An Overview of the JPEG AI Learning-Based Image Coding Standard

October 13, 2025 · The Cartographer · 🏛 IEEE transactions on circuits and systems for video technology (Print)

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"Title-pattern auto-detect: An Overview of the JPEG AI Learning-Based Image Coding Standard"

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Authors Semih Esenlik, Yaojun Wu, Zhaobin Zhang, Ye-Kui Wang, Kai Zhang, Li Zhang, João Ascenso, Shan Liu arXiv ID 2510.13867 Category eess.IV: Image & Video Processing Cross-listed cs.LG, cs.MM Citations 1 Venue IEEE transactions on circuits and systems for video technology (Print) Last Checked 4 days ago

Abstract

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.