The JPEG XL Image Coding System: History, Features, Coding Tools, Design Rationale, and Future
June 06, 2025 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Jon Sneyers, Jyrki Alakuijala, Luca Versari, ZoltΓ‘n Szabadka, Sami Boukortt, Amnon Cohen-Tidhar, Moritz Firsching, Evgenii Kliuchnikov, Tal Lev-Ami, Eric Portis, Thomas Richter, Osamu Watanabe
arXiv ID
2506.05987
Category
cs.MM: Multimedia
Citations
1
Venue
arXiv.org
Last Checked
4 months ago
Abstract
JPEG XL is a new image coding system offering state-of-the-art compression performance, lossless JPEG recompression, and advanced features. It aims to replace JPEG, PNG, GIF, and other formats with a single universal codec. This article provides an overview of JPEG XL, including its history, design rationale, coding tools, and future potential. It can be used as a companion document to the standard (ISO/IEC 18181), or as a standalone article to better understand JPEG XL, either at a high level or in considerable technical detail.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Multimedia
π
π
Old Age
R.I.P.
π»
Ghosted
Viewport-Adaptive Navigable 360-Degree Video Delivery
π
π
The Cartographer
A Comprehensive Survey on Cross-modal Retrieval
π
π
The Cartographer
An Overview of Cross-media Retrieval: Concepts, Methodologies, Benchmarks and Challenges
R.I.P.
π»
Ghosted
A Convolutional Neural Network Approach for Post-Processing in HEVC Intra Coding
R.I.P.
π»
Ghosted
Video Generation From Text
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted