Quality of Experience estimation using frame loss pattern and video encoding characteristics in DVB-H networks

K. Singh, G. Rubino
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引用次数: 9

Abstract

Automatic estimation of Quality of Experience (QoE) is of key importance for mobile television networks such as DVB-H. These networks can install network probes in order to monitor QoE. The QoE feedback can be used to take some corrective measures, in case the quality drops, to bring back QoE to satisfactory level. In this paper, we extend a previously proposed noreference QoE monitoring module for H.264 video over DVB-H networks. We consider an additional parameter called quantisation parameter (QP) and consider frame loss pattern, instead of packet loss pattern, apart from the parameters used in the earlier work such as motion activity and loss rank in a Group of Pictures (GOP). The earlier work is restricted to a fixed encoding bitrate. By considering QP this restriction is removed because QP determines the bitrate as well as the resulting video quality. The results show that our estimation module based on Random Neural Networks (RNN) captures the non-linear relationship between these parameters and QoE. Moreover, our consideration of additional parameters leads to significant improvement in QoE estimation accuracy.
基于帧丢失模式和视频编码特性的DVB-H网络体验质量估计
体验质量(QoE)的自动估计对DVB-H等移动电视网络至关重要。这些网络可以安装网络探针来监视QoE。质量值反馈可以在质量下降时采取相应的纠正措施,使质量值恢复到满意的水平。在本文中,我们扩展了先前提出的用于DVB-H网络上的H.264视频的无参考QoE监控模块。我们考虑了一个额外的参数称为量化参数(QP),并考虑帧丢失模式,而不是数据包丢失模式,除了在早期的工作中使用的参数,如运动活动和丢失等级在一组图片(GOP)。早期的工作仅限于固定的编码比特率。通过考虑QP,这种限制被消除了,因为QP决定了比特率以及由此产生的视频质量。结果表明,基于随机神经网络(RNN)的估计模块捕获了这些参数与QoE之间的非线性关系。此外,我们对附加参数的考虑导致QoE估计精度的显著提高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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