Collaborative Research: CNS Core: Small: From Capture to Consumption: System Challenges in Pervasive 360-Degree Video Sharing

合作研究：CNS 核心：小型：从捕获到消费：普遍 360 度视频共享的系统挑战

• 批准号: 2200042
• 负责人: Yao Liu
• 金额: $ 25万
• 依托单位: Rutgers University New Brunswick
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2200042&HistoricalAwards=false
• 关键词: Collaborative; Research; CNS; Core; Small

360-degree video allows users to explore a recorded scene at any angle from the camera position. This greater flexibility provided by the 360-degree video format has led to its increased popularity. However, widespread adoption has slowed because of the large amounts of resources required by the format. This work proposes methods to improve efficiency at each stage of the 360-degree video sharing path, from video capture at the sender side to consumption at the receiver side. The project addresses these challenges via four research thrusts. The first thrust explores approaches for generating high-quality frames during real-time stitching of multiple videos captured using commodity lenses. The proposed approach applies gradient decent to refine an initial rough stitching. The second thrust investigates mechanisms to spatially adapt content to be uploaded based on the available bandwidth. The third thrust aims to optimize the fine-grained representation of omnidirectional content to achieve high projection efficiency and view efficiency. The last thrust uses edge computing techniques to optimize the look-ahead window size on the receiver side to allow bandwidth-efficient content downloading.The techniques developed during the course of the project will allow users to enjoy more efficient 360-degree video streaming applications. These techniques are not only applicable to 360-degree video streaming but will also have applications in future generations of virtual reality (VR) technologies. The project will also motivate general student interest in computer science research, directly train students during the course of the project’s research, and contribute to curriculum development. The project will produce publications, code, data, and other research artifacts. All such artifacts will be made available publicly through the URLs http://www.cs.gmu.edu/~sqchen/ and http://www.cs.binghamton.edu/~yaoliu/ during the course of the project and remain available for at least five years after completion of the project. The code will be open sourced and will also be made available at code hosting website, e.g., GitHub.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.

360-度视频允许用户从相机位置以任何角度探索记录的场景。360度视频格式提供的更大灵活性导致其越来越受欢迎。然而，由于该格式需要大量资源，广泛采用的速度已经放慢。这项工作提出了提高360度视频共享路径的每个阶段的效率的方法，从发送方的视频捕获到接收方的消费。该项目通过四个研究重点来应对这些挑战。第一个推力探索在使用商品镜头捕获的多个视频的实时拼接期间生成高质量帧的方法。所提出的方法应用梯度下降，以改善初始粗糙拼接。第二个重点研究根据可用带宽在空间上调整要上传的内容的机制。第三个目标是优化全向内容的细粒度表示，以实现高投影效率和查看效率。最后一个推力是使用边缘计算技术来优化接收器端的前瞻窗口大小，以实现带宽高效的内容下载。在项目过程中开发的技术将使用户能够享受更高效的360度视频流应用。这些技术不仅适用于360度视频流，而且还将在未来几代虚拟现实（VR）技术中得到应用。该项目还将激发学生对计算机科学研究的兴趣，在项目研究过程中直接培训学生，并为课程开发做出贡献。该项目将产生出版物，代码，数据和其他研究工件。所有这些工件将在项目过程中通过URLhttp://www.cs.gmu.edu/~sqchen/和http://www.cs.binghamton.edu/~yaoliu/公开提供，并在项目完成后至少五年内保持可用。代码将是开源的，也将在代码托管网站上提供，例如，该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Exploring Spherical Autoencoder for Spherical Video Content Processing

• DOI: 10.1145/3503161.3548364
• 发表时间: 2022-10
• 期刊: Proceedings of the 30th ACM International Conference on Multimedia
• 作者: Jin Zhou;Na Li;Yao Liu-;Shuochao Yao;Songqing Chen

EVASR: Edge-Based Video Delivery with Salience-Aware Super-Resolution

EVASR：具有显着性感知超分辨率的基于边缘的视频传输

• DOI: 10.1145/3587819.3590967
• 发表时间: 2023
• 期刊: Proceedings of the 14th Conference on ACM Multimedia Systems
• 作者: Li, Na;Liu, Yao
• 通讯作者: Liu, Yao

VertexShuffle-Based Spherical Super-Resolution for 360-Degree Videos

• DOI: 10.1145/3649315
• 发表时间: 2024-02
• 期刊: ACM Transactions on Multimedia Computing, Communications and Applications
• 作者: Na Li;Yao Liu

Learning-based Homography Matrix Optimization for Dual-fisheye Video Stitching

• DOI: 10.1145/3609395.3610600
• 发表时间: 2023-09
• 期刊: Proceedings of the 2023 Workshop on Emerging Multimedia Systems
• 作者: M. Zhu;Yang Sui;Bo Yuan;Yao-Chang Liu

VQBA: Visual-Quality-Driven Bit Allocation for Low-Latency Point Cloud Streaming

VQBA：低延迟点云流的视觉质量驱动的比特分配

• DOI: 10.1145/3581783.3612486
• 发表时间: 2023
• 期刊: ACM
• 作者: Wang, Shuoqian;Zhu, Mufeng;Li, Na;Xiao, Mengbai;Liu, Yao
• 通讯作者: Liu, Yao