A Novel Visual Analysis Oriented Rate Control Scheme for HEVC

Qi Zhang, Shanshe Wang, Siwei Ma

Abstract: 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.