Histogram in Photography : How to read and use to get Perfect Exposure

Perfect exposure is always a key for a better photograph. In my previous few posts, I’ve introduced you to the exposure and factors which affect the exposure; also how to achieve the perfect exposure. While explaining these things, I’ve used one term “Histogram” to check whether desired exposure is achieved or not. It’s quite obvious that many of you are unaware of this term. So let us learn about the histogram and how to use it.

After taking a picture, we often check the image on the screen to see whether it is OK or not. But on-screen preview is never a reliable option. You can never judge the brightness or contrast of your image just by watching it on screen. Even metering fails sometimes. But Histogram is one great tool which will always help you to understand your image and to check whether it is perfect or not.

What is Histogram?

Every image is made up of pixels. Each of these pixels has a brightness value, which we can scale from 0 to 255 units. If we scan all the pixels of the image, for brightness value, we can observe that many of the pixels have same or almost same value. So if we consider the density of these similarly bright pixels, we can represent them graphically. So one can say that histogram is a computer generated graphical representation of pixels in your image.

An example of the Histogram.

An example of the Histogram.

The histogram is a graph which is calibrated from 0 to 255. consider a standard histogram. It can be divided into three main regions. Left region is dark or shadows region. The middle one is mid-tone and right one is bright or highlight region. Pixels in the image are scanned and checked how many are at each level for 0 to 255. The graph is plot according to that. So now we can check the exposure of our image correctly.


The region which represents most of the brightness value is called Tonal Range. Tonal range is very useful to check the exposure and explains a lot about the image. If this tonal range is at the left part of the histogram then there are more shadows in the image, if it is at right part then there are a more highlighted portion in it. The tonal region in the middle is considered as well configured or we can say that the image is well exposed, not too dark, not too bright.

Tonal range and three regions.

Tonal range and three regions.

So it is a good practice to keep the graph in the middle portion or in the mid-tone portion of the histogram to gain correct exposure. Evenly distributed graph from one end to another end with a pick at the middle and not much up at the edges is generally considered as correct or expected one. But it is not true for all images.

Example of an expected histogram.

An example of an expected histogram.

Practically there is no such thing called ideal histogram. Consider an example of a silhouette image of a sunset scene. There are a lot more dark portions and heavily highlighted areas. So it is obvious that histogram will not be in the middle portion. Maximum density will be at either of two edges. But that doesn’t mean the image is not correctly exposed.

Considering such conditions, we can categorize images in general terms. If tonal range for any image is in shadows then it is called “Low key”. Whereas for “High key”, the tonal range is in highlights. So while checking the histogram for any image, consider the scene and theme that you want to express. You will come to know whether generated histogram is correct or not.

Low key image.

Low key image.

High key image.

High key image.

Contrast and Clipping at the Edges

In general terms contrast is the difference in brightness between light and dark areas in a scene. The histogram can be used to check contrast. The wide tonal range reflects significant contrast in the image. While narrow tonal range represents quite dull image. Contrast is very useful to create a depth in your image.

High contrast image with widely distributed histogram.

High contrast image with a widely distributed histogram.

There is one more thing that histogram helps us to identify, it is a loss of details or pixels. This is identified by clipping or sharp spikes at the edges of the histogram. Such clipping at the either edge indicates that portion of the image is too much exposed or darkened. Pixels in that region are many times unrecoverable especially in the case of overexposed images. So we should avoid such clipping or spikes at the edges of the histogram. It is advised that graph should just touch the left or right edge for better exposure. But again it is not applied to low key or high key images.

Clipping or spikes at both of the edges. As image have huge dark area with no pixels, middle potion is quite empty and spikes are present at the edges.

Clipping or spikes at both of the edges. As image have a huge dark area with no pixels, a middle potion is quite empty and spikes are present at the edges.

Now as you are well aware of how to read a histogram, observe the histograms of various images (you may require software like photoshop or lightroom for that, or you can observe them in your camera). Try to analyse them, how they describe your image and scene.

You will observe that many of them are mid-tone images, i.e. tonal range is in the middle. For example portraits. Images like silhouettes or night scenes are low-key images and some captured in daylight like sunset, sunrise, landscapes are high key images. When there are lots of details in your image, like landscapes, images of architectures, you will find that histogram is widely spread to provide contrast in your image. Understand as many images as you can with the histogram. You will come to know that gaining correct exposure is much easier now!

At Goa. An example of landscape image with histogram.

At Goa. An example of landscape image with the histogram.

Leave a Comment

* : 16 + 8 =