CNS Core: Small: Collaborative: Content-Based Viewport Prediction Framework for Live Virtual Reality Streaming
CNS Core: Small: Collaborative: Content-Based Viewport Prediction Framework for Live Virtual Reality Streaming
批准号:
1909172
负责人:
Yanzhi Wang
金额:
$17.12万
依托单位:
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2019
资助国家:
美国
项目状态:
已结题
起止时间:
2019-08-15 至 2024-09-30
中文摘要
随着移动头盔显示器(HMD)设备在消费视频市场的迅速采用,虚拟现实(VR)视频流最近越来越受欢迎。随着沉浸式体验成本的下降,VR视频流带来了新的带宽和性能挑战,尤其是在直播中,因为提供了360度视图。该项目开发了一种新的基于内容的视点预测框架,以提高VR直播的带宽和性能,该框架通过跟踪视频中的运动对象、提取视频语义和对用户感兴趣的视点进行建模的融合来预测用户的视点。首先,通过跟踪对象的运动和语义,开发了一个基于内容的视点预测框架,用于实时VR流媒体。其次,它使用硬件和软件技术来促进实时执行,并将视点预测机制扩展到大量用户。第三,它开发了评估框架来验证该方法的功能、性能和可伸缩性。该项目独特地考虑了视频内容和用户行为之间的相关性,利用前者的确定性性质来克服后者的随机性。随着VR系统在特定领域沉浸式环境中的迅速普及,该项目将受益于几个与VR相关的研究领域,显著节省带宽并提高性能,如基于VR的直播、医疗保健和科学可视化。此外,该项目的跨学科性质将加强对若干科学、技术、工程和数学领域中代表性不足的少数群体的教育和招聘。项目储存库将存储在一个可公开访问的服务器(https://github.com/hwsel).所有项目数据将在资助期结束后保留至少五年。这一奖励反映了NSF的法定使命,并通过使用基金会的智力优势和更广泛的影响审查标准进行评估,被认为值得支持。
英文摘要
Virtual reality (VR) video streaming has been gaining popularity recently with the rapid adoption of mobile head mounted display (HMD) devices in the consumer video market. As the cost for the immersive experience drops, VR video streaming introduces new bandwidth and performance challenges, especially in live streaming, due to the delivery of 360-degree views. This project develops a new content-based viewport prediction framework to improve the bandwidth and performance in live VR streaming, which predicts the user's viewport through a fusion of tracking the moving objects in the video, extracting the video semantics, and modeling the user's viewport of interest.This project consists of three research thrusts. First, it develops a content-based viewport prediction framework for live VR streaming by tracking the motions and semantics of the objects. Second, it employs hardware and software techniques to facilitate real-time execution and scale the viewport prediction mechanism to a large number of users. Third, it develops evaluation frameworks to verify the functionality, performance, and scalability of the approach. The project uniquely considers the correlation between video content and user behavior, which leverages the deterministic nature of the former to conquer the randomness of the latter.With the rapidly increasing popularity of VR systems in domain-specific immersive environments, the project will benefit several VR-related fields of studies with significant bandwidth savings and performance improvements, such as VR-based live broadcast, healthcare, and scientific visualization. Moreover, the interdisciplinary nature of the project will enhance the education and recruitment of underrepresented minorities in several science, technology, engineering, and mathematics (STEM) fields.The project repository will be stored on a publicly accessible server (https://github.com/hwsel). All the project data will be maintained for at least five years following the end of the grant period.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
期刊论文(9)
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科研奖励(0)
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DOI:
10.1109/tnnls.2021.3063265
发表时间:
2021-03-18
期刊:
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
影响因子:
10.4
作者:
[Ma, Xiaolong, Lin, Sheng, Wang, Yanzhi]
通讯作者:
Wang, Yanzhi
DOI:
10.1145/3508352.3549379
发表时间:
2022-10
期刊:
2022 IEEE/ACM International Conference On Computer Aided Design (ICCAD)
影响因子:
--
作者:
[Yifan Gong;Zheng Zhan;Pu Zhao;Yushu Wu;Chaoan Wu;Caiwen Ding;Weiwen Jiang;Minghai Qin;Yanzhi Wang]
通讯作者:
Yifan Gong;Zheng Zhan;Pu Zhao;Yushu Wu;Chaoan Wu;Caiwen Ding;Weiwen Jiang;Minghai Qin;Yanzhi Wang
DOI:
10.1109/cvpr46437.2021.01339
发表时间:
2021-03
期刊:
2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
影响因子:
--
作者:
[Qing Jin;Jian Ren;Oliver J. Woodford;Jiazhuo Wang;Geng Yuan;Yanzhi Wang;S. Tulyakov]
通讯作者:
Qing Jin;Jian Ren;Oliver J. Woodford;Jiazhuo Wang;Geng Yuan;Yanzhi Wang;S. Tulyakov
DOI:
10.48550/arxiv.2209.11204
发表时间:
2022-09
期刊:
ArXiv
影响因子:
--
作者:
[Geng Yuan;Yanyu Li;Sheng Li;Zhenglun Kong;S. Tulyakov;Xulong Tang;Yanzhi Wang;Jian Ren]
通讯作者:
Geng Yuan;Yanyu Li;Sheng Li;Zhenglun Kong;S. Tulyakov;Xulong Tang;Yanzhi Wang;Jian Ren
A Unified DNN Weight Pruning Framework Using Reweighted Optimization Methods
使用重新加权优化方法的统一 DNN 权重修剪框架
DOI:
--
发表时间:
2021
期刊:
58th ACM/IEEE Design Automation Conference (DAC
影响因子:
--
作者:
[Zhang, T., Ma, X., Zhan, Z., Zhou, S., Ding, C., Fardad, M., Wang, Y.]
通讯作者:
Wang, Y.
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