Reinforcement Learning based Low Delay Rate Control for HEVC Region of Interest Coding

Naifu Xue, Yuan Zhang, Tao Lin

Abstract: Rate control is one of the most critical technologies of real-time video coding. It aims to make bit rate allocation and quantization parameter(QP) decisions to minimize video distortion while reducing buffering delay. However, existing solutions suffer from the problem of extra encoding delay and lack joint optimization of the region of interest(ROI) quality and buffer latency. In this paper, we propose a deep reinforcement learning(RL) based low-delay rate control method without using the information of uncoded frames to avoid the extra delay. Particularly, our RL-based rate control algorithm outputs two types of policies. The first is to make frame-level QP decisions to stabilize buffer occupancy and optimize the overall quality, while the other is in charge of adjusting the QP offset between ROI and Non-ROI regions to enhance portrait quality. Extensive experiments verify that our proposed method improves ROI and overall quality while reducing the buffer occupancy variation, compared with baseline algorithms.