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[Other resource] ychen_jasp_software DL : 0: 数字水印中Optimal Quincunx Filter Banks的设计源代码及说明，在matlab环境下实现-Digital Watermarking Optimal Quincunx Filter Banks behalf of the design source code and explanations of the environment under Matlab
Update : 2008-10-13 Size : 51.9kb Publisher : 李
[Wavelet] ychen_jasp_software DL : 0: 数字水印中Optimal Quincunx Filter Banks的设计源代码及说明，在matlab环境下实现-Digital Watermarking Optimal Quincunx Filter Banks behalf of the design source code and explanations of the environment under Matlab
Update : 2025-04-04 Size : 52kb Publisher : 李
[source in ebook] ychen_jasp_software DL : 0: MATLAB Code for Optimal Quincunx Filter Bank Design Yi Chen July 17, 2006 This file introduces the MATLAB code that implements the two algorithms (i.e., Algorithms 1 and 2 in [1], or Algorithms 4.1 and 4.2 in [2]) used for the construction of quincunx filter banks with perfect reconstruction, linear phase, high coding gain, certain vanishing moments properties, and good frequency selectivity. The code can be used to design quincunx filter banks with two, three, or four lifting steps. The SeDuMi Matlab toolbox [3] is used to solve the second-order cone programming subproblems in the two algorithms, and must be installed in order for this code to work.
Update : 2025-04-04 Size : 53kb Publisher : lzn
[Wavelet] Atoolbox DL : 0: A collection of functions is presented which includes 2nd generation wavelet decomposition and reconstruction tools for images as well as functions for the computation of moment invariants. The wavelet schemes rely on the lifting scheme of Sweldens. Rectangular grids are split into quincunx grids, also known as red-black ordering. The prediction filters include Neville filters as well as a few nonlinear ones fairly capable of preserving local maxima or minima. The decomposition and reconstruction functions are called in the style of the Matlab Wavelet Toolbox. Many small and a few elaborate examples have been included, ranging from the computation of moment invariants to multiresolution image fusion. Please see Contents.m for an exhaustive list. -A collection of functions is presented which includes 2nd generation wavelet decomposition and reconstruction tools for images as well as functions for the computation of moment invariants. The wavelet schemes rely on the lifting scheme of Sweldens. Rectangular grids are split into quincunx grids, also known as red-black ordering. The prediction filters include Neville filters as well as a few nonlinear ones fairly capable of preserving local maxima or minima. The decomposition and reconstruction functions are called in the style of the Matlab Wavelet Toolbox. Many small and a few elaborate examples have been included, ranging from the computation of moment invariants to multiresolution image fusion. Please see Contents.m for an exhaustive list.
Update : 2025-04-04 Size : 579kb Publisher : yuan
[matlab] Quincunx_wavelet_transforms DL : 0: Quincunx wavelet transforms Quincunx wavelets are non-separable transform that allows to avoid using vertical/horizontal wavelets. The scaling grows like 2^{j/2} with the scale j instead of 2^j, which can be advantageous. Biorthogonal quincunx wavelets are implemented using a simple wrapper to the code of Dimitri Van De Ville. Redundant, translation invariant transform are implemented with the lifting scheme.
Update : 2025-04-04 Size : 172kb Publisher : Swati
[Program doc] lossless-image-compression-based-on-optimal.pdf.z DL : 0: The optimal predictors of a lifting scheme in the general n-dimensional case are obtained and applied for the lossless compression of still images using rst quincunx sampling and then simple row-column sampling. In each case, the e ciency of the linear predictors is enhanced nonlinearly. Directional post- processing is used in the quincunx case, and adaptive-length postprocessing in the row-column case. Both methods are seen to perform well. The resulting nonlinear interpolation schemes achieve extremely e cient image decorrelation. We further investigate context modeling and adaptive arithmetic coding of wavelet coe cients in a lossless compression framework. Special attention is given to the modeling contexts and the adaptation of the arithmetic coder to the actual data. Experimental evaluation shows that the best of the resulting coders produces better results than other known algorithms for multiresolution-based lossless image coding. - The optimal predictors of a lifting scheme in the general n-dimensional case are obtained and applied for the lossless compression of still images using rst quincunx sampling and then simple row-column sampling. In each case, the e ciency of the linear predictors is enhanced nonlinearly. Directional post- processing is used in the quincunx case, and adaptive-length postprocessing in the row-column case. Both methods are seen to perform well. The resulting nonlinear interpolation schemes achieve extremely e cient image decorrelation. We further investigate context modeling and adaptive arithmetic coding of wavelet coe cients in a lossless compression framework. Special attention is given to the modeling contexts and the adaptation of the arithmetic coder to the actual data. Experimental evaluation shows that the best of the resulting coders produces better results than other known algorithms for multiresolution-based lossless image coding.
Update : 2025-04-04 Size : 262kb Publisher : dee
[Wavelet] quincunx DL : 0: quincunx wavelet transformation
Update : 2025-04-04 Size : 503kb Publisher : fam