| That's very interesting! My first thought, looking at the webpage: "Huh, that's neat. I didn't know that painting software didn't even attempt to do color mixing beyond naive interpolation, though I guess it figures; the physics behind all the light stuff must be fairly gnarly, and there's a lot of information lost in RGB that probably can't be just reconstructed." Scrolling down a bit: "Huh, there's some snippets for using it as a library. Wait, it does operations in RGB? What's going on here?" Finally, clicking the paper link, I found the interesting bit: "We achieve this by establishing a latent color space, where RGB colors are represented as mixtures of primary pigments together with additive residuals. The latents can be manipulated with linear operations, leading to expected, plausible results." That's very clever, and seems like a great use for modern machine learning techniques outside the fashionable realm of language models. It uses perceptual color spaces internally too, and physics based priors. All around very technically impressive and beautiful piece of work. It rhymes with an idea that's been floating in my head for a bit - would generative image models, or image encoder models, work better if rather than rgb, we fed them with wavelength data, or at least a perceptually uniform color space? Seems it'd be closer to truth than arbitrarily using the wavelengths our cone cells happen to respond to (and roughly, at that). |
For those reasons (and others), there's often a strong disconnect between stimuli and perception, which means there's no such thing as a perceptual uniform color space.