A sparsity-distortion-optimized multiscale representation of geometry

13 years 2 months ago

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This paper describes the construction of a new multiresolutional decomposition with applications to image compression. The proposed method designs sparsity-distortion-optimized orthonormal transforms applied in wavelet domain to arrive at a multiresolutional representation which we term the Sparse Multiresolutional Transform (SMT). Our optimization operates over sub-bands of given orientation and exploits the inter-scale and intra-scale dependencies of wavelet coefficients over image singularities. The resulting SMT is substantially sparser than the wavelet transform and leads to compaction that can be exploited by well-known coefficient coders. Our construction deviates from the literature, which mainly focuses on model-based methods, by offering a data-driven optimization of wavelet representations. Simulation experiments show that the proposed method consistently offers better performance compared to the original waveletrepresentation and can reach up to 1dB improvements within sta...

Osman Gokhan Sezer, Yucel Altunbasak, Onur G. Gule

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