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Predicting a viewer's quality of experience

机译:预测观众的体验质量

摘要

A method, system and computer program product for predicting a viewer's quality of experience while watching mobile videos potentially afflicted with network induced impairments. The length of a stall on a video at time t is received as a first input to a model. The number of stalls up to the time t is received as a second input to the model. Furthermore, the time since a preceding rebuffering event is received as a third input to the model. Additionally, a reciprocal stall density at time t is received as a fourth input to the model. The hysteresis effect is captured using a machine-learning-based model with an input that is an aggregate of the outputs of the first, second, third and fourth inputs to nonlinear input blocks of the model, where the hysteresis effect represents an effect that a viewer's recent level of satisfaction/dissatisfaction has on their overall viewing experience.
机译:一种方法,系统和计算机程序产品,用于在观看潜在地受网络诱发的损害困扰的移动视频时预测观众的体验质量。视频在时间t的停顿长度被接收作为模型的第一输入。接收到时间t的停顿数作为模型的第二个输入。此外,自从先前的重新缓冲事件以来的时间被接收为模型的第三输入。另外,在时间t处的倒档密度被接收作为模型的第四输入。使用基于机器学习的模型来捕获磁滞效应,该模型的输入是该模型的非线性输入模块的第一,第二,第三和第四输入的输出的总和,其中磁滞效应表示以下效果:观众最近的满意度/不满程度取决于他们的整体观看体验。

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