自适应流优化对360°虚拟现实视频感知的影响

J. Heyse, M. T. Vega, T. Wauters, F. D. Backere, F. Turck
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引用次数: 4

摘要

随着移动设备对虚拟现实视频流需求的增加,需要设计新的优化传输技术来应对这些超高带宽的视频业务。目前引起人们关注的一种方法是将基于自适应贴片的流媒体解决方案应用于VR视频领域。VR视频以不同的质量级别编码,在时间上分为片段,在空间上分为块。在流式传输过程中,每个图像都可以根据其在帧内的位置独立传输(即,在用户视野内或外)。这些方法为VR视频流媒体的带宽和延迟优化开辟了新的场所。然而,不同的自适应流优化对终端用户感知的影响仍然是一个开放的研究课题。在这个演示中,我们展示了一个VR视频平台来体验基于自适应磁贴的VR视频流服务的工作原理。通过不同的平铺方法、带宽条件和视口算法,它允许用户探索每种优化对服务感知的影响。此外,该平台还提供实时带宽节省和客观体验质量(QoE)测量,以提供优化效果的定量分析。本演示旨在为研究人员提供一个通用的平台,以基准测试和评估其优化解决方案的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Effects of Adaptive Streaming Optimizations on the Perception of 360° Virtual Reality Video
As the demand of Virtual Reality (VR) video streaming to mobile devices increases, novel optimization transport techniques need to be designed to cope with these ultra-high-bandwidth video services. One approach currently attracting attention is the application of adaptive tile-based streaming solutions to the VR video arena. The VR videos are encoded at different quality levels, temporally divided into segments and spatially split into tiles. During the streaming, each of these tiles can be transmitted independently according to its location within the frame (i.e., within or outside of the user's field of view). These methods open a new venue for bandwidth and latency optimization for the streaming of VR videos. However, the effect of the different adaptive streaming optimizations on the end-user perception is still an open research topic. In this demo, we present a VR video platform to experience the working principles of adaptive tile-based VR video streaming services. Through different tiling approaches, bandwidth conditions and viewport algorithms, it allows the users to explore the effects that each optimization has on the perception of the service. In addition, the platform provides real-time bandwidth savings and objective Quality of Experience (QoE) measurements to provide a quantitative analysis of the optimizations effects. This demo aims to provide a common playground for researchers to benchmark and evaluate the performance of their optimization solutions.
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