One of the most important and valuable tools in Photoshop for editing, retouching and restoring images is the Histogram palette. The importance of this tool is evidenced by the fact that they are used not only in Photoshop, but in many other graphic editors. Also, histograms are displayed on the displays of modern digital cameras, which allows you to view the exposure of your photographs not only after shooting, but also to select the desired angle and parameters in the preparation process before shooting the frame.
So what is a histogram? A histogram is a graph that shows the current tonal range of our image. The term "tonal range" refers to the brightness values of an image. The histogram shows us how many areas of the image are currently pure black (or the darkest), how many areas are currently pure white (or the brightest), and how much is between these extreme black and white areas. It is important in this case not to confuse the concepts of “black” and “white” with black and white images. In this case, the concepts “black” and “white” should be understood not as colors, but as extreme degrees of brightness. Histograms work equally well with a full color photograph, since we are dealing with the value of brightness, not color, and with a black and white photo.
Note. We can use histograms to look at the brightness of each individual color, but that is beyond the scope of this article.
Why do we need to know the tonal range values of an image? Well, for example, have you ever looked at a photo that seems a little dull and “flat”? Most of the time this happens due to low contrast and with the help of histogram we can easily detect and solve this problem. It will show us the existing brightness areas that are not as bright as they should ideally be, this also applies to dark areas that are not dark enough for a given image. Both of these problems result in poor contrast, but fortunately this is easy to spot and correct using a histogram.
Another potential problem we can run into when editing images is that we can reduce the dark and light parts of the photo to pure black or pure white, so that we can lose all the image detail in those areas of the photo. This error is known as shadow and highlight clipping. All this means is that we can lose image detail in the light and dark areas of the photo.
This is not always easy to notice just by looking at photographs, because... our eyes are simply not sensitive enough (although we can discern detail in the shadows much more easily than we can in the highlights). And with the help of a histogram, we can instantly see both the original flaws in the image and the mistakes made during editing. Or if we restore old photograph, the histogram will tell us about the lost light or dark areas.
Whether or not you pay attention to what the histogram shows is your personal choice. But every photographer should at least know that such a tool exists and how it can be used. In this article you will learn "read" histogram and recognize the tonality of your photo based on the histogram.
What is a photography histogram?
A histogram is a graph that shows the distribution of tones in a photograph. Please note that we will be talking about a histogram, which contains information specifically about the tones (not colors) in the photograph. If we are dealing with an image in RGB format, then such a histogram will represent all channels at once.
There are also histograms separately by channel, which show the distribution of the red, green and blue channels (colors) separately in the photo, but personally I don’t use them at all.
Where can I find a histogram of an image?
You can open a photo histogram directly in your camera or during processing in the “Histogram” information window in Lightroom and Photoshop. In Photoshop, the histogram is also presented in the Levels and Curves windows.
In a camera, the histogram is usually called up by pressing the Info button 2-3 times in a row in photo preview mode. This changes the type of preview view from photo to Full Screen Additional data about the file parameters and corresponding histograms appear.
How to read a photo's histogram?
A histogram shows how much shadow, midtone, and highlight there is in your photo. The horizontal scale controls the tonality of the pixels, from the deepest shadows on the left, to the midtones in the middle, to the brightest areas of the image on the right.
It is important to understand that the leftmost point is black point(completely blind, underlit areas without details), and the far right point is white point(the most burned out overexposed pixels, information about which is completely lost).
The vertical scale shows the number of pixels of each tonality in the photo. The higher the “peak” of the histogram, the more corresponding tones there are in the image. For example, in the histogram of the photograph shown in the examples above, there is a very high peaks fall on the left side of the histogram, which indicates that most of the photo is occupied by dark areas (in this case, the dark background).
How to use a histogram?
Most often, a histogram is used to orientate how correctly is it set? I especially recommend relying on the histogram readings for novice photographers who find it difficult to determine “by eye” whether there is enough light in a photo.
The basic rule in this case is avoid histogram peaks at extreme points, which talk about underexposure or overexposure in the photograph.
Underlight. If the histogram is heavily skewed to the left and there are high peaks at the leftmost point, this means that there are a lot of underexposed areas in the photo, i.e. There is a loss of detail in the shadows.
