256 Levels Of Brightness

If you look directly below the left or right sides of the histogram in the Levels dialog box, you'll see a number, with 0 on the left and 255 on the right. Histograms show us exactly 256 different brightness levels from black to white, with 0 being pure black and 255 being pure white, and that's because standard 8-bit images contain 256 possible brightness values (a photo saved as a JPEG file, for example, is an 8-bit image). The reason the numbers go from 0 to 255 and not from 1 to 256 is because while you and I usually start counting from the number 1, computers (and computer programs like Photoshop) start counting from 0:

If you look closely along the top of a histogram, you'll see that it's not smooth. Rather, it's made up of tiny little spikes and ridges, each one being the top of a thin vertical black bar. If you were to count them, assuming you had a continuous histogram from left to right, you'd find exactly 256 bars, with each one representing a different brightness level. The more pixels you have in your image at a specific brightness level, the higher up the bar for that level goes. Here's a zoomed-in view of the histogram to make the top easier to see:

Keep in mind that there is not a 1:1 ratio between the histogram and the actual number of pixels in the image. Most digital photos these days contain millions of pixels, and if Adobe tried to fit all of them inside the histogram, you would need several computer screens just to view it! Therefore, the histogram simply gives us an overall idea of how much of the image is at a certain brightness level compared to other brightness levels. Also note that even if you're working in 16-bit mode in Photoshop, which increases the number of possible brightness values from 256 to a whopping 65,536 of them, the histogram will still display the same 256 levels of brightness, again for the simple reason that it wouldn't fit on your screen otherwise.

The Myth Of The Perfect Histogram

Many people ask if there's such a thing as an ideal or perfect histogram. Earlier, I referred to the first histogram we looked at as an example of a "typical" histogram, but in reality, there's no such thing. Every image is different, which means that every histogram will be different. There is no ideal shape for a histogram that you should be working towards. In fact, caring too much about what your histogram looks like would be a big mistake. Histograms should be used as a guide, not a goal. What matters most is the image itself. The histogram simply shows us what we're working with and what sort of problems may already exist in the image (underexposed, overexposed, etc.). And by keeping an eye on the histogram as we work, we can make sure we don't take things too far and lose important image details. However, you should always judge the success of your images based on what the image itself looks like, not what the histogram looks like.

As an example of how different the histogram can be from one image to the next, consider this photo:

A night time photo like the one above would naturally be dark, or at least, darker than your average photo shot in the middle of the afternoon. Here's what the histogram for this photo looks like:

Notice how most of the histogram falls within the left half of the brightness range, which means that most of the image is dark and there's little in the way of lighter tones. Images like this one are commonly referred to as low-key images because they're just naturally dark. In fact, in this case, there's a large spike in the histogram on the far left, indicating that large areas of this photo are pure black. Now, everything we've learned up to this point tells us that so much pure black is a problem because it means we've lost image detail in the shadows, but as with most things in life, there are occasional exceptions to the rule. In this photo, since the plants in the foreground are meant to be silhouetted against the moonlight, it makes sense to have so much pure black in the image. It would be wrong to try and "fix" this photo just to make the histogram look more appealing.

Here's another example to consider:

This winter scene is quite the opposite of the previous image. Here, everything is bright with very few dark areas to be found. Here's what the histogram looks like:

In this case, we can see that most of the histogram falls within the right half of the brightness range, telling us that there are very few darker tones in the photo. In fact, not only is there nothing that's pure black, there's nothing even close to pure black in this photo. Images like this one are commonly referred to as high-key images because they're naturally bright. Here again, it would be wrong to make adjustments to the image simply to balance out the histogram, since the photo itself would suffer.

The rest of the tutorials in our Photo Editing section deal with how to actually correct various image problems, but since knowing how to read a histogram in Photoshop is such an essential skill for professional-level image editing, I thought it would be helpful to take a closer look at exactly what they are and what it is that histograms can tell us about our images. Whether you're viewing the histogram on the LCD screen of your digital camera, or using it to adjust the exposure in Camera Raw, fixing overall tonal problems with Levels, boosting image contrast with Curves, or simply keeping an eye on the Histogram palette as you work, hopefully you have a better understanding at this point of just how indispensable histograms really are.