Abstract
Recently the performance of nonlinear transforms have been given a lot of attention to overcome the suboptimal n-terms approximation power of tensor product wavelet methods on higher dimensions. The suboptimal performance prevails when the latter are used for a sparse representation of functions consisting of smoothly varying areas separated by smooth contours. This paper introduces a method creating normal meshes with nonsubdivision connectivity to approximate the nonsmoothness of such images efficiently. From a domain decomposition viewpoint, the method is a triangulation refinement method preserving contours. The transform is nonlinear as it depends on the actual image. This paper proposes an normal offset based compression algorithm for digital images. The discretisation causes the transform to become redundant. We further propose a model to encode the obtained coefficients. We show promising rate distortion curves and compare the results with the JPEG2000 encoder.
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