Similarly I wondered if partially decompressing video and using that format as both the input and output might work. The logic is that a fully decompressed vide

Similarly I wondered if partially decompressing video and using that format as both the input and output might work. The logic is that a fully decompressed video is huge, and that extra data is by definition wasteful: it’s exactly what’s thrown away by compression! We’ve designed compression to efficiently match the human visual system and not waste bytes on irrelevant things. So I wonder if a NN trained on something like a quantised DCT as both input and output might be dramatically more efficient, roughly in line with the compression ratio of applying the same transforms to a raw video. Obviously we’d have to avoid bit-level streaming algorithms like Huffman coding. However even reusing image tiles might work via methods such as differentiable hash tables, as seen in NVIDIA’s reverse rendering neural nets! Food for thought…