Understanding Your Histogram

//Understanding Your Histogram

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What if I told you that the LCD screen on your camera was a liar? I’m sure it seems nice enough but you really shouldn’t trust it. Especially when it’s dark outside! You see, when it’s dark out checking exposure from only looking at your LCD can be a huge mistake. It may look perfectly exposed but in reality it may be darker than you think. It just looks correct because it’s dark out and the LCD is so bright. Want to know who we trust? Our histogram……it never lies!

Your histogram is that weird looking graph you may have seen and thought how do I get this crap off of the screen so I can see my picture! It is an incredibly useful and important piece of information. Everytime you take a photo your camera records each pixel’s tonal information and populates it into your histogram. Why is this useful? It gives you a quick look to see if you got your exposure correct and remember……your LCD is a liar. If it isn’t right you can quickly make an adjustment rather than finding out the hard way once you got the image on a big screen.


So how does this magical graph work? It graphs the tonal range of your image from black (on the left) to white (on the right). There are actually five sections to a histogram.

Histogram Detail

From left to right those sections are black, shadows, midtones, highlights, white. Most cameras have lines separating these sections so it is easy to see what tones are recorded in your image. The higher the graph the more of that tone you have in your image.

Now that you know how it works let’s learn how to use it. The most important thing is to NOT have the graph pressed up against the right wall.

You may have heard of an image being “blown out”. This means that your image is overexposed and there is absolutely no detail in the blown out areas. There is no recovering from this. If you blow out an image you can not fix it in post processing. Often times people are taught to underexpose images for this reason. If you do see the histogram pressed up against the right wall you should make small adjustments to get less exposure until your histogram fits inside the wall. This can go the other way also. You can severely underexpose an image.

Although you can often recover details from an underexposed image it may get really noisy and hard to work with.

Ever heard that a perfect histogram is a bell curve?

Whoever told you that should be put in the same category as your LCD screen…..LIAR! Histograms will vary image by image. If you are shooting a scene in the snow your histogram is probably going to be biased to the right. If you are shooting a scene at night your histogram is probably going to be biased to the left. There is no single perfect histogram. You just need to make sure your histogram matches the scene you are photographing.

Sometimes your camera can’t get the full dynamic range of a scene in a single frame.

This typically happens around sunrise and sunset when the sky is really bright and the foreground is really dark. To get the correct exposure for the sky your foreground will be severely underexposed and to get the correct exposure for the foreground your sky will be severely overexposed. This is where photographers start to use exposure bracketing and photo merging (HDR or manual exposure blending) in post processing to create images. This is a whole other topic that we will get into in the near future. Now you should see the importance of a histogram so please be sure to use it…..great images depend on it.

We want to hear from you. Let us know what you think about this article. We also want to answer any questions you have. If you have a question it is likely someone else has the same one. Don’t be scared to ask, help a fellow NERD out.


  • Finally, an easy to understand explanation for histograms. Thank you.

    Charlene 01.03.2016
    • Hey Charlene, That’s the name of our game, we are trying to keep everything photography easy to understand. Thanks for reading, we sincerely appreciate you for doing so.

      JymmyJack 01.03.2016

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