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

机译:半稀疏网格压缩的两个基于提升的小波变换的基于稀疏性的优化

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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 I, -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.
机译:本文介绍了如何使用提升方案针对半规则网格优化两种流行的小波变换。目的是使多分辨率分析适合输入网格,以改善其后续编码。考虑到基于Butterfly或Loop的提升方案,我们的算法会在每个分辨率级别上找到最佳预测算子P,以使其最小化小波系数的I,norm。然后,为了考虑对P的修改,重新计算更新运算符U。实验结果表明,我们的算法在最新的小波编码器上进行了改进。

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