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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Yongfeng Huang Jin Xiao Hong, J.W. Mehaoua, A. Boutaba, R. |
| Copyright Year | 2012 |
| Description | Author affiliation: Division of IT Convergence Engineering, POSTECH, Pohang, South Korea (Yongfeng Huang; Jin Xiao; Hong, J.W.) || Laboratorie d'Informatique Paris Descartes, Paris Descartes University, France (Mehaoua, A.) || School of Computer Science, University of Waterloo, Canada (Boutaba, R.) |
| Abstract | In managing multimedia services, it is important to understand how network performance affects user experience. The model presented in this paper aims to estimate user perception of video quality based on defect events, which are automatically classified by machine learning techniques. The underlying principle of our model is that human experience is event-based and there is a strong correlation between defective events and user MOS. Through experiments, we show that our model can detect different types of defect events with good accuracy even under small data set, and we find that indeed different defect event types affect user experience with different sensitivity. |
| Starting Page | 1 |
| Ending Page | 8 |
| File Size | 257308 |
| Page Count | 8 |
| File Format | |
| ISBN | 9781467344944 |
| e-ISBN | 9781467344968 |
| e-ISBN | 9781467344951 |
| DOI | 10.1109/APNOMS.2012.6356060 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2012-09-25 |
| Publisher Place | Korea (South) |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Measurement Accuracy Humans QoE Machine learning Streaming media Feature extraction Video quality management Quality assessment H.264/AVC Video recording |
| Content Type | Text |
| Resource Type | Article |
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