A Novel Visual Analysis Oriented Rate Control Scheme for HEVC

Recent years have witnessed an explosion of machine visual intelligence. While impressive performance on visual analysis has been achieved by powerful Deep-Learning-based models, the texture and feature distortion caused by image and video coding is becoming a challenge in practical situations. In this paper, a new rate control scheme is proposed to improve visual analysis performance on coded video frames. Firstly, a new kind of visual analysis distortion is introduced to build a Rate-Joint-Distortion model. Secondly, the Rate-Joint-Distortion Optimization problem is solved by using Lagrange multiplier method, and the relationship between rate and Lagrange multiplier λ is described by a hyperbolic model. Thirdly, a logarithmic λ − QP model is established to achieve minimum Rate-Joint-Distortion cost for given λs. The experimental results show that the proposed scheme can improve visual analysis performance with stable bits used for coding.