RAWIC: Bit-Depth Adaptive Lossless Raw Image Compression
This addresses storage issues for raw images in photography and vision tasks, offering a camera-agnostic solution, though it is incremental as it builds on learned compression methods.
The paper tackles the challenge of compressing raw Bayer-pattern images with varying bit depths by introducing RAWIC, a learned lossless compression framework that adapts to bit depth, resulting in an average 7.7% bitrate reduction compared to JPEG-XL.
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.