How to Read Your Camera's Histogram


One of the magical things that digital photography gives us is the ability to review an image instantly on the back of our cameras, or inside of an electronic viewfinder.  

Frequently, one possible mistake that digital photographers make is when reviewing an image on their camera—they evaluate exposure based on the reproduced image. Why might this be a mistake? Well, both your camera’s LCD and EVF likely have adjustable brightness. Also, you might be viewing your images in bright sunlight or in the pitch black of night. Just as viewing a computer screen at work, or a television in your home, ambient light, screen brightness, and other factors affect the image that you view.

You should evaluate your LCD for composition and look to see if your depth of field, sharpness, and motion blur or freezing of action is what you were aiming for. Of course, if you have completely missed on your exposure, you may see that in an extremely bright or dark image.

So, how can we fine-tune our exposure if we cannot trust what our eyes see on the reproduced image? The answer is: the histogram. Luckily for us, the manufacturers of digital cameras have given us the histogram to use as a tool to evaluate exposure on a digital image more precisely.

The Camera’s Histogram(s)


Luminosity Histogram Color Histograms

Most modern digital cameras have four histograms. The primary one is the luminosity histogram that shows overall brightness of a scene. This histogram usually has a monochromatic display—either white data on a black chart, or vice versa. The other three histograms are the color histograms, representing the red-, green-, and blue-sensitive pixels on the sensor. These histograms are generally displayed in their respective color.

(It may help to have your digital camera with you while you read this article, so that you can figure out how to find your camera’s histogram and interpret it. Digital cameras from different manufacturers have different menus, interfaces, and settings that govern when and where your histogram or histograms will appear. Consult your owner’s manual or an online source to utilize your camera’s histogram display.)

Now that you have located your histogram, how do you read it?


This example shows a nice mid-tone image. The dark area at the top appears as the peak on the histogram’s left side, but it does not extend all the way to the left edge of the graph.

How to Read the Histogram

First of all, there is an enormous amount of math behind the histogram. Luckily for the math-challenged photographers like me, you do not need to know any of it. For those of you who like numbers, I will attempt to sprinkle a few into the article, but know that the numbers behind the chart are inconsequential to reading it on your camera. If the numbers were critical to translating the data, the graphs would have numbers labeling them. However, there are probably more than a few websites and tutorials out there that dive into the mathematical abyss of bit depths, dynamic ranges, and some other sources of the math behind the histogram. Personally, I like to stay focused on the practical applications of the histogram. If you want to know more, hit me up in the Comments section at the end of the article, and we can nerd-out a bit! OK, back to the histogram…

This histogram’s horizontal (X) axis shows the luminance of the image from pure black on the left edge of the graph to pure white on the right edge. Growth on the vertical (Y) axis indicates the relative quantity of light for the given luminance. To illustrate the functionality using an extreme example, take a photo with your lens cap on and you will produce a histogram that has one spike, from bottom to top, on the left edge of the histogram. Opposite of this, take a long exposure on a sunny day and you will achieve a spike on the right. An image with a balanced exposure will show a “hump” in the middle region of the chart that tapers off as you move left toward black or right toward white.

This middle region of the histogram is for midtone luminance—the gray area(s) between black and white. You may have heard of “50 Shades of Gray.” Your camera, if it does 8-bit sampling, has 255 shades of gray. If you must visualize numbers, the X-axis of the histogram goes from 0 (black) to 255 (white) as you move from left to right.

To effectively use the histogram, you need to know three things:

  1. How to read the histogram (you are about to learn that).
  2. The scene—A consciousness of the brightness, darkness, and contrast of the scene you are photographing is needed.
  3. Your goal—The “proper” exposure or “perfect” spread of midtones is not the goal of each photographer for each image. Know what you are trying to produce.

Let us expand on these three things a bit.

How to read it: The histogram basically shows you the brightness of an image. If you take an image and see the majority of the body of the graph toward the right, this means you have captured a “high-key” image that may appear overexposed. Opposite, a histogram with the data showing mostly on the left is a “low-key” image that might appear underexposed. If you are making an image of a high-contrast scene (very dark and bright areas), you might see a U-shaped histogram. There are almost infinite combinations of light and dark that will register on the histogram.

