The repository provides a command-line tool for computing VMAF (Video Multi-method Assessment Fusion) scores between reference and distorted video streams. It automates preprocessing steps required for accurate VMAF assessment, including deinterlacing, scaling, frame rate adaptation, and temporal synchronization. The tool supports both CPU and GPU-accelerated VMAF computation via libvmaf and libvmaf_cuda, and can process multiple video pairs using glob patterns. The architecture follows a three-layer design: the CLI layer handles argument parsing and output formatting, the VMAF layer orchestrates preprocessing and scoring, and the FFmpeg layer provides low-level wrappers for FFmpeg/FFprobe subprocesses and filterchain management. This separation of concerns makes the FFmpeg wrapper component particularly reusable for other multimedia processing tasks. The tool outputs results in JSON, XML, CSV, or to stdout, and includes features such as automatic sync window search, reverse sync, end-sync option, VMAF model selection (HD and 4K), and CAMBI heatmap generation. It is designed for Linux and macOS, with Docker support for both CPU and GPU environments.
Awesome Video is a curated resource catalog for streaming video technologies, maintained as structured JSON (contents.json) and rendered into both a README and a static documentation site (MkDocs Material) deployed to awesome.video. The catalog spans ~50 hierarchical categories covering HLS, DASH, FFmpeg, codecs (HEVC/VP9/AV1), transport protocols (RIST/SRT/RTMP), players, DRM, QoE, testing, CDN, HDR, subtitles, and learning resources. It is not a runtime application — it is a content curation pipeline: contents.json is the source of truth, convert.rb generates the README, database.json feeds the site's lunr search index, and loader.py imports bookmarks from Raindrop.io. The repo is hybrid in nature: primarily a curated data catalog with a static-site build system around it, rather than a software application or research pipeline. The build pipeline (GitHub Actions) validates the JSON against schema.json, checks external links with awesome_bot, and deploys to GitHub Pages. The curation quality is strong — entries include authoritative references (Netflix TechBlog, DASH-IF specs, Apple WWDC sessions, Xiph test media) — but the supporting code is simple utility scripting with notable weaknesses: hardcoded Raindrop.io credentials in loader.py, inconsistent category identifiers and malformed tag entries in the data, and aging CI dependencies (Python 3.7, jinja2 3.0.3). The repo is actively maintained and the data model (hierarchical categories + multi-tagged project records) is a clean, portable schema for any awesome-list-style content site.