An Overview of the JPEG AI Learning-Based Image Coding Standard
This is an incremental standardization effort for image compression, targeting both human visualization and machine consumption across diverse devices and applications.
The paper tackles the development of JPEG AI, a learning-based image coding standard, and reports significant BD-rate reductions across multiple quality metrics compared to existing standards, with completion scheduled for early 2025.
JPEG AI is an emerging learning-based image coding standard developed by Joint Photographic Experts Group (JPEG). The scope of the JPEG AI is the creation of a practical learning-based image coding standard offering a single-stream, compact compressed domain representation, targeting both human visualization and machine consumption. Scheduled for completion in early 2025, the first version of JPEG AI focuses on human vision tasks, demonstrating significant BD-rate reductions compared to existing standards, in terms of MS-SSIM, FSIM, VIF, VMAF, PSNR-HVS, IW-SSIM and NLPD quality metrics. Designed to ensure broad interoperability, JPEG AI incorporates various design features to support deployment across diverse devices and applications. This paper provides an overview of the technical features and characteristics of the JPEG AI standard.