I have a question to ask you. What do Santa Claus, this portrait of Lincoln, and great daytime television have in common?

That’s right, they’re all mythical. Well, actually, the portrait is real inasmuch as it physically exists, but it’s one of the earliest (now rather famous) examples of photo manipulation. It turns out that although the photo seems to depict a stoic Lincoln standing beside a writing desk, the only thing in that photo that is actually Lincoln’s is his head. The body is that of Southern politician John C. Calhoun.

Apparently, so few “heroic” portraits of Lincoln existed (perhaps because Lincoln was weary of posing for them) that the only logical alternative was to fake it. In addition to pasting Lincoln’s head onto Calhoun’s body, the text on the papers visible on the desk were changed from “strict constitution,” “free trade,” and “the sovereignty of the states” to “constitution,” “union,” and “proclamation of freedom.”

So who needs Photoshop, anyway?

Via Museum of Hoaxes

It’s never as simple as it sounds.

What Is Bit Depth?

If you already know what bit depth is all about, you can skip this section. Without getting into too much technical detail, bit depth describes how many colors (or shades of gray) an image is capable of storing (for the sake of simplicity, I will be discussing grayscale images in this article). Eight bits of color depth means that an eight-digit binary number is available to store the value of each pixel in the image¹. With eight digits in binary you can count up to 255. Including zero, that gives you 256 available shades of gray for each pixel in an 8-bit grayscale image. Zero is black and 255 is white. Making sense?

As you might predict, a 16-bit image gives you a 16-digit binary number to store your pixel information. With a 16-digit binary number, you can count up to 65,535! Including zero, that gives you 65,536 shades of gray to work with. Clearly there are immediate theoretical advantages to using 16 bits of color depth in photography.

The Case for More Depth

If all cameras captured images with eight bits of depth, there would be no debate; converting from an 8-bit image to a 16-bit image doesn’t do much for you, as I’ve casually proven in a few tests (which I won’t bore you with). However, cameras that capture in RAW format (which includes basically all DSLR cameras today), capture about 12 bits per pixel of data².

The savvy among you will already have thought, But 99% of printers today can only output eight bits, so who cares! You are correct.

But you’re also wrong.

The trick to this bit depth discussion is that you don’t need the extra bits to print your image. In fact, if you took pristine photos in 8-bit JPEG and printed them without ever touching a pixel, you’d be fine; you’d lose nothing. But I suspect most of you are photographers and you know that post-processing is increasingly important, if not necessary. It is for the editing that you need more bits. Let me explain why.

The Bit Crusher (aka Photoshop)

When I was discussing histograms, I mentioned in passing that making changes using e.g. levels or curves causes Photoshop (or the editor of your choice) to expand or compress the range of tones in the image, redistributing their values.

That sounds a lot more complicated than it is, which is why I like to use illustrations. Below, you will see a very subtle gradient that proceeds from a gray value of 90 on the left to 160 on the right. As you should be able to tell, there is no pure black and no pure white in this image.

In the 8-bit space, if I use levels to expand the tonal range of the image, you will see that a lot of dithering and banding occurs. What Photoshop is doing, very simply, is making the gradient start at a pixel value of zero and end at a pixel value of 255. The values of the intermediate pixels are then redistributed.

If you look closely at the image below you will see that the results aren’t too great, and it’s because there are a limited number of gray tones to choose from when averaging out the intermediate pixel values.

Here is the center area of the above image scaled by 300% so you can see exactly what’s going on.

So what happens if you have 16 bits? To find out, I created a 16-bit image exactly the same size as the ones above, drew an identical gradient, and performed the same levels adjustment (numerically, to be sure it was done precisely the same way). Because Photoshop has 256 times more gray values to choose from when redistributing the intermediate pixels, the gradient still looks pretty nice. In order to show you the result, I had to save it as an 8-bit JPEG, but as you can see below, converting to eight bits after doing the edit produces a noticeably better result.

Again, here is the middle section of the above image scaled to 300%.

I have racked my brain for a couple of days trying to come up with a lucid explanation for why editing in 16 bits and converting to eight produces a better result than editing in eight bits directly, but I’m no mathematician and regardless of the mechanics behind it, this workflow obviously delivers.

Putting 16 Bits to Good Use

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I spent a good deal of time putting this article together and building these examples, and I think I’ve proven almost without a shadow of a doubt that editing in a 16-bit space is noticeably beneficial. So you would probably think that I have a hard drive full of 16-bit Photoshop files, but you’d be wrong.

