The histogram is a tool that many beginning photographers fear to learn. At first glance, it makes little sense. This, unfortunately, results in many photographers ignoring it.
But this is a big mistake. The fact is that you need to understand the histogram to improve your photography. Because that’s what you want, right?
The good news is that it’s not nearly as difficult as it appears. It’s pretty straightforward. So, don’t be afraid to dive into this topic. My photography improved when I learned to read the histogram, and I’m confident it will be the same for you.
Let’s get straight to it and find out why understanding the histogram will make you a better photographer:
What is a Histogram?
First, we need to answer the simple yet complicated question: What is a histogram in photography?
The histogram is a graphical representation of an image’s exposure. In other words, it shows the distribution of tonal values ranging from dark to bright.
Along the x-axis, we find the exposure levels, from pure blacks on the left to pure whites on the right.
The y-axis represents the number of total pixels in that particular tonal value.
Let’s simplify it: pixels leaning to the left equal 0% brightness, and pixels leaning to the right equal 100% brightness. In between, you have what’s referred to as midtones. These are neither pure black nor white.
The histogram is different for every photo. A dark picture will have a histogram graph leaning to the left, while a bright image will have more pixels on the right. This is valuable information for you as the photographer, and when you learn to read the histogram, you can quickly recognize if the image is exposed correctly or not.
If the description above was overwhelming, skip this part for now. The explanation below is slightly more complicated but valuable to understand if you want to know how the histogram graph is created:
Let’s use the camera’s histogram as an example. As you remember from above, the histogram is a graphical representation of the tonal values in the image.
The camera creates the graph by converting an image to greyscale and dividing it into 255 brightness levels. We start at 0, representing pure black, and increase to 255, representing pure white. Your camera will then analyze each pixel within the image and plot its tonal information into the histogram chart. With the y-axis, you can see how many pixels there are of one specific tonal value.
How to Read the Histogram
We now understand that the histogram represents the distribution of tonal values within a photo, ranging from dark (left) to bright (right), and that the vertical (y) axis tells us how much of the pixels fall within a specific level of brightness.
But how can you read this graph and understand what it tells? For this, we need to look at the histogram of a photo:
By looking at the histogram of this photo, we learn that:
- There’s a healthy distribution of tonal values, meaning there’s good contrast.
- There’s a fair bit of shadows
- Most of the midtones lean towards the brighter part of the scale (i.e., more than 50% white)
- There’s a small area that’s bright
- The image has no clipping in either blacks or whites (we’ll come back to this in a bit)
Looking back at the photo, we can see that the information given by the histogram is precisely what we see. The image has a lot of contrast, meaning we have both dark and bright areas, but no parts are pure black or pure white. We can also see that we don’t have a lot of tones in the 25-50% black range but that our midtones are leaning towards highlights.
So, how about the next example? What can we say about the image that this represents:
If you said that the image contains a lot of highlights and midtones but little shadows, you’re completely correct. This histogram tells us that we’re looking at a bright picture with few dark areas. However, some midtones lean towards shadows, meaning we have some contrast. Though considerably less than in the first example.
Here is the image the histogram example above belongs to:
What is Histogram Clipping?
In the explanation of the histogram above, I mentioned that the image wasn’t clipping shadows or highlights. This is an important subject to understand, so let me explain what histogram clipping is:
Clipping is a term used when an image is over- or underexposed and has areas that are either pure black or pure white. As you remember, pure black is 0% white, while pure white is 100%.
So, how can we see clipping on the histogram?
Pure black, or 0% white, is the very first level of brightness on the histogram chart. This means that any pixels leaning all the way to the left are clipping the shadows. Pixels leaning all the way to the right are clipping the highlights.
The more pixels leaning to either side (i.e., the bigger “spike”), the more of the image is over- or underexposed.
Let’s look closer at what that means:
Clipping the Shadows (Underexposing)
Clipping the shadows means we have areas within the image that are pure black. This is often linked to underexposed photos; the main problem is the loss of shadow detail. The histogram below is a typical example of what this looks like:
Looking at the histogram, we can see that most of the pixels are leaning towards the furthest left. This means that most of the image is underexposed (too dark), and parts are pure black.
