Analysis and Enhancement of Lossless Image Compression in JPEG-XL

TL;DR

This paper evaluates and attempts to improve lossless image compression in JPEG XL, highlighting specific prediction methods that enhance compression ratios for certain image types, contributing to more efficient data management.

Contribution

It introduces modifications to JPEG XL's codebase to enhance lossless compression, providing insights into potential improvements over the original codec.

Findings

One prediction method improves compression for specific image types

Overall compression levels are below the original JPEG XL codec

Insights into enhancing lossless compression performance

Abstract

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.