Digital photography histogram explained for better photos

The ability to read histograms in digital photography is an excellent skill that every photographer should have. Getting a properly exposed shot is crucial when it comes to photography, and using the histogram is a solid way to achieve it. That 2 to 3 inch LCD screen located on digital cameras is only a quick way to look at the photo exposure and its details. In harsh lighting or vivid ambient lighting conditions, it can prove to be misleading to the human eye. Sometimes the LCD brightness level can also create issues with judging the exposure of a shot. So what does histogram explain about an image? How can histogram help you to take better images? In this beginner-friendly post, I will try to answer these questions. You can consider it as a tutorial or cheat sheet for digital photography histograms.

Photography histogram image examples:

The histogram is basically a mathematical way of representing data, and it applies to digital image information too. In digital photography, histogram represents the pixels associated with different tonal values in a given image. Each pixel in a colour image is a combination of red, green and blue colours. The brightness level of each colour ranges from 0 to 255 if the image is of 8 bits. Let's start with black and white photos first for simplicity.

Black and white histogram:

Dark or underexposed image

Bright or overexposed image

Properly exposed image

From the above 3 image examples, it is evident that an overexposed or extremely bright photo will have the curve area moved to the right. In contrast to this, an underexposed image will have the curve being squished to the left. For a properly exposed or a balanced exposure shot, the curve will cover the whole axis without abrupt spikes or cut-offs. A spike or cut off means image information is being clipped or lost.

Colour histogram:

A colour image histogram will show a bit more information as compared to a black and white image histogram. It has 3 different curves representing the red, blue and green colour pixels present in the image.  Overlapping of any 2 colour curves with each other makes cyan, yellow and magenta. One colour being clipped (overexposed) can be bad as it means you won't be able to recover much information without losing the natural look of the image.
Most of the modern day DSLR cameras allow separate representation of all three curves in 3 different histograms at the same time.

Colour image histogram example

Image histogram graph:
Vertical axis: Pixel count for each brightness or tone level.
Horizontal axis: Brightness level. Leftmost is pure black, the rightmost is pure white. As we move left to right, the brightness increases. Every image has dark areas, bright areas and some normal areas. The darker part is called shadows, bright part is called highlights and mid-normal range is called mid-tones.

Histogram explanation

How to use a histogram?

Ideally, the histogram curve should be smooth and well spread but perfect situations are not always present. What if the scene has a lot of light variation like say Sunset? Or shooting indoors and there are windows showing bright daylight. In cases like this, it is very hard to avoid a bit of highlight clipping or shadow clipping. To help with this, some digital cameras have a functionality called as "blinkies" or "highlight clipping warning". It is a flashing animation that tells the user if highlights are being clipped in your image when previewing.  In addition to this, usually exposed to the right is considered as a safe bet when taking photos. We will talk about this technique in a separate post, don't want to overwhelm beginners in one single post. :)

Why can't a camera show histogram from the raw image?

Even when shooting raw, a histogram is based on the jpeg preview of the raw file. But why don't cameras show a histogram based on the raw data instead of jpeg preview? One possible reason could be because it's faster to show information based on the jpeg review. Raw files are huge in size and for a camera processor to read that 25-40 MB image data would definitely introduce some delay. Another reason is that a raw file has no contrast curve, white balance and saturation applied to it. 

If you still want to view the raw file histogram, you can use Magic lantern software to view histogram in live view and image review. Still, that wouldn't be a very meaningful representation of the raw data.

Lastly, the ability to read and understand histograms helps a lot with post processing images too. All the major photo editing software use sliders such as highlights, shadows etc. and knowledge of histogram can make processing a lot faster. Hope this post helped you to understand the basics of camera histograms.
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