Learn to Create PERFECT EXPOSURE Every Time Using Histograms in Adobe Photoshop

1. Introduction: Hi, My name is Meredith. I'm a landscape photographer and outdoor educator based in Denver, Colorado. In this course, I'll be teaching you how to read and understand image histograms in Adobe Photoshop. Histograms are essential tools and digital photography, but they can be intimidating and confusing, especially to beginners. That many people skip over learning histograms, but this is a big mistake, especially if you want to improve your photography. Here's some of the most important tools that you can use to improve the quality of your photos as they allow you to evaluate the overall exposure and color balance of your images. In this course, you will learn how to read image histograms in a way that's simple and easy to understand. We will start with the very basics from what histograms, even our two, how to open them up and analyze them in Photoshop. You will also learn about the different types of histograms that you'll most likely encounter, such as luminosity and RGB histograms. And how to use them to correct and improve the exposure of your images when you're editing them in Photoshop. This course is for anyone who wants to improve their photography using image editing and post-processing tools. Beginners who are brand new to the concept of histograms will gain a lot from this course. And more advanced photographers looking to master this topic will benefit from this course as well. So if you are ready to join me in learning how to nail the exposure in your photos using histograms. Then I look forward to seeing you in the very first lesson. 2. Understanding pixels and image sensors: Welcome to the class. You-all, I'm so excited to have you here. In this first lesson, we're gonna be discussing what pixels are and how pixels are used and how they work. In the context of digital photography, you might already be familiar with what pixels are. And if so, that's great. This will mostly be review for you. But this is a really important concept for you to understand in digital photography. And it's a concept that really lays the foundation for how histograms work. So what is a pixel? A pixel is the smallest unit of information in a digital image. Now, what does that actually mean? You're looking at here is a photo that I took in Rocky Mountain National Park in Colorado, which is a beautiful national park in the Rocky Mountains near where I live in Denver. I shoot a lot in this area. And this is a digital image in the raw file format that I have done no editing or processing to. In order to understand what a pixel is, let's zoom all the way into this photo. I'm going to zoom in all the way that I can possibly zoom in until I can't zoom in anymore. You'll start to see all of these little squares. If you're trying this out at home on your computer and you're having a hard time seeing all of these little squares. Which you can do is you can come up here to View. Then down to show over to this sub-menu to pixel grid. And you'll want this to be checked. I usually don't have this checked when I'm editing, but it's a great way to see individual pixels. If we come back to our image, every single one of these squares that you're looking at is a single pixel. And the word pixel is just an abbreviated word for picture element. And each one of these pixels represents a single point in the image. And each pixel has two distinct qualities. The first is color, so each pixel has a single color, and the second is brightness. So each pixel has its own unique brightness level. And this is going to be something that's important to remember, especially as you edit photos. So again, each pixel has two distinct features. One is color and the other is brightness. So each one of these pixels is just a unit, a single unit of information. And when combined, if we start to zoom out, you'll see how they all come together. They combine into a single photograph. As we zoom out, we get bigger and bigger perspective. And these pixels are so tiny that we can't see them when we zoom all the way out. This photograph in particular is composed of a little over 45 million pixels. One of the reasons I know that is because my camera, which I shot the photograph with, has a 45 megapixel sensor. And we'll discuss what that means in just a moment. So where do pixels come from? How are they created by the camera and transferred into this digital image that you see here. Well, to understand that, let's talk about how a camera works. In digital photography, a camera captures an image by recording the amount of light that falls on the image sensor. And the image sensor, which is represented in this illustration right here, is just a light-sensitive piece of hardware in your camera that captures light from the scene and transfers it into a digital image. So let's say we're out shooting a photograph of this mountain. When sunlight hits this scene, the sun could be anywhere from this side, it could be behind this mountain or it could be behind us when we're shooting. All that matters is that when light hits this scene, it's reflected off of this scene and back towards you, the viewer of the scene. So wherever you are with your camera, that reflected light is going to pass through your camera lens directly from the scene as it bounces off. And it's going to travel through your lens and then into your camera at where it hits the image sensor. And the image sensor just lies directly behind the lens. So in this illustration, the lens has been removed. And if you were to be at home looking at your camera, if you have, say a DSLR or mirrorless camera, when you take off your lenses, you'll be able to see the square image sensor in this opening where the back of the lens attaches to. I don't recommend that you take your lens off your camera very often or frequently for an extended amount of time, because it exposes the image sensor to dust and other types of particles that can damage your image sensor. You definitely never want to touch your image sensor and definitely protect it from the elements. Don't let anything touch it. Again, the image sensor is just a piece of hardware in your camera that collects light information from the scene. And what will happen when the sensor collects this light information. Your camera, through a very technical process, will transfer this light information into digital information, so it'll turn it into the digital image. Another way to think about this image sensor is it's kinda like how film, like 35-millimeter film, used to be in cameras in the old days. Film is just a light-sensitive medium that picks up light information. Your image sensor is doing the exact same thing just in a lot more sophisticated way. Now, let's take a look at this illustration, which is a very, very simplified model of an image sensor. Image sensors, just like your digital photo, has individual pixels. So each one of these squares in this image sensor represents a single pixel. If we were to count up all of these pixels, the total amount of pixels on this sensor is 63, so we have nine columns and seven rows. So seven times nine is 63. In reality, modern image sensors have millions and millions of pixels. Again, this is an extremely simplified version, just to help you grasp the concept of how image sensors work. Getting back to pixels, what each one of these individual pixels does is it captures light and color information coming from this scene that you're photographing. And each one of these pixels will translate into the pixels that you'll ultimately end up with in your digital image. So imagine each one of these pixels is kind of like a bucket collecting rainwater. But instead of a bucket collecting rainwater, it's really just a collector or at theoretical bucket collecting light. Each one of these pixels records a specific amount of light and produces a corresponding digital value that represents a specific color and brightness of an image. Let's look at an example of what that means. Here we have our 63 pixel image sensor, which we just looked at. Let's say we were shooting a photo with a camera that had this 63 pixel image sensor. If you look over here, the corresponding image. So if you use your imagination, imagine this is a photo that you took with that 63 pixel camera. The image could look something like this. The colors could be a little bit different depending on what you're shooting, but it would have 63 pixels in total. You can see here too that 63 pixels gives you almost no detail in a photo, which is why modern day cameras have image sensors with millions and millions of pixels that so that you can get more pixels on an image sensor and get a higher resolution. So you can get much greater detail in an image when you have a lot more pixels on your image sensor. Again, this is an extremely simplified illustration. Not just because of how few pixels that are on this sensor in the image, but also because image sensors and the physics of how they work and turn light into images are much more complex than this, which I encourage you to read up on this stuff if you're interested. And I'll provide some links about image sensors and how this stuff works in the resources section of this course. Now, in talking about pixels, you might be familiar with the term megapixels, which is one of the major specs of a camera that manufacturers usually used to sell them. So usually higher megapixel cameras are thought to be better, sharper, just generally more desirable. With this term really refers to how many pixels the image sensor contains. One megapixel is equal to 1 million pixels. E.g. if you have a 20 megapixel camera, your image sensor has 20 million pixels, and it will produce an image that has 20 million pixels. So if we look back at this image of this mountain, we can look at the number of pixels in this image by going to Image down to Image Size. Right here, you can see that the width of this image is 8,256 pixels and the height is 5,504 pixels. If we were to multiply the width times the height to get the total number of pixels in this image, that total would be a little over 45.4 million pixels, which is the exact same thing as 45.4 megapixels. If you remember, one megapixel equals 1 million pixels. You can try this out on your own images at home. So for this, you'll want to take a raw image, look up the width and the height in pixels, multiply them by each other and you'll get the total number of pixels in your image. And that should correspond to the number of megapixels at your camera sensor has. So to review, a pixel is just a single unit of an image. If we zoom all the way in, we start to see individual pixels. In each one of these pixels has a color and a brightness. Those two pieces of information were collected by your camera's image sensors. So the pixels on your cameras image sensor. So that's a general overview of pixels. Now that you have that understanding, we can jump into what histograms are and how image histograms work. So I will see you in the next lesson. 3. How to read a histogram: Welcome back to the class you-all. In this lesson, you're going to learn what histograms are and how image histograms work. I find that a lot of people skip over what the histogram is when learning photography. And I really think that this is a big mistake. Histograms are an extremely important aspect of digital photography that will help you to properly expose your photos both in the field and also when you're editing photos at home on your computer. Now, histograms do involve just a tiny bit of math, but don't freak out if you're not a math person. I'm going to break it down into really simple terms. And I promised that by the end of this lesson, you're going to have an understanding of what histograms are before diving into what an image histogram is. So the histogram right up here. Let's first talk about what a histogram actually is. So histograms aren't something that are completely limited to digital photography. They're used in a lot of different ways outside of photography. Let's look at this illustration to help you understand what histograms are. Which you're looking at right here is just a simple histogram. Histogram is just a bar graph that's used to visually describe information or data. Let's take a look at an example of what that means. So right here on the left is a bag that contains four different colored marbles. We have red, blue, green, and yellow marbles. And as you can see, these different colored marbles, very indifferent amounts. We have three red marbles, five blue marbles, one green marble, and two yellow marbles. What we can do using a histogram is we can map out or graph the frequency that each color of marble appears in this bag. If you look at the histogram, all we're doing here is counting the number of times a specific marble occurs, and then plotting it using this bar on this graph. At the bottom here, on the x-axis, we have the different colors or categories. Sometimes these are also called bins, which is really just a fancy term for category. On the y-axis here we have the number of times that the marble appears. So if we look over here, we have three red marbles. So this bar reaches the three on the y-axis. It's