Machine Learning-Based Rate Distortion Modeling for VVC/H.266 Intra-Frame
Miaohui Wang, Jialin Zhang, Lirong Huang, Jian Xiong · 2021
Rate-distortion (R-D) optimization has been widely adopted to improve the coding efficiency on the video encoder side. However, there are few studies related to modeling the R-D characteristics of the latest Versatile Video Coding (VVC) reference software. In this paper, we investigate the R-D modeling of the intra-frame on the VVC encoder by utilizing four traditional machine learning algorithms. We extract four highly descriptive features to capture the relationship between the video content and the R-D model. Moreover, it is applied to the initial intra-frame rate control of VVC. Experimental results show that our method outperforms VTM-7.0, which improves the accuracy by up to 8.65% with affordable computational complexity increase1.