이벤트 기반의 비디오 품질 지표가 사용자 경험에 미치는 영향

Abstract

Multimedia services have become an important part of today’s network offering. How to guarantee user’s perception of delivered video quality is a critical problem for all content providers, especially when the underlying transportation uses a shared network infrastructure.In this thesis, we present an event-based model to correlate video quality metrics to user experience, as part of VIDAR - a comprehensive VIDeo quality Analyzer in Real time. Our model maps objective frame-level metrics to perceivable video defects with correlated user experience, using machine learning techniques.Through experiments, our model shows good classification accuracy of defect events while keeping the training time reasonable. Furthermore, we have developed user perception models for each event, which also show good prediction of the user experience when subjected to the specific type of events.