BEGIN:VCALENDAR VERSION:2.0 PRODID:-//swoogo.com//NONSGML kigkonsult.se iCalcreator 2.41.90// CALSCALE:GREGORIAN UID:66346566-6637-4863-b963-313530376334 BEGIN:VEVENT UID:01cacd1c1634a9dbd4983f0dd130d83d1bb15032@swoogo.com DTSTAMP:20260817T135639Z DESCRIPTION:Content-Adaptive Encoding (CAE) has transformed video compressi on by enabling encoding settings to be dynamically tailored to the unique characteristics of each video or scene. Originally pioneered by Netflix\, CAE began with per-title encoding in 2015 and evolved into per-shot and ch unk-based optimization approaches in the years that followed. However\, Ne tflix’s implementations typically required multi-pass encoding and complex analysis pipelines\, which are computationally intensive and challenging to scale cost-effectively in production environments. To overcome these ch allenges\, VisualOn introduces an AI-powered\, one-pass framework—Universa l CAE VisualOn Optimizer—that delivers comparable or even superior results with significantly reduced complexity. This tutorial presents the archite cture and methodology behind the Optimizer\, which uses a machine learning classifier trained on spatial and temporal texture features extracted fro m video segments. DTSTART:20251014T222200Z DTEND:20251014T224500Z LAST-MODIFIED:20260817T135639Z LOCATION:Session Room AB SEQUENCE:0 STATUS:CONFIRMED SUMMARY:Scalable AI-Powered Content-Adaptive Encoding for Next-Gen Video De livery TRANSP:OPAQUE X-ALT-DESC;FMTTYPE=text/html:

Content-Adaptive Encoding (CAE) has transfo rmed video compression by enabling encoding settings to be dynamically tai lored to the unique characteristics of each video or scene. Originally pio neered by Netflix\, CAE began with per-title encoding in 2015 and evolved into per-shot and chunk-based optimization approaches in the years that fo llowed. However\, Netflix’s implementations typically required multi-pass encoding and complex analysis pipelines\, which are computationally intens ive and challenging to scale cost-effectively in production environments.  To overcome these challenges\, VisualOn introduces an AI-powered\, one-pas s framework—Universal CAE VisualOn Optimizer—that delivers comparable or e ven superior results with significantly reduced complexity. This tutorial presents the architecture and methodology behind the Optimizer\, which use s a machine learning classifier trained on spatial and temporal texture fe atures extracted from video segments.

BEGIN:VALARM UID:39363064-3463-4036-b738-646538656264 ACTION:DISPLAY DESCRIPTION:Content-Adaptive Encoding (CAE) has transformed video compressi on by enabling encoding settings to be dynamically tailored to the unique characteristics of each video or scene. Originally pioneered by Netflix\, CAE began with per-title encoding in 2015 and evolved into per-shot and ch unk-based optimization approaches in the years that followed. However\, Ne tflix’s implementations typically required multi-pass encoding and complex analysis pipelines\, which are computationally intensive and challenging to scale cost-effectively in production environments. To overcome these ch allenges\, VisualOn introduces an AI-powered\, one-pass framework—Universa l CAE VisualOn Optimizer—that delivers comparable or even superior results with significantly reduced complexity. This tutorial presents the archite cture and methodology behind the Optimizer\, which uses a machine learning classifier trained on spatial and temporal texture features extracted fro m video segments. TRIGGER:-PT15M END:VALARM END:VEVENT END:VCALENDAR