Peresvet. If the histogram is very skewed to the right, and the high peaks are on the far right, then the exposure was set too high, i.e. some parts of the image are overexposed (loss of detail in highlights).
Both situations are two extremes that should be avoided when selecting exposure settings.
Correct exposure. In most cases, a histogram in which the peaks are located in the middle part of the graph indicates that the exposure has been correctly set. But this does not mean that all photos need to be reduced to some standard medium-gray histogram. This does not happen and should not happen.
It is important to understand that each photograph has its own set of lights and shadows, and depending on the subject of the shooting and the artistic idea of the author, light colors or, conversely, shadows may predominate. Accordingly, the histogram of such a photograph will be shifted in one direction. But this does not mean that the exposure was set incorrectly. Let's look at a few examples.
An “ideal” histogram only indicates the predominance of mid-gray tones in the image. This is what the photo above would look like if it were fitted to the “ideal” histogram.
As we can see, the main distribution of histogram peaks is in the middle (midtones). At the same time, the photo looks flat, low-contrast, and clearly lacks saturation in the shadows and highlights. But we got maximum detail in both highlights and shadows. But is this really important from an artistic point of view?
If you initially shoot a plot with a lot of dark tones(dark background, dark clothes, etc.), then the histogram will naturally shift to the left. Wherein gaps in the shadows are allowed, if these failures occur in plot-insignificant areas of the photograph (background, small areas in the shadows on clothing or environmental objects).
The opposite situation is when we shoot very light story(on a white background, against the light, a model with light skin, in light clothes, etc.), then the histogram will be shifted to the right. At the same time d overexposures (completely white pixels) are omitted in plot-unimportant parts of the photo(background, details in the background, etc.).
Applied to portrait photography plot-wise important details- this is primarily the skin (face, hands, figure of the model), hair, and to a lesser extent the model’s clothes.
Therefore, the basic rule for checking exposure in portrait photography is no overexposure on the model’s skin. Small overexposures in highlights on clothing and accessories, and even more so against the background, are quite acceptable.
For example, in the photo below, the exposure has been adjusted to capture detail on the subject's face while at the same time producing a clear line of light and shadow across the face. At the same time, the shooting turned out to be almost silhouetted, against the light, against the backdrop of a large window.
Why should you be more afraid of overexposure than failures in the shadows?
In digital photography (as opposed to film photography), the biggest problem is overexposure, because when it hits too much large quantity light, part of the photo turns out completely white, which means complete lack of information about the image. Such overexposed areas cannot be restored - even the RAW format cannot save you, because an error was made during shooting and the necessary data for constructing the image was not obtained.
Information in underexposed shadows is still preserved, so details even in the deepest shadows can, in principle, be pulled out in Lightroom (with the inevitable appearance of strong noise). We are not talking about maintaining image quality now.
For clarity, I will give the following example. Photograph of a high-contrast scene with a large variation in illumination between the lightest and darkest areas. Some average exposure value was chosen (neither yours nor ours). As a result, the bright sky outside the window became overexposed (overexposures are marked in red), and the deep shadows inside the room fell into blackness (gaps in the shadows are marked in blue).
When we try to bring back detail in the shadows by lowering the exposure to the limit, we essentially end up with a gray fill in those areas where there were overexposures. It was not possible to return any details (clouds, tree outlines, tonal transitions, etc.).
If we try to restore details in the shadows, then when we increase the exposure to the limit, we can quite clearly see the texture of the wood on the legs of the chairs.
Conclusion
On the one hand, it is much easier to “extract” image details from the shadows, but noise inevitably creeps in; It is impossible to return details from overexposure, but a slightly overexposed (up to +1 exposure stop) photograph can be brought to a decent appearance without the risk of noise.
This is what I personally do (this does not mean that this is the only correct option).
1. When shooting, I avoid overexposure in areas important to the subject.
2. In critical situations, I prefer to slightly overexpose the frame to avoid strong noise when trying to pull out underexposed shadows. Then, during processing, I dim the lights, returning them to “normal”
One of the most valuable tools for photo editing in Photoshop is the histogram! In fact, histograms are so important that their uses are not limited to just Photoshop. Most photo editors have them, including Adobe Lightroom, Photoshop Elements, and even Camera Raw in Photoshop.
