Netflix/vmaf

Health v2 · maintenance only

Flags: no_license

Adoption not part of the score

5460 stars · 830 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

VMAF is Netflix's Emmy-winning perceptual video quality assessment algorithm, provided as a standalone C library (libvmaf) with a wrapping Python library. It fuses multiple quality metrics into a single score and also includes implementations of PSNR, PSNR-HVS, SSIM, MS-SSIM, CIEDE2000, and the CAMBI banding detector, plus tools for training custom models.

Use cases

• measure perceptual quality of encoded video against a reference
• compare video codecs with a quality metric
• train a custom video quality model for my content
• detect banding artifacts in video with CAMBI
• compute PSNR, SSIM, or MS-SSIM between two videos
• integrate video quality scoring into an encoding pipeline via ffmpeg

When to choose

• you need an industry-standard full-reference video quality metric
• you're evaluating or tuning video encoders and codec settings
• you want to train a quality model tailored to your content distribution

When to avoid

• you need no-reference quality estimation without a source video
• you need audio quality assessment
• you want a simple GUI tool rather than a library/CLI

Facets

library · maturity stable

video-processing machine-learning benchmarking media machine-learning windows python cpp video-quality perceptual-metrics vmaf psnr ssim ciede2000 cambi ffmpeg libvmaf video linux macos docker

Member repositories

For agents

markdown · JSON · MCP: product_card(name="Netflix/vmaf")