research-article

Spherical Convolution Empowered Viewport Prediction in 360 Video Multicast with Limited FoV Feedback

Authors:

Zhi LiuAuthors Info & Claims

ACM Transactions on Multimedia Computing, Communications and Applications, Volume 19, Issue 1

Article No.: 3, Pages 1 - 23

https://doi.org/10.1145/3511603

Published: 05 January 2023 Publication History

Abstract

Field of view (FoV) prediction is critical in 360-degree video multicast, which is a key component of the emerging virtual reality and augmented reality applications. Most of the current prediction methods combining saliency detection and FoV information neither take into account that the distortion of projected 360-degree videos can invalidate the weight sharing of traditional convolutional networks nor do they adequately consider the difficulty of obtaining complete multi-user FoV information, which degrades the prediction performance. This article proposes a spherical convolution-empowered FoV prediction method, which is a multi-source prediction framework combining salient features extracted from 360-degree video with limited FoV feedback information. A spherical convolutional neural network is used instead of a traditional two-dimensional convolutional neural network to eliminate the problem of weight sharing failure caused by video projection distortion. Specifically, salient spatial-temporal features are extracted through a spherical convolution-based saliency detection model, after which the limited feedback FoV information is represented as a time-series model based on a spherical convolution-empowered gated recurrent unit network. Finally, the extracted salient video features are combined to predict future user FoVs. The experimental results show that the performance of the proposed method is better than other prediction methods.

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Index Terms

Spherical Convolution Empowered Viewport Prediction in 360 Video Multicast with Limited FoV Feedback
1. Information systems
  1. Information systems applications
    1. Multimedia information systems
      1. Multimedia streaming

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cover image ACM Transactions on Multimedia Computing, Communications, and Applications

ACM Transactions on Multimedia Computing, Communications, and Applications Volume 19, Issue 1

January 2023

505 pages

ISSN:1551-6857

EISSN:1551-6865

DOI:10.1145/3572858

Editor:
Abdulmotaleb El Saddik
Mohamed Bin Zayed University of Artificial Intelligence, UAE and University of Ottawa, Canada

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Publication History

Published: 05 January 2023

Online AM: 12 March 2022

Accepted: 12 January 2022

Revised: 12 January 2022

Received: 09 July 2021

Published in TOMM Volume 19, Issue 1

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