Almost any method of correcting images works some of the time. For those interested in PPW, or in taking a four-day class on how it works, the question has to be how much of the time. The MIT study we’ve been looking at offers a unique opportunity to answer that question. It shows the real-world advantages of the PPW and its various components, most of which I knew already, but there were a few surprises. I will discuss the two categories in this and the next post.

My book on the uses of LAB in Photoshop is subtitled The Canyon Conundrum. The first chapter shows corrections of nothing but canyons. Back in the first edition, I listed all the Photoshop books of the time that briefly mentioned what LAB was. Each one illustrated the point with a shot of a canyon.

As I pointed out then, one might conclude that working in LAB has major advantages for canyon images. Since the Picture Postcard Workflow uses LAB as its standard way of bringing out color, it follows that PPW also is a superior way of working with canyon images. For example, from the MIT 5k dataset, here is a “par” correction, compared to a PPW version prepared by me from the same original.

0018-par: The averaged result of five retouchers,

0018-par: The averaged result of five retouchers.


0018-PPW: A PPW correction based on the same original.

0018-PPW: A PPW correction based on the same original.

It is a trap to believe that since PPW is greatly superior on this image it ought to be similarly superior on whatever the next one is. That next one might not be a canyon. Human nature ensures that when we believe we have invented a better mousetrap, we try to prove that it is decisively to existing mousetrap technology in every conceivable circumstance. In our field, some misguided people (and I am one of them, far too often) try too hard to get something spectacular when the competition’s method works just as well.

One question is, how often does the competition have methods just as good? That’s hard to know from what I’ve written in the past. I save the images in which PPW does well for demonstrations and articles, but I basically ignore the images in which it does not have an advantage. That’s why the MIT study is so valuable: if we pick enough of the 5,000 MIT images at random we’ll get our fair share of both varieties. Originally I thought 50 images would be sufficient, but even when I raised the number to 100 it seemed like it might be somewhat biased toward images that favored PPW. So, I went up to 150 randomly chosen images, which fall into the following categories.

44 mostly people (29.3%) in the following subcategories:
images dominated by faces 7 (4.6%)
moderately large faces 13 (8.7%)
smaller faces/full figure shots 24 (16.0%)

30 scenic shots (20.0%) in the following subcategories:
greenery predominates 11 (7.3%)
desert or canyon 10 (6.7%)
other scenic 9 (6.0%)

14 studies of animals or birds (9.3%)
8 night or twilight shots of cities (5.3%)
15 architectural shots (10.0%) in the following subcategories:
interiors 7 (4.7%)
exteriors 8 (5.3%)

8 studies of flowers (5.3%) in the following subcategories:
basically all one color 4 (2.7%)
multiple colors 4 (2.7%)

5 studies of a strongly colored object other than flowers (3.3%)
5 food (3.3%)
3 sports (2.0%)
20 miscellaneous (13.3%)

As described here, each MIT image was corrected in Lightroom by five compensated student retouchers. I created a sixth “par” version, in which each of the five results was weighted 20%. Because the par version was often better than any of its five parents, I was most interested in comparing the par to the PPW version. But I also compared both the par and the PPW versions to each of the five parents, limited only to the question of whether any of the five were competitive with, or even better than them.

In evaluating the results (ground rules described here) it must be conceded that the MIT retouching team was made up of persons who, though reasonably skilled, don’t have nearly as much experience in the field as I do. So in trying to determine how often PPW makes a major difference I look at all of the cases where the PPW version was decisively better than the par version, but excluding ones where at least one of the five retouchers created something competitive. Then, I apply some kind of mental fudge factor downward on the theory that these retouchers are more likely to do something foolish than I am.

I’ll show the full results at the end of this post, but the bottom line is that PPW appears to add significant value to slightly less than half of these images. Certain categories are quite predictable. Ten shots of deserts and canyons? I’d expect a clean sweep, and that’s what happened. Of 50 individual and 10 par versions, only one retoucher managed to get something competitive to PPW.

One must expect similar results in scenic shots that feature greenery, due to the MMM action in PPW, and sometimes H-K as well. For example,

4197-par: The averaged result of five retouchers.

4197-par: The averaged result of five retouchers.


4197-PPW: A PPW correction based on the same original.

4197-PPW: A PPW correction based on the same original.

Other types of scenics also favor PPW. In this post I showed an image of Waikiki Beach where the extra action in the water was a big plus compared to the work of the Lightroom retouchers. So far, this is a good explanation why more landscape photographers seem to prefer LAB than anyone else.

