智能化视频码率控制技术研究OA

Video coding rate control technology is primarily responsible for dynamically adjusting the encoder's output bitrate based on video content and network bandwidth conditions to adapt to varying transmission environments while maintaining video image quality.However,traditional rate control methods face significant limitations in dynamic and bandwidth-constrained networks due to their lack of learning capability.To enhance the encoder's adaptability in fluctuating network conditions,an intelligent video coding rate control algorithm that jointly controls frame rate and bitrate is designed based on a reinforcement learning framework.By employing a dual-action output policy network and a QoE-oriented reward function,the coordinated optimization of frame rate and bitrate is realized,which balances the subjective quality and smoothness of the video.Experimental results show that the proposed algorithm can fully ultilize network resources to deliver better subjective quality experiences under low-bandwidth,highly dynamic network conditions,which can improve the adaptability of video encoders in dynamic network environments.