We can also see a poor distribution of pixels towards the rest. This tells us that the photo also lacks both midtones and highlights. Looking at the image, we can see that this is accurate:
Just as we could tell from reading the histogram, we can see that the majority of the image is nearly black, while only the sky and some details in the landscape are represented as highlights or midtones.
The peak to the left in the histogram represents the area with 0% brightness, pure black. This is the clipped area.
Let’s quickly jump into Lightroom to see what this means:
In Lightroom’s Develop Module, we can reveal pure black or white areas by clicking on the arrows in the histogram’s upper left or right corner. In this case, we click on the upper left arrow to reveal the shadow clipping. These underexposed areas are shown with a bright blue color.
Generally, you want to avoid having such areas in your images. Especially if they cover a large area of the picture. It’s possible to increase exposure and bring back shadow details in post-processing, but depending on your camera and what file format you shoot in, this could result in a significant amount of digital noise.
Recommended Reading: How Good is Topaz DeNoise AI, Really?
The best way to avoid clipping is by increasing the exposure before taking the shot. Do this by lengthening the shutter speed, increasing the ISO, or opening your aperture. Not sure which one to choose? Then make sure to learn how to use the Exposure Triangle.
Clipping the Highlights (Overexposing)
It should come as no surprise that the opposite of an underexposed image is an overexposed one. In that case, we’re dealing with highlight clipping, meaning some areas are pure white (100% brightness).
A quick look at the histogram below shows that we’re dealing with a much brighter image. There are a lot of midtones, which is good, but there’s a big spike of pixels leaning towards the right wall. That means we’ve got a lot of pixels clipping the highlights.
Looking at the image itself, we see that this information is accurate. We have a lot of details in the landscape, but the sky is pure white. In this example, I’d argue that the landscape is well-exposed, and we can easily introduce contrast in post-processing, but the overexposed sky is the problem.
Let’s return to Lightroom’s Develop Module and examine which areas are pure white. We reveal this by clicking the arrow in the upper right corner of the histogram.
The clipped area is shown by a bright red overlay, which, in this case, is more or less the entire sky.
Notice that the sky in the left corner isn’t marked red, which means that area is not pure white. Even though it might look pure white, it isn’t bright enough to be considered 100% bright.
Highlight clipping is also something you want to avoid. Recovering details from a blown-out sky like the one above is complicated. In that case, I recommend you expose for the sky or bracket multiple exposures.
What is the Perfect Histogram?
The truth is that there isn’t such a thing as the perfect histogram. It all comes down to your style and creative vision. For example, it’s not right to say that a high-key photo is bad just because the histogram has no shadows or midtones.
That being said, there is such a thing as an ideal histogram for landscape photography in general. Such a histogram has a good distribution of pixels throughout the chart with a “mountain” in the midtones.
An ideal histogram looks something like this:
As you can see, there is a lot of information in the mid-range, and no pixels are touching either side. Do you remember what that means? Yes, that means there are no clipped highlights or shadows.
Images with similar histograms to this give you much more wiggle room in post-processing. It means we have a lot of information and that we can both increase the shadows and decrease the highlights a lot without seeing a loss of quality.
Color Channels in the Histogram
Now, there is one more topic we need to cover to fully understand how the histogram works. Look at the histogram view in your camera, and you’ll notice that it’s not only the white/black graph displayed. You also find a histogram for each of the three primary color channels: Red, Green, and Blue.
The RGB histogram can also be seen in Lightroom but as a condensed version where all four graphs overlap.
So, what is the RGB histogram, and why do you need to pay attention to it?
Just as with the standard histogram (also known as the Luminance Histogram since it just refers to the tones), the RGB histograms display the brightness from dark to bright. The main difference, however, is that they represent the distribution of tones within a single color.
It’s essential to keep an eye on these channels as believe it or not, colors can also be under- or overexposed.
