Streaming Learning Center provides specialized training and consulting services for streaming media professionals, focusing on technical onboarding and practical skills for tools like FFmpeg and Wowza Streaming Engine. The company offers courses on encoding, codecs (H.264, HEVC, AV1), video quality metrics, and live streaming, helping video producers optimize encoding ladders and deploy new codecs efficiently. Led by industry expert Jan Ozer, the company also provides encoding-related testing services for encoder developers and contributes to Streaming Media Magazine. Its differentiated approach combines hands-on training with consulting to solve real-world streaming challenges.
The Munich I Regional Court has issued non-binding FRAND guidelines proposing aggregate monthly codec royalties for streaming services, specifically citing Netflix and Disney+ as examples. The court rejected arguments that device-level licensing exhausts patent rights, asserting that streaming providers are independently liable for video codec royalties.
Amazon's acquisition of MX Player and Dolby's $429 million purchase of GE Licensing's patent portfolio suggest a potential increase in industry momentum for the VVC codec. These developments highlight the ongoing interest in VVC for software-based playback and the strategic importance of codec patent portfolios for major technology players.
Major platforms like Amazon Prime Video and YouTube are increasingly deploying AI-driven dubbing to scale content localization and reach global audiences. The technology presents new integration opportunities for encoding vendors and localization agencies, though early user feedback highlights challenges regarding quality and lack of viewer control.
Microsoft has open-sourced its Machine Learning Video Codec (MLVC) under the MIT License, which is designed for real-time video conferencing on consumer-grade NPUs. The codec utilizes a scale-sharing hyperprior mechanism to resolve cross-platform floating-point drift and is currently undergoing production testing within Microsoft Teams.
Streaming Learning Center has launched SLC Bitrate Explorer 2.0, adding a Portfolio feature to aggregate BD-Rate and cost-benefit analysis across full video libraries. This update allows streaming engineers to compare performance metrics and distribution costs across multiple codecs and encoding configurations.
Jan Ozer published a June 26, 2026 article on capped CRF and QMAX encoding strategies across multiple codecs, based on a real-world consulting engagement.
Streaming Learning Center analyzed how missing color space and color range metadata in video files causes automated scaling steps in VMAF analysis tools to fail, resulting in inaccurate quality scores. The issue affects tools like Bitrate Explorer and FFMetrics when encoding outputs omit header metadata flags. Declaring the color metadata explicitly during scaling resolves the discrepancy, preventing false-negative QC results.
The article discusses three emerging compression standards tailored for machine vision applications: MPEG's Video Coding for Machines (VCM) and Feature Coding for Machines (FCM). It also mentions V-Nova's Low Complexity Enhancement Video Coding (LCEVC) as a solution that integrates AI and human perception requirements for video compression. These new approaches address the limitations of traditional codecs like H.264 and HEVC, which were designed for human viewing rather than AI-driven workflows.
This article introduces Media over QUIC (MoQ), a new QUIC-based protocol aiming to bridge the gap between WebRTC's real-time, low-latency interactivity and HLS/DASH's large-scale, CDN-friendly delivery. MoQ is described as delivering sub-second latency while maintaining CDN-scale capabilities, addressing a fundamental tension in live streaming protocols.
The article discusses the common tool stack used by video engineers for codec comparison, highlighting the limitations of current tools for analyzing codecs like H.264, HEVC, VP9, and AV1. It specifically mentions MediaInfo, Bitrate Viewer, and Moscow State University VQMT, noting their critical gaps in providing comprehensive data for RD curves and BD-Rate.
