If you’re into photography, then you have probably heard about the image histogram; however, maybe you’ve been a little hesitant to truly master its use. Histograms can seem intimidating at first, for any creative person who is hoping to avoid the “charts and graphs” feeling at all cost. Personally, the first time I saw a histogram, I had a flashback to high school or college, and a statistics class that I probably failed.
What Is A Histogram
Believe it or not, however, histograms are incredibly simple, and also extremely useful! Whether you’re a total beginner or a working professional, having a full understanding of all your images’ histograms is very important. A true mastery can even help you work faster in the field, and be more confident that you’re getting exactly the results you want, both creatively and technically.
So, let’s demystify the histogram! This article will help you quickly understand what a histogram is, and how to interpret any histogram. (Spoiler: they’re literally a graph of all the different tones in your image.)
Next, we will talk about how creativity plays a role in a “correct” histogram, plus finally, we will include a few final technical details for any photographers who are truly geeky and wish to become a complete master of in-camera histograms.
This article is designed to be the ultimate guide to understanding histograms, and comes from our full-length course, Photography 101. However this article, and all of the education on SLR Lounge, is not designed to be a technical manual, but rather a field guide. We create real-world resources, designed to get you out and shooting as quickly as possible.
For those who are interested, we will include additional technical details and facts at the end of the article.
Let’s dive right in, and learn everything about your histogram!
How To Read A Histogram
A histogram is basically your image that has been turned into a simple graph of the different tones, from white to black, as well as all the color tones.
The left edge of the histogram is where pure black would be, while the right edge of the histogram graph is where pure white would be. In the RGB (individual red, green and blue color channels) histogram above, you can see that the image is of a firey sunrise, with lots of bright, warm colors, and a few various cooler tones in the shadows and mid-tones. And, lo and behold, the histogram reflects that! The entire right (bright) half of the histogram is almost entirely red and yellow on the graph, and then on the left (shadow) half of the histogram, there are many mixed tones with a few “bumps” of different blue tones.
So, it’s that simple. Next, as you might imagine, when you brighten your exposure, the entire histogram will shift to the right, and when you darken the exposure, the histogram will shift tothe left. Brighter, and darker. Again, super simple, right?
Reading the specific details of your histogram is indeed that simple: Each “bump” on the graph represents certain tones in the image itself. This is why you’ll often see a specific, large bump in a histogram when a large part of the image is a single tone.
How To Get A Correct Histogram | What Is ETTR
Okay, before we continue, we must caveat: there is no such thing as the “right” exposure, for reasons we discussed in our Official Guide on Exposure. Since every exposure has a histogram, the same rule applies- We’re going to use terms like “correct” and “right” histograms, however, creatively speaking there is always more than one way to expose an image. So, be sure to read that Official Guide on Exposure and especially check out the high-key and low-key sample images. Or, just scroll down to the next section, “Creative Use of Your Histogram”.
With that said, now we will explain what a “correct” histogram looks like, if only from a technical perspective.
Here’s the traditional method when it comes to gauging your exposure by using your histogram: Expose To The Right, or, ETTR. The concept is to set your exposure so that the histogram’s graph is close to the right (white/highlight) edge of the histogram, instead of in the middle or towards the left (black/shadow) edge. Why? Because simply put, the brighter parts of an image offer better image quality. Highlights tend to be very smooth and noise-free, while deep, dark shadows tend to get more and more “noisy” especially when they’re boosted in post-production.
Put another way: As long as you don’t actually clip a highlight, then you’re generally better off shooting a bright exposure and darkening the image in post-production, instead of accidentally under-exposing the image and brightening it in post-production.
The critical step in the method of ETTR is, to expose the image as brightly as possible, without actually clipping any (important) highlights. To do this, it is important to carefully check your histogram, whether live or after clicking a photo.
Actually, it’s very useful to turn on your highlight clipping warning in-camera, which causes any clipped highlights to “blink” at you. On most cameras this feature is displayed using the grey, all-inclusive RGB histogram, however on some cameras, such as many Nikons, you can set the camera’s image playback to display clipped highlights for individual Red, Green, or Blue histograms.
In a red-hot sunset, for example, the Red channel will clip long before the G/B channels, and therefore the grey RGB histogram may not correctly show a clipping warning.
Creative Use Of Your Histogram
As we mentioned already, there is no such thing as one single “correct” exposure or histogram, due to the creative nature of photography of course.
In short, although it is true that practicing the ETTR method correctly will give you the absolute best possible image quality, you can still creatively expose a scene with great success. See High-Key and Low-Key in our glossary for more examples!
Technical Details About Your Histogram
You’re now a master of your histogram, congratulations! If you want, you can just grab your camera and head out to shoot some photos. However, if you’re interested in diving even deeper, and truly becoming a master of your in-camera histogram, here are a few more details about the histogram when viewed in-camera.
The In-Camera Histogram Is Not A RAW Histogram
Unfortunately, the histogram you see in your camera, whether during live view or after clicking a picture, is NOT actually the raw image data’s histogram. It is a histogram created from the JPG image which is created as a thumbnail for the raw image data, based on the in-camera processing settings.
The Vertical Lines On the Histogram Graph
Most cameras display their histogram with 3-4 vertical lines that break up the histogram data into 4-5 different chunks. A photographer might look at these lines, and for example say to themselves, “hmm, my image appears to be exactly 1 EV under-exposed!” So, they brighten their exposure by 1 EV and don’t think twice.
Unfortunately, this is usually not a precise representation of actual EVs. In fact, most digital cameras today have 10-12 or more EVs of total dynamic range. Also, to complicate matters further, as we already discussed: the raw image data itself will have a different histogram than the in-camera JPG histogram, especially when applying highlight and/or shadow recovery in the raw conversion.
Therefore, the smartest thing you can do is to test your camera to see exactly how a 1-EV adjustment looks on your in-camera histogram, and also, what it turns into when you get the image on the computer.
Now you have not only a basic understanding of how histograms work, but also a mastery of the more subtle nuances of what to watch out for when your exposures get tricky!
If you have any questions about histograms or exposure, please comment below!
Now, go take some photos, practice, and experiment with new techniques! Capture creative images of all different types, and notice what their histograms look like, both on the back of your camera, and in post-production. Then, share your photos with our community! SLR Lounge’s Critique Page and Facebook Group are both excellent places to share the latest creative imagery you’ve made!
Written by Pye Jirsa and Matthew Saville