VMAF Oriented Perceptual Optimization for Video Coding

Abstract

In the light of low costs and automatic assessment, objective visual quality metrics enjoy many important applications such as perceptual coding. Recently multiple metrics obtain further improvement by means of machine learning. However, due to the absence of specific formulas, it’s often hard to incorporate learning based metrics into video coding. In this paper, taking the state-of-the-art learning based metric VMAF for example, we propose a method of perceptual coding in an inferential manner for learning based metrics. The rate distortion optimization is adapted during coding as well. Experimental results show that compared with conventional methods, the proposed method can achieve obvious bitrate saving under HEVC coding.

Publication
2019 IEEE International Symposium on Circuits and Systems (ISCAS)
Yan Huang
Yan Huang
PhD Student
Li Song
Li Song
Professor, IEEE Senior Member

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