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This paper introduces two new orthogonal multiwavelets with supercompact support, constructed using Fast Bauer's method for matrix spectral factorization. The new filters exhibit orthogonality and improved coding and smoothness compared to existing supercompact multiwavelets. Comparative analyses demonstrate that these multiwavelets outperform traditional filters like GHM and SA4 in image compression and denoising, achieving superior human visual measures and SSIM metrics.
New orthogonal multiwavelets outperform traditional filters in image compression and denoising, achieving superior visual quality metrics.
The paper considers the construction of two new orthogonal multiwavelets with supercompact support by using the Fast Bauer's method for matrix spectral factorization on the matrix product filter of the orthogonal CL multiwavelet filter. The new multiwavelets possess orthogonality, symmetry/antisymmetry, and one of them provides better coding and smoothness than other supercompact multiwavelets. The performance of the new multiwavelet filters in subband-based edge detection, grayscale and color image compression and 1D and 2D signal denoising is compared with the GHM, SA4, CL, Integer Haar and Alpert multifilters. The comparative analysis shows that new multiwavelets can provides better human visual measures, SSIM and MS-SSIM in image compression and denoising applications.
