Natural color profile adjustment for professionals
Nachlieli, Hila (Presenter)
Morovič, Peter
Morovič, Ján
Arnabat, Jordi
Albarrán, Ángel
While there may be no point in arguing about taste, creative professionals make a living from sharing theirs. Making specific, individual color preferences that a creative professional knows how to achieve when creating content on a display also propagate into print is a significant challenge since it lacks real-time feedback. The present paper introduces a method for allowing creative professionals to use the tools they know and love to also personalize the color behavior of their devices. This is achieved by analyzing color changes applied to images and applying them to a device’s ICC profile. As a result the personalized device results in customized color behavior regardless of the workflow used. The paper describes the ICC profile transformation algorithm in detail and provides a color error analysis of its performance.
 Hi. Okay, so the one thing I love about working in the industry is that you get to meet real problem of real people, that real people have. And this is what you have in this presentation. Okay, so my colleagues from Spain, by the way, they're all working on big printers, big and high quality printers for artists. And what they saw was that when artists come to print, artists come and he has an exhibition tomorrow. It may be a photographer, it may be a real artist that got his painting photo. And he has an exhibition tomorrow. You need high quality images to the show tomorrow and you need to print them. And what they came across over and over again, that even though the printers are well-cuned and ICC file and you have it, when the artist prints what he wanted, the photo that he took, he gets something and it's accurate. Sky blue, everything is accurate, but it's not his picture. It's not what he intended. And this person here says it, if I can make it work, never mind. He says, sometimes I have not even recognized my own photographs. I have even hesitated to call them my own. And whoever controls the editing controls the artist's fate. This is not my work. This is not what I intended. This is not as good as I wanted it to be. So this is an example. This is the image when you just print it. And this is after ICC profile. And you can see that the skies are blue, the color are correct. Not in this projector, but basically there are. And that, this is what he wanted. He might have preferred the sky to be more blueish. He might have wanted the grays to be more reddish or the contrast to be bigger. This is what he wanted. It is, the color is still accurate, but it's not that. That's his own taste. He's an artist. He might even not be able to tell you why he likes it. But that, his preference, that's his art. And you should give him what he wants. So another thing that goes without saying that the printer should be consistent and accurate. That goes without saying that it should also match his taste, the artist's taste. So that's the problem they were facing, Morovi-Kanol. And this is the state of the art. You have the pipeline. You have, this is the image on screen in the RGB space. And you translate it to LAB. And then to the printer color space. And today what the artist can do is just to go over the image, to take the image, to tune it in Photoshop, print it, look at it. Is it okay? If not, tune it again, print it, calibrate so forth until he is happy. And then he can save the image with the profiles, the new profiles that he adjusted. And then he has the printed, the image which is printed the way he wanted it to be printed. But you have to do it all over again for each of the images that you want to print. It's the right for each image. And another problem with that is that you have two copies of this image. One is original and the other is a copy to be printed for this show in this printer. And this is the ground for a lot of trouble when you have two copies of the same thing which are slightly different. So what Morvik and all propose to do is to take this image, the first image, to tune it thoroughly and then to save only the profile. Not the image with the profile, but to learn the new profile, the new taste of the artist and to keep it and use it to print the other images. And this is how they do it in the pipeline sense. They have their original image. Once again, they translate it from the screen to LAB. And this is the original transformation to the ICC printer's color space. And this is the transformation after tuning the image, after adjusting it to the taste of the artist. And what they propose to do, and it's actually in the next slide, but it is easier to see here, what they propose to do is to make transformation from here to here, to learn this transformation and to use it on all the other images. And they