VMAF, Video Multimethod Assessment Fusion, is the full-reference metric Netflix built and open-sourced to predict, on a 0-to-100 scale, how good a compressed video looks to a person. Instead of measuring one thing, it fuses several elementary features — in the classic v0 design Visual Information Fidelity (VIF), the detail-loss metric (ADM/DLM), and a motion feature — through a machine-learning model (a support vector regressor) trained on subjective opinion scores, so a jump from 60 to 70 means roughly as much as 80 to 90. The default model is vmaf_v0.6.1, with separate phone and 4K models for other viewing conditions. The catch is that the training that makes VMAF accurate is also its baggage: a bare score is meaningless without naming the model, the pooling, and the content, and it can be inflated by sharpening unless you use VMAF-NEG. It is full-reference, weakest off its training distribution, and the industry default for streaming quality.
