The Joint Photographic Experts Group (JPEG), a committee that maintains various JPEG image-related standards, has started exploring ways to involve AI to build a new compression standard.
In a recent meeting held in Sydney, the group released a call for evidence to explore AI-based methods to find a new image compression codec.
The JPEG Committee launched a learning-based image coding activity more than a year ago, also referred as JPEG AI. This activity aims to find evidence for image coding technologies that offer substantially better compression efficiency when compared to conventional approaches but relying on models exploiting a large image database.
In the last few years, several efficient learning-based image coding solutions have been proposed, mainly with improved neural network models. These advances exploit the availability of large image datasets and special hardware, such as the highly parallelizable graphic processing units (GPUs). JPEG has created the JPEG AI AhG group to study promising learning-based image codecs with a precise and well-defined quality evaluation methodology.
A Call for Evidence (CfE) has been issued as outcome of the 86th JPEG meeting, Sydney, Australia as a first formal step to consider standardization of such approaches in image compression.