For example, when photographing the Northern Lights, I need to watch the green histogram. The Luminance Histogram might look fine for a night photo, but it’s easy to clip the green channel if the Northern Lights are strong. Here’s an example:
As you can see, the luminance histogram looks good, but there’s a green spike touching the right-side wall. This tells us that we have an overexposure in the green channel. In other words, we’re clipping the greens.
We wouldn’t have seen this if we only looked at the luminance histogram. But by also referring to the RGB histogram, we can identify and correct these mistakes immediately.
Exposure and the Histogram
The histogram is directly linked to the exposure of an image. What this means is that any change you make to the exposure will show on the histogram.
For example, increasing the shutter speed with one stop results in the graph moving toward the right as the image becomes brighter. Similarly, reducing the exposure by one stop will move it toward the left as the image becomes darker.
Understanding the histogram and the Exposure Triangle will give you the tools to create well-balanced images.
How to use the Histogram in Camera
The histogram plays an important role at two stages of your workflow: in-field and during post-processing. In the first stage, we use the histogram to create the best possible image file, while in the second stage, we refer to it while putting our finishing touches onto the photo.
In camera, there are also two ways you can view the histogram: you can use the Live Histogram, which is available in most modern cameras, or you can view the post-shot histogram in the image preview.
Live Histogram is a great feature that you should refer to if available in your camera. This tool gives you an updated histogram based on the camera settings and subject/light you photograph. I always refer to this when my camera is mounted on the tripod.
The post-shot histogram is equally essential to look at. This is often on by default but can easily be activated from your camera menu or by using one of the information buttons. As I previously mentioned, look at the RGB histogram at this stage as well.
It’s important to always refer to the histogram after taking a shot. This will give you valuable information about the exposure and warn you if there’s shadow- or highlight clipping.
Note: The camera display doesn’t always accurately display the brightness of an image. This could be due to outside light or the screen brightness being increased or decreased. Because of this, it’s crucial to look at the histogram to get an accurate representation of the tonal values.
How to use the Histogram in Lightroom
The second stage in which you’ll use the histogram is during post-processing. Whether this is in Adobe Photoshop, Lightroom, Luminar, or other software, it’s a function that should be turned on and referred to.
The histogram you begin with will differ from what you end with. This is normal, as you’ll work with contrast and colors throughout the post-processing workflow. For this reason, we want to get the best possible histogram in the field so we don’t need to worry about digital noise when we reveal details in dark areas.
The main thing you need to keep an eye on when processing the photo is that you’re not introducing clipping in the shadows or highlights. To ensure this isn’t happening, you can turn on the Clipping Warning in Lightroom by clicking the arrows in the histogram tab.
As we saw earlier in this article, this warning will introduce a bright blue color in areas that are pure black and a bright red in the regions that are pure white.
After reading this article, I hope that your understanding of how the histogram is better than what it was before. But why is it so important to understand? Why will understanding the histogram elevate your photography to the next level?
Most of these questions have already been answered, but let me summarize:
- It accurately represents the tonal values within an image.
- It reveals shadow or highlight clipping that you won’t see from just looking at the image preview.
- Understanding the histogram improves your understanding of exposure and helps create well-balanced images.
- Capturing images with a good histogram improves the file quality and gives you more flexibility in post-processing.
It’s not uncommon that an image looks good on the camera but appears slightly under or overexposed when importing it to the computer. This can be because of the brightness setting on your camera LCD, or perhaps the sun reflected on the screen, making it hard to see.
This could’ve been avoided by simply looking at the histogram. Then you would’ve learned that the image preview wasn’t accurate, and you could’ve made the appropriate changes.
Hopefully, this article has shown you that the histogram isn’t as scary as first feared. By spending a little time playing around with it, I’m confident that you’ll quickly realize how powerful this tool is.
Now, I challenge you to actively use the histogram during the next few days and adjust the shutter speed, ISO, or aperture to see how it changes as the image gets brighter or darker.
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