通过空间对齐的亮度预测提升原始图像无损压缩效率。
Spatially-Aligned Chroma from Luma Prediction for Lossless JPEG XS Raw Image Compression
- 用对齐采样网格的亮度预测生成色度分量，提升相关性建模。
- 在多个数据集上实现稳定编码增益，最高提升1.2%率失真性能。
- 适合需要精确可逆的原始图像压缩场景，如专业摄影与医疗影像。
本文提出一种增强型星-四面体变换（CfL-STT），用于改进JPEG XS中的原始图像无损压缩。该方法将色度从亮度（CfL）预测引入星-四面体变换，利用线性插值获得与色度采样网格对齐的亮度样本，从而在去噪的同时保持跨通道相关性，得到更去相关的Y-Delta-Du-Dv色彩空间。在JPEG XS参考软件中实现并评估后发现：直接应用传统CfL预测因缺乏空间对齐，表现依赖图像且可能降低编码效率；而所提CfL-STT在所有测试数据集上均一致提升编码效率，同时保证完全可逆性。
原文摘要 · Abstract (English)
This study proposes a Chroma from Luma (CfL)-enhanced Star-Tetrix transform (STT), referred to as CfL-STT, for improving raw image compression in JPEG XS. The proposed CfL-STT integrates CfL prediction into the STT to predict chroma components from the luma component in CFA-sampled raw images. Unlike conventional CfL prediction designed for full-color images, the proposed method employs spatially aligned luma samples obtained via linear interpolation along the horizontal and vertical directions to match the chroma sampling grid. This spatial alignment suppresses high-frequency sensor noise while preserving cross-channel correlation, resulting in a more decorrelated Y-Delta-Du-Dv color space. The proposed method was implemented in the JPEG XS reference software and evaluated on raw image datasets. Experimental results demonstrate that a direct application of CfL prediction yields image-dependent performance and may degrade coding efficiency due to the lack of spatial alignment, whereas the proposed CfL-STT consistently improves coding efficiency in lossless raw image compression while preserving exact reversibility.
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