A histogram is a graph that shows the current tonal range of an image, which allows you to evaluate it and, if necessary, correct it. By tonal range I mean the brightness levels in a photograph/image. The histogram shows which part of the image is as black as possible, which part is as white as possible, and everything in between.
Histograms are also great when working with color photographs. Even if we don't perceive color as more than just color, each color in an image has its own level of brightness. For example, yellow shades are usually very light, blue shades are much darker. This difference in brightness greatly affects the tonal range of the entire photograph.
How can the tonal range of a photograph help us? For example, have you ever looked at a photograph and thought that it was somehow “incomplete”? The subject of the photo is interesting, the composition is good, but overall it doesn’t catch anything. And all because the picture lacks contrast. The highlights are slightly washed out and the shadows are not dark enough.
Now it may seem that such defects can be seen with the naked eye, but you cannot always rely on your own vision. It's visually easy to compare two side-by-side images, but if you look closely at one image, your eyesight can easily deceive you.
It is also worth considering that the monitor is not always able to show colors as they are. If you set the screen brightness to full, the photo will look great on it, but you will be disappointed when you print it. The histogram will help you avoid similar situations, because it takes information directly from the image, always showing accurate tonal range data, and helps identify contrast problems.
Another common problem when editing images is limited shadows and highlights. That is, the shadows are so dark that they turn into a solid black canvas without any information, and the light areas are so bright that they are simply replaced with a white fill. You've probably heard terms like " overexposed photo" This is when the light areas in a photo are just white with no information and cannot be corrected by software.
As with low contrast, it is not always possible to identify these types of problems visually. Our eyes are simply not sensitive enough to such things, and the monitor may also not give us an entirely accurate picture. But the histogram always gives accurate data, especially when someone went overboard with the settings during the editing process and accidentally ruined the colors in the photo.
Or, if we have to restore a very old photo, then the histogram will help identify the missing details and indicate where to start working. In the end, if you often process photos and don’t know how to work with a histogram, then there’s nothing good about it.
Viewing a Histogram in Photoshop
There are several ways to view a histogram in Photoshop, including the well-known Histogram panel ( Histogram). However, to best understand exactly how a histogram works, we advise you to use the Levels panel ( Levels).
I mentioned earlier that histograms work equally well with both black and white photos and color ones. But to make the information easier to perceive, let's start with a black and white photo:
Black and white portrait photo
To go to the "Levels" panel ( Levels), you need to open the “Image" menu ( Image) at the very top of the screen, then the “Correction” section ( Adjustments), and then “Levels”:
Menu Image > Adjustments > Levels
The “Levels” window will open in front of you. But let's leave the topic of using levels when adjusting images for the next tutorial. For now we simply use this window to evaluate the performance of the histogram:
Levels Window
The histogram is a black shape in the center of the window, resembling the silhouette of mountains. Each image has its own unique histogram, and now you will understand why:
Histogram in the center of the Levels window
If you look just below the histogram, you can see a gradient strip. On the left it starts with black and fades to white on the right:
Black and white gradient below the histogram
As we've already seen, a histogram shows the current range of brightness levels in an image, and those levels correspond exactly to the gradient below. Histograms start black on the left—like a gradient—and end white on the right. Plus, the brightness level increases from left to right, just like a gradient!
The brightness levels of the histogram correspond to the gradient located below it
So why does a histogram resemble mountains? Because it shows the current brightness levels or tonal values in the image. In other words, it shows how much of the image is at a certain brightness level compared to other brightness levels. Therefore, some histogram elements are longer than others.
The higher the histogram bar at a certain brightness level, the more pixels in the image are at that level. Shorter histogram bars indicate brightness levels where the fewest pixels are present. And if in a certain area of the gradient the bar is not displayed at all, this means that in this part of the image there are no pixels that cover this brightness level.
It is important to know that the histogram does not show us a specific number of pixels. This is due to the fact that most modern cameras are capable of taking pictures with a resolution of 10-20 megapixels at the same image size.
It would take several huge monitors to fit all those pixels into a histogram! So instead, the histogram only gives us general idea about the tonal range of the image, distributed into highlights, midtones and shadows, and also shows completely black or white areas.