As I hope I have demonstrated in the examples above, 8-bit files are most vulnerable to what I call “broad tonal changes,” which describes levels, curves, or any adjustment that expands or compresses your tonal range. These are the types of edits that would cause your 8-bit histogram to be divided into many separate lines, as I showed in my histograms article. For that reason, those types of edits should be performed in the largest bit depth possible within your workflow to prevent banding and aliasing.

I like to make my broad tonal changes with the Develop module within Lightroom while the file is still a native 12-bit RAW. Once the histogram is compressed or expanded to my satisfaction, I will drop it into an 8-bit PSD for local edits if I have to, and save it that way. If, somewhere down the line, 16-bit printing becomes the norm, I can always re-process those files as 16-bit PSDs and print them. Somehow, though, I think I’ll be on to bigger and better things by then. One can hope, at least, right?

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The 8-bit versus 16-bit debate is one steeped in personal preference, and somehow has mutated into a rather divisive topic. I hope I’ve shown that no matter what your workflow preferences may be, you can benefit from the flexibility of 16-bit editing without filling your hard drives or waiting for your computer to crunch huge files.

I’d love to hear about your personal workflow preferences, too. Just leave a comment or drop me an e-mail!

1. In a color image, eight bits are used to store each of the red, green, and blue values for each pixel, which is why you sometimes see images referred to as “24-bit.” An 8-bit color image and a 24-bit color image are the same thing.
2. That means 12 bits each for red, green, and blue per pixel, roughly, but the specifics of different sensors and the exact math is not important for this discussion.

Today I will unclothe the common histogram and show you not only how to read it, but also how you can use it to strengthen your work.

The histogram is a very powerful tool because it provides an instant window to image information that is otherwise very difficult for a person to get a sense of. According to the Photoshop CS manual,

A histogram illustrates how pixels in an image are distributed by graphing the number of pixels at each color intensity level. This can show you whether the image contains enough detail in the shadows (shown in the left part of the histogram), midtones (shown in the middle), and highlights (shown in the right part) to make a good correction.

Most importantly for photographers, the histogram shows you this subtle highlight and shadow information in a way that is completely independent of your monitor’s capabilities and color profiling.

Great, so it shows us detail information, or something like that. What does that mean exactly? As stated in Adobe’s definition above, the left side of the histogram represents the darkest pixels in the image, while the right side represents the lightest pixels. The height of each “bar” (in a Photoshop histogram, each bar is one pixel wide) represents how many pixels of that precise brightness exist in the image relative to all the other brightnesses. All of this is much easier to understand if you can see it. So, here:

I have conveniently numbered each shaded area of the image and their corresponding histogram bars. The first thing you should notice is that the bars move toward the right of the histogram as the shade gets lighter. Because areas one, two, and three get progressively lighter by exactly the same amount, their bars in the histogram are evenly separated. Likewise for areas four through seven.

The next thing to notice is the height of the bars. How are the heights of these bars calculated? An important note when looking at any histogram in Photoshop is that the graph is scaled so there is never any “wasted” vertical space. In this case, bars one, two, and three are precisely one quarter the size of the entire image, so even though the number of pixels in each of those sections is precisely one quarter the number of pixels in the entire image, the bars are not one quarter the height of the histogram. Why? Because there is no single shade of pixel that has a higher count than any of those sections (one, two, or three). The graph is scaled vertically.

Okay, so why are the bars for sections four through seven as tall as they are? Each of those four sections of the image is one quarter the size of section one or two or three (as you can probably tell just by looking), so their bars in the histogram are one quarter the height of the bars for sections one, two, and three. Is this all making some sense?

Just by looking at this histogram (which is harder to read by virtue of its sparsity, but bear with me), we can actually see that the image must contain precisely seven distinct pixel brightnesses ranging from completely black to completely white (and look, it does!). Furthermore, we can see (for example) that there are about one quarter as many pixels of brightness four than there are of brightness three. Fantastic.

So what happens when the image has many more shades in it? Let’s say, for example, that it has every shade in it.

Be honest: is the histogram at all what you would’ve expected it to be? I was a bit surprised to see that curve in there, but it does show us some things about the Photoshop gradient tool that we might not have known before!

We have already drawn the first conclusion from this histogram: it contains every possible shade of pixel. We know this because the data begins at the very left edge and proceeds all the way to the very right edge without a single gap. What happens if a shade is removed?

The attentive among you will notice that two changes have occurred. First, there is a pretty obvious gap where that particular shade of gray was removed. Second, the bar at the very right edge is now filled in. Why? Because I covered the gray shade with a white line, so the count of white pixels has now increased! Brilliant.

