Event-based estimation of user experience for network video streaming

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.