5 ms·

I haven't gone through the whole article, but it seems to be conflating chroma and saturation. If lightness of a color is scaled by a factor c, then chroma need

I haven't gone through the whole article, but it seems to be conflating chroma and saturation. If lightness of a color is scaled by a factor c, then chroma needs to be scaled by that same factor, or saturation won't be preserved, and the color will appear more vibrant then it should.

refulgentis 2y ago
Well, no, it's not straight up scaling. (Not directed at you) Color science is a real field, CAM16 addresses all of the ideas and complaints that anyone could have, and yet, because it's 400 lines of code, we are robbed of principled, grounded, color. Instead people reach for the grab bag of simple algorithmic tricks
- SideQuark 2y ago
  While CAM16 helps, it doesn't address all the ideas and complaints. The field that brought you CAM 16 has many more advanced models to address shortcomings, and there's papers published nearly daily addressing flaws in those models. It's by no means a solved problem or field.
- jacobolus 2y ago
  > CAM16 addresses all of the ideas and complaints that anyone could have A statement this emphatic and absolute can't possibly be true. Here's a concrete complaint that I have with CAM16: the unique hues and hue spacing it defines for its concept of hue quadrature and hue composition are nontrivially different than the ones in CIECAM02 or CIECAM97s, but those changes are not justified or explained anywhere, because the change was just an accidental oversight. (The previous models' unique hues were chosen carefully based on psychometric data.) > because it's 400 lines of code, we are robbed It's not really surprising that people reach for math which is computationally cheap when they need to do something to every pixel which appears in a large video file or is sent to a computer's display.
  - 8n4vidtmkvmk 2y ago
    Then give us both. fast_decent_colormap() and slow_better_colormap() and hide away all your fancy maths. Give me some images and the kinds of transforms each color space is good at, and let me pick one, already implemented in a library in a couple different languages. What's the best color space if I want pretty gradients between 2 arbitrary colors? What's the best color space if want 16 maximally perceptually unique colors? What color space do I use for a heat map image? What color space do I use if I want to pick k-means colors from an image or down sample an image to 6 colors?
    - refulgentis 2y ago
      This is a category error question and that's what makes these hard to answer. There's very good & clear answers, but you see the reaction to them in this thread. I wish I could share the tools I made these problems visible at BigCo, and thus easy to resolve, but alas. TL;DR: A color space just tells you where a color is in relation to others. From there, focus on your domain (eg. mine was "~all software", 2D phone apps) and apply. Ex. the gentleman above talking about specularity and CAM16 is wildly off-topic for my use case, but, might be crucially important for 3D (idk). In general, it's bizarre to be using something besides CAM16, and if that's hard to wrap your mind around, fall back to Lab*, (HCL) and make sure you're accounting for gamut mapping if you're changing one of the components.
      - 8n4vidtmkvmk 2y ago
