An Overview of the JPEG AI Learning-Based Image Coding Standard
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 7 of its 49 references that are in the map. First posted on arXiv 13 Oct 2025.
Builds on
7 of 49 references.
References · 7
- 2018Variational image compression with a scale hyperpriorcited by 935
- 2016End-to-end Optimized Image Compressioncited by 696
- 2018Context-adaptive Entropy Model for End-to-end Optimized Image Compressioncited by 143
- 2018Conditional Probability Models for Deep Image Compressioncited by 133
- 2021Learned Image Compression with Gaussian-Laplacian-Logistic Mixture Model and Concatenated Residual Modulescited by 35
- 2021Transform Network Architectures for Deep Learning based End-to-End Image/Video Coding in Subsampled Color Spacescited by 6
- 2025Overview of Variable Rate Coding in JPEG AIcited by 2
Built on by
Citing papers per year: 2026: 6.
6 papers.
- 2026UniTAC: Universal Task-Aware Compression via Weighted Distortion Measurescited by 1
- 2026ARCHE: Autoregressive Residual Compression with Hyperprior and Excitationcited by 0
- 2026TAFA-GSGC: Group-wise Scalable Point Cloud Geometry Compression with Progressive Residual Refinementcited by 0
- 2026Deployed trusted-node quantum key distribution over 300 km with a multi-core fiber access linkcited by 0
- 2026JOMP: Jointly-Optimized Mixed-Precision Quantization Across Neural Video Coding Frameworks and Buffering Strategiescited by 0
- 2026JPEG AIC2026: A large-scale dataset for fine-grained assessment of image codingcited by 0
- 2026UniTAC: Universal Task-Aware Compression via Weighted Distortion Measures17 Aug 2026
- 2026JPEG AIC2026: A large-scale dataset for fine-grained assessment of image coding24 Jul 2026
- 2026JOMP: Jointly-Optimized Mixed-Precision Quantization Across Neural Video Coding Frameworks and Buffering Strategies11 Jun 2026
- 2026Deployed trusted-node quantum key distribution over 300 km with a multi-core fiber access link4 Jun 2026
- 2026TAFA-GSGC: Group-wise Scalable Point Cloud Geometry Compression with Progressive Residual Refinement30 Apr 2026
- 2026ARCHE: Autoregressive Residual Compression with Hyperprior and Excitation10 Mar 2026