Wavefront Parallelization for Efficient Learned Image Compression

TL;DR - A training-free wavefront scheduling method accelerates inference in pretrained autoregressive learned image compression models by over 13× without changing rate-distortion performance.

• Reorders inference into an optimal staggered wavefront while preserving exact autoregressive dependencies.
• Requires no architecture changes or retraining, unlike checkerboard context methods.
• Works with existing pretrained models, including Cheng et al.
• Offers additional decoding speed by optionally relaxing precise context dependencies.