As expected, PPW also did well in two other categories. When an image is dominated by one object of a single color, or in any of the flower studies that featured only one color, the PPW blending capabilities, sometimes assisted by H-K, created a decided advantage, as in

4537-par: The averaged result of five retouchers.

4537-par: The averaged result of five retouchers.

4537-PPW: A PPW correction based on the same original.

4537-PPW: A PPW correction based on the same original.

The final category in which one would expect a near clean sweep for PPW is in shots of faces. The MMM action creates attractive hue variation within the face, something missing in other applications.

3498-D and 3498-PPW: Left, the best of the five retoucher versions, which was better than the averaged entry. Right, the PPW version.

3498-D and 3498-PPW: Left, the best of the five retoucher versions, which was better than the par entry. Right, the PPW version.

This category offered two surprises, at least to me. First, I thought that the advantage would hold even when the faces were fairly small. It did not. Medium-sized faces generally were better with PPW, but it wasn’t as decisive as when the faces were larger. And even with larger faces, an exception showed up: African-American flesh is not as flattered by hue variation as lighter skintones are.

The categories listed above comprise about a third of all the images. PPW often has advantages in the others but they are image-specific as opposed to being common to a category. For example, PPW has a high win rate in architectural images because its false profile/multiply routines can correct for poor lighting conditions. Also, PPW has excellent handling of shadow and highlight detail, which are often big factors in images of birds and animals. But if these factors don’t exist then if you use PPW virtue will have to be its own reward.

Sometimes these factors pop up in an unexpected category, such as night or twilight shots of cities. In principle there isn’t a PPW advantage because the main decisions (how dark to present the scene, and what color for the sky) are artistic choices for which technique is irrelevant, and because the bright lights in the buildings are easy to handle in any method.

An image of the famous fountains of the Bellagio in Las Vegas, however, is the exception.

4196-par: The averaged result of five retouchers.

4196-par: The averaged result of five retouchers.


4196-PPW: A PPW correction based on the same original.

4196-PPW: A PPW correction based on the same original.


True, there is plenty of room for debate over the color of the lake and sky, and how strongly and what color to make the lighting of the building. But the signature of this hotel is its magnificent array of fountains and this shot lives or dies by their detailing, which PPW’s Bigger Hammer handles well.

Here are the overall results:
Comparisons against the five parent versions:
Par version is significantly better than a given parent: 558 times out of 750. (74.4%)
Par version is significantly better than ALL FIVE parents: 36 times out of 150. (24.0%)
PPW is significantly better than a given parent: 706 times out of 750 (94.1%). On only two images were there three parents competitive to PPW; in three others two parents were competitive.

PPW vs. Par (150 comparisons)
126 total wins (84.0%) with the following subdivisions:
Decisive wins over par; significantly better than all parent versions: 72 (48.0%)
Decisive wins over par but at least one parent was competitive 16 (10.7%)
17 ties (11.3%)
7 total losses (4.6%) with the following subdivision:
Decisive losses 2 (0.7%)

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For those wishing to take color skills to the ultimate level, here are the two dates for Applied Color Theory classes in 2018.
• ATLANTA, Wednesday, May 16, through Saturday, May 19.
• SAN DIEGO, Wednesday, August 22, through Saturday, August 25.

These classes—four long days, limited to eight persons—have changed the lives of many a photographer, many a retoucher, and many a Photoshop authority. This will be the 24th year I’ve been teaching them, in a dozen different countries, in four different languages. And never the same way twice. Every group has its own character, faces its own challenge, exults in its own successes. I never got tired of it, because I never found it repetitious.

Of course, the class has changed drastically over time, adding new techniques and interesting imaging problems. In 2011 we switched from three to four days, in view of the increased complexity in our field. But the format is the same. I teach what I can, then the group works independently on a set of images, and then the results are compared. This routine is repeated seven more times over the three days. By the time the class is over, we’ll have compared our results on 28 different images. The first 12 are the ones that every class works on. The last 16 are customized to and chosen by the class. Often they include images that class members themselves have provided as representative of their own work. This year, we’ll be adding some of the images from the MIT set that I’ve been writing about in this blog.

All of us who are serious about the topic have developed our own methods which seem to work well for us. Unfortunately, we normally have nothing to compare the results to. Many methods, some quite crude, still can make original photographs look a lot better. The question always is, how much better could it have been?

Nothing can answer that question better than seeing what others can do with them. In principle, you ought to be able to get what you think is the best result, because nobody else can read your mind to know what you’d like to do with the picture. In practice, especially during the first two days, there is a lot of wailing and gnashing of teeth from students whose work did not measure up to their own expectations, in comparison to that of others.