showing us that there are three red marbles. The same thing follows for the other colors. We have five blue marbles. So this part will extend all the way up to number five, representing the frequency or the number of times the blue marble appears is five times. Same thing here for grain, we only have one, so it reaches up to one on the y-axis. And same for yellow, we have two, so it reaches up to two on the y-axis. Now, we can use histograms in any situation where we have a set of data that can be broken down into categories. And then each one of these categories appear in a specific frequency. E.g. we could graph the frequency of people's ages and a movie theater by grouping people into categories that represent different decades. So we could count the number of people that were born in each discrete decade. So again, anytime we can break up data into categories and count how many times each individual piece of data appears within that category or the frequency, we can create a histogram. In the case of digital photography, as we'll discuss here next, the frequency of pixels in a digital image can be grouped by their level of brightness or tone. Let's go back to our tiny 63 pixel image, which we produce from our 63 pixel image sensor. This time our photo over here is in black and white. So I've removed all of the color information. And now all you can see are the tones in the image or the levels of brightness from solid black to pure white. Now, just like in the last example, if we were to graph out the frequency of tones in this image, we get a histogram that looks something like this. So if we broke these pixel tones down into five separate categories based on how dark or bright they are. We could divide them into these five categories. Black, dark gray, 50% gray, light gray, and white. We could count over here how many pixels fell into each one of these categories. And I've already done this for you here. And you can see how many pixels there are for each tone based on what these bars are showing, we have eight black pixels, 19 dark gray pixels, 16, 50% gray pixels. 13 light gray pixels and white pixels. Again, we are graphing the frequency distribution. So the number of pixels for each one of these tone categories. Now, in reality, if you remember, the image that we've been looking at has over 45 million pixels. The image histogram in Photoshop works the exact same way. It takes all of the brightness or tonal values of the image pixels and it maps them onto this histogram. They're going to be millions and millions of pixels graft onto the histogram. You can imagine that it's going to be a little bit larger than what you're seeing right here. So let's take a look at that now. Back in our image. If we take a look at the image histogram up here, if you see this little triangle with the exclamation mark, you can just click on it and it will refresh the histogram. It just updates it. If you're not seeing your histogram open in Photoshop, you need to do is go to Window, come down to histogram and make sure that it's checked. In our previous example, we were looking at an image histogram for image with five different pixel tones. And in reality, the histogram that you're looking at right here has 256 different tones graphed on it, rather than five. When Photoshop reads this image, it assigns each pixel in that image a tonal value or a brightness value. And it distributes them along this histogram, just like we saw in the previous example. But instead of just mapping five tones, we're mapping 256 tones. If you were to zoom all the way in on this histogram, you would see all of those 256 individual bars making up this histogram. But because there's so many bars packed in together in such a small space, it looks like a smooth curve. To better visualize what's going on in this histogram. Let's take a look at this illustration. What you see here is depicting exactly what you see on your histogram in Photoshop, broken down into everything that's going on. In this example, you can see in a little bit more detail how the tones have been graphed out from black. So zero being pure black, which in this particular histogram, there are 0 bar at zero, which means there are zero black pixels in this image. If we move all the way to the right, we reach pure white. So the 255th bar would represent pure white. Here again, we don't have any pure white pixels because we don't have a bar right here representing any white pixels. Again, each one of these bars is counting how many pixels in an image are at each one of these tones. If we were to pick out a single tone here. So around middle gray, a little bit darker. This single red bar represents the number of pixels a little bit darker than 50% gray. Now, these ranges of pixel tones can be broken up into five different ranges. So here we have the blacks, which is the range of the darkest tones. A little bit lighter is the shadows in the middle or the medium light tones. We have the mid tones. Lighter tones are going to be the highlights, and the lightest tones are the whites. Let's go back to our image. Now. If we look at this histogram, we can see a spike in the highlights and then a large amount of pixels and a spike here in the darker tones. Looking at this image, we can see that all of this light area up here in the sky is probably what's causing this spike in the lighter tone. So all of these are going to be highlights and whites. We don't have too much in the mid tones, just a little bit in the mid tones. But the bottom part of this photograph that takes up the majority of the photograph are darker tones, shadows down in here, and really dark, dark so blacks down in here. Now, one of the most important things for you to understand when you're learning histograms is how to correlate what you're seeing in the image to the histogram and vice versa, just like I showed you. And we'll be going through more examples of how to do this in future lessons. But I want you to start paying attention in your photos. If you have a photo open in Photoshop, pay attention to how the lightest part of your photo corresponds to the histogram and how the darkest parts correspond to the histogram. Now one thing I want to mention right here, you can see how this spike in the shadows and the blacks kind of gets cut off right here. If you see that in a histogram, don't worry because it doesn't necessarily mean there's anything wrong with the photo. It just means that there are so many pixels in this area that reach above the graph that we can't even see them. So we can assume that this bike could come off of the histogram and there are more pixels than can be visibly graphed here. Since we're trying to squeeze all of this pixel information into such a small space. Sometimes you'll see these spikes that jump off of the histogram. Now, if you want to see the actual number of tones that are in each of these 256 tonal categories. You can come over here to this little menu icon. Click on it, and then click expanded view. Now you'll see an option to select different channels and don't worry about all of these channels. Now, I'm going to click luminosity. We're going to talk about what these different channels mean in a future lesson. Now, if you click on the histogram, anywhere on the histogram, you can see the individual bar that you're on. So the level is just on a scale of zero to 255, you're at bar 213. And then you can see the number of pixels that are at that tone. Again, this is more in the highlight tones. There are over 682,000 pixels just at that tone alone. Quite a lot of pixels at that particular spot. And again, that's probably somewhere up in here where those tones exist. So that is how histograms work and image histograms work in Photoshop or whatever editing software that you choose to use. At this point, you might be wondering why any of this matters. Why is this important? And we're going to start discussing that in the following lessons, where you're going to learn how to use the histogram to dial in proper exposure. In other ways that histograms are great tools that can benefit you as a photographer. So I will see you in the next lesson. 4. Interpreting histogram shapes: Hey, welcome back to the class. In the last lesson, we talked a little bit about how you can look at an image histogram. And based on the distribution of tones in that histogram and the frequency of tones that you see. You can correlate that to what you're seeing in your actual image. In this lesson, we're going to dive a little bit deeper into this. We're going to cover some really common histogram shapes and the types of images that they correlate to. This is really going to help you start to be able to look at a histogram and immediately know what's going on in your image. Let's now look at several different types of photographs and how their histograms correlate with the varying levels of tones and brightness that we see in them. The image that you're looking at right here is a very typical type of image called a high key image. And what that means is that it's a photo of a scene that is mostly very bright and has a majority of its tones and the highlights with just a little bit of shadows and blacks. This photograph that you're looking at, a tree on a foggy, cloudy day. As you can see, most of this photo is lighter or brighter. So all of this guy here that takes up most of the image, we can just tell by looking at it that all of these pixels are pretty bright. We would expect to see a lot of bright pixels on the image histogram from this photograph. If we look over here at our histogram. And by the way, right now for these examples, I have the channel on luminosity and we'll talk about what that means in later lessons. But what we're looking at right here is the brightness of the pixels in this image over here, we expected to see a lot of highlights and brighter tones. And indeed, if we look at the histogram, we can see that most of the pixels are in the upper range of this histogram, which means that the histogram shows that there are a lot of bright pixels in this image. Again, looking right here, we can see that I showed you in the last lesson, this histogram is cropped. So there's a large spike right here that extends off of this window. But this huge spike right here just means that we have a lot of pixels in this image that are really bright and tone. So mostly in the highlights and also up to the whites range of the histogram. Now, the tree on this image where we see the branches are, where the darker tones are, the shadows and also maybe a few tones in the blacks range. If we look back at our histogram, we can see that there are very few tones that are in the darker range of the histogram. Remember that all the way on the left side of the histogram is pure black. And all the way on the right side of the histogram is pure white. So down in the darker shadow areas, we have very few pixels, especially compared to up here. This shape of histogram is very typical of a high key image, which again is just an image that has mostly highlights and whites and very few blacks and shadows. We also have very few mid tones is indicated in the middle range of the histogram. The most prominent feature is this big spike. All of these bunched up pixels up here in the upper region of the histogram. Another way to think about this is say you, I'd never seen the photo that this image histogram was created off of. You could immediately tell that this was a high key image just by looking at this histogram. Let's look at another example over here. This image taken in winter is mostly composed of lighter tones. We can see the brightest tones in the image here and up in the sky and some less bright but still pretty bright tones down in here and along here, and especially up in the foreground here. This image isn't quite as high key as the previous image. But if we look at the histogram, we can see a similar pattern. This time though, we have two spikes in the brighter tones. We have one spike here, which correlates to the brightest tones in the image. So back in our photograph, these tones up in here and as well as the sky, these are the pixels that correlate with these whites and bright highlights up at the top of the histogram, really close to pure white but not touching the edge of the histogram. So we know that there aren't any pure white pixels in this image. We have another spike here in the highlights, which indicates tones that are still pretty bright, but a little bit darker than the tones in this spike here. So we can probably interpret that this bike right here represents some of the lesser bright tones. So some of the darker highlights down in here and as well as in the foreground. For the rest of the image, we see that there are very few darks and shadows. And that is represented in the histogram as well. We can see that most of the tones that aren't highlights and whites are somewhere in the mid tones range. We don't really have any shadows or blacks. And we definitely don't have any pixels that are pure black because we don't have any pixels stacking up at the left edge of this histogram. Let's look at an image that is pretty much the opposite of a high key image. And that is a low key image that is really exactly