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Great work. Thank you for making it so accessible online and sharing it on HN! The first thought I had, after looking at some of the examples, is that it shoul
by cs702 6y ago
Great work. Thank you for making it so accessible online and sharing it on HN!
The first thought I had, after looking at some of the examples, is that it should be fairly trivial to train or finetune the compression network to hallucinate missing detail with "BPG-style" or "JPG-style" smoothing artifacts by feeding compressed images as "real" samples to the adversarial network during training/finetuning.
I wonder if doing that would make it possible to achieve even greater compression with loss of detail that is indistinguishable from traditional compression methods and therefore still acceptable to the human eye.
- gtoderici 6y agoWhat you suggest has already been done: train a neural network with the output of BPG or JPEG, and ask to reconstruct the input with just the decompressed pixels being available. It definitely is a valid approach but the limitation is that if the network needs some texture-specific information that cannot be extracted from the decoded pixels, it can't really do much. There were approaches where such information was also sent on the side, which yielded better results, of course. The field is wide-open and each approach has its own challenges (e.g., you may need to train one network for quantization level for example if you're going to do restoration).