A Dual-Critic Reinforcement Learning Framework for Frame-Level Bit Allocation in HEVC/H.265

Yung Han Ho, Guo Lun Jin, Yun Liang, Wen-Hsiao Peng, Xiaobo Li

研究成果: Conference contribution同行評審

10 引文 斯高帕斯(Scopus)


This paper introduces a dual-critic reinforcement learning (RL) framework to address the problem of frame-level bit allocation in HEVC/H.265. The objective is to minimize the distortion of a group of pictures (GOP) under a rate constraint. Previous RL-based methods tackle such a constrained optimization problem by maximizing a single reward function that often combines a distortion and a rate reward. However, the way how these rewards are combined is usually ad hoc and may not generalize well to various coding conditions and video sequences. To overcome this issue, we adapt the deep deterministic policy gradient (DDPG) reinforcement learning algorithm for use with two critics, with one learning to predict the distortion reward and the other the rate reward. In particular, the distortion critic works to update the agent when the rate constraint is satisfied. By contrast, the rate critic makes the rate constraint a priority when the agent goes over the bit budget. Experimental results on commonly used datasets show that our method outperforms the bit allocation scheme in x265 and the single-critic baseline by a significant margin in terms of rate-distortion performance while offering fairly precise rate control.

主出版物標題Proceedings - DCC 2021
主出版物子標題2021 Data Compression Conference
編輯Ali Bilgin, Michael W. Marcellin, Joan Serra-Sagrista, James A. Storer
發行者Institute of Electrical and Electronics Engineers Inc.
出版狀態Published - 23 3月 2021
事件2021 Data Compression Conference, DCC 2021 - Snowbird, United States
持續時間: 23 3月 202126 3月 2021


名字Data Compression Conference Proceedings


Conference2021 Data Compression Conference, DCC 2021
國家/地區United States


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