like a high key image, but in reverse. Instead of having mostly light tones in the image, we have mostly dark tones. So this is an image of the moon at night and the sky is very dark and just the moon is bright in this scene, as you might have already started to expect based on your knowledge of histograms so far, if we look over here, you can see a massive spike in the shadows area of the histogram. And again, this one spikes so high that it is cropped off of this window. If we look on the right side closer to pure white, we see a tiny little bit of bright pixels up in the upper highlights and whites region of tones that represents the moon here. So we just have a small portion of our photo, just a small percent is the bright moon. And those are represented by these few pixels at the right side of the histogram here. Again, if you were to look at this histogram, you didn't know what the image was that it was taken from. You could immediately tell that this pattern, which is a very typical pattern of a low key image, you could immediately tell that this image was mostly dark and had a little bit of brightness in it. So again, this is a very typical pattern you see in a histogram for a low key image. Next, let's see a different pattern of histogram. In this type of scene is what you would call a high contrast scene or a high contrast image. These types of images are very common when you're shooting at sunrise and sunset. Especially when you have part of the sun in the sky. And you have a very bright sky with perhaps the sun on the horizon or somewhere in the sky. Then because the sun is so low in the sky because it's at such a low angle, you get a lot of dark shadows in the foreground. So in a high contrast image, we get a lot of bright pixels and a lot of dark pixels as you see down here. And looking at the image histogram, we can see a corresponding pattern. We see a lot of brighter pixels that take up about half of the image. So this area up in here is where brighter pixels exist. And we see that the other half of the image is composed of mostly shadows and blacks. We have a big spike over here, which corresponds to all of these darker tones, all these darker pixels down here. We can also see from the histogram that we don't have a ton of mid tones. We do have some up in here and we can see that the pattern of brighter pixels is more of a bell curve shape, which really means that there's more of an even distribution of tones and the brighter regions rather than one homogenous bright patch that would spike at a particular point on the histogram. This is a more gradual distribution and that kinda makes sense when we look at this image, we see some really bright pixels where the sun is, which corresponds to these pixels all the way towards pure white. And it does look like this histogram is actually touching the right edge of the graph, which means that there are pure white pixels in the image. That can happen when you're shooting into the sun because the sun is so bright, sometimes those pixels turn into pure white, and we'll talk about that a little bit more in the next few lessons. But we see around the sun kind of a range of highlight tones. So even though they're all pretty bright, we have some brighter highlights and some darker highlights. And down in here as well, some darker highlights. So that's why we get more of a gradual curve here. The darks, however, this big spike means that all of these dark tones, these shadows, are really within a similar tonal range, which is why they all collect together in this one spike. When you're looking at a histogram for a high contrast image, you typically see this pattern. So you see a big spike or maybe even a more gradual spike in the shadows, then the histogram will dip down in the mid tones. So there'll be relatively few mid tones in this area. And then you'll see another jump again in the highlights. Sometimes you'll see a much higher spike in the highlights here. But it's usually this U-shaped curve with a spike, darker tones and a spike in the brighter tones. Let's look at another high contrast image. This image is quite a different scene than the previous image. We still get a similar pattern. If you look over at the histogram, we have a big spike in the darker tones with a smaller spike here as well, but we are really missing a lot of mid tones in this area. Then we get another spike up here in the highlights. Looking over here in the image, we can see how this histogram correlates. We have a lot of highlights up in this area, which looks like it could be about 20% or so of the entire image. And if we look over here, we don't see a huge spike in highlights. And that makes a lot of sense because we don't have a ton of highlights in this image compared to how many pixels are in the overall image. Most of the tones in this image, down in here, especially at least 50% of the image are dark tones. And we see that represented on this end of the histogram. We can see that a lot of these tones are getting very close to black, and many of them are in the blacks tonal range. Through here. Looking at the photo, we see a lot of really dark tones down in here. And as well as down in here. These are some extremely dark tones compared to appear where we have very bright tones. So again, this would be a high contrast image. Let's look at a third example here. Again, this is a very different scene than the last few scenes that we looked at, but still a relatively higher contrast image. So we have a region of brighter tones and a region of darker tones. And we don't really have a lot of mid tones in this image. If we look over here at the histogram, we see a giant spike in the shadows here, which presumably correlates to all of these shadows down in here, as well as some of the shadows up in the mountains. And then we have two spikes and the highlights here, we have a brighter spike closer to pure white. We have really bright white or bright highlights. These will correlate to the brightest part of this image. So right down in here is likely where that spike is coming from, this bright region right around in here. Then we have another spike in the highlights. So a little bit darker tones then these cones up here. And if we look at our image, we do see some darker highlights. So up in the sky here, as well as some of the light reflecting on the sand dunes. You can see in the image histogram, it looks a little bit different than the other high contrast histograms that we looked at in the last few images. But it still follows the same general pattern. We have a lot of darker tones on the left side of the histogram. A U-shape where the mid tones would be, then a region of much brighter tones. So even though these are broken up into two spikes, these together, I'll represent the brighter regions of the image. So overall we have a region of darker tones and brighter tones with few mid tones. If we look at a low contrast image, we get an opposite shaped histogram. A low contrast image mostly has mid-tones, so the majority of tones in the image lie within the mid tones. And there are very little tones, if any at all, in the darker areas and in the brighter areas. And if you look at this image, you can see that there aren't any really, really bright areas. There are a little bit over here, but not that much. There's not really a lot of dark, dark tones. There are a little bit over here, but again, not a huge spike in tones. Most of the tones in this image, so mostly up in here and as well as down in the sand, are in the mid tone areas. So they are about half as bright as pure white and about 50% brighter than pure black. And again, we can see on this histogram, most of the tones lie within the mid-tone range of the histogram with a huge spike up in here, with just a little bit of shadow tones and maybe just a little bit of highlight tones up in care. So if you see an image histogram that looks like this, maybe like a bell curve or it just a giant spike in the mid tones. You can infer that this image histogram is of a photo that is a low contrast photo. Another example here, this is just an image of the texture of the sand. And this is really obviously a low contrast photo. We really don't have any dark areas and we don't really have any light areas. And if we look at the histogram, that's exactly what we see. We don't see any lights, bright tones, and we don't see any dark tones. Maybe just a few, but a huge spike here in the upper mid tones. Because the brightness of almost all of the pixels, most of the pixels in this photo. Lie within this upper mid tone range. So those are the foremost typical shapes that you'll see on an image histogram. So a high key, a low key, a high contrast and a low contrast histogram. And understanding these image histogram shapes will allow you to more easily correlate what you're seeing an image histogram to what's going on in terms of the distribution of tones and brightness in your photograph. Now, you might be wondering at this point if there is a right or a wrong histogram shape. And the truth is that there is really no right or wrong shape of a histogram. The shape simply just providing us with tonal and exposure information about a photo. And it's just a tool that we can use to calibrate the digital image that we created with what we saw in the field with our eyes. So our goal as photographers, at least most of the time as landscape photographers, is to create images that match what we see with our eyes when we're looking at a scene. And we do this by adjusting exposure through the aperture, the shutter speed, and the ISO. When we're looking at a scene of a landscape, say e.g. this scene right here. And we're looking at it in the field with our eyes. We want to capture this scene really as closely as we can to what we saw with our actual eyes. And the histogram is a really great tool that you can use as a photographer to determine how well what you saw with your eyes matches the photo that you took. E.g. if we're out shooting this scene of this mountain, we know by looking at it with our eyes that we have some really bright areas as well as some really dark areas. We don't have any blacks in this scene when we're looking at it with our eyes, we don't have any black pixels. So we don't have any true black parts of this scene. And we don't have any true white areas of this scene. We know that we have brighter tones and a lot of darker tones. So I know by looking at this histogram, these tones, the way that these tones are distributed, really makes a lot of sons, and it really corresponds to what I saw with my own eyes. If e.g. I. Shot this photo and I weigh over-exposed it. And I'm just going to add an adjustment layer here to just demo what I mean. And you don't have to understand how this layer works at this point. We'll go over that more in my editing courses. But let's say I overexposed the scene and it looked something like this. When I uploaded this photo to my computer, I got a histogram that looks something like this. And as you can see right here, there are a lot of pure white pixels which correspond to the sky here, which is completely white. When I look at the histogram, I know that this wasn't what I saw with my eyes. I didn't see any pure white parts of the sky when I was looking at this scene of the mountain. So I know that this histogram does not accurately reflect what I sought out in the field. This spike in the histogram where it's pure white doesn't mean that the histogram is wrong or that there's anything wrong with the histogram. It just means that I overexpose the photo. So ultimately the shape of the histogram should be used as a tool to evaluate the exposure and tonality of an image, rather than as a strict guideline for what is right or wrong in terms of aesthetics. In the next lesson, we'll dive more into this topic and how you can use the histogram to determine the proper exposure of your photos and to adjust exposure as well. And using histograms to accurately judge exposure in addition to how well the scene that you saw with your eyes matches the histogram on your computer is really the essence of how we as photographers can use this tool to our advantage. We will explore that more in the next lesson. So I will see you there. 