An Ensemble Learning-Based No Reference Qoe Model For User Generated Contents
An Ensemble Learning-Based No Reference Qoe Model For User Generated Contents
复制标题
针对用户生成内容的基于集成学习的无参考 Qoe 模型
DOI：
10.1109/icmew53276.2021.9455959
复制
发表时间：
2021
期刊：
影响因子：
--
通讯作者：
T. Thang
中科院分区：
文献类型：
--
作者：
Duc V. Nguyen;Huyen T. T. Tran;T. Thang
用户生成内容 (UGC) 视频的爆炸式增长需要针对这种新型内容的新体验质量模型。在本文中，我们提出了一种新颖的 UGC 视频无参考 QoE 模型。所提出的模型不仅考虑了比特流级和数据包级信息，还考虑了编码参数。从帧级别的输入视频中提取特征，然后通过池化层进行组合。此外，利用集成学习来提高整体预测性能。实验结果表明，我们的模型可以以非常高的精度预测 UGC 视频的 MOS 值。此外，所提出的模型很简单，因此可以用于实时 QoE 监控。
The explosive growth of user-generated content (UGC) videos demands new Quality of Experience model for this new type of content. In this paper, we propose a novel No Reference QoE model for UGC videos. The proposed model takes into account not only bitstream-level and packet-level information, but also encoding parameters. Features are extracted from input videos at frame level, then combined by a pooling layer. In addition, ensemble learning is utilized to improve overall prediction performance. Experimental results show that our model can predict MOS values of UGC videos with very high accuracy. Also, the proposed model is simple, and so can be used for real-time QoE monitoring.