        Is it a category error? I can see that if I blend linearly in one color space vs another space I'll get a different result. And if a try to cluster colors using one color space vs another I'll get different results. Surely the color space is relevant and my questions aren't completely non-sensical? CAM16 can't be the best answer to all of them, can it? It's possible but I'd think some color spaces are better suited for some tasks than others. Which CAM16 are we even talking about? A quick Google reveals CAM16 UCS, SCD and LCD. CIELAB I've heard good things about but then OKLAB became all the rage and now I don't know what's better.
- itishappy 2y ago
  > CAM16 addresses all of the ideas and complaints that anyone could have... Here's some complaints that better color scientists than me have had about CAM16: > Bad numerical behavior, it is not scale invariant and blending does not behave well because of its compression of chroma. Hue uniformity is decent, but other models predict it more accurately. https://bottosson.github.io/posts/oklab/ https://bottosson.github.io/posts/oklab/ Here's more: > Although CAM16-UCS offers good overall perceptual uniformity it does not preserve hue linearity, particularly in the blue hue region, and is computationally expensive compared with almost all other available models. In addition, none of the above mentioned color spaces were explicitly developed for high dynamic range applications. https://opg.optica.org/oe/fulltext.cfm?uri=oe-25-13-15131 https://opg.optica.org/oe/fulltext.cfm?uri=oe-25-13-15131 Color is hard.
  - refulgentis 2y ago
    You've discovered my White Whale. It spells out a CAM16 approximation via 2 matmuls, and you are using as an example of how CAM16 could be improved. The article, and Oklab, is not by a color scientist. He is/was a video game developer taking some time between jobs to do something on a lark. He makes several category errors in that article, such as swapping in "CAM16-UCS" for "CAM16", and most importantly, he blends polar opposite hues in cartesian coordinates (blue and yellow), and uses the fact this ends up in the center (gray) as the core evidence for not liking CAM16 so much. > better color scientists than me Are you a color scientist?!
    - Sesse__ 2y ago
      > The article, and Oklab, is not by a color scientist. He is/was a video game developer taking some time between jobs to do something on a lark. As a non-color scientist sometimes dealing with color, it would probably be nice if the color scientists came out sometimes and wrote articles that as readable as what Ottosson produces. You can say CIECAM16 is the solution as much you want, but just looking at the CIECAM02 page on Wikipedia makes my brain hurt (how do I use any of this for anything? The correlate for chroma is t^0.9 sqrt(1/100) J (1.64 - 0.29^n)^0.73, where J comes from some Chtulhu formula?). It's hard enough to try to explain gamma to people writing image scaling code, there's no way ordinary developers can understand all of this until it becomes more easily available somehow. :-) Oklab, OTOH, I can actually relate to and understand, so guess which one I'd pick.
- creata 2y ago
  Like most people, I think, I'm just using Oklab for interpolation between colors on my screen, and in color pickers that feel a little easier to use than the usual HSV one. As you mentioned, it's easy to throw in anywhere. Is there a reason why it would be more appropriate to use CAM16 for those use cases? I think an even simpler approximation than Oklab might be appropriate for these cases - it'd be nice if the sRGB gamut was convex in Oklab, or at least didn't have that slice cut out of it in the blue region.
deleted 2y ago
[deleted]

