Impressive work, Daniel! Do I understand correctly that any image prediction for which the deltas are smaller in absolute value than the full JPEG/DCT coefficients would offer continued compression benefits? As in, if you could "name that tune" to predict the rest of the entire image from the first few pixels, the rest of the image would be stored for close to free (and if not, it essentially falls back to regular JPEG encoding).

If that's the case, then not only could we rely on the results of everything we've decompressed so far to use for prediction (which is like one-sided image in-painting), but we also could store a few bits of semantic information (e.g. from an image-net-based CNN, from face detection) about the content of the original image before re-compression, and use that semantic information for prediction as well via some generative model. All of this would obviously be trading computation for storage/bandwidth, but it this seems like an exciting direction to me. Again, nice work.