Generative Visual Compression: A Review
It addresses the problem of improving visual compression for digital content acquisition, but it is incremental as it reviews existing advances rather than presenting new research.
This paper reviews generative visual compression, which uses deep generative models to achieve competitive performance gains and diverse functionalities over traditional codecs, with applications in ultra-low bitrate communication and intelligent machine analysis.
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.