Universal Video Adaptation Model for Contents Delivery Using Focus-of-Choice Model

Abstract

Personalized video adaptation is expected to satisfy individual users' needs on video content. Multimedia data mining plays a significant role of video annotation to meet users' preference on video content. In this paper, a comprehensive solution for personalized video adaptation is proposed based on video content mining. Video content mining targets both cognitive content and affective content. Sometimes, users might prefer "emotional decision" to select their interested video content. The situation encourages the need for the personalized contents to provide the user in the best possible experience. We address the problem of video personalization. For the personalized content, we suggest the UVA (universal-video adaptation) model that uses the video content description in MPEG-7 standard and MPEG-21 multimedia framework.