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User-Perceived Quality Assessment of Streaming Media Using Reduced Feature Sets

机译：使用缩减功能集的用户感知的流媒体质量评估

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摘要

While subjective measurements are the most natural for assessing the user-perceived quality of a media stream, there are issues with their scalability and their context accuracy. We explore techniques to select application-layer measurements, collected by an instrumented media player, that most accurately predict the subjective quality rating that a user would assign to a stream. We consider three feature subset selection techniques that reduce the number of features (measurements) under consideration to ones most relevant to user-perceived stream quality. Two of the three techniques mathematically consider stream characteristics when selecting measurements, while the third is based on observation. We apply the reduced feature sets to two nearest-neighbor algorithms for predicting user-perceived stream quality. Our results demonstrate that there are clear strategies for estimating the quality rating that work well in specific circumstances such as video-on-demand services. The results also demonstrate that neither of the mathematically-based feature subset selection techniques identify a single set of features that is unambiguously influential on user-perceived stream quality, but that ultimately a combination of retransmitted and/or lost application-layer packets is most accurate for predicting stream quality.
机译：尽管主观测量是评估用户感知的媒体流质量的最自然方法，但它们的可伸缩性和上下文准确性仍然存在问题。我们探索了一种选择技术，这些技术可以选择由仪器化媒体播放器收集的应用程序层度量，从而最准确地预测用户将分配给流的主观质量等级。我们考虑了三种特征子集选择技术，这些技术可将正在考虑的特征（度量）数量减少到与用户感知的流质量最相关的特征。三种技术中的两种在选择测量值时在数学上考虑了流特征，而第三种基于观察。我们将简化的功能集应用于两个最邻近算法，以预测用户感知的流质量。我们的结果表明，有一些明确的策略可以估算在某些特定情况下（例如视频点播服务）效果良好的质量等级。结果还表明，基于数学的特征子集选择技术均无法识别对用户感知的流质量有明确影响的单个特征集，但最终重传和/或丢失的应用层数据包的组合最准确用于预测流质量。

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