In the last couple of lessons I showed you what a histogram is, and talked about what a good histogram looks like for a typical image. I also showed you a few bad-looking histograms for overexposed and underexposed images.
Sometimes, a strange-looking histogram is actually OK. This happens if the image that you’re capturing has an unusual distribution of levels, such as a lot of very light or very dark areas. It can also occur if you have a very narrow range of colors, or several other things that are out of the ordinary.
Look at the histogram above. It doesn’t look at all like a bell curve, but rather has several fairly well-defined peaks. There might be a little bit of clipping on the left and right, but there might also just be some solid black and solid light-color areas. It’s hard to know for sure from the histogram whether the image is well-exposed or not, but because there’s a good distribution across the range I would guess that it’s probably not too far off.
At first glance, this looks like a massively-underexposed image. There’s a huge amount of clipping on the blacks, and what looks like a little bit of clipping of whites as well. On the other hand, it could be an image with fast swaths of black and very dark colors, and a few bright spots.
As luck would have it, both of these images are within shouting distance of being well-exposed.
When we look at the actual images, it’s pretty easy to see why the histograms were weird. The image with spikes has a very distinct color breakdown. I probably could have done a slightly better job with the exposure, but it’s not far off. The image on the right does indeed have vast swaths of black in the form of the night sky and the ground. A few of the highlights are blown out, as we can see at the far right of the histogram, but that’s really hard to avoid when taking pictures of lights against a black background.
What did we learn from this? Well, if I’m any good at all, we’ve learned that you need to consider your subject matter when you’re looking at a histogram and use that to judge what the histogram should look like. I know that sounds impossible, but most of the time it’s actually rather easy to do once you get the hang of it. In fact, here’s one to think about. If you were taking a picture of the full moon at night, what should the histogram look like?
Oh yes. The other day I showed you a histogram with almost all of the pixels left of center and asked what you thought about the exposure. Both Sarah and TJ had excellent things to say about the image, but TJ completely nailed it– “the object… is mostly black.” In fact, it was my black camera bag shot against a dark carpet. There’s a blip at the far right for the embroidery on the logo, but other than that the image is just naturally dark. It was perfectly reasonable to guess that the image was underexposed, but it was sort of a trick question. I’m just mean like that.
Do you feel like you have a good basic understanding of how the histogram works?