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 this https URL.
Abstract (translated)
原始图像保留了线性传感器测量值和高位深度信息，这对高级视觉任务和摄影应用至关重要，然而其存储仍因文件体积庞大、位深度多变以及传感器特性差异而面临挑战。现有基于学习的无损压缩方法主要针对8位sRGB图像，而原始图像重建方案本质上有损且依赖特定相机假设。为应对这些挑战，我们提出RAWIC——一种面向拜耳模式原始图像的位深度自适应学习无损压缩框架。首先将单通道拜耳数据转换为四通道RGGB格式并分割为图像块，针对每个图像块计算其位深度作为辅助输入指导压缩。随后设计位深度自适应熵模型，根据图像块的位深度条件估计其分布。该架构使单一模型能处理不同相机与位深度的原始图像。实验表明，RAWIC持续超越传统无损编解码器，在比特率上平均比JPEG-XL降低7.7%。代码已公开于此https URL。
URL
https://arxiv.org/abs/2603.28105
