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首页> 外文期刊>Consumer Electronics, IEEE Transactions on >Video quality assessment in video streaming services considering user preference for video content
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Video quality assessment in video streaming services considering user preference for video content

机译：考虑用户对视频内容的偏好，视频流服务中的视频质量评估

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摘要

In video streaming service, the user's Quality of Experience (QoE) is not only related to video signal quality received at consumer's devices, the users??? subjectivity must also be considered. In this context, a video quality assessment method that takes into account the user's preference for video content is proposed in this research. In order to perform this task, the users' profiles that include their preferences were stored in a video server. Then, subjective tests of video quality assessment were conducted, in which evaluators had different video content preferences. Results show that the evaluators' QoE is highly correlated with the user's preference for video content type. Based on these experimental results, a function named Preference Factor (PF) is defined and used to adjust the quality index values obtained by an objective video quality metric running in the end user's device. The PF function also depends on video content type and quality index score. Using the PF function, the enhanced Video streaming Quality Metric (e-VsQM) is proposed and the results of its performance evaluation demonstrate that PF improves an objective video quality metric. Furthermore, e-VsQM has low complexity and can be utilized in different video services. Thus, an application scenario is presented, in which the proposed video quality metric is implemented.
机译：在视频流服务中，用户的体验质量（QoE）不仅与在用户设备上接收到的视频信号质量有关，用户还可以？还必须考虑主观性。在这种情况下，本研究提出了一种视频质量评估方法，该方法考虑了用户对视频内容的偏爱。为了执行此任务，将包含他们的首选项的用户个人资料存储在视频服务器中。然后，进行了视频质量评估的主观测试，其中评估者具有不同的视频内容偏好。结果表明，评估者的QoE与用户对视频内容类型的偏好高度相关。基于这些实验结果，定义了一个名为“ Preference Factor（PF）”的函数，该函数用于调整通过在最终用户设备中运行的客观视频质量指标获得的质量指标值。 PF功能还取决于视频内容类型和质量指标得分。利用PF功能，提出了增强的视频流质量指标（e-VsQM），其性能评估结果表明，PF提高了客观的视频质量指标。此外，e-VsQM具有低复杂度，并且可以在不同的视频服务中使用。因此，提出了一种应用场景，其中实现了所提出的视频质量度量。

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