Overview of JPEG AI
The scope of JPEG AI is the creation of a learning-based image coding standard offering a single-stream, compact compressed domain representation, targeting both human visualization, with significant compression efficiency improvement over image coding standards in common use at equivalent subjective quality, and effective performance for image processing and computer vision tasks, with the goal of supporting a royalty-free baseline.
JPEG AI targets a wide range of applications such as cloud storage, visual surveillance, autonomous vehicles and devices, image collection storage and management, live monitoring of visual data and media distribution. The objective is to design a coding solution that requires significant compression efficiency improvement over coding standards in common use at equivalent subjective quality as well as an effective compressed domain processing for machine learning-based image processing and computer vision tasks. Other key requirements include hardware/software implementation-friendly encoding and decoding, support for 8- and 10-bit depth, efficient coding of images with text and graphics, and progressive decoding.
The Final Call for Proposals for JPEG AI was issued as an outcome of the 94th JPEG Meeting. All documents and practical information can be found in the documentation section.