Scalable multimedia delivery for pervasive computing
MULTIMEDIA '99 Proceedings of the seventh ACM international conference on Multimedia (Part 1)
Kendra: adaptive Internet system
Journal of Systems and Software
Automatic detection of 'Goal' segments in basketball videos
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
Media transcoding for pervasive computing
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
A proxy-based adaptive flow control scheme for media streaming
Proceedings of the 2002 ACM symposium on Applied computing
Adaptive Streaming of MPEG Video over IP Networks
LCN '97 Proceedings of the 22nd Annual IEEE Conference on Local Computer Networks
Adaptive video highlights for wired and wireless platforms
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 2
Adapting multimedia Internet content for universal access
IEEE Transactions on Multimedia
Layered quality adaptation for Internet video streaming
IEEE Journal on Selected Areas in Communications
Multiquality Data Replication in Multimedia Databases
IEEE Transactions on Knowledge and Data Engineering
Algorithms for video retargeting
Multimedia Tools and Applications
Adaptive multimedia content delivery in ubiquitous environments
WISE'05 Proceedings of the 2005 international conference on Web Information Systems Engineering
Quality-aware replication of multimedia data
DEXA'05 Proceedings of the 16th international conference on Database and Expert Systems Applications
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Delivering relevant video segments (video highlights) to a variety of devices operating under both wired and wireless platforms requires: (a) a mechanism for describing video content, and the environment in which it is to be delivered, and (b) a framework to support physical and semantic adaptation of a video. The aim of the adaptation process is to maximize users' viewing experiences of video quality and minimize resources required to deliver the content. Towards this, we have developed a quality driven adaptation algorithm and implemented a prototype system called DAVE. DAVE minimizes resource requirements by identifying relevant segments within a video and then scales the identified video segment in different dimensions so that it can be delivered within the available network bandwidth, and played back by the client device. Simultaneously, it maximizes the video quality by identifying and tuning relationships between perceptual quality parameters such as frame rate and frame size, with network and device resources such as bit rate, to maximize user-viewing experience. In this paper, we present a quality-driven adaptation algorithm and its implementation through the DAVE system.