TY - GEN
T1 - GENERATIVE VISUAL COMPRESSION
T2 - 31st IEEE International Conference on Image Processing, ICIP 2024
AU - Chen, Bolin
AU - Yin, Shanzhi
AU - Chen, Peilin
AU - Wang, Shiqi
AU - Ye, Yan
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
KW - deep generative models
KW - intelligent coding/analytics
KW - Visual data compression
UR - https://www.scopus.com/pages/publications/85213355755
U2 - 10.1109/ICIP51287.2024.10647820
DO - 10.1109/ICIP51287.2024.10647820
M3 - 会议稿件
AN - SCOPUS:85213355755
T3 - Proceedings - International Conference on Image Processing, ICIP
SP - 3709
EP - 3715
BT - 2024 IEEE International Conference on Image Processing, ICIP 2024 - Proceedings
PB - IEEE Computer Society
Y2 - 27 October 2024 through 30 October 2024
ER -