5. How to get perfect photo exposure using histograms: Hey there, welcome back. Now that you have a basic understanding of how to read histograms and how to interpret different histogram shapes, which you learned how to do in the previous lesson. Now we can discuss how you can use histograms to properly expose your photographs when you're processing them in Photoshop or other editing software. One of the most important things a histogram can tell you about an image is whether or not the image is underexposed or overexposed to the point that you've lost detail in your image. So e.g. here's an image that we looked at in the previous lesson. This is one of the higher contrast images that we looked at. And if you look at the histogram here, I'll refresh it. We discussed a little bit in the previous lesson about how if you have pixels that touch either the right side or the left side, it means that you have pure white pixels on the right side. So if you have pixels that touch the right edge here, you have pure white pixels in your image. And if you have pixels on the left edge or any pixels that touch the left side of the histogram, you have pure black pixels in your image. It's really important to understand that if you have pixels that touched the left or the right side, so pure black or pure white pixels, it means that you've lost detail in your image. And the reason this is so important is that you have an image that's overexposed. A histogram can tell you if your image is overexposed if you have pixels touching the right side or underexposed if pixels are touching the left side. And we'll look at examples of under an overexposed versions of this photo in just a moment, we can see that this photo that we're looking at right now is properly exposed, at least in my opinion. As a side note, proper exposure is sometimes not necessarily something that's set in stone. You, as the artist has to determine what you deem to be proper exposure. So in my opinion, this image is properly exposed for the most part. The reason I justify that is because when I look at the histogram, there are no pixels touching the pure white end of the histogram and there's no pixels touching the black end, which tells me that I haven't lost any detail in the image. It also means that what I saw with my eyes when I was out shooting the scene in the field matches what I see here in the histogram because what I saw in the field, I didn't see any pure white areas of the image. I saw a bright areas such as down over here. And I saw dark areas, but I didn't see any pure white or pure black when I was looking at this scene. This histogram accurately reflects that. Now let's say that I overexposed this photo and I've just created an adjustment layer here to intentionally overexposed this image in Photoshop. And now we can see that this area in the sky, extremely bright, it's actually pure white. And that is definitely not what I saw in the field. So this image is not an accurate representation of what I was shooting. If we look over here at the histogram, you can see this bar up at the 250 tone in the histogram. So the pure white tonal value that corresponds to all of the pure white pixels that we see in the sky. You can also see down here, all of which should be the shadows. So it should be the darker areas been pushed up into the mid tones. And I know that when I was out shooting that these areas were much darker than mid tones. This histogram definitely doesn't correspond to what I sought out in the field with my eyes. The problem here, and this is really key, is that when you have an image with pure white tones, you can't recover what was actually going on in that area of your scene. You can't recover that in post-processing. So no amount of adjustments and changes in editing that you can do in Photoshop can bring back any of the detail in this area. If I over-exposed it when I was out shooting in the field. Now, assuming that this overexposure isn't an adjustment and that it's actually the original photo that I shot in the field. There is no way that I can bring back the detail in the sky. I can darken down these shadows so that they move from the mid tones down back into the shadows. But this detail up in where the pixels are pure white cannot be recovered. That detail is lost permanently. Whenever you see a histogram like this, where you see a spike in pure white pixels. A term that you can use to describe this image or this histogram is clipped or blown out. So we know that this image has been clipped, or in other words, blown out because this whole area is pure white. Sometimes in landscape photography clipping can be okay. Say when you're shooting directly into the sun, virtually anytime you're shooting directly at the site, you'll have some pixels that are blown out in your image. But in an image like this, where I want to retain the detail in the sky. So where I really want it to look like this, where there is a lot of really beautiful clouds and light in the sky, I want to retain that information. So clipping in this example would not be a desirable effect. Often as landscape photographers, our goal is to capture as much detail as possible in our photos. So clipping is usually not a good thing and something that you're going to want to avoid in most situations. Looking back at our overexposed version of this photo, the only way that we could fix this clipping or this blown out image due to overexposure would be in the field. That would be by reducing our exposure, by changing one or more of our exposure settings. So changing the aperture or choosing a smaller f-stop, it could be by speeding up the shutter speed, or it could even be by decreasing the ISO. You can look at an image histogram on your camera when you're out in the field shooting. In this can let you know if you are clipping the image and overexposing it, but using image histograms on your camera, even though they work the same as the histograms we're looking at here. That's a topic for a different course, but definitely something that you want to learn how to do. You can look at your image histogram in the field and see whether or not you're blowing out pixels in your image, then you can adjust the exposure properly. So when you get home and load your photos onto your computer and look at them in Photoshop. You don't end up with photos that look like this, that are completely blown out and there's nothing you can really do about it. Let's look at this photo, but this time underexposed. Again, this is just an adjustment layer I've made to darken down this photo. But imagine this is the actual photo that I took in the field. This time in the underexposed photo, we can see on the left side of the histogram, all of these pixels here are touching pure black, which means that this photo has also been clipped. But instead of the light tones being clipped, the dark tones have been clipped. We have pure black in some of these areas like down in here, maybe down in here as well. And that also is typically something that you don't want to happen in your images. Because when I was out in the field shooting this, there were no pure black tones when I was looking at the scene. So this is an inaccurate representation of what I was looking at with my eyes. Just like when you have lighter tones, better clipped. So pure white tones in the image. Whenever you have pure black tones, it means that you have lost detail in your image, which like I mentioned before, is typically something that you don't want. In addition, if we have pure black tones or clipped tones in this photo, we cannot recover those details using post-processing techniques. So there's no way that I can bring back the detail in these really dark areas. The only way I could fix this photo would be to go back in time. Would be really nice when it and increase the exposure at when I was shooting this photo. E.g. I. Could have used a wider aperture, a slower shutter speed, or a higher ISO. Let's turn this underexposure layer off. And to summarize the image, histogram is a really important tool that can help you determine whether or not you've clipped or blown out any of the tones are pixels in your image. It can help you to understand whether you have a proper image exposure. So e.g. this photo would be properly exposed because the tonal ranges are in the proper areas that I saw it with my eyes. We don't have any blown out or clipped pixels touching pure white. And we don't have any pixels touching pure black. This image histogram tells me that I've retained all of the detail in my photo, which is typically an ideal thing to do when we're shooting landscapes, it generally results in a more aesthetically pleasing photograph. Now, if I wanted to do any editing to this photo that I observe as properly exposed, I could darken the image down a bit. I could lighten it up a bit, but I wouldn't lose any detail. I have a lot more to work with in terms of the data that I captured out in the field. So that is a general overview of how to use image histograms to determine whether you have a proper overall exposure in your image. We will continue to discuss how image histograms can be a powerful tool that you can use to your advantage as a photographer. And we will jump into that more in the next few lessons. You'll also learn how to use different histogram channels. So looking up here, it will finally get into what the luminosity histogram means and what some of these other channels mean as well. And with that, I will see you in the next lesson. 6. Using adjustment layers to balance the histogram: In the last lesson, you learned how to look at a histogram and determine whether or not the exposure of your image was sufficient. So whether or not the image is overexposed or underexposed due to clipping that you see in the histogram, looking for a pure white or pure black pixels in the histogram. In this lesson, you're going to learn how to take an image and adjust the exposure of that image by changing the position or redistributing the location of where these pixels are on the histogram. You're going to learn how to do this by using adjustment layers. And this will all start to make sense as we move through the lesson. There are three main ways that you can manually adjust the exposure of an image based on the histogram. The first way is to use what's called a brightness contrast adjustment layer. In order to find your adjustment layers or to add an adjustment layer, you come over to this panel where you see this circle. Click on it. And you'll see a variety of adjustment icons. For the first one, we'll click the sun. So this is the brightness contrast adjustment layer. If we click on that, you'll see down here that we've created a brightness contrast adjustment layer. What this is doing is it's adjusting this layer at the bottom. And you don't have to understand at this point the details of how layers and adjustments work. You can learn all about that in a Photoshop editing course. But for now, just understand that we are adjusting the main image that is at the bottom here. So the image that we're looking at right here, if we look over here at this window, this is where we can adjust the brightness and the contrast of the image. I want you to pay attention as we adjust these two sliders. What's going on in the histogram up here. And I'll go ahead and refresh that first if we adjust the brightness. So if we increase the brightness to lighten up the image, you can see how the histogram starts to press up against the right side of the graph. So we're getting all of these pixels blown out at pure white up here. So we know that we've adjusted the brightness too far. If I bring this back down, you can see how the histogram starts to shift to the left. So we're darkening all of the pixels in the image. So every single one of the pixels in this image is getting darker. If we bring this all the way down, we can make the image really, really dark. And obviously that looks completely unnatural. So we're not gonna do that, but this is just to give you an idea of if you wanted to adjust the brightness of your image, you can see how the histogram starts to shift as all the pixels get darker and all the pixels get brighter. So I'm just going to put this back at zero for now. Let's take a look at contrast. If we slide the contrast up, what you'll notice with the histogram is that the highlights and the lightest tones get lighter and the darker tones get darker. If you remember from earlier in this course, we talked about high contrast images and how they typically have a spike on the dark area of the histogram and a spike in the lighter tones of the histogram with this you in the mid tones separating those two spikes. As we increase the contrast, you'll notice that that pattern starts to become more and more increased or stronger and stronger. And eventually we start to clip some of these pixels on the right side. Again, obviously this is super unnatural looking. So I will bring that back down. The same if we pull it down to the left here, you'll start to see the pixels move more towards the mid tones. So when these two spikes come closer together, we're going to lose some of the contrast in the image. And that's because the darker tones are getting lighter and lighter tones are getting darker as they move to the left. We'll pull that back up. So that is the brightness contrast adjustment layer and how you can use that to shift the tones in your histogram. Let's turn off this adjustment layer by clicking here. And next, let's create the second type of adjustment called a levels adjustment. You come back up to your adjustment panel. This time we're going to click the second icon here. So this right here is the Levels adjustment. Now we've created a Levels Adjustment applied to our background layer, our image. Here we can see all of the controls and sliders for this levels adjustment. You can see that this histogram that shows up is essentially the same histogram that we're looking at up here. You only difference is this is an RGB histogram and this is a luminosity histogram, which we'll talk about what the differences between those two aren't later in this course. But for now, just assume that they work in similar ways. The levels