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Telepresence Video Quality Assessment

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Computer Vision – ECCV 2022 (ECCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13697))

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Abstract

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 \(\sim \)2k telepresence videos from different countries, on which we crowdsourced \(\sim \)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.

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Acknowledgments

This work was supported by Meta Platforms, Inc. A.C. Bovik was supported in part by the National Science Foundation AI Institute for Foundations of Machine Learning (IFML) under Grant 2019844.

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Authors and Affiliations

  1. University of Texas at Austin, Austin, USA

    Zhenqiang Ying & Alan Bovik

  2. Facebook AI, Menlo Park, California, USA

    Deepti Ghadiyaram

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  1. Zhenqiang Ying
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  2. Deepti Ghadiyaram
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Correspondence to Zhenqiang Ying .

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Editors and Affiliations

  1. Tel Aviv University, Tel Aviv, Israel

    Shai Avidan

  2. University College London, London, UK

    Gabriel Brostow

  3. Google AI, Accra, Ghana

    Moustapha Cissé

  4. University of Catania, Catania, Italy

    Giovanni Maria Farinella

  5. Facebook (United States), Menlo Park, CA, USA

    Tal Hassner

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Ying, Z., Ghadiyaram, D., Bovik, A. (2022). Telepresence Video Quality Assessment. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13697. Springer, Cham. https://doi.org/10.1007/978-3-031-19836-6_19

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