[Photography] How to Read (and Use) Histograms for Beautiful Exposures

The post How to Read (and Use) Histograms for Beautiful Exposures appeared first on Digital Photography School. It was authored by Darlene Hildebrandt.

Are you struggling to understand how histograms in photography work? Do you want to know how to read a histogram so you can capture consistently detailed exposures?

In this article, we’re going to look at everything you need to know to get started with histogram photography, including:

• What a histogram actually is
• How to understand the peaks of a histogram graph
• How to use a histogram to prevent overexposure and underexposure
• Histogram pitfalls and mistakes

So if you’re ready to become a histogram expert, then read on!

What is a histogram?

A histogram is a graph that represents the tones in an image: the highlights, the shadows, and everything in between.

Every image has a unique histogram, which is displayed on your camera and by most post-processing programs.

Why is a histogram useful?

In photography, a major goal is to capture a detailed exposure of a scene (i.e., a photo with well-rendered shadows, highlights, and midtones).

And while you can always check image exposure by looking at your camera’s LCD screen or by viewing your image on a computer, the histogram offers a more objective method of evaluating tones.

If an image has blown-out (detailless) highlights, this will be visible on the histogram; if an image has clipped (detailless) shadows, this will be visible on the histogram; if an image is just generally too dark or too light, the histogram will make this clear.

That’s why photographers love histograms so much, and why learning how to use a histogram is essential. If you can read a histogram, you can quickly and accurately check the exposure of your image while out in the field or when editing at home.

How to read a histogram: step by step

As I explained, a histogram is a graph – which represents the pixels in an image, like this:

The left side of the graph represents the blacks or shadows, the right side of the graph represents the highlights or bright areas, and the middle section represents the midtones of the photo (middle or 18% gray). 

The graph peaks represent the number of pixels of a particular tone (with each peak corresponding to a different tonal value). So a peak at the right side of the histogram (such as in the example image above) indicates a large volume of bright pixels in the image. Whereas a peak at the left side of the histogram indicates a large volume of dark pixels in the image.

Here’s how I recommend reading a new histogram:

Step 1: Look at the overall curve of the graph

Is the histogram skewed to the right? Skewed to the left? Or just generally centered?

A left-skewed histogram often (but not always!) indicates underexposure, as the shot is full of dark pixels.

A right-skewed histogram often (but not always!) indicates overexposure, as the shot is full of light pixels.

And a balanced, generally centered histogram tends to indicate a beautifully detailed, well-exposed image, because the shot is full of midtones.

Step 2: Look at the ends of the histogram

A histogram with peaks pressed up against the graph “walls” indicates a loss of information, which is nearly always bad.

So check both the right and left ends of the histogram. Look for any clipping – highlight clipping along the right side, or shadow clipping along the left side.

What will a histogram tell you?

A careful analysis of a histogram will tell you two things:

1. Whether an image is broadly well-exposed
2. Whether an image has clipped tones

You can tell that an image is well-exposed if it’s balanced toward the center of the frame, with no obvious skew. Ideally, the graph is spread across the entire histogram, from edge to edge – but without edge peaks, which indicate clipping.

Here’s an example of a well-exposed histogram:

This is how an ideal histogram might look: evenly distributed and not up the sides, stretching across the entire graph.

If your histogram looks like the one displayed above, then your exposure is likely perfect and requires no adjustment.

However, if the graph is skewed to the right and/or includes peaking against the right end, it’s a sign you should reduce your exposure (try boosting the shutter speed) and retake the image:

And if the graph is skewed to the left and/or includes peaking against the left end, it’s a sign you should increase your exposure (try dropping the shutter speed or increasing the ISO) and retake the image:

Histogram pitfalls and mistakes

In the previous section, I talked all about ideal histograms and how you can use a histogram to determine the perfect exposure for a scene.

But while this is generally true, and the histogram guidelines I shared above are generally reliable, you may run into three issues:

1. Your scene may be naturally darker or lighter than middle gray

A well-balanced, unskewed histogram is ideal for images that include plenty of midtones and is generally centered around midtone detail.

But certain scenes just don’t look like this. For instance, if you photograph a black rock against a night sky, you might end up with a histogram with a significant skew, even if you’ve captured all the detail correctly:

This is a histogram for a dark subject. It is not wrong; it is just shifted to the left to represent the tones of the subject. This might be a dark rock at night, or a black cat on dark pavement.

And if you photograph a tree against snow, you might see skew in the other direction, because the scene is naturally lighter than middle gray:

This is a histogram for a light subject (e.g., a snow-covered valley) with mostly light tones in the scene and few dark areas (e.g., trees). See how it is shifted to the right compared to the dark subject? This is what you want, assuming your scene is mostly light-toned. If you change your exposure to keep the graph centered, you will end up with gray snow, not white snow.

So before you look at your image’s histogram, ask yourself:

Should my scene average out to a middle gray? Or should it have an obvious skew? Then use this information to guide your approach.

2. You may wish to overexpose or underexpose for creative reasons

Sometimes, even though an image is technically overexposed, underexposed, or clipped, it still looks great – so if you’re after a creative result, you don’t need to worry so much about an “ideal” histogram, assuming you know exactly what you want.

For instance, you might blow out the sky for a light and airy look, or deliberately underexpose for a moody shot; really, the possibilities are endless! Just remember to check your histogram no matter what and aim for a specific, deliberate result.

3. The dynamic range of the scene exceeds the dynamic range of your camera

While it’s good to avoid clipping, you’ll occasionally run into scenes where clipping is unavoidable, simply because the scene contains both ultra-light and ultra-dark pixels (e.g., a sunset with a dark foreground).

Here’s a histogram with this exact problem:

High contrast graph

In such situations, you’ll generally need to use a graduated neutral density filter to reduce the strength of the bright pixels, or capture several bracketed shots that you’ll later blend together in Photoshop. You can also embrace the clipped exposure (see the previous section on creative overexposure and underexposure) – though it’s often a good idea to bracket anyway, just to be safe.

Here’s an example of a scene that will likely go off the histogram at both ends, thanks to the bright star and the dark walls:

In the above shot, I’ve left the exposure as is, and I think the shot looks fine. But check out this image, with bright windows and dark shadows:

Notice the skylight at the top of the roof is blown out, and the deep shadows have little detail.

Using advanced techniques like image merging and blending, HDR, or careful post-processing, you can compress the tonal range of a scene to fit within the histogram and get a result like this:

Notice how, in this image, the details have been retained in both the highlights and the shadows.

For the image above, I’ve used four bracketed images (taken two stops apart) and the HDR tone mapping process to prevent clipping.

How to read a histogram: final words

Well, there you have it:

A simple guide to reading and using histograms for beautiful exposures. No, histograms aren’t foolproof – but they certainly allow you to improve your exposures, and will significantly improve your photos.

Now over to you:

What do you think about using the histogram in photography? Do you have any advice? How will you approach the histogram from now on?

The post How to Read (and Use) Histograms for Beautiful Exposures appeared first on Digital Photography School. It was authored by Darlene Hildebrandt.

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