adjustment tool consists of three sliders, the black point right here, the white point on the right side, and the gray point in the middle. The black point slider right here, adjust the darkest areas in the image. So if we start to pull this up and slide it to the right, what you're telling Photoshop is that you want these pixels at the far end of the histogram on the left here. You want those to be darker and you want them to be pure black. If this slider touches those pixels. You can see it as I slide this slider to the right, it darkens down the darkest pixels in the image. And notice appear at how the darker tones in the image start to move to the left, they start to get even darker. Again. If I pull that down, the shadows are a little lighter. And if I pull it to the right, we are setting the black point at the edge of the histogram. We're making the darker tones darker. The white point works in a similar way, but for the whites. If we pull the white point to the left, what you're telling Photoshop is that you want the brightest pixels in the image to get brighter. So you want to have the white point established at the point where lighter tones used to be. And again, the levels adjustment is a complicated tool and I definitely recommend that you look into more in-depth Photoshop editing courses to fully grasp what's going on here. But the most important point to keep in mind is that you are making the lightest tones in the image, lighter by pulling the white point down. And you're making the darkest tones in the image darker by pulling the black point up. The gray points lighter right here, works in a similar way. As you pull it to the right, you start to darken the lighter mid tones. And as you pull it to the left, you start to lighten the darker midtones. It's a little bit counterintuitive, but I encourage you to play around with this and it'll start to make sense. So what we're really doing here is we're just shifting the tonal values of the mid tones up and then down. Since this is a high contrast image, we don't have a lot of mid tones in this image. So if you look up here, there's a lot of pixels missing in the mid tones. And for this image, sliding, the grade point isn't really doing much, at least to my eye to improve this image. For this image in particular, we definitely don't want to slide the white point down because this is causing the pixels to blow out on the right side of the histogram here. So Let's pull that all the way back up. Sliding the black point up just a little bit does actually help this photograph. I think, by adding just a little bit more contrast by turning this layer on and off. I do think that increasing the black point, shifting the shadows down a little bit does help out this image. The third type of adjustment layer we can use is a curves adjustment. So that is the third icon here. If we click on this, what you can see here is another histogram representing our histogram up here. The curves adjustment is another tool that allows us to adjust the tonal range of an image. And it's very similar to the levels adjustment, but it provides more control over the tonal range and allows for more targeted adjustments of specific tonal values. And I'll show you what that means here. Let's expand this window so that we can see this entire window here. You'll see this diagonal line going across the image histogram. Now, like I mentioned, this allows you to make more targeted adjustments. And you do that by clicking somewhere on this line where you want to adjust the histogram. And then you can drag that line up to increase the brightness of the tones, or you can drag it down to decrease the brightness of the tones. Right here. When I'm dragging this line, I'm focused on increasing these particular tones on this part of the histogram. Let's say I wanted to increase the contrast of this image. To do that, I would want to increase the tones on the right side of the histogram. If we come over here to the darker tones, I'll click on this level curve and drag this part of the curve down. And you'll notice wherever you click, you create a point and that will fix the curve at the point where you click on it. And you can adjust where you want these points to exist. You can create as many points along this line as you want. So I could place a point right here just by clicking on this line. And that will prevent this area from shifting when I move this area right here. So if I want to adjust this line right here, I am going to have more control of how light I make these lighter tones because I have placed a point right here. The same thing over here in the darker tones. I have more control. If I pull these down even more, they don't really affect the lighter tones appear. Now, this is obviously way too much contrast for this image. And you can see that we've even blown out some of the pixels on the right side. So I'm gonna pull this down just a little bit and I'll pull up the darks up as well. That does add a nice little bit of contrast to this image. If we toggle this adjustment layer on and off, we can see how that little pop of contrast adds a little nice aesthetic to this image. Depending on your personal editing preferences, you may or may not like more contrast in your image. Let me increase the contrast. Once again, just to demo real quick. We have a really high contrast image here which does not look good. But just to point out this S curve right here is a great technique you can use when editing your photos and you want to add contrast. This works a little bit better on images that are more flat. So this is a pretty high contrast image to begin with. But if you had a flatter image, you could create this S curve. And that's a really nice way to add some contrast into your image by selecting for the darker tones, as well as for the lighter tones. You can see out we have way more control over the regions of different tones compared to our brightness contrast adjustment. Where we didn't have control over where we increased or decreased the tones. All of the tones in the image were adjusted by the same amount. I hope that helps you understand how to use adjustment layers to shift where the tones are in your histogram so that you can more easily match what you saw in the field with your eyes to what the image histogram looks like at home on your computer. And these adjustment layers, especially curves and levels, There's a lot more to them and a lot more you can do with them when you're editing your images. So I encourage you again to go check out eight Photoshop editing course. But for now those are the most important aspects of how to use those adjustment layers and can really help you out when adjusting the exposure of your images. 7. Luminosity histograms part 1: luminosity and brightness explained : Welcome back to the class you-all. So far in this class, we've discussed the properties of image histograms. And we've covered how they're a graphical representation of the distribution of brightness levels in an image. Now we're going to break this down even further and you're going to learn about different types of image histograms that you will encounter when you're using Photoshop to edit your photos. If we look over here back at our histogram, you will see these different histogram channels. If you click here, you will see RGB, red, green, blue, luminosity and colors. If you are not seeing these channels right here, make sure that by clicking on this menu icon right here, that you are in expanded view. This view will allow you to see all of the channels. In this lesson, the type of histogram that we're going to be looking at is called a luminosity histogram. If we go back into the channels here, the luminosity histogram is right here. So I'm going to click on this. And if you're following along at home in Photoshop, go ahead and also click on the luminosity histogram for whatever image you have open, we're going to dive into what a luminosity histogram is. And luminosity histograms are really important for you to understand. There'll be especially important down the road when you start to use things like Luminosity Mass when you start getting into more advanced landscape photography editing techniques. Alright, so if you remember, in the very first lesson we talked about pixels and what pixels are in digital photography. Let's zoom back in here so that we can see our pixels. If you remember from that first lesson, every pixel in an image has both a color and it also has a brightness. One of the ways that we can describe the brightness of a tone is through something called luminosity. In photography, brightness and luminosity are two different concepts that are often used interchangeably, but they have distinct meanings, so they are not exactly the same thing. When talking about the brightness of a pixel and the luminosity of a pixel. Oftentimes you'll hear people use those words in the same way, but they are not the same things. The brightness of a pixel and the luminosity are different, but they are a little bit similar. So let's discuss what these two terms mean and how they're different. First, let's talk about brightness. Brightness refers to how we perceive the intensity of light that hits our eyes after it reflects off of an object or is emitted from a light source. So in other words, brightness describes the perceived intensity of light in an image or part of an image. If I zoom out here. When we're looking at this image and we're talking about brightness. In this image, our eyes perceive this area of the image to be much brighter than this part of the image, which is the darker area of the image. So the brightness up in the sky is much greater than the brightness down in the shadows. This seems pretty intuitive and obvious that this area is brighter than this area in the shadows. But since brightness is how our eyes perceive the intensity of light, brightness can change depending on our environment and the light source that's illuminating the scene that we're looking at. A good way to think about this is e.g. the brightness of your cell phone screen. Let's say you set the brightness of your cell phone screen to about 50% and you walk into a dark room. The screen at this specific brightness will appear much brighter indoors in this dark room than it would if you were to walk outside and look at your screen outdoors on a bright and sunny day. Even though you haven't changed the brightness level on your phone, when you're looking at your phone outside, usually have to increase the brightness, often to up to 100%, just so that you can see your screen on a sunny day. That's just because our eyes have perceived the intensity of light or the brightness differently outdoors than we do indoors when it's dark and it's easy to see the screen at a lower brightness. The way our eyes perceive the intensity of light or the brightness can change depending on the light in your surrounding environment. Alright, so luminosity, on the other hand, which by the way, luminosity is also called lightness. So if you hear the terms luminosity and lightness, they really mean the exact same thing. Luminosity is how we perceive brightness without color information. Relative to 100% pure white. I know that probably sounds very complicated and technical. So let's break this down into more simple terms. If we were to remove all of the color information from this image, all of the pixels in this image with lies somewhere on a scale from pure black all the way to pure white. So if we were to remove all of the color from this image, what we'd be left with is the luminosity. We can actually do that to this image in Photoshop. If you come up here to Image, down to Mode and over to gray scale. Now we've removed the color from this image. And if we zoom all the way in, you can see that all of the pixels are gray in color. So all of these pixels lies somewhere on a scale from pure black to pure white, where 0% luminosity is pure black and 100% luminosity or lightness is pure white. Let's take a look at this illustration just to help you understand luminosity. When we remove the color from the image, all of the pixels in the image will lie somewhere on this luminosity scale or tonal scale. At the far end here on the left, pure black would be 0% luminosity, and pure white would be 100% luminosity. Whenever we're talking about the luminosity of pixels or the luminosity of an image. We're comparing the pixels or the tones to pure white. So e.g. if we look at the mid tones here, are 50% or around 50%, as bright as pure white. So whenever we're talking about the luminosity of a tone, we're determining where it lies on this scale. How bright is it compared to pure white? In the highlights, we are about 75% as bright as pure white. Maybe somewhere 7080-85% as bright as pure white. The white tones up here will be somewhere in the range of 85, 90% to 99% as bright as pure white. And the darker tones down here, it will be about, around 25% as bright as pure white. And the blacks, somewhere in the range of five to 10% as bright as pure white. These are just ranges and not exact numbers. So looking back at our image that has been converted to black and white, Let's zoom out here. All of the tones in this image lies somewhere on