A histogram is a great tool and learning to use your camera’s histogram is a great idea, but you should know that it’s not 100% perfect all the time, So we’re looking at how to read a histogram and know when it won’t be right.
You shouldn’t rely entirely on your histogram
The thing is though, if you’re using it properly, you’ll know that you can’t rely on it entirely. You need to observe the scene and make a decision too, because you know more than the histogram about what you’re shooting.
About the photography histogram
Many photo editing programs also have a histogram, such as in the develop module of Lightroom.
So let’s have a quick look at the histogram, because it is actually a very simple tool to read. I’ll break it down into a few good questions…
- What is a photography histogram?
- Why do I need to know how to use a histogram?
- What are a histogram’s weak points? Histogram myths.
- How does a histogram work?
Let’s get started…
1. What is a photography histogram?
In photography, a histogram is a graph that represents the tonal variation in an image from dark shadows to bright highlights, and everything in between. You view a histogram on the back of your camera to get an indication of whether you’ve over, under or correctly exposed your image.
2. Why do I need to know how to use a histogram?
I find the histogram particularly useful when I can’t see the image in the LCD, like when I’m shooting outdoors on a really bright day. In fact, I always wonder how on earth anyone can decide if their image is correctly exposed purely by checking the back of the camera.
When processing a shoot in Lightroom, the histogram helps me to ensure that my adjustments don’t push the exposure too far, causing loss of detail. The histogram also separates the colors out, which is helpful for ensuring all colors within the image are well exposed.
However, histograms are not living beings. They don’t exercise judgement of what’s in a scene or how bright it is meant to be. They only know how to report back the data of the scene. Which leads me to my next point.
3. What are a histogram’s weak points?
In other words, why can’t you rely entirely on your histogram to determine your image is correctly exposed. I’ll give you three example scenarios:
- Polar bear in snow
- Black cat on a black background
- Black and white cows in a field
Polar bear in snow
If you find yourself lucky enough to be photographing a polar bear in the Arctic, you’ll know that there’s a whole lot of white and not a lot of other colors, except for the bear’s eyes and nose. However, your histogram doesn’t know what you’re photographing, only that there’s a whole stack of white and just a smidge of black.
So, when you look at the histogram of your polar bear photo, you’ll see that the right of the graph is full of information and the left is barren.
Black cat on black background
The reverse is true for a photograph of a black cat on a black background. Lots of black with the only other color being the eyes. Your histogram is going to bunch up on the left of the graph.
Cows in a field
Let’s imagine you’re photographing a herd of black and white cows in a green pasture with a lovely blue sky above. Now we have variety – midtones in the sky and grass, contrasting black and white in the cows.
Your graph will be mainly in the middle with information spread across it. If it’s bunching to one side or the other, your shot is under (left) or over (right) exposed.
That’s a really simple explanation. Let’s get into the details of how to read a histogram.
4. How does a histogram work?
Let’s use this photo of a two year old with a serious passion for chocolate cake:
- On one end of the tonal range we have dark chocolate cake
- In the mid range we have green grass and skin tones etc
- On the other end of the tonal range is a white dress
I’ve used Lightroom histograms to demonstrate this. The image was accurately exposed in camera and I’ve decreased and increased the exposure in Lightroom to show you how this changes the histogram.
Most of the time, to get an accurate exposure, you need to make sure that the graph doesn’t touch the left and right sides of the box. If this happens it means that your image is either under or over exposed, as you can see in the examples below. When an image is under exposed we refer to the blacks as being “clipped”, when over exposed, we say the whites have been “clipped”.
When taking a shot, if you clip the whites all detail will be lost in that area and it will be recorded as pure white. There is no coming back from that in post production.
When you clip the blacks you can still retrieve some of the information in that area in post production. So it’s better to underexpose than to overexpose. This is one of the reasons why shooting in RAW is better than JPEG.
Further reading: Image quality – the pros and cons of RAW vs JPEG
Speaking of which, remember that the image you see on the LCD of your camera is a JPEG image. If you’re shooting in RAW, which has a higher dynamic range than JPEG, more detail will have been recorded than what you see in the image on your LCD. So if there’s clipping, it might be less than it appears on your LCD.
Don’t fall into the trap of thinking you have to have a perfectly domed hill right in the middle of the histogram.
If your spike in the middle of the histogram hits the top, that’s fine. It just means there’s a lot of that particular tone in the image. Clipping only happens when the graph hits the left or right side. The histogram example from the back of my camera shows this pattern.
If you photograph a black cat on a white background, it’s a high contrast scene, because of the use of both ends of the tonal range. Your histogram will show peaks towards both ends with a dip in the middle, like a U shape.
Have you heard of exposing to the right?
This is ideal for when you’re photographing a subject of mid-tones.
So the contrast in the image is low, with no dark areas and no bright areas. In this instance, to get a correctly exposed image, the histogram should read as far to the right as possible, without clipping.
Sometimes clipping is okay
There are two types of scenes that will show clipping in the histogram and in these types of scenes, it’s okay if clipping occurs to ensure either white whites or black blacks. They are:
- High key scenes – brightly lit scenes with little or no shadow, such as in a studio with a bright white background, or scenes with a lot of white, such as the polar bear example or the image in the histogram at the start of this article.
- Low key scenes – dark scenes with a lot of shadow.
Further reading: What is high key photography, and how to master it
Different types of histograms
Now that you know how a histogram works, it’s helpful to know that many cameras are equipped with different types of histograms.
- Luminance histogram
- RGB histogram
The good news is that, as long as you know how to read a histogram, you can understand both types of histograms.
You’re probably familiar with the luminance histogram – the one at the start of this article. That’s the one that shows a white graph on a black background, or vice versa. This measures the overall brightness (luminance) of the scene.
An RGB histogram is really useful for establishing if any of the red, blue or green parts of the image have been clipped. If you have this function set on your camera, you can see separate red, blue and green graphs. See the image below.
Once you know how to read a histogram, you’re well on your way to controlling exposure. The key word in my statement at the start of the article was “entirely” – you shouldn’t rely entirely on your histogram.
As usual with photography, it’s up to the photographer, not the camera, to make the final decision on the outcome of an image. You just need a little guided help from your camera’s clever tools.
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