refulgentis 2y ago

Well, no, it's not straight up scaling. (Not directed at you) Color science is a real field, CAM16 addresses all of the ideas and complaints that anyone could have, and yet, because it's 400 lines of code, we are robbed of principled, grounded, color. Instead people reach for the grab bag of simple algorithmic tricks

SideQuark 2y ago
While CAM16 helps, it doesn't address all the ideas and complaints. The field that brought you CAM 16 has many more advanced models to address shortcomings, and there's papers published nearly daily addressing flaws in those models. It's by no means a solved problem or field.
jacobolus 2y ago
> CAM16 addresses all of the ideas and complaints that anyone could have A statement this emphatic and absolute can't possibly be true. Here's a concrete complaint that I have with CAM16: the unique hues and hue spacing it defines for its concept of hue quadrature and hue composition are nontrivially different than the ones in CIECAM02 or CIECAM97s, but those changes are not justified or explained anywhere, because the change was just an accidental oversight. (The previous models' unique hues were chosen carefully based on psychometric data.) > because it's 400 lines of code, we are robbed It's not really surprising that people reach for math which is computationally cheap when they need to do something to every pixel which appears in a large video file or is sent to a computer's display.
- 8n4vidtmkvmk 2y ago
  Then give us both. fast_decent_colormap() and slow_better_colormap() and hide away all your fancy maths. Give me some images and the kinds of transforms each color space is good at, and let me pick one, already implemented in a library in a couple different languages. What's the best color space if I want pretty gradients between 2 arbitrary colors? What's the best color space if want 16 maximally perceptually unique colors? What color space do I use for a heat map image? What color space do I use if I want to pick k-means colors from an image or down sample an image to 6 colors?
  - refulgentis 2y ago
    This is a category error question and that's what makes these hard to answer. There's very good & clear answers, but you see the reaction to them in this thread. I wish I could share the tools I made these problems visible at BigCo, and thus easy to resolve, but alas. TL;DR: A color space just tells you where a color is in relation to others. From there, focus on your domain (eg. mine was "~all software", 2D phone apps) and apply. Ex. the gentleman above talking about specularity and CAM16 is wildly off-topic for my use case, but, might be crucially important for 3D (idk). In general, it's bizarre to be using something besides CAM16, and if that's hard to wrap your mind around, fall back to Lab*, (HCL) and make sure you're accounting for gamut mapping if you're changing one of the components.
    - 8n4vidtmkvmk 2y ago
      Is it a category error? I can see that if I blend linearly in one color space vs another space I'll get a different result. And if a try to cluster colors using one color space vs another I'll get different results. Surely the color space is relevant and my questions aren't completely non-sensical? CAM16 can't be the best answer to all of them, can it? It's possible but I'd think some color spaces are better suited for some tasks than others. Which CAM16 are we even talking about? A quick Google reveals CAM16 UCS, SCD and LCD. CIELAB I've heard good things about but then OKLAB became all the rage and now I don't know what's better.
itishappy 2y ago
> CAM16 addresses all of the ideas and complaints that anyone could have... Here's some complaints that better color scientists than me have had about CAM16: > Bad numerical behavior, it is not scale invariant and blending does not behave well because of its compression of chroma. Hue uniformity is decent, but other models predict it more accurately. https://bottosson.github.io/posts/oklab/ https://bottosson.github.io/posts/oklab/ Here's more: > Although CAM16-UCS offers good overall perceptual uniformity it does not preserve hue linearity, particularly in the blue hue region, and is computationally expensive compared with almost all other available models. In addition, none of the above mentioned color spaces were explicitly developed for high dynamic range applications. https://opg.optica.org/oe/fulltext.cfm?uri=oe-25-13-15131 https://opg.optica.org/oe/fulltext.cfm?uri=oe-25-13-15131 Color is hard.
- refulgentis 2y ago
  You've discovered my White Whale. It spells out a CAM16 approximation via 2 matmuls, and you are using as an example of how CAM16 could be improved. The article, and Oklab, is not by a color scientist. He is/was a video game developer taking some time between jobs to do something on a lark. He makes several category errors in that article, such as swapping in "CAM16-UCS" for "CAM16", and most importantly, he blends polar opposite hues in cartesian coordinates (blue and yellow), and uses the fact this ends up in the center (gray) as the core evidence for not liking CAM16 so much. > better color scientists than me Are you a color scientist?!
  - Sesse__ 2y ago
    > The article, and Oklab, is not by a color scientist. He is/was a video game developer taking some time between jobs to do something on a lark. As a non-color scientist sometimes dealing with color, it would probably be nice if the color scientists came out sometimes and wrote articles that as readable as what Ottosson produces. You can say CIECAM16 is the solution as much you want, but just looking at the CIECAM02 page on Wikipedia makes my brain hurt (how do I use any of this for anything? The correlate for chroma is t^0.9 sqrt(1/100) J (1.64 - 0.29^n)^0.73, where J comes from some Chtulhu formula?). It's hard enough to try to explain gamma to people writing image scaling code, there's no way ordinary developers can understand all of this until it becomes more easily available somehow. :-) Oklab, OTOH, I can actually relate to and understand, so guess which one I'd pick.
creata 2y ago
Like most people, I think, I'm just using Oklab for interpolation between colors on my screen, and in color pickers that feel a little easier to use than the usual HSV one. As you mentioned, it's easy to throw in anywhere. Is there a reason why it would be more appropriate to use CAM16 for those use cases? I think an even simpler approximation than Oklab might be appropriate for these cases - it'd be nice if the sRGB gamut was convex in Oklab, or at least didn't have that slice cut out of it in the blue region.

deleted 2y ago

[deleted]