VMAF Based Rate-Distortion Optimization for Video Coding

被引:0
作者
Deng, Sai [1 ]
Han, Jingning [1 ]
Xu, Yaowu [1 ]
机构
[1] Google LLC, WebM Codec Team, 1600 Amphitheatre Pkwy, Mountain View, CA 94043 USA
来源
2020 IEEE 22ND INTERNATIONAL WORKSHOP ON MULTIMEDIA SIGNAL PROCESSING (MMSP) | 2020年
关键词
VMAF; rate-distortion optimization; perceptual quality;
D O I
暂无
中图分类号
TP31 [计算机软件];
学科分类号
081202 ; 0835 ;
摘要
Video Multi-method Assessment Fusion (VMAF) is a machine-learning based video quality metric. It is experimentally shown to provide higher correlation with human visual system as compared to conventional metrics like peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) in many scenarios and has drawn considerable interest as an alternative metric to evaluate the perceptual quality. This work proposes a systematic approach to improve the video compression performance in VMAF. It is composed of multiple components including a pre-processing stage with a complement automatic filter parameter selection, and a modified rate-distortion optimization framework tailored for VMAF metric. The proposed scheme achieves on average 37% BD-rate reduction in VMAF, as compared to conventional video codec optimized for PSNR.
引用
收藏
页数:6
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