do it by looking for in the source image. They look for all the pixels with exactly the same color in the source image and they ask what happened to those pixels in the target image. And they did the mean over those pixels for each of the colors in the image. And you can go by other, you can do 3D transformation, you can do separable transformations. They have several methods to do that. But basically they just learn the transformation. Okay, of course it has limitation. The first limitation is obvious. You cannot, if you tune it to one artist, it will not work for another artist because they have different tastes. So this is kind of redundant. The second problem is that it's an average. So if I have, for example, a photo of someone in the mirror and they're totally identical, the mirror color are totally identical to the person and they have to want to make the mirror look darker, then it just wouldn't, it will take the mean between the, you have the same color in two places in the image and you made them just in one region be different. The mean will not match either of them. So it wouldn't work in such an area. Another problem is that if you are not sampling the whole color space, then you'll have problem. For example, if you're tuning a portrait of a person and then you get a sky, image of a sky, then you don't have any knowledge about the artist's preferences in blue. So you just don't know what to do with it. So we're around it is just to put two images, to tune it over a combination of two images that samples the taste of the artist. And also they say that this is a rare scenario. But the artist usually very conservative and they either like a portrait or scenery, they usually don't mix. But if you have this scenario, you can get over it. But this is something you should know that is not working. And last but not least, you should bear in mind that this is all local changes. Because they assume that the ICC profile was performed and that those are minute changes, not something very big. It's all about the differences. Okay so this is the test they did. And this is Morvik and his family. And this is because they have copyright issues with working with the artist's photographs. So they took their own photograph. And they gave it to 24 artists. And they asked them to tune the first image according to their preferences, their taste. And then they applied it for all of the set of images. And this is just a sanity check. Look at the original image with, sorry, look at the adjusted image with reference for the original profile and compare it to the unadjusted image with a completed profile. So this is a sanity check and it worked. There was no noticeable distance between the two. And the other is they asked them to charge other images that were printed and they all liked the result. And they really preferred, they said that this does reflect their taste in images. So and this is what it's all about. And this is a more quantitative test. They took the images, they let them tune and they compared is the transformation work well. And they really succeed in capturing the transformation. And they did it by comparing the, again, the adjusted image with the original profile to the unadjusted image with the computed profile. And the blue are the median and in the difference of the color. And the red are the 95% in the difference of the color. You can see that most of the values are below one delta E. With this small exception, still it's under 2.5 delta E, which is an noticeable difference. So to conclude, the problem was capturing the artist, the taste of the artist. And they did that by, and they captured this transformation. And they showed that it does capture the taste of the artist on 24 artists. That's the past test. And well, thank you. So we have some time for questions. Yes. So I'm also working with artists in another domain. But so is your conclusion that the artist working with printed image has a single taste for all kind of images of this result? That was the original assumption, but the test showed it because they like, this is what they meant. I mean, that's what they showed in this test, basically. That was the original assumption, which they tested here. So yes. Which is interesting. You're right. You would think that not. So then I have one if you can answer it. So why was the work done on LAB, not on some more equidistant color space like C-CAMO2 model? Okay. So what I understood, and I'm not sure I got the whole of it, but what I understood is that it's because they are doing only modulation, small local differences. And then it doesn't really matter about the big distance in the color space. They want the local neighborhood to be linear. And that's why they work on LAB. So that's what I understood about this region. Okay. Thank you. Thank you very much. Thank you.
