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How to read a histogram
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How to read a histogram

The key to understanding exposure

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Low key? High key? Learn how to read histograms and you’ll understand how to manipulate them until you get the kind of digital image you want.


A histogram indicates the amount of each light level (and in more sophisticated versions, amount of each color at each amount of brightness your photos have. An experienced photographer can glance at a histogram and know in an instant if the exposure is accurate, while a Photoshop jockey can manipulate an image’s Levels (which shows the histogram) to improve an image.

Let’s look at three kinds of histograms: One for a good average scene, one for a darker, low-key scene, and one for a bright, high-key image.


What does a good histogram look like?

A histogram is a graph of 256 vertical bars, one for each tonality in an 8-bit image. (For a 16-bit image it will show it reduced to 8-bit.) The left end represents black and the right end white. The two ends are very important. In most cases you want them to tail off as shown here. You don’t want them to “push up against the wall” unless the image has a lot of dark or light.

   
There is no right way for the middle to look. It is as variable as images are. (The three shown here are all for the same image at different contrasts.) For an average exposure image the center of the “mountain peaks” will be near the center of the graph.

An average contrast image will have the vertical bars spread from darks to lights, as shown above. A lower contrast image will have them more in the center (below, left); A higher contrast image will have them distributed more widely (below, right.)

These principles can also be used for reading and evaluating in-camera histograms.

A low-key histogram

A low-key image contains primarily dark tones, and its histogram will be skewed to the left, with fewer numbers of lighter-toned pixels. Here is an example image and its histogram.


In this image the histogram is pushed against the left “wall” because there are significant areas of pure black in it, and significant areas of very dark tones.

But in many more realistic images, this sort of histogram would indicate a loss of detail in the darkest areas, blocked up shadows. In a more conventional image where it is desirable to have detail retained in the dark areas, the histogram should taper to just touch the left edge at the bottom corner, as shown here:

The shape of the histogram will vary with each different image. There isn’t a right or wrong way it should look. It is simply a reflection of the tonalities in an image.


High-key histogram
A high-key image contains primarily light tones, and its histogram will be skewed to the right, with fewer darker-toned pixels. Here is an example image and its histogram.



 

 

This histogram is pushed against the right “wall” because there are significant areas of pure white in it, as well as significant areas of near-white and light tones.
In this image it is an intentional artistic effect.

But in many images that are more realistic, this sort of histogram would indicate a loss of detail in the lightest areas, also known as blown-out highlights. In a more conventional image with detail retained in the highlights the histogram would taper to just touch the right edge at the bottom corner:



 

The shape of the histogram will vary with each different image. There isn’t a right or wrong way it should look. It is simply a reflection of the tonalities in an image.



Diane Miller is a widely-exhibited freelance photographer who lives north of San Francisco, in the Wine Country, and specializes in fine-art nature photography. Her work, which can be found on her web site, www.DianeDMiller.com, has been published and exhibited throughout the Pacific Northwest. Many of her images are represented for stock by www.MonsoonImages.com.


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