No matter what type(s) of photography you like to pursue, mastering exposure is key to creating successful images. While it can be tempting to use your camera’s screen to judge exposure, that display can be quite unreliable for such a task. Instead, one of the most useful tools for evaluating exposure in digital photography is the histogram, a graph that reveals the distribution of brightness levels across the image. Understanding how (and why) to use the histogram can help prevent accidentally losing details in the highlights or shadows, allowing you to get consistent, quality exposures.

What is the histogram?

Histograms help visualize exposure, but can also show the distribution of red, green and blue. Image: Mitchell Clark

Histograms are not specific to photography and are simply graphs that show the distribution of continuous numerical data. They help visualize data by revealing a shape, spread and central tendency in a dataset.

In digital photography, the histogram is an exposure-assessment tool. It graphs how many pixels fall at each brightness value. Pure black (0% brightness) is on the left side, and pure white (100% brightness) is on the right side. The height of each bar tells you how many pixels have that particular brightness value in the image.

Histograms are based on the camera’s JPEG output, even if you are shooting in Raw.

There are also histograms that show the distribution of tones for each color channel (red, green and blue). These help you identify color casts and understand if certain colors are completely overexposed, often called ‘clipped,’ resulting in a loss of color information.

It’s important to note that histograms are based on the camera’s JPEG output (which includes the camera’s picture style settings), even if you are shooting in Raw. So while a histogram may show clipping in a specific color or for shadows or highlights, you may still be able to bring that detail back when editing the Raw file, since it has greater dynamic range than the JPEG.

Why does the histogram matter?

Histograms provide an objective assessment of image lightness, which differs from your camera’s display, which can be very misleading. For example, looking at the back of your camera in full sun will give you a very different experience than looking at it in a dark room. Likewise, adjusting your display’s brightness will impact how your image looks, even though the image’s lightness level hasn’t changed at all.

The histogram doesn’t rely on how things look but is based on brightness values. That means that no matter how or where you view your image, the histogram will be the same, making it possible to ensure your photographs are not misexposed.

If you’re shooting Raw, you may have slightly more leeway than what the histogram shows, but there’s still a limit.

One of the most important things to gather from the histogram is whether you are losing details in the highlights or shadows. You can change the general distribution of tones in editing, but if you are missing detail at either end (called clipping), you won’t be able to bring that back. For example, if you’re taking a photo of a sky with bright white clouds and you overexpose enough to have clipped highlights, those clouds will be pure white blobs in the photo with no texture. As mentioned above, if you’re shooting Raw, you may have slightly more leeway than what the histogram shows, but there’s still a limit.

Histograms make it easy to accurately assess exposure at a glance, helping to prevent clipping. Once you understand the common shapes of different types of exposure levels, you can quickly glance at the graph to determine whether your image is over- or underexposed and adjust from there.

How to use a histogram

When looking at the histogram, you want to assess the distribution of pixels by looking at the general shape of the graph. Certain shapes will typically indicate underexposure, while others suggest overexposure. You also want to pay particular attention to the edges. After all, the edges are where you will see if there is clipping. Data bunched up or spiking on either edge means you are losing detail that won’t be recoverable.

It’s important to know that there is no such thing as a perfectly shaped histogram for all photos, as every photo will have its own ideal histogram based on the tones within that particular scene. Instead, you want to check for abnormalities and dramatic shifts that don’t fit a given scene. For example, it’s normal for a histogram to be skewed to the left when shooting the night sky, but not when photographing a sunny beach.

If your histogram is skewed to the left

This image is very underexposed, with a histogram that’s very shifted to the left, indicating that the JPEG risks having clipped shadows. Photo: Mitchell Clark

If your histogram has most of the data bunched on the left side of the graph, it typically means your image is underexposed. Again, some situations will naturally have a left-skewed histogram, so it might be okay. However, if you see data spiking and touching the left edge of the graph, that means that you have shadow clipping and are losing detail in your shadows.

While you can bring a substantial amount of shadow detail back in Raw files, brightening shadows in editing can emphasize noise that’s present from a lack of exposure. Additionally, though it generally isn’t recommended to edit JPEG files, if your shadows are too heavy in those, you simply won’t get detail back, and it will be a black mass in the photo.

To fix a histogram skewed to the left, you need to lighten your image by, ideally, using a wider aperture or a slower shutter speed. If that’s not possible, you can lighten the image by using a higher ISO.

If your histogram is skewed to the right

This image is very overexposed, and the resulting histogram has a large spike on the right side, indicating clipped highlights. Photo: Mitchell Clark

If your histogram is skewed to the right, that typically means your image is overexposed. The important thing to check for is, once again, if you have a spike of data on the right edge of the graph. When that’s the case, it means your highlights are clipped (often referred to as ‘blown out’) and you won’t be able to get tonal information back in those areas.

You need to darken your image to correct an image with a histogram skewed to the right. To do so, start by lowering your ISO, if you can, or by reducing exposure with a smaller aperture or faster shutter speed.

Using the histogram when editing

Adobe Photoshop and other editing programs make it easy to keep an eye on the histogram while editing. Image: Abby Ferguson

The histogram isn’t just an in-camera feature. Most editing programs also feature a histogram, allowing you to balance the distribution of tones and color to maintain detail while editing. Just like your camera’s display, computer screens can all look different, making it hard to judge if your exposure or colors are properly balanced. The histogram takes some of the guesswork out of the editing process, helping you fine-tune your edits and preserve critical image detail.

Check your histogram often

The histogram is an often misunderstood and underutilized tool. However, it can prevent you from realizing only too late that your shadows or highlights are completely clipped and lack detail. Checking often – both when photographing and when editing – can save you from some headaches later on and help you improve your photographs.

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