Tektronix has introduced a cloud-based over-the-top (OTT) video monitoring solution intended to cover ingest through delivery. The solution is designed for both live and file-based content over cloud, virtual or physical networks. The company will demonstrate its new OTT monitoring solutions at IBC2017 in Amsterdam.
The Tektronix OTT monitoring solution for live content, based on the company's Sentry platform, is designed to provide monitoring and diagnostics anywhere along the media workflow, from cloud ingest through to content delivery networks (CDNs). With new support for Microsoft's PlayReady digital rights management (DRM) technology, Sentry offers video quality of experience (QoE) and picture quality analysis on encrypted networks. The software-based solution has cloud, virtual private network (VPN) and data center options.
For file-based analysis, Tektronix' Aurora file-based QC solution is designed to let customers verify the correct encoding for all ABR profiles, and ensure their packaging is error-free and correctly aligned. It also features perceptual video quality tests to reveal any encoding issues.
"With so many fundamental changes in technology and workflows, broadcasters need scalable, future-proof and highly flexible monitoring solutions to adopt cloud-based OTT video delivery models that ensures quality and regulatory compliance," said Charlie Dunn, general manager, Video Product Line, Tektronix. "For both file and live OTT, our scalable solutions allow customers to start out at any level, big or small, and to see what their viewers see with QoE and video quality measurement that validate picture quality."
The company will also be demonstrating its new TekMOS picture quality measurement and scoring technology for the first time at IBC. It's available in both Sentry and Aurora. TekMOS is intended to provide an automated single-ended mean opinion score (MOS) evaluation system for picture quality. The single-ended analysis solution uses machine-learning techniques to evaluate picture quality and identify distortions based on a large training set of subjectively graded images.