The histogram of a well-exposed frame will show the entire range of brightness levels from black to white, and an example of such an image is given below. Left side The histogram starts from the left edge, above the blackest shade of the gradient ( these are the darkest shades that can be):
The left side of the histogram starts at the very edge, with pure black
On the other side of the histogram, we see that it extends to the very right edge, covering all kinds of lights. The small bar at the very end of the histogram tells us that the image contains the whitest possible colors. That is, we can say with confidence that our black and white photo looks correct:
The right side of the histogram ends at pure white
Typically a histogram is read from left to right ( from dark to light). If you start on the left and move towards right side, then you can see that the image is dominated by dark colors, and then, as you approach halftones, there is a sharp decrease in the number of pixels. Then there is a rapid increase again, after which there is a sharp drop to complete absence pixels:
The highest bars in the histogram are in the highlights and shadows; the fewest pixels are in the midtone area
Since most of the pixels are in bright areas, we can conclude that the image will be light. We also have sufficient quantity dark shades, and this is reflected by the tall bars located in the dark part of the gradient. And finally, the small height of the bars in halftones tells us that there are still such areas in the image, but there are fewer of them than light or dark ones.
Let's take a look at the photo again and make sure that most of it is really light ( girl skin, eyeballs, clothes, background). We also have quite a few dark areas ( hair, eyebrows, eyes, and some background), but few halftones. They are there, but not as many as other brightness levels, which gives more contrast. It's safe to say that the histogram told us perfectly about the tonal range in the photo:
Photo and histogram match each other
Now let's take a look at the full color photograph:
Full color photography
Let's open the Levels window again and look at the histogram. But this time I'll use the keyboard shortcut Ctrl+L ( Win) / Command+L ( Mac). The same window opens, only using hotkeys everything happens faster:
Levels window with histogram of the second image
Let's see what this histogram tells us. Here again we see that the photo is well exposed. On the left side, the histogram starts with black, and the right side ends completely in white, meaning that the tonal range spans both ends:
The histogram starts with black and ends with white
Starting to study the histogram from left to right, you can see that the number of pixels increases sharply almost from the very beginning, in the dark areas, but, unlike the previous example, their number does not fall in the midtones. Up to the light areas, the number of pixels remains approximately the same, but after reaching this range on the gradient, the height of the bars in the histogram rapidly increases. After which there is a sharp decline in the white areas:
This suggests that the image is quite detailed in all three ranges ( shadows, midtones and highlights), and that there are more white areas here than others. Let's take a look at the image again. White shirt The groom and bride's dress make up the majority of the frame, which explains why there are so many more pixels in the highlights in the histogram:
Photo and histogram match again
Using a histogram to identify problems
So far we've looked at histograms of photos with the correct exposure, but a histogram can also help identify problem areas. For example, what could that high “peak” at the beginning of the right side of the histogram mean?
High peak on the right side of the histogram
This usually indicates that the image is overexposed. The high rise is outside the limits because too much of the photo has turned white without any information. To see this in the image, let's compare the groom's shirt. On the left is a correctly exposed photo showing all the details. On the right is an overexposed ( or overexposed) photo where white shades have turned into a solid white canvas.
Notice how many parts of the shirt are missing:
Exceeding the maximum brightness level leads to loss of image detail
The same rule works on the opposite side of the histogram. Take a look at the very left side of the histogram - there is a high peak there too:
High peak on the left side of the histogram
This indicates that the photo is underexposed and many areas have become a solid black canvas, losing detail. To see the defect in the photo, let's compare the groom's hair in the two photos. On the left we again have a well-exposed shot that shows all the right details. On the right, many details are lost due to the fact that many areas have turned into a “canvas”:
Insufficient brightness in dark areas results in loss of detail and makes them appear black
If your camera has a histogram view or highlighting areas that are too dark or light, be sure to use it. If you find such problems, we recommend that you retake the shot by changing the exposure settings. However, these errors can be corrected using “Levels”, “Curves” or “Camera Raw” in Photoshop. But today we are talking exclusively about histograms.
How many brightness levels are there in the histogram?
Now you know that a histogram shows the tonal range of an image from completely black to completely white. But how many tones are there in a histogram? The histogram contains 256 brightness levels for each of these “bars”. But it also happens that the bar is not displayed on the histogram at all.
