The JPEG Committee has launched the learning-based image coding activity, also referred to as JPEG AI. This activity aims to find evidence for image coding technologies that offer substantially better compression efficiency than available image codecs with models obtained from a large amount of visual data and that can efficiently represent the wide variety of visual content that is available nowadays.

A Call for Evidence (CfE) was issued as an outcome of the 86th JPEG meeting, Sydney, Australia as a first formal step to consider standardization of such approaches in image compression.

This document reports the decisions made at the 88th JPEG Meeting with respect to the subjective evaluation of the JPEG AI Call for Evidence (CfE) proponents’ submissions.

To stay informed on JPEG AI activities, please regularly consult our website at jpeg.org and/or subscribe to our e-mail reflector.