 Hi.  Okay, so the one thing I love about working in the industry is that you get to meet real  problem of real people, that real people have.  And this is what you have in this presentation.  Okay, so my colleagues from Spain, by the way, they're all working on big printers,  big and high quality printers for artists.  And what they saw was that when artists come to print, artists come and he has an exhibition  tomorrow.  It may be a photographer, it may be a real artist that got his painting photo.  And he has an exhibition tomorrow.  You need high quality images to the show tomorrow and you need to print them.  And what they came across over and over again, that even though the printers are well-cuned  and ICC file and you have it, when the artist prints what he wanted, the photo that he took,  he gets something and it's accurate.  Sky blue, everything is accurate, but it's not his picture.  It's not what he intended.  And this person here says it, if I can make it work, never mind.  He says, sometimes I have not even recognized my own photographs.  I have even hesitated to call them my own.  And whoever controls the editing controls the artist's fate.  This is not my work.  This is not what I intended.  This is not as good as I wanted it to be.  So this is an example.  This is the image when you just print it.  And this is after ICC profile.  And you can see that the skies are blue, the color are correct.  Not in this projector, but basically there are.  And that, this is what he wanted.  He might have preferred the sky to be more blueish.  He might have wanted the grays to be more reddish or the contrast to be bigger.  This is what he wanted.  It is, the color is still accurate, but it's not that.  That's his own taste.  He's an artist.  He might even not be able to tell you why he likes it.  But that, his preference, that's his art.  And you should give him what he wants.  So another thing that goes without saying that the printer should be consistent and accurate.  That goes without saying that it should also match his taste, the artist's taste.  So that's the problem they were facing, Morovi-Kanol.  And this is the state of the art.  You have the pipeline.  You have, this is the image on screen in the RGB space.  And you translate it to LAB.  And then to the printer color space.  And today what the artist can do is just to go over the image, to take the image, to tune  it in Photoshop, print it, look at it.  Is it okay?  If not, tune it again, print it, calibrate so forth until he is happy.  And then he can save the image with the profiles, the new profiles that he adjusted.  And then he has the printed, the image which is printed the way he wanted it to be printed.  But you have to do it all over again for each of the images that you want to print.  It's the right for each image.  And another problem with that is that you have two copies of this image.  One is original and the other is a copy to be printed for this show in this printer.  And this is the ground for a lot of trouble when you have two copies of the same thing  which are slightly different.  So what Morvik and all propose to do is to take this image, the first image, to tune  it thoroughly and then to save only the profile.  Not the image with the profile, but to learn the new profile, the new taste of the artist  and to keep it and use it to print the other images.  And this is how they do it in the pipeline sense.  They have their original image.  Once again, they translate it from the screen to LAB.  And this is the original transformation to the ICC printer's color space.  And this is the transformation after tuning the image, after adjusting it to the taste  of the artist.  And what they propose to do, and it's actually in the next slide, but it is easier to see  here, what they propose to do is to make transformation from here to here, to learn this transformation  and to use it on all the other images.  And they do it by looking for in the source image.  They look for all the pixels with exactly the same color in the source image and they  ask what happened to those pixels in the target image.  And they did the mean over those pixels for each of the colors in the image.  And you can go by other, you can do 3D transformation, you can do separable transformations.  They have several methods to do that.  But basically they just learn the transformation.  Okay, of course it has limitation.  The first limitation is obvious.  You cannot, if you tune it to one artist, it will not work for another artist because  they have different tastes.  So this is kind of redundant.  The second problem is that it's an average.  So if I have, for example, a photo of someone in the mirror and they're totally identical,  the mirror color are totally identical to the person and they have to want to make the  mirror look darker, then it just wouldn't, it will take the mean between the, you have  the same color in two places in the image and you made them just in one region be different.  The mean will not match either of them.  So it wouldn't work in such an area.  Another problem is that if you are not sampling the whole color space, then you'll have problem.  For example, if you're tuning a portrait of a person and then you get a sky, image of  a sky, then you don't have any knowledge about the artist's preferences in blue.  So you just don't know what to do with it.  So we're around it is just to put two images, to tune it over a combination of two images  that samples the taste of the artist.  And also they say that this is a rare scenario.  But the artist usually very conservative and they either like a portrait or scenery, they  usually don't mix.  But if you have this scenario, you can get over it.  But this is something you should know that is not working.  And last but not least, you should bear in mind that this is all local changes.  Because they assume that the ICC profile was performed and that those are minute changes,  not something very big.  It's all about the differences.  Okay so this is the test they did.  And this is Morvik and his family.  