Paper:
Learning to Inpaint for Image Compression
We study the design of deep architectures for lossy image compression. We present two architectural recipes in the context of multi-stage progressive encoders and empirically demonstrate their importance on compression performance. Specifically, we show that: (a) predicting the original image data from residuals in a multi-stage progressive architecture facilitates learning and leads to improved performance at approximating the original content and (b) learning to inpaint (from neighboring image pixels) before performing compression reduces the amount of information that must be stored to achieve a high-quality approximation. Incorporating these design choices in a baseline progressive encoder yields an average reduction of over 60% in file size with similar quality compared to the original residual encoder.

Review:
This paper proposes a progressive image compression method that's "hybrid". The authors use the framework of Toderici et al (2016) to setup a basic progressive encoder, and then they improve on it by studying how to better propagate information between iterations. The solution involves using "temporal" residual connections, without the explicit need to have an RNN per se (though this point is a bit debatable because in theory if the residual connections are transformed by some convolution, are they acting as an additive RNN or not?). However, the authors also employ a predictor (inpainter). this allows them to encode each patch after trying to predict an "inpainted" version first. It is important to note here that this will only work (in practice) if all the patches on which this patch depends on have been decoded. This introduces a linear dependency on patches, which may make the method too slow in practice, and it would be nice to see a bit more in the text about this issue (maybe some timing in formation vs. not using inpainting). 
Overall, I think the paper was well written and an expert should be able to reproduce the work.
Given that the field of neural image compression is still in its infancy and that most of the recent papers have been focusing on non-progressive methods, and this paper proposes a *progressive* encoder/decoder, I think we should seriously consider accepting it.