使用田口和模糊逻辑方法建模视频流体验质量

Alex Mongi
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引用次数: 0

摘要

近年来,移动视频流的普及程度显著提高,预计在不久的将来将占全球互联网流量的三分之二。然而,基于网络参数准确地确定最终用户的满意度仍然是一个挑战。现有研究通常使用丢包、延迟和抖动等网络参数来估计用户的体验质量(QoE)。然而,大多数模型以平均意见分数(Mean Opinion Scores, MoS)表示QoE估计,这是不容易被客户理解的。在本研究中,我们使用田口方法在无线测试中进行QoE实验。我们研究了丢包、损坏、延迟和抖动对视频流QoE的同时影响,以及它们的交互影响。此外,我们在MatlabR2016a中开发了一个模糊逻辑模型来建立输入变量与视频流QoE之间的关系。该模型以易于理解的语言术语(如优秀、良好、一般、差和差)表示结果。此外,该模型预测得分与用户得分之间的相关性为0.875,均方根误差为0.344。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Modeling Video Streaming Quality of Experience using Taguchi and Fuzzy Logic Methods
The popularity of mobile video streaming has increased significantly in recent years, and is expected to account for two-thirds of global internet traffic in the near future. However, determining accurately end-users' satisfaction based on network parameters remains a challenge. Existing research often uses network parameters, such as packet loss, delay, and jitter, to estimate users' Quality of Experience (QoE). However, most models present QoE estimates in Mean Opinion Scores (MoS), which are not easily understood by the customers. In this study, we used the Taguchi approach to conduct QoE experiments over a wireless tested. We investigated the simultaneous effects of packet loss, corruption, delay, and jitter on video streaming QoE, as well as their interaction effects. Furthermore, we developed a Fuzzy logic model in MatlabR2016a to establish the relationship between input variables and video streaming QoE. The model presents the results in an easily understandable linguistic terms such as excellent, good, average, bad, and poor. Additionally, the proposed model achieves a correlation of 0.875 between the predicted and user scores, with a Root Mean Square Error of 0.344.
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