RAWIC: Bit-Depth Adaptive Lossless Raw Image Compression

TL;DR

RAWIC is a novel lossless raw image compression framework that adaptively handles varying bit depths and sensor characteristics, outperforming traditional codecs with a single model.

Contribution

Introduces RAWIC, a bit-depth-adaptive learned lossless compression method for raw images, capable of handling diverse cameras and outperforming existing codecs.

Findings

RAWIC achieves 7.7% bitrate reduction over JPEG-XL.

The model effectively adapts to different bit depths and sensor types.

Experiments validate superior performance of RAWIC on raw images.

Abstract

Raw images preserve linear sensor measurements and high bit-depth information crucial for advanced vision tasks and photography applications, yet their storage remains challenging due to large file sizes, varying bit depths, and sensor-dependent characteristics. Existing learned lossless compression methods mainly target 8-bit sRGB images, while raw reconstruction approaches are inherently lossy and rely on camera-specific assumptions. To address these challenges, we introduce RAWIC, a bit-depth-adaptive learned lossless compression framework for Bayer-pattern raw images. We first convert single-channel Bayer data into a four-channel RGGB format and partition it into patches. For each patch, we compute its bit depth and use it as auxiliary input to guide compression. A bit-depth-adaptive entropy model is then designed to estimate patch distributions conditioned on their bit depths. This architecture enables a single model to handle raw images from diverse cameras and bit depths. Experiments show that RAWIC consistently surpasses traditional lossless codecs, achieving an average 7.7% bitrate reduction over JPEG-XL. Our code is available at https://github.com/chunbaobao/RAWIC.