Let’s make a more drastic change and see how that affects the histogram display:

I have now removed most of the dark tones in the example image. Notice that the histogram has a huge gap on the left side. No matter how large or small your image is, the histogram always ranges from 100% black on the left to 100% white on the right. Also notice that the tallest bar is the one at the far right, for 100% white. This is because there are more completely white pixels than pixels of any other brightness in the image, so the histogram is now scaled to the height of that bar (the tallest bar).

By now you should be pretty comfortable getting the following information out of a histogram:

1. The range of tones in an image; does the image contain completely black and completely white tones, and is it missing tones anywhere in the range?
2. Which tones occur most often in the image; which bars in the histogram touch the top?

That’s a good start, but it won’t make you more proficient at editing your photographs. Let’s take a look at a real example to see how what we’ve learned applies to a photo.

Here is a photograph that I made at one of the (many) tobacco fields up in Windsor, Connecticut. It was a foggy morning and, as you can see from the histogram, there are no very light tones in this image at all (you can tell by the large gap on the right side). Notice also that there is not a significant amount of black (the curve on the left doesn’t begin immediately at the left edge).

I want to evenly brighten this image so that the sky area becomes white. I will use levels for this (which I won’t explain; there are ample articles for using levels), but you could also use curves. Once I’ve brightened the image, it might look more like this:

There are two important things to notice about this histogram. First, you may wonder why it’s full of gaps. These gaps are a side-effect of expanding the tonal range of an 8-bit image; Photoshop shifts and redistributes the tones in the image to give the best perceptual result, but it doesn’t (and cannot) create more pixels with intermediate tones to complete the entire tonal range. This is one of the linchpins in the 8-bit versus 16-bit editing debate, which I may cover in a later article.

Second, you will notice that I now have a significant amount of pure white in the image (the very last bar on the right goes all the way to the top of the histogram), and the curve appears to be “cut off.” This is usually a bad thing, because it tells you that you have lost highlight detail. If this were a high-key portrait, or if your intent was to wash out the sky to pure white, you would have succeeded. I didn’t mean to do that, though, so I’m going to undo that change and try again.

The changes in the photograph itself might be very subtle and difficult to see on your monitor. This is the entire point of the histogram. By reading the histogram, you can tell what’s going on even if you can’t see it in the photo. Notice that I have not clipped as much of the highlight area of the tone curve, leaving almost no pure white at all. This tells me that my sky is going to be filled almost entirely with delicious digital sensor noise, which will give it a more continuous tone and realistic feeling.

By keeping an eye on your histogram, you can quickly and easily evaluate whether or not you’ve achieved the result you were after. If your camera has the ability to display a histogram you can even do this evaluation in the field. I personally use the histogram display on my Canon 5D just about 100% of the time; once I have seen the composition through the viewfinder, what I’m most concerned with is the exposure, and the histogram allows me to see that no matter how bright or dark it is, and no matter how accurate the camera’s LCD display is. A truly valuable tool!

That’s it for our in-depth examination of the histogram and all of its exploits! I hope you’ve enjoyed digging into the mechanics of this very useful tool and if you have any remaining questions or if you think I screwed up anywhere along the way, please leave a comment!

Lightroom, Lightroom, Lightroom, Lightroom

While reading histograms directly on your camera is the first step toward complete control of your exposure, no digital photographer should overlook the importance of post-processing. When Adobe released Lightroom, I was already using Apple’s Aperture, but after only a few hours, I was ready to switch. I was a Lightroom pre-release beta tester and I have purchased every upgrade to Lightroom ever since; it’s literally the killer app of killer apps.

Lightroom takes the histogram to the next level with RGB graphs and over- and under-exposure views that actually show you on the photo itself where you’re losing detail. Not only is Lightroom an indispensable tool for developing photographs, it’s an awesome way to learn how imaging works.

If you haven’t purchased Lightroom yet, let me urge you now, please, please buy it. It will change the way you develop and organize your photos. On top of that, if you buy it from B&H at their great price, and use one of the links below, you help me keep this site online… Which I would really appreciate. Using these links does not change your cost in any way, shape, or form.

• Adobe Photoshop Lightroom 3 Software for Mac & Windows
• Adobe Photoshop Lightroom 3 Software for Mac & Windows – Student & Teacher Edition

If you are a student or a teacher, you can get a huge discount by purchasing the second one; it’s absolutely identical in every way (except the box, it says “Student & Teacher Edition” on it), but you do need to produce proof of your student or teacher status.