And this is because they have copyright issues with working with the artist's photographs.  So they took their own photograph.  And they gave it to 24 artists.  And they asked them to tune the first image according to their preferences, their taste.  And then they applied it for all of the set of images.  And this is just a sanity check.  Look at the original image with, sorry, look at the adjusted image with reference for the  original profile and compare it to the unadjusted image with a completed profile.  So this is a sanity check and it worked.  There was no noticeable distance between the two.  And the other is they asked them to charge other images that were printed and they all  liked the result.  And they really preferred, they said that this does reflect their taste in images.  So and this is what it's all about.  And this is a more quantitative test.  They took the images, they let them tune and they compared is the transformation work well.  And they really succeed in capturing the transformation.  And they did it by comparing the, again, the adjusted image with the original profile to  the unadjusted image with the computed profile.  And the blue are the median and in the difference of the color.  And the red are the 95% in the difference of the color.  You can see that most of the values are below one delta E.  With this small exception, still it's under 2.5 delta E, which is an noticeable difference.  So to conclude, the problem was capturing the artist, the taste of the artist.  And they did that by, and they captured this transformation.  And they showed that it does capture the taste of the artist on 24 artists.  That's the past test.  And well, thank you.  So we have some time for questions.  Yes.  So I'm also working with artists in another domain.  But so is your conclusion that the artist working with printed image has a single taste  for all kind of images of this result?  That was the original assumption, but the test showed it because they like, this is  what they meant.  I mean, that's what they showed in this test, basically.  That was the original assumption, which they tested here.  So yes.  Which is interesting.  You're right.  You would think that not.  So then I have one if you can answer it.  So why was the work done on LAB, not on some more equidistant color space like C-CAMO2  model?  Okay.  So what I understood, and I'm not sure I got the whole of it, but what I understood  is that it's because they are doing only modulation, small local differences.  And then it doesn't really matter about the big distance in the color space.  They want the local neighborhood to be linear.  And that's why they work on LAB.  So that's what I understood about this region.  Okay.  Thank you.  Thank you very much.  Thank you.
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Hi. Okay, so the one thing I love about working in the industry is that you get to meet real problem of real people, that real people have. And this is what you have in this presentation. Okay, so my colleagues from Spain, by the way, they're all working on big printers, big and high quality printers for artists. And what they saw was that when artists come to print, artists come and he has an exhibition tomorrow. It may be a photographer, it may be a real artist that got his painting photo. And he has an exhibition tomorrow. You need high quality images to the show tomorrow and you need to print them. And what they came across over and over again, that even though the printers are well-cuned and ICC file and you have it, when the artist prints what he wanted, the photo that he took, he gets something and it's accurate. Sky blue, everything is accurate, but it's not his picture. It's not what he intended. And this person here says it, if I can make it work, never mind. He says, sometimes I have not even recognized my own photographs. I have even hesitated to call them my own. And whoever controls the editing controls the artist's fate. This is not my work. This is not what I intended. This is not as good as I wanted it to be. So this is an example. This is the image when you just print it. And this is after ICC profile. And you can see that the skies are blue, the color are correct. Not in this projector, but basically there are. And that, this is what he wanted. He might have preferred the sky to be more blueish. He might have wanted the grays to be more reddish or the contrast to be bigger. This is what he wanted. It is, the color is still accurate, but it's not that. That's his own taste. He's an artist. He might even not be able to tell you why he likes it. But that, his preference, that's his art. And you should give him what he wants. So another thing that goes without saying that the printer should be consistent and accurate. That goes without saying that it should also match his taste, the artist's taste. So that's the problem they were facing, Morovi-Kanol. And this is the state of the art. You have the pipeline. You have, this is the image on screen in the RGB space. And you translate it to LAB. And then to the printer color space. And today what the artist can do is just to go over the image, to take the image, to tune it in Photoshop, print it, look at it. Is it okay? If not, tune it again, print it, calibrate so forth until he is happy. And then he can save the image with the profiles, the new profiles that he adjusted. And then he has the printed, the image which is printed the way he wanted it to be printed. But you have to do it all over again for each of the images that you want to print. It's the right for each image. And another problem with that is that you have two copies of this image. One is original and the other is a copy to be printed for this show in this printer. And this is the ground for a lot of trouble when you have two copies of the same thing which are slightly different. So what Morvik and all propose to do is to take this image, the first image, to tune it thoroughly and then to save only the profile. Not the image with the profile, but to learn the new profile, the new taste of the artist and to keep it and use it to print the other