Comparison of multimedia communications QoE models by Bayesian networks and Bayesian statistics: a case study
Comparison of multimedia communications QoE models by Bayesian networks and Bayesian statistics: a case study
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贝叶斯网络和贝叶斯统计多媒体通信 QoE 模型的比较：案例研究
DOI：
10.1007/s42452-019-0983-5
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
Shuji Tasaka
中科院分区：
文献类型：
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作者：
源田 斗輝;尾崎 光紀;八木谷 聡;今村 幸祐;塩川 和夫;三好 由純;大山 伸一郎;片岡 龍峰;海老原 祐輔;細川 敬祐;Shuji Tasaka
本文比较了贝叶斯网络（BN）和贝叶斯统计（BS）两种建模方法在多媒体通信中的QoE（Quality of Experience）估计和预测中的应用，特别是在预测方面。作为比较的一个例子，我们采用了有保证的带宽的触觉-视听交互式通信系统。这里采用的QoE度量是在由每个主体的视频保证带宽、视频编码比特率、接收器的播出缓冲时间和性别的组合指定的条件下，主体执行交互任务的总体满意度（平均得分）。对于BN建模，我们利用一个R包学习器创建一个离散BN模型，该模型是一个有向无环图，四个节点对应于四个参数。对于BS建模，我们建立了（1）一个贝叶斯分层回归模型的协变量的四个参数和随机效应项反映用户的个性和性别，（2）贝叶斯回归模型没有随机效应项。利用OpenBUGS软件对这两种BS模型进行了MCMC仿真分析。然后，我们发现BN和BS模型提供了近似相同的QoE度量估计。然而，关于预测，具有随机效应项的BS模型优于BN模型和不具有随机效应项的BS模型。因此，我们了解到随机效应项增强了贝叶斯方法在QoE预测中的能力。
This paper presents a comparison ofBayesian Network(BN) andBayesian Statistics(BS) modeling forQoE(Quality of Experience) estimation and prediction in multimedia communications, with special attention to prediction. As an example of the comparison, we employ a haptic-audiovisual interactive communication system with guaranteed bandwidth. The QoE measure adopted here is subjects’ overall satisfaction (average score) of performing an interactive task under conditions specified by combinations of the video guaranteed bandwidth, video encoding bit rate, receiver’s playout buffering time and gender of each subject. For BN modeling, we utilize an R packagebnlearnand create a discrete BN model of adirected acyclic graphwith four nodes corresponding to the four parameters. For BS modeling, we build (1) a Bayesian hierarchical regression model with covariates of the four parameters and random effect terms reflecting users’ individualities and gender, and (2) a Bayesian regression model without the random effect terms. The two BS models are analyzed byMarkov chain Monte Carlo(MCMC) simulation with the softwareOpenBUGS. We then find that the BN and BS models provide approximately the same estimates of the QoE measure. Regarding the prediction, however, the BS model with random effect terms outperforms the BN model and BS model without random effect terms. We thus learn that the random effect terms enhance the ability of Bayesian approaches in QoE prediction.