Histograms – It’s all Greek to me… not!

Histogram titleby Maggie Terlecki.

Looking at the graph of a histogram used to give me a bit of a panic attack and I would try to avoid that screen on the LCD of my camera with all my might. I didn’t like to tell people, as I was afraid they might laugh… but hey, it just looked like a bunch of hills and valleys and obviously this camera had way too much cholesterol and was headed for by-pass surgery. For many budding or non-technical photographers, their eyes just glaze over when reading this section in their camera manual.

Well, I’ve since learned that it really isn’t that scary after all and actually very useful.

Here is an example of a histogram:

histogrampart1example

To start with the name. It’s isn’t anything as frightening as a cardiogram or mammogram, where you tremble to find out the results; it is simply a graph shown as a drawing. Histos (Gk) means ‘graph’ and gram means ‘drawing’. (also Greek, I guess Gus Portokalos was right, the Greeks did invent everything, after all!)

Basically, the histogram is the fastest way to tell if your image is useable. It won’t tell you if your image is pretty, if it’s well composed or if is a great concept, but it will tell you if you have captured the lights and darks properly. It is the only truly reliable way of knowing if your image is well exposed short of actually printing it.

You would then say to me, but Maggie, I have an LCD on my camera, I can see that it looks fine. Well, sorry to break the news, but LCDs are not really reliable as they are only as good as the LCD screen they are showing up on. Screen qualities and color gamut are different even within the range of the same make of camera. Ever noticed that on sunny days it’s harder to see what’s displayed on your LCD screen because it looks too dark? This could easily make you over-expose your photographs. You may have set your LCD to be brighter to avoid this problem, but you still cannot trust that what you see is accurate, and you may actually under-expose because of it. All of these factors explain why you should use the LCD as your compositional tool only and not what you use to decide if your image is actually well-exposed.

So let’s get into the nitty-gritty, it is really not as painful as you may think.

For the purposes of this first article on histograms, we’ll touch on the basics.  In future articles we’ll look more in-depth and why you may want to use ETTR, Expose to the right, (yes, a bit of photo jargon there, but we’ll break it down to something that will be easy to understand).

Most point-and-shoots and all of today’s DSLR’s have a histogram screen, if you don’t know where to find it on your camera, here is the dreaded moment you must pull out your camera manual to the index page and find it.

As you can see, in the image provided, it is a graph. It is actually divided into 128 bars from
0 on the left to 127 (middle grey) another 128 bars to  255 on the right.
The 0 represents true black and the 255 represents pure white.

The vertical part is telling you how many pixels each segment contains. The higher the bar, the more your image is concentrated in that range of tone. If most of the pixels are to the right, the brighter the image is, and the more they are to the left, the darker the image is.

So, what does this all mean and why should we even care.

histogram1

Best case scenario, we want an image that gives us as much information as possible in both the dark and light zones. If our image is over-exposed, information in the whites are blown out and you cannot retrieve that which you haven’t recorded. Good-bye to the little detail in those flower petals and the pretty swirls in the bride’s lace. The dark zones may seem more forgiving as you may still be able to lighten the image and bring detail back, but now you are also introducing noise. In actuality if the camera sees something black and it is actually gray, it doesn’t know the difference so detail below 0 is also lost, just as detail over 255 is. Every post-process you do degrades the image quality, so getting it right in the beginning gives you that much more to work with without making your photograph suffer.

 

 

 Here is an example of an over-exposed image:
histogram2

Here is an example of an severely under-exposed image:

histogram3

If you are shooting .jpg as most point-and-shoot cameras don’t have RAW (although expect to see this feature in more and more lower-end cameras) you may not have any option. In that case, you will be looking at your histogram to see if you are actually recording what you are trying to produce.

An image of a concert pianist with a black curtain behind should show a histogram almost completely to the left (mostly blacks) with information at a smaller level to the right.. the details in what the musician was wearing for instance. Although the histogram should show basically almost everything to the left (Black); if you have no information in the light zone, then you know that you are under-exposed and if clipped, then you need more light and you are losing information that is existent in the shadows and that you may no longer be able to retrieve.

Example of a low-key image (mostly to the left on the histogram)

lowkey1

An image of a bride’s dress will be predominately to the right as it would be very white, but still needs information in the middle ground to see the details of the lace and some in the darker zone for the little bits of contrast. All to the right… and  blown out…or clipped?  you’ll have lost detail in the lace. Mostly in the middle? The dress will look dingy and gray.

Clipped you say, Maggie? What do you mean? Think of of your histogram as a piece of paper. that has a graph on it.  The graph would extend to the edges of the paper from left to right and starts at the bottom and goes up and over the edge of the top.  Normally very low at left. build up into a mountain towards the right and back down.  Now, if your image is clipped, it would be as if your information at either one side or both were cut off like a border, so that all the information that would have been clipped past the left registers  as black and all the information that would have been clipped past the right registers as pure white. Normally, it should instead be several shades of very dark gray to black and several shades of very light gray to white. By making sure your histogram isn’t clipped, you are making sure all shades available.  Now, an image that is supposed to be High Key (predominantly light and bright with little contrast) would naturally have clipping to the right as you want to lose most of that information. The same thing would apply to the left side of the graph for Low Key (predominantly dark with a high amount of contrast) Images.

Here is an example of a high-key image where most of the information naturally is to the right:

highkey1

The only way to take full advantage of your histogram, is to shoot RAW.

Think of .jpg as a baked cake. All the ingredients are mixed in camera, and the end result is what you snap. Sure you can do post-processing on a .jpg but you are limited. If you forgot the sugar in your cake, adding icing on top will make it better, but won’t ever replace having the sugar in the first place.

The histogram is showing you what your image will look like (or taste like, ;-) . )  If you are going to shoot  .jpg,  play around with your camera styles and settings to get the contrast and color the way you like it right away. Using your histogram will make sure that you are getting the highest quality image possible.

RAW is a whole different bakery game. Instead of the baked cake, it’s the ingredients. It’s the flour, sugar, eggs, baking soda, salt, spice etc., The advantage of using RAW is that you get to play with the quantities of the ingredients as much as you want before baking it, and if it doesn’t come out as you like, you can revert to the ingredients and start all over and create a completely different tasting cake.

The advantage of using the histogram is that you can make sure all the ingredients (detail throughout the entire range of the tones) are available.. cause if you don’t have the sugar.. no matter how you mix them, that cake is not going to be sweet.

Ideally the histogram of a RAW file should have no clipping to the right or left side. Detail should be predominantly in the middle and probably mostly to the right. (we’ll get into the reasons why they should most often be towards the right in a future article.)

Here is an example of a well-balanced and therefore well-exposed image.

histogram4

There is no good or bad histogram. It all depends on what you are shooting and how you are shooting.

When shooting RAW you will not want to use styles; you will also want to keep contrast to a minimum and color to a true or neutral setting as this will ensure that the histogram is not giving you a false reading. Pushing the contrast beforehand will create the illusion that you have detail in the blacks but the actual bars have been squished together allowing less information.. ergo, less ingredients.

RAW files NEVER record the style information .. remember it’s only the ingredients, not fully-baked. It has not decided beforehand that you want a spice, chocolate or buttery vanilla cake; it’s waiting for you, the master baker to play with the recipe.

See? That wasn’t hard, was it.. and now you will be sure your images are always as good as they can be, and when someone sees you reading the graph.. you’ll simply smile and say, this.. oh.. it’s just the histogram, easy as cake!

Remember, stay tuned for the follow-up on shooting to the right!

Creative Commons License
Histograms – It’s all Greek to me… Not! by Maggie Terlecki is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License.

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