[londons_explore]

I suspect if lossless reconstruction was your goal, you would want a different architecture. You would want the model to give you a conditional probability distribution for each pixel, conditioned on all previous pixels, so you could use a regular entropy coder to encode exact data.

[tonic_section]

As u/londons_explore mentioned, in theory you can train a model for lossless reconstruction - there are several papers about this, e.g. [1] is a good recent example. Lossless compressors need to learn a probability distribution over each input pixel, which amounts to maximum likelihood estimation in the original image space.

The model in the demo is a lossy compression method because it first projects the input to a lower dimensional space and performs quantization of this representation to integer values so the result can be ultimately entropy coded. It uses the mean-scale hyperprior model introduced in [1] to estimate the necessary probability distributions in the lower-dimensional space for entropy coding.

[1]: https://arxiv.org/abs/1811.12817 [2]: https://arxiv.org/abs/1802.01436