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Arriving at the best exposure for a photo is part science and part art. In Foundations of Photography: Exposure, Ben Long helps photographers expand their artistic options by giving them a deep understanding of shutter speed, aperture, ISO, and all other critical exposure practices. This course covers the basic exposure controls provided by all digital SLR cameras, as well as most advanced point-and-shoot models. Learn how to master a camera's metering modes, how to use exposure compensation and bracketing, and much more. By the end of the course, you'll know how to develop an "exposure strategy" that will allow you to effectively employ your exposure knowledge in any shooting situation.
Now that you are intentionally over- and underexposing, you have a great level of control over a number of things, from tonality, to ensuring that highlights and shadows hold the details that you want. But here's the bad news: while it's great that you have the little screen on the back of your camera for reviewing your images, it's important to understand that the screen is only good for judging composition. Really, it tells you nothing at all about color or exposure, because the image on the screen is brightened and saturated by the camera to make it easier to view in bright light. So just because an image looks okay on the camera screen doesn't mean that you've actually got a good exposure.
Now, in the old days when facing a difficult exposure situation, photographers had to bracket their shots. They had to shoot the same image with multiple exposures, and there's still a lot of times when that's a good tool for a digital shooter. But fortunately, as a digital photographer, you have an additional tool in the form of the histogram. So I have got a histogram right here, and it's going to look a little math-like, but don't worry about that. It's actually very simple. What I've got here is just a graph. This is an image of a grayscale going from black to white, and what I have here is a histogram generated by this image.
A histogram is just a bar chart representing the distribution of tones in an image with black on the left and white on the right. So my gray ramp goes from black to white with a bunch of intermediate shades of gray. My histogram is showing a whole lot of black and a whole lot of white and a full range of intermediate tones. So if I think about, over there I have got 100% black and for every 100% black pixel, another little dot is added to the 100% black bar, and so on and so forth. Each line, each tiny little line just represents one bar in this chart.
It's somewhat easy to understand a histogram when we are looking at a very simple thing like a grayscale ramp. So let's look at more of a real-world situation. What we've got here is a simple set. We've got a darker gray backdrop with a table in front, with a lighter gray tablecloth on it. This is what it looks like in my camera. Now you should recognize this. This particular camera has the ability to generate a live histogram on the fly while I am shooting. So what I am seeing here is a camera-generated histogram of my scene.
I've got black over here, white over here, and here's some data. Now, I have nothing, I have no black in the image. I have no really dark gray in the image. I know that because there is no data here. There are no bars. I have no white in the image. I have no really bright tones. I know that because there is no data in this part of the image, there are no bars. What I have is this big mess here. I have got a big blob of data here, and that's going to be all of this dark gray that's back here. I have got a smaller blob of data here.
That's going to be all of this light gray on the tablecloth. I've got some intermediate gray tones. Those are the little shadows and folds and all of that kind of thing. So I can see that I have no black in the image. I have no white. I've just got couple of pieces of gray. Now, let's watch what happens as Samara brings in our black antique film projector and puts it in the scene. Now, as you'll recall, there was--I am waiting for her to get out of the frame because she was in the frame, so she was becoming part of the histogram. As you'll recall, there was no data over here, and now there is.
Here's all the data that represents these tones in the image. So there's some black right here. So the histogram generation software says, "Well, there's some black. That means I've got to put a little bar right there, and here's some lighter black. I've got to put some right there." So we've filled in this part of the graph with some of that data. Now, you may have noticed also that there is a little bit less gray data than there was before. That's because the projector here has replaced these gray pixels that were there before, so they've dropped out of the graph and been replaced with these new pixels. Now, take a look over here on the right. We have no light data, no white, nothing really bright.
Watch what happens as, once again, she brings in a white orchid and places it in the frame, and we wait for her pixels to be taken out of the graph. It settles down a little bit, and look at here. Sure enough, well, I'll move the camera around some, we've got white data in our image now. Not completely white. We've got a bunch of white data over here. This is because these tones get placed in the histogram right here. Something that is very important to understand about the histogram is the shape doesn't matter.
There's no correct shape for an image. Some people think, "Oh! I am supposed to aim for a histogram that looks like a bell curve or a hippopotamus or something like that." It's not like that. You are not trying to control the histogram. The histogram is simply telling you what's in your image. Now, you've learned, using your exposure compensation control, how to control tone in your image, how to underexpose and overexpose to properly represent black or white. Let's do that here. I have nothing in my image that is actually real black.
