A sparsity-distortion-optimized multiscale representation of geometry

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...

• Type: Journal
• Year: 2010
• Where: ICIP
• Authors: Osman Gokhan Sezer, Yucel Altunbasak, Onur G. Guleryuz
• Keywords: Coefficient Coders, ICIP 2010, Image Processing, Wavelet, Well-known Coefficient Coders