Summary
The existing "AI and Machine Learning" section contains only two links, both from 2018, focused on video AI platforms and metadata. It completely misses the 2024–2026 story: AI-driven encoding is now production-mature and is changing how streaming services deliver video quality.
What's missing
Per-title (and per-scene) encoding
Netflix's per-title encoding approach — using ML to analyze content complexity and generate an optimal ABR ladder per title — is now baseline for premium OTT. The concept needs a clear explanation:
- What it is (vs. fixed ladder encoding)
- Why it matters (40–50% bitrate savings at same quality, or higher quality at same bitrate)
- How AI accelerates it in practice
VMAF as quality measurement standard
VMAF (Video Multi-Method Assessment Fusion) developed by Netflix is now the standard loss-quality metric for encoding optimization. Any streaming engineer working with encoding quality needs to know it exists and what it measures.
AI-accelerated AV1 encoding
Netflix and Meta report 40% faster AV1 encoding with ML-optimized pipelines. This is directly connected to AV1's adoption trajectory (see also related issue on AV1 positioning).
AI subtitling
AI subtitling now covers 50–60% of live broadcasts at leading European broadcasters (SVT, NRK). Whisper and commercial alternatives (AWS Transcribe, Google Speech-to-Text) are production-ready. The media supply chain workflow (transcode + package + subtitle) is a standard automation pattern.
Eyevinn's auto-subtitles tool is a directly relevant OSS reference: https://github.com/Eyevinn/auto-subtitles
Suggested approach
Replace or substantially expand the "AI and Machine Learning" section with 2024–2026 relevant content covering:
- Per-title/per-scene encoding and VMAF
- AI subtitling (with Eyevinn auto-subtitles as reference)
- AI-driven media supply chain orchestration
References
Summary
The existing "AI and Machine Learning" section contains only two links, both from 2018, focused on video AI platforms and metadata. It completely misses the 2024–2026 story: AI-driven encoding is now production-mature and is changing how streaming services deliver video quality.
What's missing
Per-title (and per-scene) encoding
Netflix's per-title encoding approach — using ML to analyze content complexity and generate an optimal ABR ladder per title — is now baseline for premium OTT. The concept needs a clear explanation:
VMAF as quality measurement standard
VMAF (Video Multi-Method Assessment Fusion) developed by Netflix is now the standard loss-quality metric for encoding optimization. Any streaming engineer working with encoding quality needs to know it exists and what it measures.
AI-accelerated AV1 encoding
Netflix and Meta report 40% faster AV1 encoding with ML-optimized pipelines. This is directly connected to AV1's adoption trajectory (see also related issue on AV1 positioning).
AI subtitling
AI subtitling now covers 50–60% of live broadcasts at leading European broadcasters (SVT, NRK). Whisper and commercial alternatives (AWS Transcribe, Google Speech-to-Text) are production-ready. The media supply chain workflow (transcode + package + subtitle) is a standard automation pattern.
Eyevinn's auto-subtitles tool is a directly relevant OSS reference: https://github.com/Eyevinn/auto-subtitles
Suggested approach
Replace or substantially expand the "AI and Machine Learning" section with 2024–2026 relevant content covering:
References