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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


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.

Original languageEnglish
Title of host publicationProceedings - DCC 2021
Subtitle of host publication2021 Data Compression Conference
EditorsAli Bilgin, Michael W. Marcellin, Joan Serra-Sagrista, James A. Storer
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages10
ISBN (Electronic)9780738112275
StatePublished - Mar 2021
Event2021 Data Compression Conference, DCC 2021 - Snowbird, United States
Duration: 23 Mar 202126 Mar 2021

Publication series

NameData Compression Conference Proceedings
ISSN (Print)1068-0314


Conference2021 Data Compression Conference, DCC 2021
CountryUnited States


  • Dual critic
  • Frame level bit allocation
  • HEVC/H.265
  • Reinforcement learning

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