images. And this is how they do it in the pipeline sense. They have their original image. Once again, they translate it from the screen to LAB. And this is the original transformation to the ICC printer's color space. And this is the transformation after tuning the image, after adjusting it to the taste of the artist. And what they propose to do, and it's actually in the next slide, but it is easier to see here, what they propose to do is to make transformation from here to here, to learn this transformation and to use it on all the other images. And they do it by looking for in the source image. They look for all the pixels with exactly the same color in the source image and they ask what happened to those pixels in the target image. And they did the mean over those pixels for each of the colors in the image. And you can go by other, you can do 3D transformation, you can do separable transformations. They have several methods to do that. But basically they just learn the transformation. Okay, of course it has limitation. The first limitation is obvious. You cannot, if you tune it to one artist, it will not work for another artist because they have different tastes. So this is kind of redundant. The second problem is that it's an average. So if I have, for example, a photo of someone in the mirror and they're totally identical, the mirror color are totally identical to the person and they have to want to make the mirror look darker, then it just wouldn't, it will take the mean between the, you have the same color in two places in the image and you made them just in one region be different. The mean will not match either of them. So it wouldn't work in such an area. Another problem is that if you are not sampling the whole color space, then you'll have problem. For example, if you're tuning a portrait of a person and then you get a sky, image of a sky, then you don't have any knowledge about the artist's preferences in blue. So you just don't know what to do with it. So we're around it is just to put two images, to tune it over a combination of two images that samples the taste of the artist. And also they say that this is a rare scenario. But the artist usually very conservative and they either like a portrait or scenery, they usually don't mix. But if you have this scenario, you can get over it. But this is something you should know that is not working. And last but not least, you should bear in mind that this is all local changes. Because they assume that the ICC profile was performed and that those are minute changes, not something very big. It's all about the differences. Okay so this is the test they did. And this is Morvik and his family. And this is because they have copyright issues with working with the artist's photographs. So they took their own photograph. And they gave it to 24 artists. And they asked them to tune the first image according to their preferences, their taste. And then they applied it for all of the set of images. And this is just a sanity check. Look at the original image with, sorry, look at the adjusted image with reference for the original profile and compare it to the unadjusted image with a completed profile. So this is a sanity check and it worked. There was no noticeable distance between the two. And the other is they asked them to charge other images that were printed and they all liked the result. And they really preferred, they said that this does reflect their taste in images. So and this is what it's all about. And this is a more quantitative test. They took the images, they let them tune and they compared is the transformation work well. And they really succeed in capturing the transformation. And they did it by comparing the, again, the adjusted image with the original profile to the unadjusted image with the computed profile. And the blue are the median and in the difference of the color. And the red are the 95% in the difference of the color. You can see that most of the values are below one delta E. With this small exception, still it's under 2.5 delta E, which is an noticeable difference. So to conclude, the problem was capturing the artist, the taste of the artist. And they did that by, and they captured this transformation. And they showed that it does capture the taste of the artist on 24 artists. That's the past test. And well, thank you. So we have some time for questions. Yes. So I'm also working with artists in another domain. But so is your conclusion that the artist working with printed image has a single taste for all kind of images of this result? That was the original assumption, but the test showed it because they like, this is what they meant. I mean, that's what they showed in this test, basically. That was the original assumption, which they tested here. So yes. Which is interesting. You're right. You would think that not. So then I have one if you can answer it. So why was the work done on LAB, not on some more equidistant color space like C-CAMO2 model? Okay. So what I understood, and I'm not sure I got the whole of it, but what I understood is that it's because they are doing only modulation, small local differences. And then it doesn't really matter about the big distance in the color space. They want the local neighborhood to be linear. And that's why they work on LAB. So that's what I understood about this region. Okay. Thank you. Thank you very much. Thank you.
was that when artists come to print, artists come photographer, it may be a real artist that got wanted it to be. So this is an example. This is other images. And this is how they do it in the propose to do is to make transformation from here to here, to learn this transformation and to use it on all the other images. And they do it by The second problem is that it's an average. So if of the artist. And also they say that this is a that this is all local changes. Because they assume that the ICC profile was performed and preferences, their taste. And then they applied it for all of the set of images. And this is just other is they asked them to charge other images really matter about the big distance in the color