Sparsity-based optimization of two lifting-based wavelet transforms for semi-regular mesh compression

Abstract : This paper describes how to optimize two popular wavelet transforms for semi-regular meshes, using a lifting scheme. The objective is to adapt multiresolution analysis to the input mesh to improve its subsequent coding. Considering either the Butterfly- or the Loop-based lifting schemes, our algorithm finds at each resolution level an optimal prediction operator P such that it minimizes the L1 norm of the wavelet coefficients. The update operator U is then recomputed in order to take into account the modifications to P. Experimental results show that our algorithm improves on state-of-the-art wavelet coders.
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Aymen Kammoun, Frédéric Payan, Marc Antonini. Sparsity-based optimization of two lifting-based wavelet transforms for semi-regular mesh compression. Computers and Graphics, Elsevier, 2012, 36 (4), pp.272-282. ⟨10.1016/j.cag.2012.02.004⟩. ⟨hal-00687445⟩

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