Viewport History as a Heuristic for Quality Enhancement and Quality Variation Control in Viewport-Aware Tile-Based 360-Degree Video Streaming
Viewport History as a Heuristic for Quality Enhancement and Quality Variation Control in Viewport-Aware Tile-Based 360-Degree Video Streaming
复制标题
视口历史作为基于视口感知图块的 360 度视频流中质量增强和质量变化控制的启发式方法
DOI：
10.1109/access.2022.3204331
复制
发表时间：
2022
期刊：
影响因子：
3.9
通讯作者：
Bandai Masaki
中科院分区：
文献类型：
--
作者：
Dziubinski Kiana;Bandai Masaki
尽管虚拟现实（VR）越来越受欢迎，但360度视频通常被认为是具有挑战性的，因为它们需要大量的带宽。作为一种解决方案，360度视频内容在空间上被划分为瓦片，并且基于用户的网络环境和视口信息来选择每个瓦片的质量级别。为了确定高质量的图块，视口预测和视口历史方法用于估计用户的视口。然而，由于用户头部移动的不可预测性，生成准确的视口估计是困难的，这可能严重降低用户的体验质量（QoE）。在本文中，为了维持高用户QoE，我们详细介绍了一种新的瓦片质量选择算法，采用视口预测，视口历史，视口扩展，和视口瓦片计数限制。此外，我们还对内容节奏不同的六个360度视频进行了比较分析。基于模拟，使用视口历史作为瓦片质量选择的启发式方法，与八种参考方法相比，在抑制视口内部和跨段的质量变化的同时，表现出感知质量的显著增加;其次，内容节奏较慢的360度视频往往会导致较低的视口预测准确性，QoE性能，与内容速度快的360度视频相比，视口历史趋势较弱。
Despite the growing popularity of Virtual Reality (VR), 360-degree videos are often regarded as challenging to stream due to their large bandwidth requirement. As a solution, the 360-degree video content is spatially divided into tiles, and the quality level for each tile is selected based on the user’s network environment and viewport information. To determine the high quality tiles, viewport prediction and viewport history methods are used to estimate the user’s viewport. However, due to the unpredictability of user head movements, generating accurate viewport estimates are difficult, which can severely degrade the Quality of Experience (QoE) for the user. In this paper, to sustain high user QoE, we detail a novel tile quality selection algorithm that employs viewport prediction, viewport history, viewport extensions, and a viewport tile count limit. In addition, we also include comparison analysis on six 360-degree videos that vary in content pace. Based from simulations, using viewport history as a heuristic for tile quality selection demonstrated a significant increase in the perceived quality while suppressing quality variation inside the viewport and across segments compared to eight reference methods; and secondly, 360-degree videos slow in content pace tended to result in lower viewport prediction accuracy, QoE performance, and weaker viewport history trends compared to 360-degree videos fast in content pace.