Here is a low-key image and its accompanying histogram. You can see how the “weight” of the histogram is on the left side of the graph and it tapers rapidly as you move toward the center. The hump in the middle corresponds with the illuminated areas under the benches. Note that the data barely touches the left edge of the histogram. That would indicate clipping of the shadow detail.


The scene: I feel this is important, and something I do not see discussed very much on the topic of exposure and histograms. If you take a night photograph of a building and half of your image is a sky as black as a raven’s wing at midnight, you should expect to see a large spike on the left edge of your histogram. Conversely, if the sun is in your image, get ready for a right-side spike. If you are taking photos of a scene with dark shadows or bright sunlight, you need to expect to see this on the histogram and you should not be surprised when the spikes appear on the right or left edge.

Your goal: For those of you who read my article about “Understanding Aperture”, you are familiar with my thoughts about the term “proper” when it comes to exposure. Many guides will say that a certain histogram shows a proper exposure. “Proper” is subjective and photography is art. Art is subjective. If your artistic vision is a photo that is overexposed or one that is underexposed, and you intentionally cause that effect for your image, then “proper” is what you have achieved.

Here is an example of the U-shaped histogram caused by a high-contrast image. The band of LED lights in the middle of the frame and the bright foreground give the histogram a short peak at the right while the majority of the pixels are dark, and this is reflected by the mass on the left side of the histogram.


So, you take those three elements—knowledge of what the histogram is showing you, knowledge of the scene you are capturing, and knowledge of the final image you wish to produce—and then you look at the histogram and evaluate how and if you want to adjust your exposure for the next image by tweaking your aperture, shutter speed, ISO, or by recomposing the scene to reduce the amount of dark or light area in the image.

Beside personal artistic goals, the biggest reason to adjust the exposure, based on histogram data, is if your histogram tells you that you have experienced the horrible, yucky, and unfortunate phenomenon known as “clipping.”


Your camera’s digital sensor is much more limited than the human eye in its ability to gather information from a scene that contains very bright and very dark areas—a scene with a broad “dynamic range.” In photography, dynamic range is defined as the ratio between the maximum and minimum areas of luminance in a given scene. The camera will, unless you are manually controlling exposure, try its hardest to create an image that is exposed for the widest possible range of lights and darks in a scene. Because of the limited dynamic range of the sensor, this solution might leave the image with pitch-black shadows or pure white highlights.

A spike touching the left edge of the histogram means that there is shadow clipping. The dark areas of the image are outside of the camera’s dynamic range to the point that the camera cannot discern any information from those regions. The camera says, “By exposing for the major portion of the image, I have created an area of the photo so dark that I cannot see anything there, so I am going to call it pure black.” Spikes touching the right edge are representative of the camera saying the opposite, “When I expose for the major portion of the image, this one region is so bright that I cannot tell if there is an object in that region, so I will call it pure white.”

Here is a simulated (no, I did not blow the exposure!) high-key image. All of the data in the histogram is off to the right and—oops—lights of highlight clipping too!

Here is the same shot, as it was taken, which shows a low-key exposure. Note the spike on the right, corresponding to the illumination from the light bulbs, that shows up on the histogram and shows clipping. This is a good example of the sneakiness of highlight clipping, as there is virtually no buildup of brighter sections leading to the spike on the right edge of the histogram. We also have some shadow clipping as well. In scenarios like this there is really no way to avoid clipping on one or both edges. You just need to adjust your exposure to get the effect you want. Your own eyes will have the ability to see into the shadows while not getting blinded by the lights.

Clipping represents, unfortunately, the loss of data from that region of the image. Digital cameras are known for their ability to extract detail from dark shadow regions of an image, but once the histogram touches the left edge, that data is all but lost to a black abyss, and no amount of post-processing will pull detail from those shadows. Areas of pure white will also be just that—pure white—on your screen or on a print.

