Content-Driven Adaptation of On-Line Video

This work presents an on-line approach to the selection of a variable number of frames from a compressed video sequence, just attending to selection rules applied over domain independent semantic features. The localization of these semantic features helps to infer the non homogeneous distribution of semantically relevant information, which allows to reduce the amount of adapted data while maintaining the meaningful information. The extraction of the required features is performed on-line, as demanded for many leading applications. This is achieved via techniques that operate on the compressed domain, which have been adapted to operate on-line. A subjective evaluation of online frame selection validates our results.