Four days, 40 hours. It remains the fastest way that a professional—or someone who wishes to produce professional quality work—can upgrade color correction skills. If you’d like more information, or to consider signing up, here’s the link.

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“And yet,” I wrote in my first book 25 years ago, “most color correction could be handled by monkeys…a numerical, curve-based approach calling for little artistic judgment…all the advanced techniques are inevitably based on these surpassingly simple ones. The by-the-numbers rules can be stated in a single sentence: Use the full range of available tones every time, and don’t give the viewers any colors that they will know better than to believe.”

Today’s “advanced techniques”, such as PPW, could not have been dreamed of back then. The lesson remains. Without those basics, neither PPW nor any other system works. The failure of some of the MIT retouchers to keep this in mind is a warning to the rest of us. This post will discuss the most basic of the basic things: the importance of setting proper endpoints, a/k/a setting highlight and shadow.

Like any other system, PPW offers advantages on certain types of image, but few or none on others. The temptation is always to call attention to the ones on which it does well. And, it is easy to show what seems to be impressive results on certain images, but usually know way to prove that people couldn’t have done it just as well another way.

The MIT study solves both these problems. It samples a wide variety of images, not chosen to favor any system over another. And for each one, they have commissioned five different corrections, so that we know what others find possible.

As described previously, I created a sixth “par” version, in which each of the five parents was weighted 20 percent, of each of 100 randomly chosen images. I also did a PPW correction myself, limited to methods that could be automated. I then compared the PPW versions to the others. Because the par version was often better than any of its parents, the key comparison was between it and mine. I rated these, according to rules laid out here, as either a decisive win, a win, a tie, a loss, or a decisive loss.

#1828: Although the cast seems ominous, there is little difficulty in correcting this because the uniform's color is known.

#1828: Although the cast seems ominous, there is little difficulty in correcting this because the uniform’s color is known.

#2082: The original is extremely flat, but the clouds have more than enough detail.

#2082: The original is extremely flat, but the clouds have more than enough detail for a retoucher to exploit.

#0091: This Hawaii scene should be ideal for PPW, which can add variation in the water.

#0091: This Hawaii scene should be ideal for PPW, which can add variation in the water.


If the PPW version scores a decisive win against par, it may indicate a superior method. It may also indicate that the retouching group found the exercise too difficult—or that it was not too difficult, but they made silly mistakes, such as failure to set a proper highlight and shadow.

On the other hand, I occasionally made silly mistakes that prevented PPW from winning more decisively. The big difference, though, between someone with a mountain of experience, and people like the five retouchers, is that experienced people make far fewer mistakes.

When we see that the PPW version is clearly superior to the par, we must ask (to use an old sports analogy) did I win, or did they lose? We will now look at three images where they did worse than they should have. Here are the three original files as given to the five retouchers. How would you think their work will compare with mine, if they do a competent job?

The first two originals seem at first glance to be much worse than the third. I have surely had to correct more such horrors in my career than the five retouchers have. Someone inexperienced might infer that I would therefore be likely to do much better than they would on these two. Indeed, that would be a good argument in many similar-looking situations. But in these two, the apparent difficulty is illusory. The chef’s uniform in #1828 obviously should be be white. His hair, judging by his ethnicity, must be black. It is easy, even for beginners and even when the color of the original is this far off, to make the necessary corrections. PPW’s hammers may add texture to the uniform and the skin, which may cause the viewer to prefer it, but it’s hard to visualize a decisive win.

The clouds image, #2082, is only scary because it’s so flat. Clouds don’t need to be as impeccably white as the chef’s uniform but white does have to be their basic color. The hammer actions aren’t needed to build highlight detail because there’s more than enough of that already. There seems to be sunlight coming in from the right and patches of blue where the sky peeks through the clouds. PPW may be able to exploit those, in which case it becomes a slight favorite. Otherwise, I predict a tie, maybe even a loss if somebody finds a creative solution.

The Waikiki image of #0091, now that ought be a decisive win for PPW, which can put attractive color variation in the water in a way that Lightroom can’t. And it should be able to present the high-rises as a more attractive rosy-orange.

How did these predictions pan out? The chef first.

1828-par&PPW: The par version looks flat because it has no highlight (white point).

1828-par & PPW: The par version looks flat because it has no highlight (white point).

To say that PPW wins this comparison is misleading. It would be more accurate to say that the par version loses. It loses because its detailing is so poor. The detailing is poor because the version ignores the advice at the top of this post that it should always use a full tonal range. And it does not do that because, unlike the PPW version, nothing is represented as a bright white. No proper highlight has ever been set.

Believe it or not, the par version, which as usual has decent color, beats all five of its parents. Let’s have a look at three of them, plus a modest suggestion.