Shadow clipping is usually pretty noticeable on the histogram, since there is usually a buildup of data on the left side of the scale that reaches the left edge. Highlight clipping can be a bit more subtle, especially when taking night photographs, as only a small number of pixels in the image might be blown-out highlights. Keep an eye out for the narrow spike on the right edge of the histogram. Many of today’s digital cameras will have a flashing highlight indicator, or “blinkies” function that causes the blown-out light and/or dark regions of a previewed photo to blink so that the photographer can see, without the histogram, that areas are lost to black or white. A good practice is to use both the blinkies and histogram so that you can evaluate what regions of the photos have been clipped and whether you want to make adjustments to the exposure to prevent the occurrence in the next shot. Of course, you can always leave well enough alone and move to your next great image and accept the shadows or highlights. Sometimes, depending on the scene and your camera’s dynamic range, there will not be a practical remedy to the clipping; however, if you can adjust your exposure to avoid clipping, by all means, do so.

Remember that RGB histogram that I mentioned? Well, basically, it shows distribution and clipping in the red, green, or blue channels. Pay attention to the RGB histograms because you might see color clipping in one or more channels while the luminance histogram shows no clipping. This “hidden” clipping might be a big deal for your image, depending on your photographic vision for the picture.

Another thing to mention: the histogram and blinkies are usually based on a JPEG rendition of your image. If you are shooting a raw file, your actual image will have a slightly greater dynamic range and, the clipping, if there is some, should be reduced.

In summary, the histogram is a remarkable tool to have in your tool box and one of many gifts that digital photography has bestowed on photographers of all skill levels. When reviewing your images, be sure to base your exposure adjustments moving forward on the histogram data, look for clipping and blinkies, and do not judge an exposure based on the preview image. 

Do you use your camera's histogram? Let us know in the Comments section, below!


Good, durable, article. B&H's robot included it in an email after I (finally) bought another lens. I see a direct parallel between the camera's luminance histogram and the one that comes up when using PhotoShop (Elements) enhance/levels tool. It has been helpful for decades in dragging sliders to perk up highlights and shadows, and now I see more clearly why. I didn't feel up to reading 140 comments and hundreds of responses to them, so here, perhaps repeating, is my question: In the color histograms, other than noticing things that might be clues to color-balance skewing, what are they good for?

Hi John,

Thanks for the kind words on the article! That darn robot is up to its tricks again!

No worries about not diving through the comments as well. Interestingly, there is a comment from Bill and in-depth discussion a very short scroll down the page. CTRL-F "color" and you'll see it right away!

My summary was that the color histogram shows the intensity of a particular color channel in the image (red, green, or blue)—not necessarily the brightness of the color—and, as such, do show color balance shifts. For overall exposure evaluation, the standard histogram is going to be your tool of choice.

If you want to dive deeper into the math of it, Will, replying to Bill, got super technical down the page!

Standing by for follow-up questions, John!

Thanks for reading!



Very helpful and concise. Since I started shooting with e cameras (I came from film) I have had a phenominal advantage being able to review each shot using the feedback of the luminance histogram. Even with a bit of knowledge it creates a whole new world.

Thank you for your help.

Hey Joe,

Thanks for the kind words on the article! I, too, came from film. :)

Let us know if you have questions as you explore the digital world and thanks for reading!



Excellent and informative article. Thanks.

Hi Stephen,

Thank you very much for the kind words! I am glad you enjoyed the article.

Thanks for reading!



Nice. I actually just watched a video that can help it. They show how to read they histogram and what to look for live. [Link removed] it worth watching it

Thanks, Alexandre! Unfortunately, we cannot publish external links here, but thanks for stopping by!



I am interested in certain color histograms I see on various websites.  It seems that the left side varies between white and black (top to bottom) while the right side varies between some color and black both based upon the red, green and blue values.

It seems that various individual red (or green or blue) values may vary but when the red, green and blue integer values are similar the pixel falls on the left side of the color histogram.

Is there a formula for determining whether a pixel falls on the left, right or somewhere along the horizontal of the color histogram?  Where might I find the formula?

If the color is "black" it seems that the individual red, green, blue values are each similar small numbers on the left.  But, on the right the red, green and blue values can vary significantly but still end up black.  Is there a way to use the red, green and blue integer values to determine you have a "black" on the horizontal of the color histogram?

Hi Bill,

I am not sure I completely understand your question, but here is my stab at a reply...

There is actually very little written about RGB histograms (including in my article above) as, I believe, they aren't very well understood.

It is my understanding that the RGB histograms show the intensity of colors in those three wavelengths from a value of 0 on the left to 255 on the right.

