GENERATIVE VISUAL COMPRESSION: A REVIEW

Artificial Intelligence Generated Content (AIGC) is leading a new technical revolution for the acquisition of digital content and impelling the progress of visual compression towards competitive performance gains and diverse functionalities over traditional codecs. This paper provides a thorough review on the recent advances of generative visual compression, illustrating great potentials and promising applications in ultra-low bitrate communication, user-specified reconstruction/filtering, and intelligent machine analysis. In particular, we review the visual data compression methodologies with deep generative models, and summarize how compact representation and high-quality reconstruction could be actualized via generative techniques. In addition, we generalize related generative compression technologies for machine vision with different-domain analysis. Finally, we discuss the fundamental challenges on generative visual compression techniques and envision their future research directions.