VMAF (Video Multimethod Assessment Fusion) is a video quality metric developed by Netflix. It uses a machine learning approach to combine multiple video quality metrics into a single score, providing a more comprehensive and accurate assessment of video quality.
VMAF uses both objective and subjective metrics to evaluate the quality of a video. Objective metrics include factors such as resolution, bit rate, and frame rate, while subjective metrics are based on human perception of video quality and include factors such as color accuracy, sharpness, and distortion.
By combining multiple metrics into a single score, VMAF provides a more robust evaluation of video quality and reduces the likelihood of missing important aspects of video quality. This makes it a useful tool for video content creators, broadcasters, and streaming services, as well as for researchers studying video quality.
VMAF is available as an open-source tool, and it can be run on a variety of platforms, including Windows, Linux, and macOS.
https://en.wikipedia.org/wiki/Video_Multimethod_Assessment_Fusion