TY - GEN
T1 - Statistical modeling based fast rate distortion estimation algorithm for HEVC
AU - Meng, Xiang
AU - Huang, Xiaofeng
AU - Yin, Haibin
AU - Zheng, Shengsheng
AU - Wang, Shiqi
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/3
Y1 - 2020/3
N2 - Rate distortion optimization (RDO) is the basis for algorithm optimization in video coding [1], such as mode decision, rate control and etc. Minimizing the rate distortion coding cost is usually employed to determine the optimal coding parameters such as quantization level, coding mode, and etc. However, rate and distortion calculations for optimal solution decision from massive possible candidates suffer from dramatically high computation complexity. To resolve this problem, this paper proposes a fast TU level rate model with higher accuracy by fully imitating the behavior pattern hid in entropy.
AB - Rate distortion optimization (RDO) is the basis for algorithm optimization in video coding [1], such as mode decision, rate control and etc. Minimizing the rate distortion coding cost is usually employed to determine the optimal coding parameters such as quantization level, coding mode, and etc. However, rate and distortion calculations for optimal solution decision from massive possible candidates suffer from dramatically high computation complexity. To resolve this problem, this paper proposes a fast TU level rate model with higher accuracy by fully imitating the behavior pattern hid in entropy.
UR - https://www.scopus.com/pages/publications/85086875794
U2 - 10.1109/DCC47342.2020.00057
DO - 10.1109/DCC47342.2020.00057
M3 - 会议稿件
AN - SCOPUS:85086875794
T3 - Data Compression Conference Proceedings
SP - 385
BT - Proceedings - DCC 2020
A2 - Bilgin, Ali
A2 - Marcellin, Michael W.
A2 - Serra-Sagrista, Joan
A2 - Storer, James A.
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 Data Compression Conference, DCC 2020
Y2 - 24 March 2020 through 27 March 2020
ER -