Hi everyone, I've been working on an implementation of a model for learnable image compression together with general support for neural image compression in PyTorch. You can try it out directly and compress your own images in Google Colab [1] or checkout the source on Github [2].

This project is based on the paper "High-Fidelity Image Compression" by Mentzer et. al. [3] - this was one of the most interesting papers I've read this year! The model is capable of compressing images of arbitrary size and resolution to bitrates competitive with state-of-the-art compression methods while maintaining a very high perceptual quality. At a high-level, the model jointly trains an autoencoding architecture together with a GAN-like component to encourage faithful reconstructions, combined with a hierarchical probability model to perform the entropy coding.

What's interesting is that the model avoids compression artifacts associated with standard image codecs by subsampling high-frequency detail in the image while preserving the global features of the image very well - for example, the model learns to sacrifice faithful reconstruction of e.g. faces and writing and use these 'bits' in other places to keep the overall bitrate low.

The overall model is around 700MB - so transmitting the model wouldn't be particularly feasible, and the idea is that both the sender and receiver have access to the model, and can transmit the compressed messages between themselves.

If you have any questions or notice something weird I'd be more than happy to address them.

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[1] Colab Demo: https://colab.research.google.com/github/Justin-Tan/high-fid...

[2]: Github: https://github.com/Justin-Tan/high-fidelity-generative-compr...

[3]: Original paper: https://hific.github.io/

[4]: Sample reconstructions: https://github.com/Justin-Tan/high-fidelity-generative-compr...