I’ve remembered that back in the day there was an encoder for RoQ format (the format that uses a codebook with 2×2 YUV vectors, what a coincidence!) called Switchblade and it was using NeuQuant before it was integrated into FFmpeg where it started to use ELBG. So I decided to give it a try.
If you have forgotten, NeuQuant is an application of Kohonen neural network to the task of generating palette for an image. I’ve implemented that kind already so I tried my hoof at adapting it for a larger vector size. Good thing: it works and it’s reasonably fast (2-3 times slower than median cut, faster than partitioned ELBG—and that’s the code that uses doubles for the majority of its calculations). Bad thing: the result quality is mediocre. The results obviously can be improved by adjusting various factors (wait, am I talking about neural network or string theory?) and changing the pseudo-random order in which the candidates are sampled but I don’t feel enthusiastic about tweaking all those parameters and see which ones work good for the widest selection of video sequences.
So I’m drawing a line here. It was a quick and failed experiment, I should find something better to do.
Using ffmpeg solutions all the time.
Nope, NeuQuant has never got into FFmpeg codebase in any form, Vitor has implemented proper ELBG for vector quantisation right from the start when he ported the encoder.