Those who have read my previous articles probably remember that I love uploading my images to aviation photography websites. The picture placed in the queue has to go through a screening process and needs to meet many requirements to get accepted. If the screener thinks the image has some faults, it’s going to be rejected.
I can clearly remember my first few rejections. The screener sent me a message like this: “Sorry, but the picture is underexposed. The histogram is shifted to the left.” To be honest, the first time I didn’t really know what he was talking about, all I understood was that the picture is underexposed. Next time when I received the same message, I asked other fellow photographers if they ever had a rejection like that and what is that histogram thingy, but didn’t get any meaningful answer. Although it really annoyed me and I wanted to know what is it, so I took the time and started browsing the web to find out more about this mystery.
What is a histogram?
The histogram is a graphical illustration of the tonal range in our photo that allows supporting us to get well-exposed images. It can be found in most digital cameras and really easy to use once we understand how does it work. Many years ago before digital photography, it was much more difficult to achieve the correct exposure, because it was impossible to review our images through the LCD screen or EVF (electronic viewfinder). Although presently we have the ability to check our images instantly when we captured something. Then we can evaluate the exposure, and make any adjustments if necessary, which is a huge help to increase our efficiency.
However, the LCD screen and EVF both have their own adjustable brightness’s which can mislead us when judging exposure, that’s where the histogram comes into play. This handy feature helps us to review our images more accurately. If we wish to take our photography to the next level, we should understand how does it work.
How to read the histogram?
First, we should turn the feature on to enable the histogram in our camera. Some models also offer a histogram in live view mode, which is very helpful, especially at night time when we need to change our settings more often than during the day. It helps us to judge the exposure. Let’s see the diagram below:
We can divide our histogram into two axes. The horizontal axis displays the brightness level, and the vertical one shows the number of pixels at each particular tone. The left side of the graph represents the blacks or shadows the right one corresponds the whites or highlights and we can find the mid-tones between them. We can also split our horizontal graph into five subsections such as blacks, shadows, mid-tones, highlights, whites from left to right. There are 256 different tonal values of brightness in a histogram and 0 stands for the absolute black, while 255 corresponds the pure white.
Most cameras have 4 histograms and the main one is the luminosity histogram, which shows the overall exposure of the picture, while the other three are the colour histograms (red, green, blue – RGB). It’s also important to check the RGB histograms because sometimes the luminance histogram doesn’t show any clipping, however, it’s there, just hidden.
The perfect histogram doesn’t even exist, however, the ideal graph growths from the left and descents on the right without any detail loss in shadows or highlights. If we adjust one of the settings (ISO, aperture, shutter speed), the details in the histogram changes too. Let’s analyze a few examples!
Low key image
The histogram in the image above tells that there are a lot of dark tones in the picture, as we can see plenty of pixels on the left side. We call this kind of picture as a low key / dark key image.
Mid key image
A correctly exposed photo is also called mid key image, as the exposure looks balanced without any clipping.
High key image
The image above is kind of a high key photo with lots of detail on the right side. There are no pixels in the blacks, shadows area. If our aim is to get an “ideal” looking histogram, by pushing all of the information to the middle section, our snow will look grey instead of white. The logic is the same when shooting a dark object at night time but the pixels will stay on the left side apparently. The histogram is only a guide, so we should expose our images the way we imagined.
Clipping means we have areas in our image without any details, also called blown out areas. There are two types of clipping exists. One of them is shadow clipping while the other one is called highlight clipping.
If the histogram reaches the left border of the chart, it means our picture has pure black pixels and it’s called shadow clipping.
The problem is the same when the histogram touches the right side of the graph and it’s called highlight clipping.
This means, that our exposure setting is incorrect in both cases. Unfortunately, we can’t fix those pixels in post-processing as we lost data from those areas. All we can do is modify our settings to prevent clipping next time.
Enable highlight alert/blinkies on the camera
On most camera’s we can activate a highlight warning setting, which shows us when clipping occurs in our image. The overexposed areas of the photo will blink when we preview the image on the LCD display.
High and low contrast images
At high contrast images, the photo contains a lot of contrast and it’s hard to catch an ideal looking histogram, as most of the tones are packed at the shadows or highlights area and possibly not so many amongst them. Moreover, we can’t really fix the exposure in post-processing, so we have to decide if wish to preserve the details in highlights or shadows area.
When we are looking at low contrast photos, they have lots of mid-tones, and our histogram has a bell shape.
I hope that I covered the most important things about the histogram. Basically it’s useful to analyze our image after a shot by checking the histogram on the display and adjust settings to compensate if needed. No doubt, it will improve our skills! Also good to know that the histogram displayed on the LCD display is not the RAW image data’s histogram, but the in-camera processed JPEG’s. When we open up our image in post-processing, we will see that the RAW image’s histogram looks slightly different. Apply adjustments if necessary, but don’t push your post-processing too far!