Getting Started
VMAF Studio is a web-based interface for running VMAF (Video Multi-Method Assessment Fusion) video quality analysis on OSC. It connects to the EasyVMAF service to compare a reference video against a transcoded version and compute a perceptual quality score.
VMAF is Netflix's open source quality metric, widely used to validate encoded renditions before publishing to end-users. A score of 100 indicates the distorted video is perceptually identical to the source.
Prerequisites
- An OSC account (sign up here)
- Your OSC personal access token (from Settings → API) — VMAF Studio uses it to call the EasyVMAF service on your behalf
- Video files accessible via a URL or stored in a MinIO bucket on OSC
Step 1: Create a VMAF Studio instance
Via web console
- Go to app.osaas.io/dashboard/service/ablindberg-osc-vmaf-studio
- Click Create studio
- Fill in the fields:
| Field | Description |
|---|---|
| name | A short identifier for the instance, e.g. myvmaf |
| oscAccessToken | Your OSC personal access token. Used server-side only — never sent to the browser |
- Click Create and wait for the instance to show status Running
Via CLI
Store your token as a service secret first:
npx -y @osaas/cli secret set vmafstudio osctoken <your-personal-access-token>
Then create the instance:
npx -y @osaas/cli create ablindberg-osc-vmaf-studio myvmaf \
-o oscAccessToken="{{secrets.osctoken}}"
Step 2: Run a quality analysis
Open the VMAF Studio URL in your browser. The UI lets you:
- Enter the URL of a reference video (the original, uncompressed or high-quality source)
- Enter the URL of a distorted video (the transcoded rendition to evaluate)
- Click Analyze to start the job
VMAF Studio submits the job to EasyVMAF, polls for completion, and displays the aggregate VMAF score and per-frame score chart when ready.
Interpreting results
| Score range | Interpretation |
|---|---|
| 95–100 | Excellent — visually transparent |
| 85–94 | Good — suitable for most streaming delivery |
| 70–84 | Acceptable — noticeable quality loss on large screens |
| < 70 | Poor — visible artefacts, consider re-encoding |
Resources
- VMAF Studio on GitHub
- Netflix VMAF on GitHub — upstream quality metric
- Service: EasyVMAF — batch VMAF analysis via S3