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.

[1]  Andreas Girgensohn,et al.  Time-Constrained Keyframe Selection Technique , 2004, Multimedia Tools and Applications.

[2]  Edward J. Delp,et al.  Automated video summarization using speech transcripts , 2001, IS&T/SPIE Electronic Imaging.

[3]  Ajay Divakaran,et al.  Constant pace skimming and temporal sub-sampling of video using motion activity , 2001, Proceedings 2001 International Conference on Image Processing (Cat. No.01CH37205).

[4]  Guoliang Fan,et al.  Key-frame extraction for object-based video segmentation , 2005, Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005..

[5]  Stephen W. Smoliar,et al.  An integrated system for content-based video retrieval and browsing , 1997, Pattern Recognit..

[6]  Jesús Bescós,et al.  An Engine for Content-Aware On-Line Video Adaptation , 2006, SAMT.

[7]  SangKeun Lee,et al.  A fast clustering algorithm for video abstraction , 2003, Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429).

[8]  Jesús Bescós,et al.  Real-time shot change detection over online MPEG-2 video , 2004, IEEE Transactions on Circuits and Systems for Video Technology.

[9]  Stefanos D. Kollias,et al.  A stochastic framework for optimal key frame extraction from MPEG video databases , 1999, 1999 IEEE Third Workshop on Multimedia Signal Processing (Cat. No.99TH8451).

[10]  Janko Calic,et al.  Efficient key-frame extraction and video analysis , 2002, Proceedings. International Conference on Information Technology: Coding and Computing.

[11]  Guoliang Fan,et al.  Joint Key-Frame Extraction and Object-Based Video Segmentation , 2005, 2005 Seventh IEEE Workshops on Applications of Computer Vision (WACV/MOTION'05) - Volume 1.

[12]  Ajay Divakaran,et al.  Framework for measurement of the intensity of motion activity of video segments , 2004, J. Vis. Commun. Image Represent..

[13]  Daniel DeMenthon,et al.  Automatic Performance Evaluation for Video Summarization , 2004 .

[14]  Aggelos K. Katsaggelos,et al.  Rate-distortion optimal video summary generation , 2005, IEEE Transactions on Image Processing.

[15]  Jesús Bescós,et al.  Camera Motion Analysis in On-line MPEG Sequences , 2007, Eighth International Workshop on Image Analysis for Multimedia Interactive Services (WIAMIS '07).

[16]  Christian Timmerer,et al.  Bitstream syntax description-based adaptation in streaming and constrained environments , 2005, IEEE Transactions on Multimedia.

[17]  Lie Lu,et al.  A generic framework of user attention model and its application in video summarization , 2005, IEEE Trans. Multim..

[18]  Anthony Vetro,et al.  Video transcoding architectures and techniques: an overview , 2003, IEEE Signal Process. Mag..

[19]  Jenq-Neng Hwang,et al.  An integrated scheme for object-based video abstraction , 2000, ACM Multimedia.

[20]  Alan Hanjalic,et al.  An integrated scheme for automated video abstraction based on unsupervised cluster-validity analysis , 1999, IEEE Trans. Circuits Syst. Video Technol..

[21]  Wayne H. Wolf,et al.  Key frame selection by motion analysis , 1996, 1996 IEEE International Conference on Acoustics, Speech, and Signal Processing Conference Proceedings.