The histogram on your camera can give you some valuable exposure information, including whether color information is being clipped.
- [Deke] In this chapter, we're going to take a look at a few elements of wide angle exposure, starting with the in-camera histograms. So this is those histograms that you see on your LCD screen, on the back of your camera. And they're basically yet another way of viewing your image, because if all you rely on is the image preview, then you're going to get a distorted idea of what your photograph really looks like. All right, so, for those of you who are unfamiliar with histograms, they're column graphs, that is to say, vertical bar graphs, of the distribution of tones, from black on the left and white on the right.
So, here we're seeing the straight-ahead luminance histogram, and, again, black is on the far left, white is on the far right, and so, in this case, we're seeing a lot of dark colors, and they're tapering off when we get into the brighter colors. But, in addition to the luminance graph, we also have the independent red, green, and blue histograms, because, after all, your camera sees the world in terms of red, green, and blue light. And just so you fully appreciate what's going on here, tones are specific luminance values measured from black, again, over here on the left, to white, over on the right.
So, it's a kind of gradient. And, incidentally, just so we're defining our terms, the shadows are the darkest colors, the highlights are the brightest ones, and then, the midtones are the colors in between. And your camera captures these tones on a red, green, and blue basis. So here, we're seeing the red tonal range, from black to bright red. Next, we have the green tonal range, from black to bright green. Very bright green, as you can see. And then we have the blue tonal range, from black to a deep blue.
And so, Hergen, tell us how the histogram is going to help us out. - [Hergen] Okay, Deke, so we're going to use that histogram to help us achieve proper exposure. As Deke mentioned, if you're just relying on that review image to judge your exposure, you're getting the wrong picture. What it's showing you is an idealized, processed version of your photo. It's not actually showing you the raw image. And many of you have probably already experienced this, where the image looked great in the back of your camera, you pulled it into light room, and it looks like somebody turned the dial way down on every single aspect of it.
And that's because the camera's processing the image and showing you a processed result. So the histogram is a tool that we can use to get proper exposure. And what we mean by proper exposure is the full tonal range in the image captured. So, here again, we have that RGB histogram. So this is showing us the full tonal range of the red channel, the green channel, and the blue channel in our image. So, as Deke explained with the tonal range scale in the previous slide, you can see the dark red tones on the left, over to the bright red tones on the right, and the same within the green, and the same within the blue.
And in this particular set of histograms, you can see we have values all the way across each histogram. This means that we have captured the full tonal range of the image. Now, some of you may associate the shape of the histogram representing something to do with the exposure of the image, but it's actually not. What we can tell from the histogram in this image is that it's going to have a bright foreground with a dark background. The reason being, the majority of our tones are over on the left edge of the histogram. So we're in the shadows and the darker regions.
Now, we do have bright highlights. We just don't have a whole lot of them. But, again, it is critical, the histogram is a graph. It is not a map. We're not saying that all the bright stuff is on the right side of the photo and the dark stuff is on the left side of the photo. That's not what we're saying. We're saying that the distribution of the image is such that there are more darker tones than there are brighter tones in this image. - [Deke] Which is just fine, by the way. We don't need to have bell curves for our histogram, or any sort of item like that, nor is it important that you have more midtones than highlights, or more highlights than shadows.
It depends entirely on the photograph that you're trying to create. - [Hergen] As you'll see in the next few movies, we're going to tie some of these individual channels to elements of our exposure, so that you can use these channels to help adjust certain elements of your exposure. - [Deke] All right, now let's talk about color clipping, at least where highlights are concerned. - [Hergen] So, when a single channel becomes overexposed, that means the affected pixels are going to become infused with pure color. So if you remember back to the chart Deke showed us in the beginning, goes from black on one side to a very bright, pure color on the other.
