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YIQ colorspace and YIQ distance #55
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@makew0rld thanks for noting this! It might make my work in mccutchen/palettor#8 (replacing RGB euclidean distance with a better perceptual distance measure) faster. That code does As I understand it, there are two frequent arithmetic operations:
Implementing a The second operation remains essentially the same speed: whether in HCL or YIQ, I'm taking a euclidean distance in 3D space. It seems to me the bigger performance improvement would come from actually storing HCL and YIQ values. That might all seem out-of-scope for this package, but I figured I'd write it up in case that's of interest! Also, for future readers: see dmtrKovalenko/odiff#81 for an updated link to the YIQ paper referenced here. |
Storing HCL representations made a significant impact. My benchmark results are here – compare 'Main branch' (using |
## Changes Link in README.md appears to point to an old address for "Measuring perceived color difference using YIQ NTSC transmission color space in mobile applications" (Kotsarenko & Ramos 2010). This change substitutes a link to the current journal site. ## Motivation Here from lucasb-eyer/go-colorful#55.
Thanks for the updated paper link. I do think |
The YIQ colorspace can be useful for quickly measuring the perceived difference between two colors. This is what odiff uses to measure the difference between images. The README references this paper.
The YIQ conversion and special YIQ distance algorithm have already been implemented in Go here, but should be compared with the paper just in case.
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