Is there a formula for determining where the pixels fall? I would imagine there is, because there has to be a way for the computer to calculate and display the information, but I do not know it. Where can you find it? You may be able to email your camera manufacturer and see if they will put you in touch with the sensor engineers or software folks.

Black is the absence of light, so, if something is pure black, it should not register on an RGB histogram. If it is a dark area that is barely red, blue, or green, it should register on the left. More intense colors (not necessarily brighter) will appear to the right.

Regarding your last question, I do not know how you would determine a black on the horizontal of a color histogram. My thought is that black is represented only on the luminosity histogram, and never on the RGB color histograms.

I did find this in my research:

I hope I was a bit of help. Feel free to follow-up if I completely misread your questions, or if you have follow-ups. Also, be careful not to get too buried in this stuff. If you are a photographer, your focus and concentration should be on the image, not necessarily on the formulas behind your histograms. Just my $0.02.

PS. Once you used the word "integer" I had a complete seizure and fell off of my chair panicked that my mission to work in the arts and never have to deal with mathematics again had failed miserably. Thanks for that! :)




Hi Bill,

Each pixel you see is a composition of red, green, blue, and transparency channels. Let's leave out transparency as it is a synthetic value representing how much the superimposed colours diffuse into one another.

If I understand Todd's explanation of histograms correctly, the red/green/blue histograms aren't that important as the luminance (light intensity) histogram.

It is worth noting that luminance (light intensity) is the combined brightness of red, green, blue channels of the pixel. The shades of grey all have equal levels of luminance (intensity) for each of the RGB channels.

Any other colour than grey will have various intensity of RGB channels, which is the primary reason that we see a particular colour.

If we present the colour space as a triangle where the spikes are noting one pure colour (red or green or blue), then at the red spike the intensity of red is maxed, the intensity of blue and green are nil. All the intermediate points can be inferred from these three extremities.

Now let's put that in a practical, observable way:

Suppose you have a pur red light emitting bulb and you've taken a picture. The luminance histogram and the red histogram should theoretically have identical shapes and characterisitcs because there is only red light in the image and the overall image luminance is the same as the red luminance.

Same thing will happen if you have pure green or pure blue light emitting bulbs.

Now, take all this 3 pictures and superimpose (stack on top of each other) so that all pixels diffuse completely, you'll should the picture that is exactly the same as if you've taken the picture using white light bulb in the first place.

Now, take a look at the luminance histogram of the final superimposed image (let's call it white histogram). Mathematically, you should see something that's close to the superimposed histograms of the each individual colour channel: where there was a bump in red histogram, there should be a (shorter) bump in the white histogram, where there was a dip in the red histogram, there should be a (less deep) dip in the white histogram, so on and so forth. Dips and bumps cancel each other and make the spot flat, so if your red histogram had bump or hypothetical 5, the green had dip of hypothetical 3 and blue had dip of hypothetical 1, then white histogram should have a hypothetical bump of 1; (+5) + (-3) + (-1) = 1

The reason I use words "should" and "hypothetical" is that I am not delving into exact mathematical equasions, instead, explaining how to interpret shapes and put them in the context of one another.

So, yes one can derive white histogram from the RGB histogram components.

There is one important aspect that RGB individual histograms can tell you, which is the colour accuracy.

It is very common to see oversaturated colours especially on AMOLED screens and especially on PenTile matrix displays where there are twice as many green pixels as either red or blue, and the blue pixels are larger and less intensive as reds.

If the live scene you see is blue, and you see navy on the camera display, don't be surprised. Your blue histogram should tell you if in fact the camera sees navy or blue.

Purple is the combination of blue and red, Yellow and shades of brown are combination of blue and green. So take photoshop and play with colour combinations to learn what to expect of each colour when you see it live...

If your expectations match the indications of the RGB histogram shapes, then the camera lens sees what you see and the picture will be an accurate capture.


Hope this was helpful.

This hs been the most informative article that I've seen on this subject. Thanks for unfogging my head. Haha. Great comments also.

No worries, Bob! Thanks for stopping by and thanks for the kind words!

