VMAF is a perceptual video quality assessment algorithm that measures video quality by fusing multiple assessment methods into a single metric.
Video quality assessment traditionally relies on simple metrics like PSNR that correlate poorly with human perception. VMAF addresses this by combining multiple quality metrics through machine learning to produce assessments that align with how viewers actually perceive video. The tool includes a C library for efficient computation and a Python wrapper that enables users to train and test custom models tailored to specific use cases or codecs. Beyond VMAF itself, the package provides implementations of several established metrics including PSNR, PSNR-HVS, SSIM, MS-SSIM, and CIEDE2000.
Organizations evaluating video encoding pipelines should consider VMAF when perceptual quality matters more than raw bitrate efficiency. It suits streaming services, video platforms, and codec developers who need reliable quality measurements that reflect viewer experience rather than mathematical similarity. The tool is particularly valuable for codec evaluation and optimization work. The README does not compare VMAF to alternative quality metrics, so no comparative guidance can be offered.
The project maintains active development with regular updates addressing performance optimization and API improvements. Work includes refinements to the core assessment model and introduction of specialized modes such as NEG mode for codec evaluation scenarios. The codebase receives ongoing attention to both the C library implementation and the Python tooling for model training and testing.