I can tell that because there's no real dark black over here, and all of these tones in this nice, dark-black film projector are coming out kind of gray. So I am going to meter my scene here. I am going to intentionally underexpose my image. And look what's happened. I've got more black down here. These black tones have piled up a little higher. But also watch what happened to the rest of the tones. Everything in the histogram shifted to the left. In other words, all the tones are darker. Even my lighter tones, the ones that were up here, have shifted down here.
Everything in the image is darker, so my tones are shifted much more to the left. Now, let's do the opposite. Let's overexpose the image to brighten it up. Maybe I originally metered it, and it looked like this, and I think, I don't know, these nice white flowers, they should be a little whiter. So I am going to overexpose, and sure enough, everything in the image has moved to the right. That means that these tones are no longer as dark as they were, and that's reflected here. My white tones are much brighter. But look at this, these have blown out to complete white. That's showing up over here in the histogram as the spike on the right side.
You don't want this. Anytime you see a spike on the right side of your histogram, it means you've got overexposure. It means you have lost detail, things have gone out to complete white, and that's what I am seeing here. The opposite is true also. That's going to be a little bit harder for us to get. I can underexpose too much and end up with a spike of complete black, meaning detail lost into shadows. So you may look at this and say, "You needed a graph to figure out that this image was overexposed? You can tell just by looking at it that it's too bright," and that's true. But remember, you can't tell that by looking at your LCD screen.
It's never going to be accurate in terms of exposure. Histogram gives me a quick way to immediately see that I have got this spike over here on the right. Now, color has a tone as well, and that can be a little bit harder to understand. So we are going to take this set out of here and start over with some color objects. All of our histogram examples so far have been grayscale or predominantly black-and-white examples, and of course, the real world is colored. So how does color show up in the histogram? Color has a tone, a gray value, just like any black or white object.
I think this will become more obvious as we build a new scene here. We've got an empty vase on our table. You can't really see the table. I've cropped it out of the shot. And now Samara's going to bring in some lovely yellow flowers and place them in the histogram, and place them in the histogram. This is how I think now. Everything in the world is in terms of histograms. She places them in the shot, and before we had just the gray blob over here representing the background, and now we've got a whole bunch of detail up here representing these yellow flowers. Why do they come in here? Because they are very bright. They are very bright yellow.
That's a little out of focus. Let's just fix that up. So if I think of these in terms of a corresponding shade of gray, they are very light gray, and so they end up here in this part of the histogram. Let's bring in some darker flowers now. She is going to put those in the shot. When she does that, she fills in a lot of the black tones down here, or darker gray tones. These red flowers are much darker than these yellow flowers. So when I think of that in terms of tone, this is a darker tone than these yellow flowers, you may wonder, well, where did some of our yellow go? That's just in the process of putting these flowers in she has blocked out these, so they are not--there aren't as many yellow pixels in the frame as there were before, so there aren't as many data points in this part of the histogram.
She is going to bring in some more flowers now. These are all kind of mid-tone flowers and a couple of other bright and dark tones. They're just going to fill in the histogram even more. So all I know now is I have a lot of dark tones. This image has far more dark tones than light tones. And that's going to be all of this stuff here, the shadows back here, these, and these, and these. As you can see, there's no relationship between location on the frame and place in the histogram. The histogram is just representing how many different points of tone there are in the image.
Then I've got these lighter tones over here. For the most part, she has completely blocked out the gray background, so that big spike that we had there is gone. Watch what happens as I zoom out. It's starting to grow here. There's this weird growth in my histogram. That's because I am getting these gray tones back in here. As I zoom out further, that goes up higher, and I just start getting more and more and more of it as more of the frame becomes gray. And I am dropping out a lot of color here because fewer pixels in the frame are these light and dark tones.
Again, the histogram is a critical tool when you are shooting in the field for understanding when you've over- and underexposed, because you can't trust your LCD screen. The histogram also has phenomenal use in post production obviously. We are not going to cover that here. A live histogram is not something that I use a lot. I find it's a little too confusing to seeing it change all the time as I move my camera around. I mean if I start doing this, the histogram is just constantly moving, and if I am trying to handhold it, I just don't find that useful. It's much easier to simply take a shot and then afterwards review it on the camera.
We are going to take a look at some camera-generated histograms and some more real-world examples in the next lesson.
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