  Video conferencing, which includes both video and audio content, has
contributed to dramatic increases in Internet traffic, as the COVID-19 pandemic
forced millions of people to work and learn from home. Global Internet traffic
of video conferencing has dramatically increased Because of this, efficient and
accurate video quality tools are needed to monitor and perceptually optimize
telepresence traffic streamed via Zoom, Webex, Meet, etc. However, existing
models are limited in their prediction capabilities on multi-modal, live
streaming telepresence content. Here we address the significant challenges of
Telepresence Video Quality Assessment (TVQA) in several ways. First, we
mitigated the dearth of subjectively labeled data by collecting ~2k
telepresence videos from different countries, on which we crowdsourced ~80k
subjective quality labels. Using this new resource, we created a
first-of-a-kind online video quality prediction framework for live streaming,
using a multi-modal learning framework with separate pathways to compute visual
and audio quality predictions. Our all-in-one model is able to provide accurate
quality predictions at the patch, frame, clip, and audiovisual levels. Our
model achieves state-of-the-art performance on both existing quality databases
and our new TVQA database, at a considerably lower computational expense,
making it an attractive solution for mobile and embedded systems.