that scale we just looked at. There's somewhere between 0% as bright as pure white and 100% as bright as pure white. And we know by looking at the histogram up here, if I refresh this, that we have no pure white or pure black pixels in this image, because we have no pixels stacking up on the right side at pure white and we have no pixels stacking up on the left side at pure black. Let's bring the color back to this image. And I'm going to zoom in again to the pixel level. Now, the best way to look at the actual brightness and the luminosity of each one of these pixels is to use the color picker in Photoshop. The way to access your color picker is to come over here. Click on this square here. And here we have our color picker. Now, when you move through the image, you will see this eyedropper tool and you can click on an individual pixel. So if I click on this pixel right here, the color that we just picked will appear here. And now we have some really important values that tell us everything about the color, the brightness, and the luminosity of that particular pixel. So the particular pixel that I just clicked on, the brightness, which is represented by b, is 32%. So our eyes perceive the intensity of the light coming from that pixel as 32%. The luminosity or the lightness is right here, represented by the L. The lightness or the luminosity of that pixel is 34, which means that that pixel is 34% as bright as pure white. If we were to go over and look at our gray scale over here, that pixel would lie somewhere around here at 34% as bright as pure white. So that pixel will lie somewhere in the shadows on the luminosity scale. You can see that the brightness and the luminosity art similar, but they're not the exact same numbers. And that's because they are not the exact same thing. Let's pick one of these brighter colors down here. And now you can see that both the brightness and the luminosity has gone up. Now the brightness is 57% and the lightness is 50. Again, luminosity would be 50% as bright as pure white. That is literally right down the middle here at 50% gray. Remember that this luminosity doesn't have anything to do with the color. It's just how our eyes perceive the brightness compared to pure white. Let's close this up and look at another example. We zoom out here and we look at the brighter tones in the image. Let's open our color picker back up. Now I'm going to click on the sky. Here we can see that the luminosity is 95%, so it's extremely close to pure white. It's 95% as bright as pure white. But it is not pure white even though it might look like it is to our eyes. And that's a good thing to know because we know that we haven't blown out our image because these pixels aren't pure white. So again, these pixels appear, these really bright pixels with a luminosity of 95. We jump over to our luminosity scale. They will lie somewhere up in here, so they are extremely light but not pure white. Alright, let's try one of the shadows down here. We can see the luminosity is 24. So that is about right in the middle of the shadows. We look back at our scale. 24% will be about right here. 8. Luminosity histograms part 2: luminosity and color: I want to show you this illustration, I think will help the concept of luminosity sink in for you. Right here on the left side of this illustration, we have the typical color wheel. So all of these colors right here on the color wheel are fully saturated colors at their full intensity. So this is what we call hues on the color wheel. If you are unfamiliar with the term humans, or saturated means, which are just terms that refer to the basic qualities of color. I definitely recommend that you take some time to read up on or take a class on color theory, just to learn some of the basic concepts of color theory as this will not only help you understand this stuff better, but it will also help you to become a better photographer. So I'll provide some links in the course resources on color theory so that you can read up on and study some of this color theory stuff. If the concept of saturation, hue, brightness, things like that are really foreign to you at this point. Back to our color wheel. So we have all of these hues on the color wheel. What I want you to understand about all of these colors is that they all have the same brightness. But our eyes perceive this brightness differently. If I open my color picker here and I click on all of these colors, you can see that the Brightness, the brightness of pure red on this wheel is 100%. If I go around the wheel here, clicking the different colors, if you pay attention, the brightness of all of these colors is 100%. You'll notice though that the lightness or the luminosity changes when I click on these different colors. So even though the brightness of all of these colors is the same, the luminosity is different. If you look over at the right here, we have the exact same color wheel. But in the middle of this wheel, the hues of the color wheel have been converted to grayscale. So they retained their luminosity values. They are the same perceived brightness when compared to 100% white. Around the color wheel here, I've shown what percent of pure white that our eyes perceive these colors to be. So in other words, what the luminosity of these colors are. You'll see here red, e.g. red is 54% as bright as pure white. If we convert red to the gray scale, retain the luminosity. So we keep this gray tone 54% as pure white. This gray tone is how our eyes are actually perceiving this color. You can see here that even though all of these colors have the same brightness, our eyes perceive certain colors to be darker than others. E.g. if we look at Blue, Blue has a luminosity of 30, so it's 30% as bright as pure white. And yellow is 98% as bright as pure white. The luminosity is 98, so our eyes perceive blue to be much darker in tone than it perceives yellow. Our eyes perceive the yellow to be very close to pure white. We can check all of this here. If we pull out our color picker again, if we click on read, you'll see the brightness is 100%, but the luminosity or lightness is 54, 54% as bright as pure white. Looking at our gray scale like we've looked at before, that tone will lie somewhere around here. So a little bit more than 50% gray. If I click on this gray slice of the color wheel, which is just read with all of the color information removed, you can see that the lightness is still 50 for the brightness has gone all the way down to 50. But our eyes still perceive the brightness at 54% as bright as pure white. If I click on blue or blue converted to the gray scale with the same luminosity. You can see that this dark gray tone is 30% as bright as pure white. But that's still the same luminosity. If we were to click on the actual color, the luminosity values are the same. So again, even though the brightness changes, the luminosity, the way our eyes perceive the brightness stays the same. Again, with yellow, the brightest tone. It's at 98% as bright as pure white. If I click on this part of the color wheel, we see the luminosity is 98. And I will provide a link to download this illustration in the course resources so that you can open this up on Photoshop, pull up the color picker, and test this out for yourself. Pick different colors and see where they lie on the luminosity scale. So orange, e.g. 68% as bright as pure white, orange. If we took all the color information out, converted it to the gray scale, it would lie somewhere around right here. So somewhere in probably the highlights or upper mid tones range. The most important thing for you to understand here though, if you take anything away from this lesson, is that luminosity is the perception of brightness without color information on a scale from black to white. Let's go ahead and close this up and come back to our image. Okay, so now hopefully you're starting to have an understanding of what luminosity is. So let's talk a little bit about what a luminosity histogram is. We come back to our histogram and I'm going to reset this on the luminosity histogram and refresh that. Just like you've been learning throughout this course, how histograms work by mapping the frequency of the different pixels in the image. What this luminosity histogram is showing is it's reading the luminosity of each one of the pixels in this image, and it's plotting them on this graph. Photoshop is reading every single luminosity value for every pixel. It's determining on a scale from black all the way on the left side to white, pure white all the way on the right side where that pixel will lie on the histogram. So e.g. if we take the color picker and we look at our sky, so the luminosity of our sky, many of the pixels in the sky here are around 94, 95, quite bright, really in the 90s range. And that's why we see this spike up in here. We see a lot of pixels with a luminosity value higher than '90s. These are about 95% as bright as pure white. Then we have the highlights. So really down in the sand dunes here. So these tones in here are about 50 to 70%. Some of the brighter ones in here around 55, 56. If we look up in here, we also have more highlight tones. So these tones in here are around 83, 86. So in the '80s, these values have been here. In the '80s, will correlate to this spike up here because this is about 75, 80% as bright as pure white on the scale from black to white. Then we have this huge spike in luminosity values and the shadows down in here. If we use our color picker, luminosity of 2025, 18, it's really in the 15 to 25%, as bright as pure white range, luminosity of 15 to 25, which corresponds exactly to the pixels that bunch up in this area of the histogram. Again, this histogram is just graphing the frequency of how many pixels exist at each luminosity value. On a scale of black, 0% luminosity to white, 100% luminosity. Now what I want you to do is open up a photo in Photoshop if you haven't already, and open up the color picker, and I'd like you to just move around your photo, guessing what the luminosity of each pixel or area of the photo is and seeing if you're correct. So see if you can guess things like the luminosity of the shadows and the luminosity of the highlights. And check right here at the luminosity value to see if the luminosity you guessed or the lightness you've guessed is correct. This type of practice will really help you start to read the tones in the image. They'll help you to understand how our eyes perceive the brightness of the pixels in an image. So take some time to practice this. Next, we're going to move on from luminosity histograms and we're going to discuss RGB histogram. So I will see you in the next lesson. 9. RGB histograms part 1: the primary colors of light: Hey, welcome back to the class. In the last lesson, we did a deep dive into what luminosity histograms are. Luminosity histograms are really important to understand before we move on to what are called RGB histograms. If you feel a little fuzzy or unclear, or if you haven't watched those lessons on luminosity histograms in the previous part of this course, I definitely recommend that you go and look at that now before you watch the RGB histogram lesson. Because the previous lessons that you saw are really going to lay the foundation for the concepts we're going to talk about in this lesson. In this lesson, we're going to focus on the RGB histogram. And if you look up here to our histogram window, you can see that one of the channel options is RGB, and that's what I have it set to right here. We went over Luminosity histograms and RGB histograms work in a similar way to the luminosity histograms that we've been looking at. But they're slightly different and perhaps a little bit more confusing, especially when you start to learn about them. So stick with me. We're going to break all this down. And by the end of this course, you're going to start to understand what an RGB histogram is, as well as what all of these other color channel histograms are. So red, green, and blue. Rgb stands for, is just red, green and blue. Rgb histogram, as we'll discuss, is a combination of a red, green, and blue channel histogram. One thing that's important to know about RGB histograms is that these are the types of histograms that are gonna be on your camera. So if you look at the back of your camera after taking a photo, or if you pull it up on your live view, on your camera. If your camera has live view, if you pull up the histogram while you're shooting, that histogram is going to be an RGB histogram. Your camera might even give you the option to see the red, green, and blue histograms independently in that religious depends on your camera, but most cameras at least have the RGB histogram. So you won't see the luminosity histogram on your camera, at least at the time of this filming? There are no cameras that I personally know of that have a luminosity histogram or the option to C1. You also won't see a luminosity histogram in light room. So if you're using Lightroom to edit your photos, the histogram you'll see in Lightroom is going to be the RGB histogram. And that's one of the main reasons I've discussed in other classes. The main reason that I use Photoshop to edit my photos because the luminosity histogram