How you can Read (and Use) Histograms for Beautiful Exposures

The best way to Read (and Use) Histograms for Beautiful Exposures

how to use histograms for beautiful exposures

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

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

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

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

What is a histogram?

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

how to use the histogram

Every image has an unique histogram, that is displayed on your camera through most post-processing programs.

Why is a histogram useful?

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

And while you can always examine 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 noticeable on the histogram; if a picture has clipped (detailless) shadows, this will be visible on the histogram; if an image is just usually 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 go through a histogram, you can quickly plus accurately check the exposure of the image while out during a call or when editing at home.

How to go through a histogram: step by step

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

histogram with well-balanced exposure

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

The graph highs represent the number of pixels of a particular tone (with every peak corresponding to a different tonal value).   So a peak at the right part of the histogram (such as in the example histogram above) indicates a large volume of shiny pixels in the image. Whereas a peak at the left side of the histogram signifies 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 simply generally centered?

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

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

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

Step 2: Go through the ends of the histogram

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

Therefore check both the right and left finishes of the histogram. Look for any clipping – highlight clipping along the right side, plus shadow clipping along the still left side.

What will a histogram tell you?

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

  1. Whether an image is generally well-exposed
  2. Whether or not an image has clipped tones

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

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

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

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

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

an overexposed histogram

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

an underexposed histogram

Histogram pitfalls plus mistakes

In the previous section, I talked all about ideal histograms and how you may 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 usually reliable, you might run into three issues:

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

A a well ballanced, unskewed histogram is ideal for pictures that include plenty of midtones and are generally centered around midtone detail.

But particular scenes just don’t appear like this. For instance, if you picture a black rock towards a night sky, you might end up with a significantly skewed histogram, even if you’ve captured all the detail correctly:

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

And if you photograph a whitened tree against snow, you may get skew in the other path because the scene is naturally lighter than middle gray:

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

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

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

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

Occasionally, even though an image is technically overexposed, underexposed, or trimmed, it still looks excellent – 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 blowout the sky for a lighting and airy look, or deliberately underexpose for a moody shot; really, the possibilities are endless! Just remember to check your own histogram no matter what and strive for a specific, deliberate result.

3. The powerful range of the scene surpasses the dynamic range of your own camera

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

Here’s the histogram with this exact issue:

a high-contrast histogram
High contrast chart

In such situations, you’ll usually need to use a graduated neutral density filter to reduce the effectiveness of the bright pixels, or capture several bracketed shots that you’ll later mix together in Photoshop. You can even embrace the clipped direct exposure (see the previous section in creative overexposure and underexposure) – though it’s normally a good idea to bracket anyhow, just to be safe.

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

neon star sign

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

wide-angle cathedral with a blown-out ceiling and deep shadows
Notice the skylight at the top of the roof is offered out, and the deep dark areas have little detail.

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

cathedral with better detail
Notice how, in this picture, 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 might 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 enhance your photos.

Now over to you:

What do you think about using the histogram in photography? Do you have any advice? How will you technique the histogram from now on? Discuss your thoughts in the comments beneath!

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