Technical Session - 6
Date & Time
Thursday, November 11, 2021, 8:45 PM - 10:15 PM
Andy Rayner Yoshitaka Ikeda

Live Production System to Handle Video Signals with Various Aspect Ratios

Speaker: Yoshitaka Ikeda

  • With the aim of providing a service that allows viewers to watch more attractive TV programs using various aspect ratios freely selected according to the creator's intent, we studied the system requirements and specific transmission methods for live production in broadcast stations. We proposed the use of a 16:9 active video area in conventional video signals such as HD/4K as containers, and determined the range that the user-specified aspect ratio video occupies in the container using identifier ancillary data. The advantage of this method is that it is highly compatible with existing systems and conventional workflows. We also considered the scalability using multiple containers.

Automatic essence timing alignment in IP production

Speakers: Geoff Bowen and Andy Rayner

  • This session will look at the existing timing mechanisms baked into IP media connectivity standards and specifications and how these are typically used in system deployments. We will look at these alongside the ideal timing requirements of a full system. As more of the production chain becomes virtualized, these timing requirements need to be able to be realized in both broadcast appliance infrastructure and virtual server infrastructure (local or cloud based). We will explore some of the technical work still outstanding to realise end-to-end time-aware systems that can deliver the true potential of fully 'time savvy' solution architecture. Fundamental to this work is the concept of timing planes in a production workflow and how these domains are managed in an end to end system.

Super Resolution Application - Content Modernization

Speaker: Adam Mitchell

  • At Warner Bros, we have decades of digital-native content in old standards - lower resolution, higher framerate, interlacing, etc. Bringing that content to modern expectations is currently a manual, labor-intensive process. We are using machine learning techniques as an alternative to that process, which will increase the breadth of content we can bring to streaming audiences by decreasing cost and turnaround time. Our in-development process executes multiple algorithms for each step (upres/frame-interpolation/interlacing) and measures the quality compared to a ground truth via objective algorithms.
Virtual Session Link