If you zoom in very close to the histogram graph, you can see that in fact it is not smooth and consists of separate bars. Each peak represents the top of a separate vertical column. If you view a histogram with a full spectrum in grayscale, then when you count the bars you will get exactly the number 256.
In previous articles, we discussed how to interpret histogram graphs on your camera. You can use this handy tool when shooting to adjust highlights and shadows so you don't cut out the information you need. Unfortunately, if the problem is with the camera, the best thing you can do is change the settings to improve subsequent shots.
Today we'll use the same principles in post-processing in Lightroom 3. Here, the histogram is not only an indicator of the current state of the photo, but also a tool you can use to improve the photo.
Note: Although we will be using Lightroom in in this example,techniques and principles are applicable in any software.
What is a Histogram?
If you don't understand what we're talking about we're talking about, launch Lightroom, select a photo and go to the Develop module. At the top right you should see a color graph that seems too complex to be useful.
Although the histogram is actually very easy to use, basic level. You can understand the basic concepts in a few minutes, be able to apply them, and immediately change the way you analyze photos.
Why do we need a Histogram at all?
If you are a beginner and don't know much about the histogram, you may wonder why you should pay attention to it at all? You've been good at editing your photos in the past and didn't pay attention to that creepy graph - so why do you need it now?
For me, the answer lies in the print results of the client's images. Editing a photo for the screen and for printing are completely different things. When you're editing photos for the web, you'll have to deal with a ton of different monitor calibrations, color settings, and other quality settings.
What I notice all the time is that photos that look amazing on my Apple monitor will often be too dark on a $300 Dell laptop. The best thing you can do in this situation is to experiment until you have a general feel for how your photos would look on a hypothetical typical monitor with standard settings (though understanding the histogram can help with this a lot).
In general, just because a photo looks good on your screen doesn't mean it will look just as good when printed. Obviously, working with your photo printer and calibrating your monitor to their setting guidelines are important steps, but a histogram can also be an invaluable tool for working with highlights, shadows, contrast, and brightness in preparation for printing.
What does this mean?
Perhaps now you are looking at this rainbow histogram and asking yourself: “what does this mean”? As I said above, although it seems complicated, you actually don't have to learn much to use this tool effectively.
To understand what the histogram is telling us, let's take a look at the modified version of the image below.
Here I have noted some important parts of the graph. The graph plots image data in pixel-by-pixel rage coordinates. The left side of the graph is responsible for the dark parts of the image, and the right side is responsible for the light parts.
In the picture above, we can notice that almost all the pixels in the image are concentrated on the right side of the graph. Even without looking at the photo, we can immediately tell that this is an image that is too bright, but without overexposure.
Lost Parts
Very important element, which is worth paying attention to when working with the histogram in Lightroom, as well as with your camera, is clipping (cropping, data loss). Clipping occurs when detail is lost due to overexposure in the photo, or when detail disappears in the shadows. You can determine the presence of clipping by looking at the left or right side of the graph. If the data is pressed against the edges of the graph, then there is clipping.
In the picture above, you can see four different scenarios. The top histogram on the left tells us that we haven't lost any data in the shadows or highlights (although this image has very low contrast, which we'll discuss later).
The top histogram on the right tells us that there is clipping in both the shadows and the highlights, and the next two graphs show the difference between these two options (below on the left - highlighted, below on the right - gaps in the shadows).
If we return to our conversation about printing, we can bet that two out of three histograms will result in very poor print quality, regardless of what we see on the screen. However, keep in mind that sometimes clipping is acceptable, and even desirable. The trick here is to find where clipping occurs and decide whether data loss at that location is acceptable or not.
Definition of Clipping in Photography.
In most cases, you will find that when working with the histogram, you will be making minor adjustments to the shot. However, for the purposes of this tutorial we need to actually see what effects take place, so we will have to use some extreme examples. Consider the photo below:
Looking at the photo, we can see that we have problems in the shadows and the histogram confirms this. Notice that the small arrow at the top of the histogram is highlighted, indicating clipping has occurred.
If we click on the arrow for the highlighted parts, we will see that the pixels that are out of range are highlighted in red in the photo. If we hover the mouse over the shadow arrow, the lost pixels will be indicated in blue. And finally, if we press the "j" key, we will see all the clipping at once.