I second that sentiment! It is llike I am now armed and dangerous.  :-)

Hi Todd, Thanks for the informative article. Two questions. 1. When your histogram has a large lump say in the midtones so that it is cut off at the top of the y-axis are you losing any data?  2. In high contrast shots (e.g., a sunrise or sunset) I find that if I under expose a bit so that the left side of the histogram is clipped that I can recover quite a bit using the shadows tool (I use Capture One) in post processing on a raw file.  When I do this am I creating data or is the raw converter able to see image data that the histogram on the camera could not see?  Thanks in advance.

Have the same question: What happen when graph is cutoff in middle tones?


Anonymous wrote:

Have the same question: What happen when graph is cutoff in middle tones?


I rad your answer below and you already answer that...:) thanks again

Thanks, Jhim! Also, see what I just posted in response to Ray's questions. 

Hi Ray!

Thanks for your questions!

1. I honestly don't think I have ever seen a large section of a mid-tone "hump" hit the top of the y-axis, but I have seen it peak there. But, to answer your question, you will not be losing any information. The histogram shows the amount of information gathered at a certain brightness (for non RGB histograms). I suppose if you took a normally exposed photo of a very gray sky, you might see a lot of information in the center of the histogram, but you cannot "clip" mid-tone data. Gray is gray!

2. I have a couple of answers to this one question...mixed into one. When you pull shadow detail out of an image you are technically brightening the non-clipped regions of the photograph. Not every dark area is actually pure black...the camera has many different shades of dark gray that it is capturing on its way to black. You might think that the histogram shows a lot of clipping, but you were able to get a lot of information out of the shadows in post-processing. This may be a function of the fact that the histogram is based on a JPEG copy of your image, not the raw file. The information is there, but the histogram may not show it totally accurately. 

If you are curious, take a photo and upload it to Capture One. Compare the raw histogram on the computer while looking at the camera's version of the histogram after you pop the card back in. I bet they are close, but not carbon copies.

Also, remember, the histogram is a guide...if we were meant to give it a scientific analysis, our LCD screens would impose grid lines over the histogram, or allow us to zoom in on it. Don't over-think the histogram. It isn't for in-depth analysis.

Thanks for reading, Ray! Great questions!

Hi Todd, Simple explanation to what I thought was a complex subject. One question. Will a high end camera with a very good dynamic range seldom give clipped Histograms?



Hey Patrick,

I think there is a simple answer, so here it goes!

The better the camera's dynamic range, the less clipping you should see for a given scene. However, you can always get clipping by over or underexposing a photograph—regardless of the sensor. 

Take a photo with your lens cap on while standing in a closet inside of a room with the lights off at 1/1000th shutter speed and ISO200, you will clip the shadow details of the inside of that lens cap on every camera! 

Good question! Thanks for reading!

Thanks for the article.  My camera manual and photo software all say "here's the histogram" without explanation, and show examples without axis labels or scales.  This definitely helped me to understand.

Glad to be of assistance, Rod! Thanks for reading and taking the time to comment!

Wow!  So glad I found your article.... This totally explains why, when photographing an event - I am so happy with what I 'seem' to be getting as far as exposure goes, but when I get home and look at them in LR, I find they are usually underexposed.  I will come back to this as I learn, and I bet it will make more and more sense to me.  Thanks !!!


I am glad the article was enlightening! Underexposing in the digital world usually isn't the worst thing in the world as a lot of detail can be pulled out of the darker areas of the image. Better than overexposing!

Thanks for reading and please let me know if you have any questions!

Todd, what does spiking in the middle of the histogram indicate?

Hello Major,

Thanks for your question!

Spike(s) in the middle of the histogram are usually what you want to see on a histogram unless you are intentionally trying to over or underexpose an image. If you see a very spiky (is that a word?) histogram, it is likely the result of artifacts from the JPEG compression. You generally will not see those on a raw file.

You will not get mid-tone "clipping" in an image.

I hope this answers your question. Thanks for reading!

You are very welcome, Major! Semper Fi!

Can you explain what the difference is between a high-key image and an over-exposed image? Or a  low-key image and an under-exposed shot? Thanks.

Hi Hal,

Thanks for your question. The two terms are mostly synonymous. The semantic difference is that a high key image may appear overexposed, depending on your subject and artistic vision. The opposite applies to a low key photo. High key photos have the bulk of the information to the right of the middle of the histogram, but may or may not be overexposed. Low key shows the weight to the left of the histogram and may be underexposed, but not necessarily.

