A dependency-free (pure standard-library) Python companion that wires perceptual quality measurement into a pipeline without computing any metric itself (FFmpeg/libvmaf does that; the gate DECISION lives in 5.5's video-quality-gate.py). --scaffold {github,gitlab,jenkins,docker} prints a ready-to-use, version-pinned config: a GitHub Actions workflow (matrix fan-out per rendition, container run, content-hash reference cache, JUnit + artifact upload), a GitLab CI file (GPU runner tag, image pin), a
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The handbook · 7 volumes
Fora Soft Video Engineering Handbook
This Python companion scaffolds GitHub, GitLab, Jenkins or Docker configs that run VMAF per rendition, estimates CI cost and consolidates reports into one gate result. Our seven-volume handbook covers the full video stack; start with Volume 1 for quality metrics, rate control and codecs, then Volume 3 for ABR ladders and Volume 6 for AI quality models. Written by Nikolay Sapunov, CEO at Fora Soft.
Want every encoder change checked for quality before it reaches viewers?
Tell us your CI system and encoding pipeline, and we will wire VMAF gates per rendition with reference caching and a cost estimate. 250+ video products shipped since 2005.
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