is so important. But the RGB histogram is important as well. So we'll talk about what that is. Like I mentioned, RGB stands for red, green and blue. And red, green and blue are the three primary colors. When we're talking about light, when we're talking about digital screens, like what you're looking at here, say on your computer or on your cell phone. Every pixel on your computer or any LCD screen that you look at is composed of a combination of only red, green, and blue light. When you look at an RGB histogram over here, the RGB histogram shows how many pixels in the image have a particular combination of red, green, and blue values. So it's giving us information about how red, green, and blue is combined in the image to create all of the colors that we see. Now, when you combine red, green, and blue at their full intensity, so at their full brightness, you get pure white. On this canvas here, half of it is pure white. So what that means is that each pixel in this white area that you're seeing on the screen is composed of red, green, and blue at their full intensities. In contrast to that, the black pixels on your screen are a combination of red, green, and blue pixels at their lowest intensity, so they are completely turned off. You could say. Let's take a little closer look at what that means by opening up our color picker. And let's click on the color white to sample white. And if you look on your color picker, right in this area, you will see are for red, g for green, and B for blue. What you see to the right is a level of intensity that these colors are at, at that specific pixel that we just sampled. Now, the intensity of each color is on a scale 0-255. So there are 256 total different intensity values. If you remember from earlier in this course, we talked about how histograms that you'll be using in Photoshop. Graph tones or brightness on a scale from zero all the way on the left edge here. So zero being pure black and 255 being pure white. So that's what this is over here. On the RGB values. It's showing where on a scale of zero to 255, the intensity of that color lives. You can see here that all of these three colors in the white pixel that we sampled are. Full intensity. In other words, they're turned completely on and you'll see them all the way on the right side of the graph, which represents the full intensity of the colors. And this will start to make a lot more sense in a moment when we start to break down these colors individually. But first, let's take a look at black. Let's sample a black pixel and you see how the values have all gone down to zero. That's because each one of these colors, red, green, and blue, are now at zero intensity. They are all at pure black. Let's now look at each one of these colors individually. Let's turn red all the way on. So if I type 255, I have turned red on at its full intensity, as you can see in the color I've created right here. You can also see that green is at zero and blue is at zero as well. This means is that we have a pure hue of red at its full brightness or intensity level. Once you start mixing in any green or blue, this will no longer be pure red. So if I added in a little green here, you can see how that color changes. Let's try this out for green. So if I change this to zero and green to 255, so we've turned it all the way up to its maximum intensity. We get a pure green hue. And we can check that as well right here. The hue is set at 120 degrees. So this right here is the color wheel. And pure green is always at 120 degrees on the color wheel. Again, if color theory is a little unfamiliar to you, definitely go read into and study up on how basic colors work and how we get pure hues. Let's try this out for blue. So I'll set this back to zero and set this to 255. So let's full brightness and intensity. Now we have pure blue, which you can see here. We can also check because pure blue is also 240 degrees. It's always 240 degrees on the color wheel. If you want to decrease the brightness or the intensity of one of these colors, you do that by adding black to the color. So if we take our pure blue, which is fully saturated, we start to add in black by dragging this down. If you watch the value for blue, It's going to start to go down. As you bring this all the way down to zero, you can see that pure black is represented by zero. If we look over here on the color picker, what we're just looking at is on a scale from zero, so right down here at black to 255 at the full intensity of the color up here, our value for blue will lie somewhere along this scale. So if we wanted to choose a value of blue that was somewhere between black and 100% intensity of blue. We could type in something like 120, which is about half of 255. And you can see how this circle has moved halfway up the scale. It's important to understand what these values mean right here and how they lie along the scale from black to full intensity. Because these are the values that are going to be graphed on our histogram. When we look at the RGB histogram, Let's break down how this works. I'm going to refresh the histogram here. I'm also going to sample white again. So this histogram over here is an RGB histogram of this image, this black and white image that you're looking at in the background. And you'll see on the RGB histogram that we have a spike appear in pixels, and we have a smaller spike on the left edge of the histogram. So this is pure black and this is full color intensity. When we're looking at this image, we can see that about half of the pixels are pure white. And again, that means that red, green, and blue are turned on at full intensity. This spike all the way at the top here is representing all of the pixels that are pure white. And it's graphing how many pixels are at that 100% color intensity. So they're fully turned on. Now, this spike on the left side here, if we sampled black, it's doing the exact same thing, but it's showing all of the pixels in the image that have no red, green, or blue. So those colors are completely turned off. So again, this spike here on the left side is just showing all of the pixels that do not have any red, green, or blue in them. Now, the caveat to the RGB histogram is that even though it's graphing all three different colors and their intensities separately, and it's averaging them on this graph. The caveat is that each of these colors are weighted differently. And that's because our eyes perceive green to be a slightly lighter color. So Photoshop ways green twice. So it takes one red, two greens, and one blue and then averages those channels of light rather than just one red, one green, and one blue. And again, that's just because our eyes perceive green to be a lighter color. This can definitely be a little bit confusing. And I don't think it's necessary to really understand the physics of how this works unless it's something that you're interested in, then by all means, go and explore and learn about how RGB histograms work by averaging the color channels differently. But it is important to have a general idea of how these RGB histograms work, as we'll discuss towards the end of this lesson, how it can help you as a photographer. Looking back at our color picker, one thing I wanna show you is how you can mix different channels of light to make secondary colors. So before we looked at each channel or color individually, Let's see what happens if we were to add separate colors together at their full intensities. So let's add red at its full intensity at 02:55. And let's add green at its full intensity. So it's combining red and green at their full intensities. What you'll see here is that when you combine pure red and pure green, you get pure yellow. We can do the same thing by combining other colors. So if I set this back to zero, keep Green at full intensity, and let's turn blue all the way on. We get the color cyan. And if we turn green off and turn red all the way up, we get magenta. And yellow, cyan and magenta are the secondary colors that we can create by combining the red, green, and blue primary colors. 10. RGB histograms part 2: reading RGB histograms and color channels: Alright, so now let's check out a Canvas that is pure red. And I think this is going to help you more to visualize what's going on in these RGB histograms. Let me turn this background layer off and I will select this red layer, and let's click refresh the histogram. So now instead of looking at white, which all of the colors were at their full intensity, and black were all the colors. We're at zero intensity. Now we're looking at a single pure color. We're looking at pure red. Let's take a look at what the histogram looks like. The RGB histogram for pure red. So we get a spike on the right side here and a much larger spike on the left side. The spike on the right represents red. And again, this is read at its fullest or brightest intensity. The spike on the left that's touching the left edge of the graph represents the green and blue channels at 0% intensity. So they're completely black, or you could say turned off. And you'll notice the spike on the right is smaller than the spike on the left. And that's because this bike represents one color channels. So just read. And this bike represents two color channels, so green and blue. Now, unlike the luminosity histogram, which is a histogram that graphs the lightness values on a scale 0-100% lightness. This histogram is looking at three different colors and graphing them individually based on how intense they are from pure black to the full color without any black added to it. If we look here at our luminosity histogram for this red canvas, you can see we have a single spike in pixels. That's because the pixels on this canvas all have a single lightness or a luminosity value. We get out our color picker and sample this color. You can see anywhere you click, the luminosity value stays the same. So that's why we get one single spike, because all of these pixels have identical lightness or luminosity. If we switch this over to RGB. Now this histogram is showing the different colors and the intensity of the three different primary colors. So red, which is at 02:55, it's at its full intensity. We get those pixels stacked all the way up at the right edge. And then green and blue, which are fully turned off, they're fully at Black. Those pixels representing blue and green are going to stack up at the black edge of the histogram. In addition to looking at the RGB histogram, you can also look at each individual channels separately. So let's first look at the red channel, which just pulls out that single color. Rather than having it averaged out with the other two colors in the RGB histogram. So if we select the red channel, we can see one single spike now. And that's because this histogram is ignoring the green and the blue colors. All this is saying is that every single pixel in this image is at level 255 and intensity, they're fully turned on. Now, up to this point, we've been looking at the three primary colors that have been turned all the way on. Let's see what happens if we add some red. But at a lower intensity value. If we take our color picker, we can drag it down to add some black, say maybe about halfway down. We get read at about 120. Now we have a darker shade of red. It's still the exact same hue of red. It's still pure red since we can see red is at zero degrees, right? Is always at zero degrees on the color wheel. So we know we have pure red still. We've just added some black into the red. So we've lowered the intensity of pure red. If I exit out of this and select the brush tool, I'm just going to brush in some of that darker red color. So if I brush this in here, we come up here and refresh this histogram. Now you can see that there are two spikes. The one all the way at the right side, like we'd been looking at, represents all of this red that is at its full intensity. Then we have a darker red or red that has black added to it. We see that at about the middle of the histogram here, we open our color picker backup. You can see how this color of red is about 50% of the way down on the scale from black to full intensity. And this spike right here is doing the same thing. It's just showing that we have red pixels in our image that are about 50% intensity. We could drag this down even more, create a darker shade of red. If I paint this onto the canvas. And we'll refresh this here, you can see we have a third spike that is representing the darker red in this image. It is getting closer and closer to pure black. Or in other words, the red is turning off as we add more and more black. Now, if we go back to the RGB histogram, we have three different red spikes are the same ones that we just looked at in the pure red color channel. Then we're getting the information about the blue and the green channels, the blue and the green colors. Let's try out this experiment with blue. If I turn this off and we pull up our pure blue Canvas, I will refresh this histogram here. We also need to select that layer. Alright? This histogram looks identical to the first RGB histogram that we looked at. We have a smaller spike on the right and then a larger spike on the left. But this time it's analyzing this blue Canvas. So