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SUMMARY:Streaming of rendered content with adaptive frame rate and resolut
 ion - Yaru Liu\, University of Cambridge
DTSTART:20250529T130000Z
DTEND:20250529T140000Z
UID:TALK232780@talks.cam.ac.uk
CONTACT:Yancheng Cai
DESCRIPTION:Streaming rendered content is an attractive way to bring high-
 quality graphics to billions of mobile devices that do not have sufficient
  rendering power. Existing solutions render content on a server at a fixed
  frame rate\, typically 30 or 60 frames per second\, and reduce resolution
  when bandwidth is restricted. Here\, we argue that when streaming graphic
 s content with fast motion\, higher quality is achieved when both the fram
 e rate and the resolution are adjusted dynamically based on the content an
 d its motion. We propose a system in which a small neural network predicts
  the optimal frame rate and resolution for a given transmission bandwidth\
 , content\, and motion velocity. This prediction maximizes perceived rende
 ring quality and reduces computational cost under constrained transmission
  bandwidth. The network is trained on a large dataset of rendered content\
 , which was labeled with a perceptual video quality metric.
LOCATION:SS03 - William Gates Building
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