.png){kind=icon}
Return
Graph Learning Based Head Movement Prediction for Interactive 360 Video Streaming
.png){kind=icon}
PRE
AI
DOI：10.1109/TIP.2021.3073283.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Ultra-high definition (UHD) 360 videos encoded in fine quality are typically too large to stream in its entirety over bandwidth (BW)-constrained networks. One popular approach 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 foretelling a viewer's future FoVs is essential. In this paper, we cast the head movement prediction task as a sparse directed graph learning problem: three sources of relevant information-collected viewers' head movement traces, a 360 image saliency map, and a biological human head model-are distilled into a view transition Markov model. Specifically, we formulate a constrained maximum a posteriori (MAP) problem with likelihood and prior terms defined using the three information sources. We solve the MAP problem alternately using a hybrid iterative reweighted least square (IRLS) and Frank-Wolfe (FW) optimization strategy. In each FW iteration, a linear program (LP) is solved, whose runtime is reduced thanks to warm start initialization. Having estimated a Markov model from data, we employ it to optimize a tile-based 360 video streaming system. Extensive experiments show that our head movement prediction scheme noticeably outperformed existing proposals, and our optimized tile-based streaming scheme outperformed competitors in rate-distortion performance.
Keywords:
Head
Streaming media
Predictive models
Data models
Optimization
Directed graphs
Servers
360 video streaming
directed graph learning
head movement prediction
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
13.7
Papers:
1.0W
Citations:
8.4W
Organization
Cited Papers
ShareSaveIsolation of Infectious Bovine Rhinotracheitis Virus from the Soft-Shelled Tick,
Ornithodoros coriaceus.png)
Science
ScienceIF0
PREAI
ShareSave
La nappe alluviale de Petit-Spiennes et le début du débitage levallois dans la vallée de la Haine
OAAI
OAAIShareSaveShareSave
View more