标题: HEVC/H.265 CODING UNIT SPLIT DECISION USING DEEP REINFORCEMENT LEARNING
作者: Chung, Chia-Hua
Peng, Wen-Hsiao
Hu, Jun-Hao
資訊工程學系
Department of Computer Science
关键字: HEVC/H.265;deep reinforcement learning;mode decision
公开日期: 1-一月-2017
摘要: The video coding community has long been seeking more effective rate-distortion optimization techniques than the widely adopted greedy approach. The difficulty arises when we need to predict how the coding mode decision made in one stage would affect subsequent decisions and thus the overall coding performance. Taking a data-driven approach, we introduce in this paper deep reinforcement learning (RL) as a mechanism for the coding unit (CU) split decision in HEVC/H.265. We propose to regard the luminance samples of a CU together with the quantization parameter as its state, the split decision as an action, and the reduction in rate distortion cost relative to keeping the current CU intact as the immediate reward. Based on the Q-learning algorithm, we learn a convolutional neural network to approximate the rate distortion cost reduction of each possible state-action pair. The proposed scheme performs compatibly with the current full rate-distortion optimization scheme in HM-16.15, incurring a 2,5% average BD-rate loss. While also performing similarly to a conventional scheme that treats the split decision as a binary classification problem, our scheme can additionally quantify the rate-distortion cost reduction, enabling more applications.
URI: http://hdl.handle.net/11536/147200
期刊: 2017 INTERNATIONAL SYMPOSIUM ON INTELLIGENT SIGNAL PROCESSING AND COMMUNICATION SYSTEMS (ISPACS 2017)
起始页: 570
结束页: 575
显示于类别:Conferences Paper