we see these pixels up here showing that we have a spike in blue pixels at 100% intensity. And then over here we have both red and green, which combined to form a much larger spike on the histogram. The same thing here. If we turn on this green Canvas, the histogram looks exactly the same. But this time, this bar right here represents pure green, and this one over here on the left represents red and blue. So again, if I pull up the color picker and we sample for green, we can see that green is that it's full intensity. And this represents these pixels. They're just graphing all of the pixels that are at their full brightness or intensity level in these pixels right here that are being graphed are the ones that are at zero intensity. So the red and the blue. And if we open up channel, so if we open up the green channel, we see a giant spike in pure green pixels. If we were to change this to say red, we would see all of the pixels line up on the left edge there. And that's because we don't have a single bit of red on this image. Red is completely turned off, and this channel completely disregards any other color information. It's only showing you information about the intensity of red for every single pixel in this image. If we were to say, add a little bit of red into this image. So if I select our color picker and I set red 255 and turn this down to zero so we could get pure red. If I paint some red onto this canvas, you can see up here that we start to get a spike at the pure red area of the histogram. So as we start to add pure red in at its full intensity, we see the spike here. Let's turn the RGB histogram back on. And just to summarize, the RGB histogram combines and averages one red, two greens, and one blue for every pixel in the image weighing green two times because we see it as a brighter color. And the RGB histogram is looking at the image and graphing the distribution of color intensity for each one of these three primary colors from zero at black, 255 or full intensity. Let's go try a different experiment here. So let's go back to our color picker. And I'm just going to select a random color. So if I move along the color wheel, I am not going to pick a fully saturated or fully bright color this time. Let's say I pick something around here, maybe a little bit more orange. Alright, so now if we look down at our RGB values, we can see a combination of different intensities for red, green, and blue individually. So again, all of these values for the three primary colors are just mixing together to create literally any color that you could imagine. These colors are just getting mixed to create the specific color that we're seeing up here. All close this up. What I'm gonna do is I'm going to paint this color onto the canvas. I'm going to make this canvas entirely our new color. Now, let's check out our RGB histogram. Now we see three separate spikes. The reason for that is that we have a combination of three different color intensities. We open our color picker backup. We can start to deconstruct what each one of these spikes mean. Despite closest to pure intensity, is going to be the highest number of these three numbers. Because remember this is just a scale 0-255. This would be 212. So this would be the red color channel. The second largest number, 171, that's going to be our pure greens. So we see this spike right here at 171. This represents all of the pixels that have green at a 171 intensity level. Then we have the lower intensity color, the darker one, which is blue. So this spike right here is going to represent blue. Let's say that we add a, another color to our Canvas. So let's add a shade of blue. I'm just going to paint this color on just like that. And now looking at the histogram, we can see that it's starting to get a little bit more complicated. So now we have six spikes. We have the three spikes from before, which represent this yellowish orange color. And then we have three additional spikes that were created from the three color channels that make up this blue color. So now it starts to get a little bit difficult to figure out which spike corresponds to which color. You can imagine if we have a photograph with millions and millions of pixels and millions and millions of different colors. This RGB histogram is gonna be extremely complex. You might be wondering at this point, why does all of this matter? Why do you need to understand and RGB histogram and the different color channels. Well, the truth is that when you are editing a photograph, the RGB histogram is something that you're probably not gonna be using too much. You're really going to be using the luminosity histogram to see the distribution of tones throughout the image. But when you are taking photographs in the field shooting your camera, this is where an RGB histogram is extremely important. And the reason for that is that each individual color, so red, green, and blue, can be overexposed or underexposed individually. You think of the entire photo being overexposed, but you can actually overexposed one or more of these colors. If you're looking at an RGB histogram, especially when you're out shooting, when you can actually correct this problem. Any of these channels are clipped or blown out like we talked about clipping in the previous few lessons. It can indicate that the image is over or underexposed in that particular channel. It might actually not be perceptible to your eyes. But it does mean that you've lost information in your digital image. The histogram can be used as a tool to ensure that you're not losing any subtle information that maybe isn't very obvious to your eyes, at least out in the field shooting. It's helping you to see whether or not you've blown out any of these color channels. Because if you've blown out or clipped any of these color channels, you've lost detail in your image and that is something we want to avoid. We want to capture as much light information, as much detail as possible. Now, just like with a luminosity histogram. So if we were to look at the luminosity histogram for this image, which is really just two different colors. Remember how you learned that you want to avoid the brightest tones touching pure white and the darkest tones touching pure black. The same thing holds for the RGB histogram, even though it works a little bit differently, it tells you more about each individual color, primary color of light in your image. And you want to avoid having any of these channels are colors touching the right edge of the histogram. And any of these colors touching the left edge, especially if you didn't have any pure red, green, or blue. And the image which you are pretty much never going to see in nature, any pure black pixels because you're probably not going to see pure black when looking out at a landscape. So again, you want to avoid having any of the pixels touching the right edge or the left edge because that means you've lost detail in your image. I want to jump back to the photo that we've been looking at for the last few lessons and briefly go over an example of what an RGB histogram looks like on a photo that has colored channels that have been blown out or clipped. You look up here to the histogram. I've turned on the RGB histogram. And if you look all the way to the right here, you can see that this histogram has been clipped. In other words, we have some colors in this photo. We don't quite know exactly what colors yet, but either red, green, or blue has been blown out. Either 12 or all of those colors have been clipped or blown out because we see them touching the right edge of the graph. Here. If we compare that to the luminosity histogram, you can see that this histogram isn't blown out. So we don't have any pixels in this image that are pure white. We do, however, have pixels in the specific color channels. That are overexposed. So we have some colors in this image that are individually overexposed. Again, even though the luminosity histogram isn't clipped, we're getting additional information about exposure from this RGB histogram. And what it's telling us is that I actually did overexpose this photo. The reason for that is because these colors or one could be two or all three of the colors were clipped. Like we discussed in previous lessons, whenever you clip pixels, you lose detail in that image. And that's fine. Rgb histogram can be so valuable because with the naked eye, I can't really tell that this photo was overexposed. But the RGB histogram is showing me that some of the pixels are touching the right edge of the graph, which means that I've lost some detail. Now if I go into each individual color channel starting with red, I can start to piece out which one of those colors was blown out. And it looks like red is definitely overexposed. I have pixels in this image that contain red at their highest intensity. They are touching the right side of this graph. In my guess would be that those pixels lie somewhere over here. If I use my color picker and look at some of these pixels, you can see that red is very close to 255. The ones I've selected here aren't exactly 255, but they're very close. So somewhere around here, I probably have pixels that have an intensity of 255, which again corresponds to the pixels that touch the right edge of this graph. Let's look at some of the other channels here. Let's go to green. And you can see here that I have not blown out green. So green is actually within a proper exposure level. There are no pixels in this image that contain green at its maximum intensity. I know that I have not lost detail in the green channel. Let's look at Blue here. The same thing for Blue. Blue has not been overexposed, are blown out here. I have not lost any detail in the blue channel. If I go back to the RGB histogram, now I know that the pixels that are touching the right edge of this combined graph. So the average of all three red, green, and blue channels by separating them out and looking at them individually. Now I know that these are red pixels up here. And that's really how you use an RGB histogram to evaluate if you've properly exposed an image by not clipping the histogram. The other thing in RGB histogram can help you with is when you're adjusting the color balance of your image. If the histogram shows that one channel has a higher peak than the others, It's possible that this could indicate that the image has a color cast or a tint color throughout the entire image. You can correct things like that by adjusting the white balance and using color correction tools. And those are more advanced editing techniques which you may or may not be familiar with yet at this point. And if you're not, don't worry about that now, but just so you know, when you get to the point where you want to start adjusting white balance and correcting colors that this is where your RGB histogram is going to become very important. So those are the main ways that an RGB histogram can be utilized as a really powerful tool when you're both a shooting in the field and also when you are editing your photos. Now, in order to help this sink in, I really encourage you to practice this in Photoshop at home. So open up a canvas like this. It can just be a solid white Canvas. And open up the color picker. I want you to experiment changing the values of the red, green, and blue channels, testing out colors at their full intensity. And then maybe adding some different colors onto your canvas. And looking at how the RGB histogram correlates to the values that you see in your color picker. And the more you do this, the more you experiment and play around like I've shown you throughout this lesson. The more this is going to start to sink in, because this is very conceptual and it's okay if it doesn't quite make full sense at this point. But the more you play around with this and practice, I promise that it's going to start to make a lot more sense to you. So I hope that really helps you out and I will see you in the next lesson. 11. Conclusion: Well, that's it for this class. You all, I just want to thank you so much for being here. We covered a lot, so I hope you learned a lot. I hope you took a lot of weight in this course, and I hope you continue to practice with a lot of the tools and techniques that you learned here. Histograms can be a little bit of a confusing and intimidating topic. And sometimes it can take a little while for these concepts to sink. And I recommend that you continue to go over the course material. And the more you study, the more you learn, it will start to make a lot more sense. And you will start to pick this up. Now, if you've learned a lot from this class and enjoyed learning with me, I definitely recommend that you go check out some of my other classes. They're all aimed at helping you become a better photographer, especially a better landscape photographer, if that's the type of photography that you are interested in. I'd also love for you to go check out my website where I have a ton of free resources related to photography and exploring the outdoors. I want to thank you so much again for being here and I hope you continue to learn and create. And I hope to see you back here again soon. So that's it for now. Take care guys. I'll see you next time.