Papers › Practical Full Resolution Learned Lossless Image Compression
Practical Full Resolution Learned Lossless Image Compression
Fabian Mentzer, Eirikur Agustsson, Michael Tschannen, Radu Timofte, Luc van Gool
We propose the first practical learned lossless image compression system, L3C, and show that it outperforms the popular engineered codecs, PNG, WebP and JPEG 2000. At the core of our method is a fully parallelizable hierarchical probabilistic model for adaptive entropy coding which is optimized end-to-end for the compression task. In contrast to recent autoregressive discrete probabilistic models such as PixelCNN, our method i) models the image distribution jointly with learned auxiliary representations instead of exclusively modeling the image distribution in RGB space, and ii) only requires three forward-passes to predict all pixel probabilities instead of one for each pixel. As a result, L3C obtains over two orders of magnitude speedups when sampling compared to the fastest PixelCNN variant (Multiscale-PixelCNN). Furthermore, we find that learning the auxiliary representation is crucial and outperforms predefined auxiliary representations such as an RGB pyramid significantly.
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Image Compression | ImageNet32 | L3C | bpsp | 4.76 | #3 of 5 | Archive leaderboard | report |
| Image Compression | ImageNet32 | JPEG2000 | bpsp | 6.35 | #4 of 5 | Archive leaderboard | report |
| Image Compression | ImageNet32 | PNG | bpsp | 6.42 | #5 of 5 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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