First author here. First of all thanks so much for the interest in SReC! It was a pleasant surprise seeing my research on top of Hacker News. Answering a few qu

First author here. First of all thanks so much for the interest in SReC! It was a pleasant surprise seeing my research on top of Hacker News. Answering a few questions from reading the comments: How is this lossless? The entropy coder is what makes this technique lossless. The neural network predicts a probability distribution over pixels, and the entropy coder can find a near optimal mapping of pixel values to bits based on those probabilities (near optimal according to Shannon’s entropy). On practicality of the method, I don’t expect SReC to replace PNGs anytime soon. The current implementation is not efficient for client-side single-image decoding because of the cost of loading a neural network into memory. However, for decoding many high-quality images, this is efficient because the memory cost is amortized. Additionally, the model size can be reduced with a more efficient architecture and pruning/quantization. Finally, as neural networks become more popular in image-related applications, I think the hardware and software support to run neural nets client-side efficiently will get better. This project in its current form is just a proof of concept that we can get state-of-the-art compression rates using neural network. The previous practical neural network-based approach (L3C) was not able to beat FLIF on Open Images. For a detailed explanation of how SReC works and results, please refer to our paper: https://arxiv.org/abs/2004.02872 https://arxiv.org/abs/2004.02872. Btw, SReC is pronounced “Shrek”, because both ogres and neural nets have layers ;).