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Yeah. That's what an MP4 does, but I was just saying that first you have to figure out which images are "close enough" to encode this way.
It seems that we focus our interest in two different parts of the problem.
Finding the most optimal way to classify which images are best compressed in bulk is an interesting problem in itself. In this particular problem the person asking it had already picked out similar images by hand and they can be identified by their timestamp for optimizing a comparison of similarity. What I wanted to find out was how well the similar images can be compressed with various methods and codecs with minimal loss of quality. My goal was not to use it as a method to classify the images. It was simply to examine how well the compression stage would work with various methods.
and my point was explaining that that work has likely been done because the paper I linked was 20 years old and they talk about the deep connection between "similarity" and "compresses well". I bet if you read the paper, you'd see exactly why I chose to share it-- particularly the equations that define NID and NCD.
The difference between "seeing how well similar images compress" and figuring out "which of these images are similar" is the quantized, classficiation step which is trivial compared to doing the distance comparison across all samples with all other samples. My point was that this distance measure (using compressors to measure similarity) has been published for at least 20 years and that you should probably google "normalized compression distance" before spending any time implementing stuff, since it's very much been done before.