A feature-based algorithm for detecting and classifying scene breaks
Proceedings of the third ACM international conference on Multimedia
Image coding by fitting RBF-surfaces to subimages
Pattern Recognition Letters
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Learning Layered Motion Segmentation of Video
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
A Scene Change Detection Scheme Using Local x^2-Test on Telematics
ICHIT '06 Proceedings of the 2006 International Conference on Hybrid Information Technology - Volume 01
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Parametric coding is a technique in which data is processed to extract meaningful information and then representing it compactly using appropriate parameters. Parametric Coding exploits redundancy in information to provide a very compact representation and thus achieves very high compression ratios. However, this is achieved at the cost of higher computation complexity. This disadvantage is now being offset by the availability of high speed processors, thus making it possible to exploit the high compression ratios of the parametric video coding techniques. In this paper a novel idea for efficient parametric representation of video is proposed. We perform Oct-Tree Decomposition on a video stack, followed by parameter extraction using Radial Basis Function Networks (RBFN) to achieve exceptionally high compression ratios, even higher than the state of art H.264 codec. The proposed technique exploits spatial-temporal redundancy and therefore inherently achieves multiframe prediction.