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Sparse Directed Graph Learning for Head Movement Prediction in 360 Video Streaming

Abstract: 

High-definition 360 videos encoded in fine quality are typically too large in size to stream in its entirety over bandwidth (BW)-constrained networks. One popular remedy is to interactively extract and send a spatial sub-region corresponding to a viewer's current field-of-view (FoV) in a head-mounted display (HMD) for more BW-efficient streaming. Due to the non-negligible round-trip-time (RTT) delay between server and client, accurate head movement prediction that foretells a viewer's future FoVs is essential. Existing approaches are either overly simplistic in modelling and predict poorly when RTT is large, or are over-reliant on data-driven learning, resulting in inflexible models that are not robust to RTT heterogeneity. In this paper, we cast the head movement prediction task as a sparse directed graph learning problem, where three sources of relevant information---a 360 image saliency map, collected viewers' head movement traces, and a biological head rotation model---are aggregated into a unified Markov model. Specifically, we formulate a constrained optimization problem to minimize an $l_2$-norm fidelity term and a sparsity term, corresponding to trace data / saliency consistency and a sparse graph model prior respectively. We solve the problem alternately using a hybrid iterative reweighted least square (IRLS) and Frank-Wolfe optimization strategy. Extensive experiments show that our head movement prediction scheme noticeably outperforms existing proposals across a wide range of RTTs.

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Paper Details

Authors:
Gene Cheung, Patrick Le Callet, Jack Z. G. Tan
Submitted On:
20 May 2020 - 7:49pm
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Presentation Slides
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[1] Gene Cheung, Patrick Le Callet, Jack Z. G. Tan, "Sparse Directed Graph Learning for Head Movement Prediction in 360 Video Streaming", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5420. Accessed: Sep. 23, 2020.
@article{5420-20,
url = {http://sigport.org/5420},
author = {Gene Cheung; Patrick Le Callet; Jack Z. G. Tan },
publisher = {IEEE SigPort},
title = {Sparse Directed Graph Learning for Head Movement Prediction in 360 Video Streaming},
year = {2020} }
TY - EJOUR
T1 - Sparse Directed Graph Learning for Head Movement Prediction in 360 Video Streaming
AU - Gene Cheung; Patrick Le Callet; Jack Z. G. Tan
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5420
ER -
Gene Cheung, Patrick Le Callet, Jack Z. G. Tan. (2020). Sparse Directed Graph Learning for Head Movement Prediction in 360 Video Streaming. IEEE SigPort. http://sigport.org/5420
Gene Cheung, Patrick Le Callet, Jack Z. G. Tan, 2020. Sparse Directed Graph Learning for Head Movement Prediction in 360 Video Streaming. Available at: http://sigport.org/5420.
Gene Cheung, Patrick Le Callet, Jack Z. G. Tan. (2020). "Sparse Directed Graph Learning for Head Movement Prediction in 360 Video Streaming." Web.
1. Gene Cheung, Patrick Le Callet, Jack Z. G. Tan. Sparse Directed Graph Learning for Head Movement Prediction in 360 Video Streaming [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5420