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    <p>Generally any system which implements this and has serious speed issues isn't going to be doing random colors. It's going to be doing several distinct colors. Even a giant image full of different colors is typically going to only have a few thousand colors. I very highly recommend a caching algorithm. Though if speed is a concern you should roll up your own (you want primitives only, speed).</p> <p>There's not much optimizing to be done with the actual color distance routine itself, but I wrote a caching system for this thing and it went on the order of a 100 times faster. The distance routine went from the overwhelming dominant factor to a blip. You shouldn't seek to reduce the speed of that. You might eek out something. But, reduce the number of times you invoke the thing properly.</p> <p>You have two set inputs and it produces a single set output, and does so after a very very long time. 7 doubles per caching index. That's 14 bytes. For a 14 meg memory footprint (or so, ignoring hashes or what not, likely we're talking double). You can store a million entries and that's enough that if you have like 1k typical different colors you'll get high 90%s cache hits. You can even hugely reduce this if you're converting your initial colors from RGB to Lab (those conversions should be cached too). You'll see a speed up if you hit like 5% of the time. And you'll get hits likely 99% of the time (unless you're doing something odd like random color comparisons). From my observations it makes CIEDE2000 take pretty much the same about of time as Euclidean RGB.</p>
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    1. POHow can I further optimize this color difference function?
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