Generative Visual Compression: A Review
Bolin Chen Shanzhi Yin Peilin Chen Shiqi Wang Yan Ye
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-fidelity
reconstruction could be actualized via generative techniques. In addition, we
generalize related generative compression technologies for machine vision and
intelligent analytics. Finally, we discuss the fundamental challenges on
generative visual compression techniques and envision their future research
directions.