As the demand for digital information grows in fields like medicine, remote sensing, and archival, efficient image compression becomes crucial. This paper focuses on lossless image compression, vital for managing the increasing volume of image data without quality loss. Current research emphasizes techniques such as predictive coding, transform coding, and context modeling to improve compression ratios. This study evaluates lossless compression in JPEG XL, the latest standard in the JPEG family, and aims to enhance its compression ratio by modifying the codebase. Results show that while overall compression levels are below the original codec, one prediction method improves compression for specific image types. This study offers insights into enhancing lossless compression performance and suggests possibilities for future advancements in this area.

翻译：随着医学、遥感及档案存储等领域对数字信息需求的增长，高效图像压缩变得至关重要。本文聚焦于无损图像压缩——这一技术对在不损失画质的前提下管理日益增长的图像数据量具有关键意义。当前研究侧重于通过预测编码、变换编码及上下文建模等技术来提升压缩比。本研究对JPEG家族最新标准JPEG XL中的无损压缩性能进行了评估，并旨在通过修改其代码库来优化压缩比。结果表明，尽管整体压缩水平低于原始编解码器，但其中一种预测方法能够针对特定类型图像改善压缩效果。本研究为提升无损压缩性能提供了新视角，并为该领域的未来发展指出了可能性。