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A 'GAP-Model' based Framework for Online WoIP QoE Measurement

机译:基于“ GAP模型”的在线WoIP QoE测量框架

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Increased access to broadband networks has led to a fast-growing demand for voice and video over IP (WoIP) applications such as Internet telephony (VoIP), videoconferencing, and IP television (IPTV). For pro-active troubleshooting of WoIP performance bottlenecks that manifest to end-users as performance impairments such as video frame freezing and voice dropouts, network operators cannot rely on actual end-users to report their subjective quality of experience (QoE). Hence, automated and objective techniques that provide real-time or online WoIP QoE estimates are vital. Objective techniques developed to-date estimate WoIP QoE by performing frame-to-frame peak-signal-to-noise ratio (PSNR) comparisons of the original video sequence and the reconstructed video sequence obtained from the sender-side and receiver-side, respectively. Since processing such video sequences is time consuming and computationally intensive, existing objective techniques cannot provide online WoIP QoE. In this paper, we present a novel framework that can provide online estimates of WoIP QoE on network paths without end-user involvement and without requiring any video sequences. The framework features the "GAP-model", which is an offline model of QoE expressed as a function of measurable network factors such as bandwidth, delay, jitter, and loss. Using the GAP-model, our online framework can produce WoIP QoE estimates in terms of "Good", "Acceptable", or "Poor" (GAP) grades of perceptual quality solely from the online measured network conditions.
机译:对宽带网络的访问不断增加,导致对IP语音和视频(WoIP)应用的需求快速增长,例如Internet电话(VoIP),视频会议和IP电视(IPTV)。对于主动出现故障的WoIP性能瓶颈进行故障排除,这些瓶颈会因为诸如视频帧冻结和语音丢失之类的性能损害而向最终用户显示,网络运营商不能依靠实际的最终用户来报告其主观体验质量(QoE)。因此,提供实时或在线WoIP QoE估计的自动化和客观技术至关重要。客观技术通过对原始视频序列和分别从发送方和接收方获得的重构视频序列执行帧对帧峰值信噪比(PSNR)比较,开发了迄今为止的估计WoIP QoE 。由于处理此类视频序列非常耗时且计算量大,因此现有的客观技术无法提供在线WoIP QoE。在本文中,我们提出了一个新颖的框架,该框架可以在网络路径上在线评估WoIP QoE,而无需最终用户参与,也不需要任何视频序列。该框架具有“ GAP模型”,它是QoE的离线模型,表示为可测量网络因素(例如带宽,延迟,抖动和损耗)的函数。使用GAP模型,我们的在线框架可以仅根据在线测量的网络状况就感知质量的“好”,“可接受”或“差”(GAP)等级生成WoIP QoE估计。

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