Object-based and event-based semantic video adaptation

Semantic video adaptation allows transmitting video content with different viewing quality, depending on the relevance of the content from the user´s viewpoint. To this end, an automatic annotation subsystem must be employed that automatically detect relevant objects and events in the video stream. We present a composite framework that is made of an automatic annotation engine and a semantics-based adaptation module. Three new different compression solutions are proposed that work at the object or event level. Their performance is compared according to a new measure that takes into account the user´s satisfaction and the effects on it of the errors in the annotation module.