1828-C&D: One version can't handle the color cast; the other is even flatter than par.

1828-C & D: One version can’t handle the color cast; the other is even flatter than par.

1828-E&auto tone: another failed retoucher version, plus a suggestion: merely establishing a white and a black point, which the Auto Tone command does with one click.

1828-E & Auto Tone: another failed retoucher version, plus a suggestion: merely establishing a white and a black point, which the Auto Tone command does with one click.

No matter what method of color correction you choose, the following steps are absolutely mandatory. No method will succeed if they are ignored.
1) Locate the lightest significant part of the image.
2) Decide whether it should be made white.
3) Locate the darkest significant part of the image.
4) Decide whether it will be acceptable if made black.
5) Force the lightest and darkest significant parts as far apart as reasonable, while honoring the decision you made about whiteness/blackness.

These items are not always obvious. What if the lightest significant point is not the lightest literal point? And should it be forced to a true white, or some type of off-white? The same considerations apply in reverse to the choice of dark point.

This chef image has no such issue. The lightest literal point is in the uniform; it is also the lightest significant point, and it is emphatically supposed to be white. The hair, similarly, is both the darkest literal point and the darkest significant one, and it is emphatically supposed to be black.

The simplest of all Photoshop commands, Image: Adjustments>Auto Tone, establishes correct white and black points, on the assumption that literal=significant and that the endpoints are supposed to be neutral, both of which happen to be true in this case. Compare the Auto Tone version, prepared from the default with one click, to any of the three retouched versions, or even the par. The color is acceptable and the detail much better.

My suspicion is that Auto Tone is thought of as an amateur method, to be avoided by those in the know. But why not use it here, as least as a start point for something better?

2082 par & PPW: the failure to establish a white point in the clouds at right dooms the par version.

2082 par & PPW: the failure to establish a white point in the clouds at right dooms the par version.

Upwards of 99 percent of images have something that can be used as a dark point. This cloud shot is the exception. Nothing can be represented as black. But the white point issue is the same as with the chef. The lightest literal point, in the clouds at top right, is also the lightest significant point. And clouds can surely be represented as white. Establishing the highlight is the biggest reason that the PPW version works and the par does not.

Despite this flaw, the par version, with its predictably reasonable color, once again beats all five individual efforts. Here are three of the competitors, plus a ringer.

2081-A & C: These two efforts have reasonable detail but major color problems.

2081-A & C: These two efforts have reasonable detail but major color problems.

This image might benefit from slight moves toward a warmer or even a cooler color, but we must keep in mind that the basic color of clouds is white. Both 2081-A and 2082-C go way too far; there is no longer any sense of neutrality in the lightest clouds. Jumping out of the frying pan and into the fire is D:

2082-D & Auto Tone: Left, the clouds are white at a huge cost. Right, Auto Tone applied to the default forces a white and a black point.

2082-D & Auto Tone: Left, the clouds are white, at a huge cost. Right, Auto Tone applied to the default forces a white and a black point.

After looking at 2082-D we can understand why the par version wins. We have looked at two with terrible color but acceptable contrast, and one where the clouds are correctly white but grossly too flat.

Why do we have to choose one or the other, when one click with Auto Tone puts us on the right track? Sure, we can’t accept black clouds, but already the Auto Tone version is arguably the best other than the PPW one, and it is easy enough to make it much better:

2082-AT curve & AT smart blend: Left, a two-second master-curve correction of the Auto Tone version. Right, instead, an intelligent blend of Auto Tone  with 2082-D to soften the shadows.

2082-AT curve & AT smart blend: Left, a two-second master-curve correction of the Auto Tone version. Right, instead, an intelligent blend of Auto Tone with 2082-D to soften the shadows.

Even the sort of person who overuses Auto Tone can apply the kind of master curve shown above to counter the excessive darkness of the straight Auto Tone version. Someone wishing a more conservative effect might prefer the version at right, which is an example of an “intelligent” blend, as opposed to the “stupid” blending that produced the par versions. I lightly blended 2082-D into 2082-Auto Tone, using a mask that emphasized changes to darker areas.

How do the two images we’ve looked at differ? Well, you can’t ask for a more perfect target for Auto Tone than that chef shot of #1828. PPW methods generally work better with flatter originals, but Auto Tone moves so obviously in the correct direction that I used it myself, fading it back to 80% opacity to allow easier later adjustments.

This cloud image is another story. Auto Tone sets an excellent highlight, and knocks out most of the cast. We have other ways of doing these things, so Auto Tone would not be on my agenda here. But not everybody knows these other ways. Apparently the student retouchers did not. Auto Tone is much better than nothing.

