How to Use the Histogram to Adjust Images for Printing
Explaining the histogram with a practical example
Ben: All right, Amber! You have gone and you have made three really exceptional images here. I think, like so much of the work here, anyone who looks at this is going to say "that's a high school student?" These are just fantastic. I took your images as you gave them to me and I did these prints. So we have got this lovely shot of these hands on this wooden door. We have got Haley--is this just? Amber: She is behind the window and then she is looking out. I am on the other side of the window. It's a reflection of the scenery behind me.
Ben: Okay, so there is not compositing here. Ben: You actually shot that. Amber: That's all on camera. Ben: Okay. And then Haley is still stalking you, breaking into the abandoned house you had of Haley. Amber: Well, I'm the one with the camera, so I think I am stalking her, but yeah that's--. Ben: Okay, good point, all right! So what do you think of these prints? Amber: I love them. I mean, that's why I sent them to the print. Ben: Okay, I had given you some thing to fix before and you did a great job of fixing them. And asking what you think about them is obviously a loaded question because I brought you over here to do something with them. So I would offer to you, let's talk about this hands picture.
Black-and-white prints of course are all about brightness. They are all about light and shadow, and we want them to have all this nice luminance gray, and then you have got a lot of that here. This particular image, it's great, the light that you have put on here, and you have plainly done a lot of work with some vignetting and dodging and burning. We have got a lot of nice texture in here. the hands themselves are great. Whose hands are those? Amber: Those are Lucy's hands. They are originally Ashley's, but she had sparkly nail polish on. Ben: Okay, that's good. Louis wears a completely different nail polish I think. All Right! They look great, but you are about 10% short of where you need to be, in terms of the tonal adjustment that you make, and I would ask you to look right here.
His fingernail right here is really, really white. There is no ink there. That's just paper. That's what white should look like in this image. And it's not what we have in all of these other white bits. Amber: Yeah. Ben: We don't need this to go to complete white, but if white is all the way over here, these other secular highlights should be at least that bright, and if they are that bright, Ben: it's going to brighten up some of these tones. Amber: Right. Ben: That's going to allow us to get more texture on here. It's going to just give the image a little more punch. As good as this looks, it looks a little muddy and blah to me.
And it's an easy thing to miss when the image first comes out of the printer, because your eye will look at it and make sense of it and so on and so forth. But once you realize there is actually more to be had there, there is a very different image you can get. So let's go in here and look at what you did. Let's analyze this image by the numbers. Looking at it onscreen obviously doesn't tell us very much, so I am going to go in here and add a levels adjustment layer. So as we look at the histogram right over here, what are you seeing? Amber: There is a very sharp spike out there, which is probably that little spot on his nail.
Ben: It's probably this right here. Amber: Oh, yeah, the window too. Amber: And there is just so flat line. It's not a lot of white until the middle, around the grays. Ben: Yeah, so thinking of it that way, do you have an idea of a different white point adjustment? Now, technically, you are okay. You are saying, well, I have got my white over at the right edge of my data. Well, the right edge of your data is this thing. The bulk of your data, all of this stuff, really doesn't start till in here. So make another adjustment. See if you can get something else going.
Yeah, coming into there is making a difference. Are you thinking a midpoint adjustment? Amber: Maybe. Ben: Okay. Amber: Kind of bring some of it back. Ben: Good because as you move the white point over, you are running the risk of brightening up the blacks and washing those out. And I think it's smart to do that. You did that right. You did that with the midpoint adjustment because if you had done the blacks, then everything is going to plunge back down. All right! So what are we getting now? We are getting--turn off the eyeball. Let's see a before and after. So there is before. There is after.
And it's very slight and it's very subtle, but I think it's a huge change that's going to make a big difference. We could now start looking for how do you introduce any overexposure, has this gone out, have these gone out. Turn it off again. I want to look at this pinky right here. Yeah, we have lost some detail there. Ben: So, we are not going to be able to get around this with a single global adjustment. We are going to need to do some localized adjustments. We will look at those in a little bit. Let's move on to some of these other images and see where they stand.
This picture of Haley reflected in the window, why don't you pull that out? So same thing. Make a Levels Adjustment layer. Okay, this one is a little trickier. This is where you had it set, and again, you did the right thing. You found the right edge of your data. But there is this little area along here that's all roughly the same amount of data, and that's probably all of these tones in her hand, these bright tones over here, maybe a little bit in here.
As far as the rest of the image goes, the bulk of the image, most of it, the data doesn't start until right in here. So let's see what happens if you move the white point over to there. Yeah, right into there. Now, obviously, we have lost her hand. This is gone. But turn that adjustment layer off: before, after. If you notice in there, there is not a lot of differentiation between these tones and these tones. Overall it's a lot of middle-gray tones. With this Levels Adjustment layer on, this has gone brighter.
Again, we are going to have to do some localized things to fix all that. We will come back to that. Let's move on to the next image. And let's just do the same thing here. Pull up the levels. And again -- Amber: About the sharp spike right there. Ben: Right, and that's going to be, very good. So where do you think we ought to go with this one? Amber: Probably about there, because that's all plateaued. Ben: I think you are right. Ben: Exactly and that's exactly the word. Thank you! I was looking for that word. Where you see those plateaus, you are looking at a bunch of little tones that aren't as significant as these big piles of tones. And let's see a before and after again.
Okay. Again real, real subtle, but we have just picked up some extra stuff in there that's going to give it a little more punch. So even if these highlights get brighter, it just means there is more highlights from here coming into her, and that may or may not be a problem. So you were real close on all of these edits. Again, you were just like 10% off, and it's an easy mistake to make. You were--it's kind of heartbreaking to have to tell you, because you were doing exactly the right thing. You were going over to the edge of the histogram. It just turns out that the edge of the histogram is not actually right for the bulk of the image data.
We need to define the significant data and move white and black to there. And as you pointed out, there is a plateau of insignificant data that you needed to go away from. So now the next thing is to get some masks in place to ensure that we haven't introduced new problems into the things that you just fixed.
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