標題: Reinforcement Learning for HEVC/H.265 Intra-Frame Rate Control
作者: Hu, Jun-Hao
Peng, Wen-Hsiao
Chung, Chia-Hua
資訊工程學系
Department of Computer Science
公開日期: 1-Jan-2018
摘要: Reinforcement learning has proven effective for solving decision making problems. However, its application to modern video codecs has yet to be seen. This paper presents an early attempt to introduce reinforcement learning to HEVC/H.265 intra-frame rate control. The task is to determine a quantization parameter value for every coding tree unit in a frame, with the objective being to minimize the frame-level distortion subject to a rate constraint. We draw an analogy between the rate control problem and the reinforcement learning problem, by considering the texture complexity of coding tree units and bit balance as the environment state, the quantization parameter value as an action that an agent needs to take, and the negative distortion of the coding tree unit as an immediate reward. We train a neural network based on Q-learning to be our agent, which observes the state to evaluate the reward for each possible action. When trained on only limited sequences, the proposed model can already perform comparably with the rate control algorithm in HM-16.15.
URI: http://hdl.handle.net/11536/150871
ISSN: 0271-4302
期刊: 2018 IEEE INTERNATIONAL SYMPOSIUM ON CIRCUITS AND SYSTEMS (ISCAS)
Appears in Collections:Conferences Paper