Multi-Agent Reinforcement Learning based Bit Allocation for Gaming Video Coding

Guangjie Ren, Zizheng Liu, Zhenzhong Chen, Shan Liu

Abstract: In this paper, we propose a multi-agent reinforcement learning based bit allocation method towards quality stability for gaming video coding in Versatile Video Coding (VVC). The bits allocated to regions-of-interests (ROI) are critical to obtain a subjectively optimal visual quality but also constrained subject to the frame-level bit budgets. A multi-objective partially observable stochastic game is formulated by combining the frame-level and ROI-level bit allocation process, which optimizes both the quality and fluctuation simultaneously. The proposed method is implemented in VVC and verified with gaming video. A multi-agent reinforcement learning method is utilized for training the agents and obtaining reasonable bit allocation actions. In comparison to the reference methods, the proposed method achieves a more consistent quality at both the frame-level and ROI-level, while improving the quality of ROI.