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Mathematics

Mathematics

Journal of Applied & Computational Mathematics

Author(s): A Secker, D Taubman

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This paper proposes a new framework for the construction of motion compensated wavelet transforms, with application to efficient highly scalable video compression. Motion compensated transform techniques, as distinct from motion compensated predictive coding, represent a key tool in the development of highly scalable video compression algorithms. The proposed framework overcomes a variety of limitations exhibited by existing approaches. This new method overcomes the failure of frame warping techniques to preserve perfect reconstruction when tracking complex scene motion. It also overcomes some of the limitations of block displacement methods. Specifically, the lifting framework allows the transform to exploit inter-frame redundancy without any dependence on the model selected for estimating and representing motion. A preliminary implementation of the proposed approach was tested in the context of a scalable video compression system, yielding PSNR performance competitive with other results reported in the literature.

This article was published in Image Processing and referenced in Journal of Applied & Computational Mathematics

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