As you can see, there is only a small part of the image where there are highlights. You can lower this a little down a tone, but this is not comparable to our blockages in the shadows, of which there are really a lot.
Eliminating the Clipping Part of an Image
If you place your mouse over a histogram, you will see that different parts of the graph are highlighted as you move your mouse. This is because there are four plot elements that are controlled by four adjustable parameters in Lightroom. This is shown in the picture below.
In the "Basic" section of the Develop module, you will find the four controllable parameters shown above. These are: Blacks, Fill Light, Exposure and Recovery. You can change these options in two ways: You can work with the graph directly by clicking and dragging some parts left or right. Another way is to use the sliders in the Basic section. I find it a little awkward to use actual controls to adjust settings.
Basically all you have to do is adjust each of these parameters individually until you like both the shape of the graph and appearance picture.
Again, remember that in real-life situations you'll likely be making much more subtle adjustments. You'll find yourself working on a little more detail in the hair or trying to reduce the amount of light in just a few spots.
Adjusting Contrast Using Histogram
While for me the main use of a histogram is to work with clipping, you can use it in other cases. Another case I often use a histogram for is analyzing the contrast of an image.
If you think about what the data in a histogram graph says, you'll realize that if the histogram pixels cluster in one area of the graph, then it indicates very low contrast.
If all the data is in the middle, then there are very few truly dark and light elements; your image in halftone. If everything is on the right, then everything is bright and there is little dark, and if everything is on the left, then everything is dark and there are no light parts.
With this in mind, you can look at the histogram and decide whether you need to add contrast to the image or not. Look at two examples below. As you can see, the histogram for the first image shows very low contrast, while the second image shows comparatively higher contrast.
You can increase the contrast simply by using the contrast setting in the basic settings. Another technique I often use when working with low contrast images is to pay attention to the highlights and shadows and spread them out. different sides histograms.
To really create depth in an image, I like to bring the highlights and shadows right to the edges of the histogram, or even before clipping begins, as you can see in the example above.
Conclusion
To sum it up, Lightroom's histogram is similar to what you have in your camera, but it's actually a more robust tool for improving the quality of your images. You can use a histogram to identify areas where image detail is clipped, or to work on contrast, and you can use Lightroom's tools to correct these problems so your photo looks great once you print it.
Leave a comment below and let us know if you use a histogram when working on the quality of your images. Be sure to tell us what a “good” histogram is for you, and what techniques you use to make it that way.
A histogram illustrates the distribution of pixels in an image; This is a graph that shows the number of pixels at each color intensity level. The histogram shows detail in the shadows (left), midtones (middle), and highlights (right). The histogram allows you to determine whether the image has enough detail for effective correction.
The histogram also gives an idea of the tonal range of the image or the key type of the image. Low key images concentrate detail in the shadows; A high key image contains the most detail in the highlights; and in midtones, detail is concentrated in mid-key images. A full tonal range image contains a certain number of pixels in all areas. Determining the tonal range helps you select the appropriate tonal correction.
A. Overexposed photograph B. Correctly exposed photograph with full tonal range C. Underexposed photograph
The Histogram panel offers many options for viewing color and tone information in an image. By default, the histogram displays the tonal range of the entire image. To view histogram data for a portion of an image, first select that portion.
To view the histogram as an insert overlay in the Curves dialog box, select the Histogram option from the Curve Display Options menu, and in the Adjustments panel (CS5) or Properties panel (CS6) of the Curves window, select the command " Curve Display Options > Histogram.
Histogram panel overview
To open the Histogram panel, choose Window > Histogram or click the Histogram tab. By default, the Histogram panel opens in a compact view that lacks controls and statistics, but you can choose a different view.
Histogram Panel (Expand View)
A. Channel menu B. Panel menu C. Refresh without cache button D. Cache data warning icon E. Statistics
Selecting a Histogram panel view
Choose a view from the Histogram panel menu. Advanced View In this mode, the histogram is displayed with statistics. It also displays controls that let you select the channels represented in the histogram, set Histogram panel options, refresh the histogram to view uncached data, and select a specific layer in a document with multiple layers. Compact View The histogram is displayed without controls or statistics. The histogram represents the entire image. View All Channels In addition to all the options available in the expanded view, individual channel histograms are also displayed. Histograms do not include alpha channels, custom color channels, or masks.
