TY - GEN
T1 - Deep Video Compression for P-frame in Sub-sampled Color Spaces
AU - Lin, Rongqun
AU - Zhang, Pingping
AU - Wang, Meng
AU - Wang, Shiqi
AU - Kwong, Sam
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - In this paper, we propose a deep video compression method for P-frame in sub-sampled color spaces regarding the YUV420, which has been widely adopted in many state-of-art hybrid video compression standards, in an effort to achieve high compression performance. We adopt motion estimation and motion compression to facilitate the inter prediction of the videos with YUV420 color format, shrinking the total data volume of motion information. Moreover, the motion compensation module on YUV420 is cooperated to enhance the quality of the compensated frame with the consideration of the resolution alignment in the sub-sampled color spaces. To explore the cross-component correlation, the residual encoder-decoder is accompanied with two head-branches and color information fusion. Additionally, a weighted loss emphasizing more on the Y component is utilized to enhance the compression efficiency. Experimental results show that the proposed method can realize 19.82% bit rate reductions on average compared to the deep video compression (DVC) method in terms of the combined PSNR and predominant gains on the Y component.
AB - In this paper, we propose a deep video compression method for P-frame in sub-sampled color spaces regarding the YUV420, which has been widely adopted in many state-of-art hybrid video compression standards, in an effort to achieve high compression performance. We adopt motion estimation and motion compression to facilitate the inter prediction of the videos with YUV420 color format, shrinking the total data volume of motion information. Moreover, the motion compensation module on YUV420 is cooperated to enhance the quality of the compensated frame with the consideration of the resolution alignment in the sub-sampled color spaces. To explore the cross-component correlation, the residual encoder-decoder is accompanied with two head-branches and color information fusion. Additionally, a weighted loss emphasizing more on the Y component is utilized to enhance the compression efficiency. Experimental results show that the proposed method can realize 19.82% bit rate reductions on average compared to the deep video compression (DVC) method in terms of the combined PSNR and predominant gains on the Y component.
KW - Deep learning
KW - learned video compression
KW - P-frame
KW - sub-sampled color spaces
UR - https://www.scopus.com/pages/publications/85142525935
U2 - 10.1109/ISCAS48785.2022.9937560
DO - 10.1109/ISCAS48785.2022.9937560
M3 - 会议稿件
AN - SCOPUS:85142525935
T3 - Proceedings - IEEE International Symposium on Circuits and Systems
SP - 3200
EP - 3204
BT - IEEE International Symposium on Circuits and Systems, ISCAS 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 IEEE International Symposium on Circuits and Systems, ISCAS 2022
Y2 - 27 May 2022 through 1 June 2022
ER -