Video Multimethod Assessment Fusion Based Rate-Distortion Optimization for Versatile Video Coding

The emerging visual quality assessment metric VMAF that fuses several elementary metrics by SVM regression has shown a higher correlation with human perception. In this paper, we introduce VMAF into the traditional video coding task as the distortion metric, which needs to be optimized to explore the potential of perceptual quality improvement. Specifically, we propose a multi-granularity VMAF based rate-distortion optimization framework. A frame level visual quality adaption is first conducted by taking the quantization characteristics into account at the coarse-grained adjustment step. Within each frame, a CTU level Lagrangian multiplier and corresponding quantization parameter adaption are carried out based on the content of each CTU at the fine-grained adjustment step. The proposed method has been incorporated into the latest video coding standard – VVC. Experimental results show compared with the conventional rate-distortion optimization for SSE, the proposed method achieves an average 3.30% BD-rate reduction in VMAF.