A histogram is an essential tool in digital photography. A histogram assists you in judging the brightness values of various parts of your image. Once you understand your histogram, you'll be able to avoid over or underexposing your photos, and you'll have better control of achieving correct brightness.
Basics of Digital Brightness and Exposure
In digital photography, we measure brightness in values of 0 to 255. 0 is pure black, 255 is pure white, and everything in between makes up the various brightness levels between those two values. As shown below, we can visualize this as a gradient from 0 (pure black) to 255 (pure white).
Most of us are familiar with the terms: shadows, midtones and highlights in our photography work. These terms are simply a way of dividing the gradient of brightness into zones, as shown below. From this, we can see that shadows are roughly between 0 - 65, midtones are in the 65 - 195 range, and highlights fall between 195 - 255.
A rule of thumb in digital photography is that we shouldn't go below the value of 0 or beyond the value of 255, as doing so will result in what's known as clipped highlights or shadows.
Clipped or "blown out" highlights or shadows present some issues. Firstly, there is no detail—all you have is either pure white or pure black. A lack of detail can be problematic when your work is printed and can also create issues on websites if the edge of the image bleeds into the pure white of a webpage, which you will see later in this article.
Reading a Histogram
Everything we have learned about the gradient of brightness values from 0-255 describes the horizontal axis of the histogram. If we take the pixels of an image and stack them on this axis according to their brightness, we get a histogram. In other words, the histogram is a graph that shows the concentration (vertical axis) of pixels recorded in an image according to brightness (horizontal axis).
Histograms & Correct Exposure
Exposure is easier to understand by looking at a histogram with its corresponding image. Here are a few examples of some well-exposed photos with their corresponding histogram:
The photo above is a Capture One histogram with its corresponding image. It shows the spread of pixels from shadows on the left to highlights on the right. The red, blue and green lines correspond to the RGB channels. The peaks are higher concentrations of pixels, whereas the valleys indicate lower concentrations of pixels.
The first thing to notice on this histogram is the significant spike (concentration) of pixels on the right (highlights). The spike shows the bright highlights of the image made up of the sun and sky. The grouping of pixels on the left corresponds to the shadows of the model's skin, clothing, and other darker areas in the image.
Notice that no pixels are touching the left or right walls of the histogram, indicating that we do not have clipped highlights or shadows in this image.
In this bright image's histogram, most of the pixels are bunched to the right-hand side of the graph in the bright midtones to highlights range. Looking at the photo, we see it is mainly made up of the bright white of the surf and the light blue of the sky. There is also a small cluster of pixels grouped in the deep shadows section of the histogram that represent the shadow tones of the surfer's wetsuit and skin. Notice the significant blue spike on the histogram, which corresponds to the image's bright blue sky.
This landscape is very different from the bright image above, yet it is still a well-exposed image. The histogram shows that it is made up mostly of two brightness levels, the illuminated area of the sky and the monument and the dark shadow areas of the tree and foreground. The histogram mirrors this with a large grouping of pixels in the shadows and another grouping in the highlights. Also, note that no pixels are touching the left or right walls of the histogram, indicating that none of the shadows or highlights are clipped.
This image shows an even spread of pixels across the histogram, with most around the midtone mark--a perfect mix of all tones from shadows to midtones to highlights.
We've looked at three very different images with three very different histograms. There is no perfect histogram, but the information it provides about the exposure of your image is invaluable in creating perfectly exposed images and spotting potential mistakes in your work.
Histograms & Incorrect Exposure
To highlight some of the errors you can easily spot by looking at a histogram, we'll take the first image as an example and apply different (wrong) processing and see how it affects the histogram. We'll be looking at Clipped Highlights, Crushed Highlights, Faded Shadows and Crushed Shadows.
When we overexpose an image, we clip the highlights. Looking at the histogram, we now have pixels touching the far right-hand side of the graph, meaning they are at the maximum brightness value of 255 (pure white). If we look at the corresponding image, we see that the highlights of the sky are pure white, and in fact, you can no longer see the top right border of the image as it bleeds into the pure white of the background webpage.
Crushed highlights occur when you edit your image so that what should naturally make up the highlights of an image—like the sun or a bright sky—are pushed into the midtones of an image. This is often done by overusing your highlights recovery tool. Looking at the histogram, we see that there is no information in the highlights region: the peak showing the highlights has now been pushed leftwards and is in the upper midtones region of the graph. This creates a crushed highlights look where the highlights appear grey and muted, and the image lacks a vibrance that would make it feel more natural.
There are no pixels in the deep shadow region of the histogram, and the graph only starts in the midtones. The image appears faded with no true black/clipped shadows. As you can see on the histogram, the pixels are unevenly distributed throughout the brightness range, resulting in a less balanced and natural-looking image.
Crushed shadows occur when you have pixels touching the far left wall of the histogram, as seen in the example above. These crushed/clipped shadows are now pure black and have lost any detail. You can see this clearly in the model's clothing, which is now a silhouette with no detail.
Hopefully, this should give you a good understanding of histograms and why they're such a helpful tool. If you'd like to explore further, you can find many valuable articles and videos online. Still, the best way to learn is to start using the histogram in your everyday photography and editing workflow.