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Yeah. I understand. But first you have to cluster your images so you know which ones are similar and can then do the deduplication. This would be a powerful way to do that. It's just expensive compared to other clustering algorithms.
My point in linking the paper is that "the probe" you suggested is a 20 year old metric that is well understood. Using normalized compression distance as a measure of Kolmogorov Complexity is what the linked paper is about. You don't need to spend time showing similar images will compress more than dissimilar ones. The compression length is itself a measure of similarity.