Quality Control For HEVC: A Deep Reinforcement Learning Approach

In video coding, large quality fluctuations exist in compressed videos, significantly degrading their quality of experience (QoE). Most works in literature focus on controlling bit-rates, however, paying few attention on reducing the quality fluctuations. In this paper, we propose a novel deep reinforcement learning (DRL) method for quality control in video coding. Specifically, we first propose the formulation of quality control, which targets at both controlling the target quality and reducing fluctuations. Then, we solve the quality control formulation by proposing a DRL method, in which the DRL elements are modeled by considering the features of both current frame and previous encoded frames. Specifically, for the DRL elements, we take the encoding information, content complexity and hidden features of long short-term memory (LSTM) as the state of DRL, and the selection of quantization parameters (QP) as the action of DRL. Subsequently, an algorithm, based on proximal policy optimization, is utilized to update our DRL model for decision-making on the actions of QP selection. In this way, the videos can be compressed under given and constant quality. We implement our DRL-based quality control method on the standard of high efficiency video coding (HEVC) with the HM 16.15 platform, and experimental results show that our method achieves the state-of-the-art performance on both quality control accuracy and fluctuations, in comparison with other quality control baselines.