VMAF (Video Multimethod Assessment Fusion) is an open-source, machine-learning-based quality metric developed by Netflix to estimate perceived video quality on a 0-100 scale. Unlike older metrics such as PSNR, VMAF correlates well with how humans actually rate quality, which makes it the practical yardstick for encoding decisions.
Its main use is to decide "how much bitrate is enough": per-title and context-aware encoding pick the lowest bitrate that still hits a target VMAF, capturing quality without overspending bits. VMAF turns the fuzzy question of "does this look good?" into a measurable number that drives ladder design and automated QC.
