[cubefox]

Thanks. The paper is fascinating. I only skimmed around so far and it is full of interesting details. Even beyond compression. They really tried hard to make the USB of image formats, by supporting as many features and use cases as possible. Even things like multiple layers and non-destructive cropping. I like the section where they talk about previous image formats, why many of them failed and how they tried to learn from past mistakes.

Regarding algorithms: Searching for "learned image compression", there are a lot of research papers which use neural networks rather than analytic algorithms like DCT. The compression rates seem to already outperform conventional compression. I guess the bottleneck is more slow decoding speed than compression rate. At least that's the issue with neural video compression.

[fleabitdev]

As I understand it, very small neural networks have already been incorporated into both VVC and AV2 for intra prediction. You're correct that this strategy is limited by decoding performance, especially when predicting large blocks.

In general, I'm pessimistic about prediction-and-residuals strategies for lossy compression. They tend to amplify noise; they create data dependencies, which interfere with parallel decoding; they require non-local optimisation in the encoder; really good prediction involves expensive analysis of a large number of decoded pixels; and it all feels theoretically unsound (because predictors usually produce just one value, rather than a probability distribution).

I'm more optimistic about lossy image codecs based on explicitly-coded summary statistics, with very little prediction. That approach worked well for lossy JPEG XL.