Reduced-Reference Video Quality Assessment Based on BP-Neural Network Model for Packet Networks

摘要：

This paper investigates quality monitoring of videos transmitted over packet networks. Our goal is to develop a methodology that is both simple and accurate to support quality assessment for videos over packet networks. For this purpose, this paper focus on the parameters that affect the quality of videos and uses back propagation Neural Networks (BPNN) to mimic the way that human viewers assess the quality of videos transmitted over packet networks. Because network factors and motion change degree, that many people may ignore, are both important factors for video quality assessment, in this paper we propose a video quality assessment system considering packet loss rate (PLR), the motion change degree of video and subjective ratings as the inputs of back propagation Neural Networks (BP-NN) for training to get more precise video quality. Simulation result shows that our system can be used to measure the subjective video in real time with very good precision.

关键词： packet networks BP Neural Network packet loss rate video quality asscessment motion change degree

作者: Qingling Li Jing Yang Liang He Shaofen Fan

作者单位: Department of Science and Technology East China Normal University Shanghai, China

会议类型: 国际会议

会议名称: 2010 International Conference on Future Information Technology(2010年未来信息技术国际会议 ICFIT 2010)

会议地点: 长沙

会议语种:英文

页码: 500-503

在线出版日期: 2010-12-14（万方平台首次上网日期，不代表论文的发表时间）