Video Multimethod Assessment Fusion

The metric is based on initial work from the group of Professor C.-C. Jay Kuo at the University of Southern California. == Components ==

VMAF uses existing image quality metrics and other features to predict video quality: • Visual Information Fidelity (VIF): considers information fidelity loss at four different spatial scales • Detail Loss Metric (DLM): == Performance ==

An early version of VMAF has been shown to outperform other image and video quality metrics such as SSIM, PSNR-HVS and VQM-VFD on three of four datasets in terms of prediction accuracy, when compared to subjective ratings. Its performance has also been analyzed in another paper, which found that VMAF did not perform better than SSIM and MS-SSIM on a video dataset. In 2017, engineers from RealNetworks reported good reproducibility of Netflix' performance findings. In MSU video quality metrics benchmark, where its various versions (including VMAF NEG) were tested, VMAF outperformed all other metrics on all compression standards (H.265, VP9, AV1, VVC). VMAF scores can be artificially increased without improving perceived quality by applying various operations before or after distorting the video, sometimes without impacting the popular PSNR metric. == Software ==

A reference implementation written in C and Python ("VMAF Development Kit, VDK") is published as free software under the terms of BSD+Patent license. Its source code and additional material are available on GitHub. == See also ==