  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.
