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Comment by atorodius | Hacker News Reader

Small text is indeed one of the fail cases, and more research needs to be done here (see also my other comment [1]). We mention this issue in the supplementary materials [2], and you can check out an example here at [3]

[1] https://news.ycombinator.com/item?id=23654161

[2] https://storage.googleapis.com/hific/data/appendixb/appendix...

[3] https://storage.googleapis.com/hific/userstudy/visualize.htm...

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This might be a good opportunity to lead to research on a "good faith watermark" for GAN-compressed images that may include hallucinated details.

One of the things we discussed to address this is to have the ability to: a) turn off detail hallucination completely given the same bitstream; and b) store the median/maximum absolute error across the image

(b) should allow the user to determine whether the image is suitable for their use-case.

You could use an OCR encoder and cat that to the feature vector. Great work by the way!

Agreed! Alternatively, some semantic segmentation network could be used and a masked MSE loss. In this paper, we focussed on showing the crazy potential of GANs for compression - let's see what future work brings.

Is it possible to put the decoder into a feedback loop and search among multiple possible encodings that minimize the residual errors? Similar to trellis optimization in video codecs. http://akuvian.org/src/x264/trellis.txt

It would be possible - but by minimizing residual errors you end up in a similar regime as when minimizing MSE again, likely making reconstructions blurry!

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