TY - GEN
T1 - Machine-Learning Based High Efficiency Rate Control for AV1
AU - Chen, Yi
AU - Mao, Yunhao
AU - Wang, Shiqi
AU - Zhang, Xianguo
AU - Kwong, Sam
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Recent years have witnessed the increasing demand of video coding technologies, which have been continuously developed to meet various requirements in video-related applications. Developed by Alliance for Open Media (AOM), the AOMedia Video 1 (AVl) is an open-source and royalty-free standard. Herein, we achieve high efficiency rate control for AVI based on the machine-learning model, which establishes the rate-quantization relationship in a data-driven manner. More specifically, the Supporting Vector Regression (SVR) is used for rate model parameter estimation. The model is trained using sufficient training data, and incorporated in the encoder. Compared to the default rate control scheme in AV 1, experimental results have shown that 2.01% bitrate could be saved with tolerable bitrate error.
AB - Recent years have witnessed the increasing demand of video coding technologies, which have been continuously developed to meet various requirements in video-related applications. Developed by Alliance for Open Media (AOM), the AOMedia Video 1 (AVl) is an open-source and royalty-free standard. Herein, we achieve high efficiency rate control for AVI based on the machine-learning model, which establishes the rate-quantization relationship in a data-driven manner. More specifically, the Supporting Vector Regression (SVR) is used for rate model parameter estimation. The model is trained using sufficient training data, and incorporated in the encoder. Compared to the default rate control scheme in AV 1, experimental results have shown that 2.01% bitrate could be saved with tolerable bitrate error.
KW - Quantization parameter (QP)
KW - Rate control
KW - Supporting vector regression
KW - Video coding
UR - https://www.scopus.com/pages/publications/85139017700
U2 - 10.1109/MIPR54900.2022.00019
DO - 10.1109/MIPR54900.2022.00019
M3 - 会议稿件
AN - SCOPUS:85139017700
T3 - Proceedings - 5th International Conference on Multimedia Information Processing and Retrieval, MIPR 2022
SP - 65
EP - 70
BT - Proceedings - 5th International Conference on Multimedia Information Processing and Retrieval, MIPR 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 5th International Conference on Multimedia Information Processing and Retrieval, MIPR 2022
Y2 - 2 August 2022 through 4 August 2022
ER -