Histogram panel with all channels shown in color and hidden statistics
View a specific channel in a histogram
If you select Expanded View or All Channel View in the Histogram panel, the settings in the Channel menu become available. In Photoshop, channel settings are remembered when you switch from Expanded View or All Channels View back to Compact View.
- Select one of the channels to view its histogram, such as the color channel, alpha channel, or spot channel.
- Depending on the image's color mode, select RGB, CMYK, or Composite to display a composite histogram of all channels.
- For an RGB or CMYK image, select Luminosity to display a histogram representing the luminosity or intensity values of the composite channel.
- For an RGB or CMYK image, select the Colors option to open a composite histogram of the individual color channels in color. This option is the default for viewing images RGB format and CMYK if "Extended View" or "View All Channels" is preselected.
When viewing all channels, selecting options in the Channel menu changes only the top histogram in the panel.
View channel histograms in color
In the Histogram panel, do one of the following:
In all channels viewing mode, select the “Show channels in color” command from the panel menu.
In Expand View or All Channel View, select one of the channels from the Channel menu and choose Show Channels in Color from the panel menu. If you switch to compact viewing mode, the channels will still be displayed in color.
In an expanded view or viewing all channels, select Colors from the Channel menu to open a composite histogram of the channels in color. If you switch to compact view, the stacked histogram will still appear in color.
View histogram statistics
By default, the Histogram panel displays statistics in Expanded and All Channel views.
- Choose Show Statistics from the Histogram panel menu.
- Perform one of the following actions.
To view information about the value of a specific pixel, hover your mouse over the histogram.
To view information about a range of values, drag across the histogram to highlight the range.
The panel below the histogram displays the following statistical information.
Average Average intensity value. Std. off ( standard deviation) How much intensity values vary. Median The middle value within a range of intensity values. Pixels Total number pixels from which the histogram was calculated. Level The intensity level for the area under the mouse pointer. Count The total number of pixels corresponding to the intensity level of the area under the mouse pointer. Percentile The cumulative number of pixels corresponding to the intensity level of the area under the mouse pointer and lower levels. This value is expressed as a percentage of all pixels in the image, from 0% at the far left to 100% at the far right. Cache Level Shows information about the cache of the current image that was used to create the histogram. When the cache level is greater than 1, the histogram is displayed faster. In this case, a histogram is created from a representative sample of image pixels (based on magnification). The cache level for the original image is 1. At each level above the first, the average of four neighboring pixels is taken to calculate the value of one pixel. Thus, the image dimensions for each next level are two times smaller (1/4 the number of pixels) than for the previous level. When Photoshop needs to do a quick approximation, the program can use one of the higher levels. Click the Refresh Without Cache button to redraw the histogram using the actual image layer.
View a histogram for a multi-layer document
- Choose Expanded View from the Histogram panel menu.
- Select suitable value in the "Source" menu. (The Source menu is not available for single-layer documents.) Full Image Shows a histogram for the entire image, including all layers. Selected Layer Shows a histogram for the layer that is in this moment selected in the Layers panel. Combined Adjustment Shows a histogram for the adjustment layer selected in the Layers panel, as well as all layers below that adjustment layer.
Preview adjustments in a histogram
You can preview the results of any color and tone adjustments in the histogram.
Select the Preview check box in the dialog box of any color or tone adjustment command.
When you select View mode, the Histogram panel displays the effect of adjustments on the histogram.
Note. When you make adjustments in the Adjustments panel, any changes are automatically reflected in the Histogram panel.
Preview the adjustment in the Histogram panel
A. Original histogram B. Corrected histogram C. Shadows D. Midtones E. Highlights
Histogram update
When the histogram is read from the cache rather than the current state of the document, a Cache Data Warning icon appears in the Histogram panel. Image cache-based histograms are rendered faster and are generated from a representative sample of image pixels. In the performance settings you can set the caching level (from 2 to 8).
Note. More high level caching increases the speed of redrawing for large multilayer files, but requires the use of additional random access memory systems. If there is a shortage of RAM, or when working with small images, it is recommended to set low caching levels.