2024/04/30 by Rustam Mamedov, Mamedov, Rustam
Computer Science · #Advanced Data Compression Techniques #FOS: Computer and information sciences #Information Theory (cs.IT)
paper · pdf · doi:10.48550/arxiv.2404.19755
openalex publication_date 2024/04/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.