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

arXiv: https://arxiv.org/abs/2510.13867 · HTML: https://mlsanity.com/paper/2510.13867/

## 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 (most cited first, 7 of 7)

- [Variational image compression with a scale hyperprior](https://mlsanity.com/paper/1802.01436/) (2018)
- [End-to-end Optimized Image Compression](https://mlsanity.com/paper/1611.01704/) (2016)
- [Context-adaptive Entropy Model for End-to-end Optimized Image Compression](https://mlsanity.com/paper/1809.10452/) (2018)
- [Conditional Probability Models for Deep Image Compression](https://mlsanity.com/paper/1801.04260/) (2018)
- [Learned Image Compression with Gaussian-Laplacian-Logistic Mixture Model and Concatenated Residual Modules](https://mlsanity.com/paper/2107.06463/) (2021)
- [Transform Network Architectures for Deep Learning based End-to-End Image/Video Coding in Subsampled Color Spaces](https://mlsanity.com/paper/2103.01760/) (2021)
- [Overview of Variable Rate Coding in JPEG AI](https://mlsanity.com/paper/2503.16288/) (2025)

## Built on by (most cited first, 6 of 6)

- [UniTAC: Universal Task-Aware Compression via Weighted Distortion Measures](https://mlsanity.com/paper/2608.16696/) (2026)
- [ARCHE: Autoregressive Residual Compression with Hyperprior and Excitation](https://mlsanity.com/paper/2603.10188/) (2026)
- [TAFA-GSGC: Group-wise Scalable Point Cloud Geometry Compression with Progressive Residual Refinement](https://mlsanity.com/paper/2604.28045/) (2026)
- [Deployed trusted-node quantum key distribution over 300 km with a multi-core fiber access link](https://mlsanity.com/paper/2606.06107/) (2026)
- [JOMP: Jointly-Optimized Mixed-Precision Quantization Across Neural Video Coding Frameworks and Buffering Strategies](https://mlsanity.com/paper/2606.13110/) (2026)
- [JPEG AIC2026: A large-scale dataset for fine-grained assessment of image coding](https://mlsanity.com/paper/2607.22783/) (2026)

## 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.