AI 中文总结
研究针对JPEG XS原始图像压缩，提出CfL-STT方法，将CfL预测集成到STT中，利用空间对齐亮度样本预测色度分量，抑制高频噪声并保留相关性，在无损压缩中提高编码效率且保持可逆性。
AI 中文摘要
本研究提出一种用于改进JPEG XS中原始图像压缩的亮度色度增强星型四变换（CfL-STT）。该方法将CfL预测集成到STT中，从CFA采样的原始图像中的亮度分量预测色度分量。与传统的针对全彩色图像的CfL预测不同，此方法利用沿水平和垂直方向线性插值获得的空间对齐亮度样本以匹配色度采样网格。这种空间对齐抑制高频传感器噪声并保留跨通道相关性，形成更去相关的Y-Delta-Du-Dv颜色空间。该方法在JPEG XS参考软件中实现并在原始图像数据集上评估。实验结果表明，直接应用CfL预测会产生依赖于图像的性能，且因缺乏空间对齐可能降低编码效率，而所提出的CfL-STT在无损原始图像压缩中持续提高编码效率并保持完全可逆性。
英文摘要
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.
CommentsAccepted for publication in IEEE Signal Processing Letters. 5 pages, 4 figures, 1 table
