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

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.

Authors

Institutions

Publication Details

Journal
IEEE Transactions on Circuits and Systems for Video Technology
Published
2025-09-22
DOI
https://doi.org/10.1109/tcsvt.2025.3613244
Primary Topic
Advanced Data Compression Techniques
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

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

Semih Esenlik, Ye-Kui Wang, João Ascenso, Kai Zhang et al.
IEEE Transactions on Circuits and Systems for Video Technology
Advanced Data Compression Techniques
article

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

Semih Esenlik, Ye-Kui Wang, João Ascenso, Kai Zhang, Li Zhang, Shan Liu, Yaojun Wu, Zhaobin Zhang
article en

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.

IEEE Transactions on Circuits and Systems for Video TechnologyVol. 36(2)
Tencent (China) (CN), Beijing Dance Academy (CN), University of California System (US), Instituto de Telecomunicações (PT), KLA (United States) (US)
Openalex Percentile: Top 75%
Advanced Data Compression Techniques
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.