Traditionally, retouchers set highlight and shadow points immediately. PPW does not require this, but it and every other rational system does require that they be set eventually. The following example shows that Auto Tone can come in handy even late in the process.

0091-E: The best of the retoucher corrections of this original.

0091-E: The best of the retoucher corrections of this original.

0091-PPW: The Modern Man From Mars action creates desirable variation in the water, sky, and highrises.

0091-PPW: The Modern Man From Mars action creates desirable variation in the water, sky, and highrises.

The results here are as predicted. PPW has a routine that creates color variation without necessarily making the image more vivid. The scene is more realistic.

You may ask why the comparison is to one of the retoucher versions and not to the par, which is not shown. It turns out that three others came up with renditions similar to that of Retoucher E. As for the fifth retoucher, well, this exercise is the only one out of 150 I’ve looked at so far that one version was atrocious enough to bring down the average so much that four of the five retouchers beat par. How bad can that be?

0091-A: The worst of the retoucher versions, which brought down the average result so far that the other four retouchers beat par.

0091-A: The worst of the retoucher versions, which brought down the average result so far that the other four retouchers beat par.


My guess is that this retoucher got tired of spinning his wheels and eventually threw up his hands in despair and went on to the next image. If so, he acted too soon. The lightest literal and significant point in this shot is in what we call the whitewater. The name doesn’t prove that it should actually be set to white; a slight green or cyan might be better. The darkest literal and significant point is in the lava, which should probably be a dull dark brown rather than black. So Auto Tone is not an ideal solution, but if the alternative is 0091-A, the choice is easy.
0091-A-Auto Tone: The Auto Tone command is applied to 0091-A, forcing a white and black point.

0091-A-Auto Tone: The Auto Tone command is applied to 0091-A, forcing a white and black point.


Better even than 0091-E, no? And 0091-E was the best of the other four.

It is correct that Lightroom, which the five retouchers were using, doesn’t have an explicit Auto Tone command, but it has plenty of ways to achieve the same thing quickly. This post, I hope, will be an eyeopener to those who don’t accept that proper highlight and shadow selection is critical.

Color correction takes practice. Nobody should be ashamed of a poor result when the image was too difficult for them. When the poor result comes because the image was too easy, that’s harder to swallow.

The MIT 5k Dataset 4: More on Averaging

by Dan Margulis November 28, 2017

The previous entry described giving each of five independently corrected versions 20% weight to create a new, “par” version. This can be called a “stupid” blend, in that no notice is taken of the merits of any of the five. Nevertheless, it appears that this average is better than all five of its parents in […]

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The MIT 5k Dataset 3: Effective Averaging Close-Up

by Dan Margulis November 18, 2017

Those interested in quality have always been willing to spend time to get what they considered the best possible results. For some years now I have been suggesting that this is not the best approach in our field. Instead, I have been preaching that it is a better use of time to do the initial […]

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The MIT 5k Dataset 2: The Ground Rules

by Dan Margulis November 13, 2017

The following details the procedures used in evaluating the images in this study. It is posted separately so that I do not have to repeat it every time I discuss results in the future. I went through the set of 5,000 original images and deleted those I thought were of limited interest. I used the […]

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The MIT 5k Dataset 1: Introduction

by Dan Margulis November 11, 2017

This is the introduction to a series of posts I will make based on my work with files that are part of a remarkable archive. Researchers at MIT and Adobe have recently made available the data from a massive project they have undertaken to study what people look for when they correct color. The researchers’ […]

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Applied Color Theory classes in 2017

by Dan Margulis January 4, 2017

For those wishing to take color skills to the ultimate level, here are the two dates for Applied Color Theory classes in 2017. • ATLANTA, Wednesday, March 22 through Saturday, March 25, 2017. • SAN DIEGO, Wednesday, August 9, through Saturday, August 12, 2017. These classes—four long days, limited to eight persons—have changed the lives […]

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The Presentation of Data: When Red and Blue Are Opponent Colors

by Dan Margulis June 3, 2016

The U.S. presidential campaign offers an interesting insight on opponent colors, and on how best to present data. The conventional way of doing it leaves much to be desired. Residents of other countries have difficulty understanding the American system, where in effect the election is always decided by voters in a small minority of states. […]

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Applied Color Theory Classes in 2016

by Dan Margulis January 30, 2016

In September 1994, I took three days off from my day job in New York, and spent them teaching color correction to six people in Atlanta. We were using Photoshop 3, with no adjustment layers, no multiple undo, no actions, and computers with 16 mb RAM. Sterling Ledet gave the name “Applied Color Theory” to […]

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