How to use the Histogram?

How to use the Histogram?

When you take a photo with your digital camera, you will have a chance to view the picture on the camera’s display. In viewing mode, you can look at some information stored together with your photo. This can be as little as the file name, but it also can include the histogram. This is a graphical representation of the tonal values in your image. Digital cameras can show a combined RGB histogram, but also one for each of the three color channels. The horizontal axis shows the color values from 0 (black) to pure white (255) in the RGB histogram. The vertical axis shows how many pixels in the image have this particular tonal value.

Why is looking at the image on the back of your screen, not enough?

There are of course evaluations that you should make from looking at the image on the back of your camera. These include the sharpness of your subject, the depth of field, and your composition of the image. The image shown on your camera display is always a JPEG. Even if you are shooting in RAW only, the screen shows you a JPEG. The JPEG is created with the help of the settings you made in your camera for the finishing touches. How bright or dark the photo appears depends, of course, on your subject. However, it also depends on the brightness/darkness of your surroundings and the that of your screen. A correct exposed image can seem to be overexposed when you look at it in the dark shadows of a forest. Alternatively, the same photo seems to be underexposed if you look at it in bright sunlight.
While the brightness of your screen is adjustable, you cannot adjust the brightness of your surroundings.

How the Histogram has saved my images


When I was photographing in Iceland last winter, I was often wondering why the image that I saw on the back of my screen never added up with the displayed histogram. It always seemed underexposed; the graph could not be right, could it? Some error of my camera, maybe? However, then my exposure meter indicated that my settings were correct. So what was wrong? Well, I completely had forgotten that at some point in the past I had changed the brightness of my display. Only when a fellow photographer commented on my dark screen, I realized that this was what had driven me crazy for weeks.
Since I had looked at both the histogram and the image, I knew there was a mismatch somewhere. So I just took a few more exposures to be certain to have one correct exposed image in the end. However, if I had used the picture alone to check my exposure, I would have started to overexpose the photos to get them displayed correctly. I would have been devastated to come back home and find all my photographs overexposed beyond rescue. However, because I also looked at the histogram, I was able to figure out that there was something not adding up. So I rather took a few more photos of every scene than not going home with one correct exposed one.

How to check a histogram?

You want the graph not to be cut off at either end of the horizontal scale. It also should have a proper distribution of values across the axis.

Underexposure

If your figure ends far before the right border of your histogram, it can indicate underexposure.

Overexposure

If your figure ends far before the left border of your histogram, it can show overexposure.

Lack of contrast

If the graph ends far before the left and ahead of the right edge as well it can mean lack of contrast.


Black Clipping

A figure which is clipped on the left side indicates that your photograph lacks detail in the shadows. This means that your shadows are now not colored in shades of grey but pure black.

As you can see in the photo above, the leaves of the rose lack detail in the shadows. Compare it to the better-exposed image below to see the difference.

White Clipping

If your graph is clipped on the right side, you have blown out details. Some of the highlights in your photo will no longer be in different shades of white, but will instead be pure white.

In the photo above the sky in the bottom right is lacking detail, compare it to the better-exposed image below to see the difference.

No rule without exceptions.

Photos of dark scenes

If you are photographing a flower in front of a dark background, your histogram will, of course, show more tonal values in the black area.
If you have a very dark background, your graph can be clipped on the left side, yet your photo is still correct exposed.

As you can see, properly exposed the leaves of the rose show some detail that was lost in the example above.

Photos of bright scenes

If you take an image of a snow-landscape, most of the tonal values of that histogram will be on the right side of the graph. Again some might be clipped on the right side, while the histogram still does not indicate overexposure for the image. 
It is possible to shoot the perfect exposure and despite that have blown out highlights. This happens, such as, if you are shooting into the setting sun, or into some street light. The overexposure warning will mark these areas. It is your decision if the marked regions contain valuable detail, or not. If they do not provide details that you cannot lose, then you might leave the exposure as it is. Otherwise, adjust it to your liking.

While the histogram is pushed to the right, this fits the scene of the image. The avoided white clipping leads to a sky with much more detail/color than in the white-clipping example.

What does this mean for your evaluation?

A histogram without an image is as useful for exposure validation as an image without a histogram. You need to know what the scene looks like to decide if your histogram indicates over-/underexposure.
A bright scene combined with a histogram pushed to the left means you are safe to assume underexposure. However, the same histogram for a dark scene might as well suggest that your photo is correctly exposed.

What about the color channel histograms?

While the RGB histogram is a combination of all three color channel histograms, it can be helpful to check the channel histograms too. Sometimes your channel histograms show under-/overexposure, while the RGB histogram seems OK.
In cases like this, it is often a good idea to adjust the exposure, so that all three channel histograms are close to an ideal histogram for your image.

Worth to bear in mind

  • Histogram source

     

    If you are shooting in RAW the histogram on your display is the one of the JPEG image. If you set your camera’s image settings to a Natural finishing touch, the JPEG histogram is useful to check your RAW images too.

  • It’s easier to fix over- than underexposure

     

    I said – and this is true – the tonal values go from pure black (0) to pure white (255) in your histogram. The right quarter of your histogram stores more information than the left. Every quarter doubles the amount of information stored in the previous one. So if the far left holds 250 pixel, the far right would hold 2000 pixel!
    The reason why it is easier to rescue an overexposed image than an underexposed one: there is more information stored in the brighter pixels. Fixing an underexposed photograph means you have to fewer data to work with, and it is so harder to do a good job.
    With less information to use in the recovery, noise is introduced into the photo. To a degree, of course, that is removable, but at the cost of loss of detail. In general, it is better to expose to the right – toward overexposure – than to the left.

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    I hope this article has helped you understand your histogram better. If you are uncertain of how to fix the exposure of an image have a look at this article. If you have any questions about this article, why not ask them in the comments?

All photos that aren’t properly exposed here are created from the correct exposed versions in Adobe® Lightroom 😉

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