TY - GEN
T1 - DVC-P
T2 - 2021 International Conference on Visual Communications and Image Processing, VCIP 2021
AU - Zhang, Saiping
AU - Mrak, Marta
AU - Herranz, Luis
AU - Blanch, Marc Gorriz
AU - Wan, Shuai
AU - Yang, Fuzheng
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - Recent years have witnessed the significant development of learning-based video compression methods, which aim at optimizing objective or perceptual quality and bit rates. In this paper, we introduce deep video compression with perceptual op-timizations (DVC-P), which aims at increasing perceptual quality of decoded videos. Our proposed DVC-P is based on Deep Video Compression (DVC) network, but improves it with perceptual optimizations. Specifically, a discriminator network and a mixed loss are employed to help our network trade off among distortion, perception and rate. Furthermore, nearest-neighbor interpolation is used to eliminate checkerboard artifacts which can appear in sequences encoded with DVC frameworks. Thanks to these two improvements, the perceptual quality of decoded sequences is improved. Experimental results demonstrate that, compared with the baseline DVC, our proposed method can generate videos with higher perceptual quality achieving 12.27% reduction in a perceptual BD- rate equivalent, on average.
AB - Recent years have witnessed the significant development of learning-based video compression methods, which aim at optimizing objective or perceptual quality and bit rates. In this paper, we introduce deep video compression with perceptual op-timizations (DVC-P), which aims at increasing perceptual quality of decoded videos. Our proposed DVC-P is based on Deep Video Compression (DVC) network, but improves it with perceptual optimizations. Specifically, a discriminator network and a mixed loss are employed to help our network trade off among distortion, perception and rate. Furthermore, nearest-neighbor interpolation is used to eliminate checkerboard artifacts which can appear in sequences encoded with DVC frameworks. Thanks to these two improvements, the perceptual quality of decoded sequences is improved. Experimental results demonstrate that, compared with the baseline DVC, our proposed method can generate videos with higher perceptual quality achieving 12.27% reduction in a perceptual BD- rate equivalent, on average.
KW - Generative adversarial network
KW - Spatial interpolation
KW - Video compression
UR - http://www.scopus.com/inward/record.url?scp=85125252475&partnerID=8YFLogxK
U2 - 10.1109/VCIP53242.2021.9675350
DO - 10.1109/VCIP53242.2021.9675350
M3 - 会议稿件
AN - SCOPUS:85125252475
T3 - 2021 International Conference on Visual Communications and Image Processing, VCIP 2021 - Proceedings
BT - 2021 International Conference on Visual Communications and Image Processing, VCIP 2021 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 5 December 2021 through 8 December 2021
ER -