- [Deke] And so here we are looking at the luminance channel, and because we have clipping on that far right side, that might lead you to believe that we have absolute clipping. But that's actually not the case. - [Hergen] As you can see here in the red channel, we do have some red clipping. That means that there is a pixel or pixels somewhere in the image that have gone pure red. In other words, they have no real tonal differentiation anymore in the red channel. We also have that in the green channel, but to a much lesser degree, and in the blue channel, you can see we've actually got quite a bit of clipping.
But this does not mean that all three of these are happening in the same pixel. So we need to be careful in deciding that because we have clipping in the red, green, or blue channel, that we actually have something that has gone pure white in the image itself. - [Deke] Now, this is a problem, by the way, but it's not nearly as big of a problem as our next two examples. For example, here, we've got straight-ahead highlight clipping. And so that means when all channels are overexposed, the affected pixels become absolute white.
So now we have a pretty dangerous-looking composite histogram right here, but we'll know even more when we take a look at the independent histograms for the red, green, and blue channels. - [Hergen] So with these red, green, and blue channels, we can see we have extreme clipping in all three channels. Which means, more than likely, that's going to be occurring at the same pixel, which is going to give us blown highlights. In other words, some part of the image has become pure white. - [Deke] Any anytime when we have blown highlights, we have ultimately unrecoverable detail, which is never something you want.
Now, the opposite of highlight clipping is shadow clipping. And so, when all channels are underexposed, the affected pixels become black. So, again, we have a pretty dangerous-looking luminance-only histogram here, with a whole bunch of stuff over on the far left side of the graph, but we're most concerned with those vertical bars that are on the very far left side. And, again, to really see what's going on, we have to look at the histograms for the red, green, and blue channels, and because every one of those histograms is smashed up against the left side, that means we are clipping to absolute black.
And I will stress, as Hergen mentioned, just because we're seeing everything over on the left-hand side doesn't mean the same pixels are being affected. You could have some pixels that are not clipping in the blue channel, but are clipping in red and green. The fact of the matter is, however, in 90% of the cases, these histograms are going to overlap, and so you are going to get black. And in this case, we're going to see a ton of black, which is never a good thing. - [Hergen] So let's talk about some histogram takeaways.
So, just to mention again, even these RGB histograms that we're looking at are not a silver bullet, nor is the luminance histogram. Because these are also based off of a JPEG, not of the raw information. But this is sort of the best tool available to us to get as close as possible to good exposure. But nothing is going to tell us whether we've really managed to get it spot-on. So these are just tools that you can use to get yourself as close to perfect exposure as possible, but you have to understand the limitations of each method.
- [Deke] But I do want to insert that if you're not seeing any clipping on your red, green, and blue histograms, then your raw image will not be clipping either. So, in many ways, these histograms are worst-case scenarios. - [Hergen] So, again, we want to be using this histogram, especially the RGB portion of the histogram, to capture as much tonal range as possible from each channel. The other thing we want to be doing is we want to be exposing those channels to the right. That means we want to be moving that histogram towards the right, but not touching the edge.
'Cause, remember, if we touch the edge, we're clipping. Another takeaway from this is pay attention in the moment, because adjusting exposure in post introduces false tonal data. That's why it's called posterization. That's what happens after the fact. - [Deke] And that's because, what you're doing in post is you're taking the original tones, and you're mapping them on top of each other, and that's where the term posterization comes from. I will stress, however, as a person who loves post, that some degree of post-processing will have to occur.
- [Hergen] The actual shape of the RGB histogram, as Deke touched on earlier, only really speaks to the distribution of tones. It does not represent the overall exposure. And it's going to change wildly based on whatever it is you're taking a photo of. - [Deke] And so that's our look at the first element of wide angle exposure, the in-camera luminance-only and RGB histograms.
- Wide-angle optics
- Blending and contrasting exposure
- Controlling exposure with aperture
- Lighting underwater
- Shooting on walls and slopes
- Composing underwater shots
- Capturing rays of sunlight
- Going in for close focus
- Post-processing in Lightroom