An official website of the United States government
Predicting where users will look inside head-mounted displays (HMDs) and fetching only the relevant content is an effective approach for streaming bulky 360 videos over bandwidth-constrained networks. Despite previous efforts, anticipating users’ fast and sudden head movements is still difficult because there is a lack of clear understanding of the unique visual attention in 360 videos that dictates the users’ head movement in HMDs. This in turn reduces the effectiveness of streaming systems and degrades the users’ Quality of Experience. To address this issue, we propose to extract salient cues unique in the 360 video content to capture the attentive behavior of HMD users. Empowered by the newly discovered saliency features, we devise a head-movement prediction algorithm to accurately predict users’ head orientations in the near future. A 360 video streaming framework that takes full advantage of the head movement predictor is proposed to enhance the quality of delivered 360 videos. Practical trace-driven results show that the proposed saliency-based 360 video streaming system reduces the stall duration by 65% and the stall count by 46%, while saving 31% more bandwidth than state-of-the-art approaches.
more »
« less
Full UHD 360-Degree Video Dataset and Modeling of Rate-Distortion Characteristics and Head Movement Navigation
We investigate the rate-distortion (R-D) characteristics of full ultra-high definition (UHD) 360° videos and capture corresponding head movement navigation data of virtual reality (VR) headsets. We use the navigation data to analyze how users explore the 360° look-around panorama for such content and formulate related statistical models. The developed R-D characteristics and modeling capture the spatiotemporal encoding efficiency of the content at multiple scales and can be exploited to enable higher operational efficiency in key use cases. The high quality expectations for next generation immersive media necessitate the understanding of these intrinsic navigation and content characteristics of full UHD 360° videos.
more »
« less
- Award ID(s):
- 1901137
- PAR ID:
- 10341362
- Date Published:
- Journal Name:
- Proceedings of the 12th ACM Multimedia Systems Conference
- Page Range / eLocation ID:
- 267 to 273
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
After the emergence of video streaming services, more creative and diverse multimedia content has become available, and now the capability of streaming 360-degree videos will open a new era of multimedia experiences. However, streaming these videos requires larger bandwidth and less latency than what is found in conventional video streaming systems. Rate adaptation of tiled videos and view prediction techniques are used to solve this problem. In this paper, we introduce the Navigation Graph, which models viewing behaviors in the temporal (segments) and the spatial (tiles) domains to perform the rate adaptation of tiled media associated with the view prediction. The Navigation Graph allows clients to perform view prediction more easily by sharing the viewing model in the same way in which media description information is shared in DASH. It is also useful for encoding the trajectory information in the media description file, which could also allow for more efficient navigation of 360-degree videos. This paper provides information about the creation of the Navigation Graph and its uses. The performance evaluation shows that the Navigation Graph based view prediction and rate adaptation outperform other existing tiled media streaming solutions. Navigation Graph is not limited to 360-degree video streaming applications, but it can also be applied to other tiled media streaming systems, such as volumetric media streaming for augmented reality applications.more » « less
-
null (Ed.)We demonstrate a video 360 navigation and streaming system for Mobile HMD devices. The Navigation Graph (NG) concept is used to predict future views that use a graph model that captures both temporal and spatial viewing behavior of prior viewers. Visualization of video 360 content navigation and view prediction algorithms is used for assessment of Quality of Experience (QoE) and evaluation of the accuracy of the NG-based view prediction algorithm.more » « less
-
Use of video as a representation of practice in teacher education is commonplace. The current study explored the use of a new format (360 video) in the context of preservice teachers’ professional noticing. Findings suggest that preservice teachers viewing 360 videos attended to more student actions than their peers viewing standard video. In addition, using a virtual reality headset to view the 360 videos led to different patterns in where preservice teachers looked in the recorded classroom, and to increased specificity of mathematics content from the scenario. Thus, findings and results support the use of 360 video in teacher education to facilitate teacher noticing. However, future research is needed to further explore this novel technology.more » « less
-
As virtual reality (VR) offers an unprecedented experience than any existing multimedia technologies, VR videos, or called 360-degree videos, have attracted considerable attention from academia and industry. How to quantify and model end users' perceived quality in watching 360-degree videos, or called QoE, resides the center for high-quality provisioning of these multimedia services. In this work, we present EyeQoE, a novel QoE assessment model for 360-degree videos using ocular behaviors. Unlike prior approaches, which mostly rely on objective factors, EyeQoE leverages the new ocular sensing modality to comprehensively capture both subjective and objective impact factors for QoE modeling. We propose a novel method that models eye-based cues into graphs and develop a GCN-based classifier to produce QoE assessment by extracting intrinsic features from graph-structured data. We further exploit the Siamese network to eliminate the impact from subjects and visual stimuli heterogeneity. A domain adaptation scheme named MADA is also devised to generalize our model to a vast range of unseen 360-degree videos. Extensive tests are carried out with our collected dataset. Results show that EyeQoE achieves the best prediction accuracy at 92.9%, which outperforms state-of-the-art approaches. As another contribution of this work, we have publicized our dataset on https://github.com/MobiSec-CSE-UTA/EyeQoE_Dataset.git.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3458305.3478447