Does that answer your question? Thanks for reading!

Thanks for the clarity. It is a great help since I have been shooting manual at the beach with a Canon 70D and it is tough to get exposure exactly right in the viewfinder. Understanding the luminance histogram better has been a huge benefit. Especially with highly reflective sand and big patches of sky.

Hi John,

You are very welcome! Yeah, beaches can be tough on the camera's dynamic range limitations. Thanks for reading and writing in!

Great article. I have often wondered about the value of viewing and evaluating jpg images when the RAW image is what I'm going to use. How do I know that the image preview and histogram are accurate?

I assume the jpg image and histogram shown on the image preview are "as processed" with whatever settings are in the camera. Does this include exposure compensation? What is the safest camera settings to use so the jpg image and histogram are most accurate?

thanks again / Kozmo

Hey Kozmo,


To answer your first question, you do NOT know that the preview is going to be accurate. Why? Because your LCD screen has adjustable brightness and you might be viewing it in a very dark or bright space. Therefore, you cannot really evaluate exposure on a preview. Well, you can, but beware of certain pitfalls. If your camera has an option for "blinking" highlights and/or shadows, that can be helpful on the preview screen.

Is the histogram accurate? Well, we all hope so! A primary mission of the digital camera is to either give you a correct automatic exposure, or let you manually dictate the exposure. The histogram is the thing that shows you if everything (your brain or the camera's brain) is working. I hope histograms, in general, are accurate. I have not heard tales about an inaccurate one.

The histogram and preview should definitely show you the exposure you captured—with or without any exposure compensation dialed in. As far as safest settings, RAW is always your safest setting. If you want to shoot JPEG, I recommend shooting JPEG+RAW, especially if you are going to funky in-camera JPEG processing.

Don't over think too much. Unless you are doing heavy in-camera JPEG editing, the JPEG and RAW file will be pretty darn close as far as exposure appearance and the histogram. In fact, most times, it will be difficult to tell the difference between the two.

Thank you for your questions! I hope this helped. Standing by for follow-ups!

Unfortunately, due to a lack of transparency as regards to raw in general, photographers, even the most experienced ones, are left somewhat in the dark in all things raw.

If you're shooting in raw, the image preview and histogram displayed on the back of your camera are most of times not representative of raw data. The JPEG rendering compresses and chops off highlights and crushes shadows. White balance is applied to JPEGs, and thus the histogram is affected by the white balance, which may result in a false clipping indication in the highlights. To see the difference, you can use FastRawViewer,… It also has a free 30-day trial if you download it from the fastrawviewer website. Toggle J, and you can switch between the JPEG preview and the raw. It also toggles between the histograms for raw and JPEG, so you'll be able to see just how "similar" they are. Spoiler alert, they're not in the least.



Thanks for sharing this info, Fabian! 

Regarding Fabians comment on RAW histograms, can you tell me if the histogram that I see in Lightroom represents the jpg image I see on the screen, or does it represent the RAW histogram

Hey Manohar,

The histogram in Lightroom will show you the histogram for whatever type of file you have selected in the Develop or Library module. If it is a .DNG file, you will see the histogram for the .DNG image.

Thanks for your question and thanks for reading!


Thanks for sharing the link to the app and thanks for creating it! Awesome!


I am very impressed with this article. Very easy to follow and extremely well explained.  One thing I cannot work out is that when I  preview my images , for some reason the blue (B) off the RGB keeps blinking and I not sure how I can fix this problem on camera.

Keep the good work.. Much appreciated.


Michael Camilleri 



Hi Michael,

Thanks for the kind words!

Can you tell me what kind of camera you have and what is actually blinking? Is it the histogram or blue areas inside the previewed image?


Thank you for the clarity with which you communicate your knowledge.

Thank you for the compliment and thank you for reading, AnthonyL!

Thanks a lot ...You explain the subject in a easy way. I appreciate your efforts.

Thanks for the kind words, Dipeshkumar! I am glad you enjoyed the article!

thank you so much for the easy to understand tutorial much appreciated 

You are welcome, Steven60! Thanks for reading!

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