(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
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/
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
Color is hard.
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?!
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.
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?