Design and Implementation of New Computation Method for Multiwavelets Decomposition as 1st and 2nd Approximations

One of the important differences between multiwavelets and scalar wavelets is that each channel in the filter bank has a vector-valued output as scalar-valued input signal must somehow be converted into a suitable vector-valued signal .This conversion is called preprocessing[1]. Preprocessing is mapping process which is done by a prefilter. A postfilter just does the opposite. The most obvious way to get two input rows from a given signal to repeat the signal [2]. Two rows go to into the rnultifilter bank. This procedure is called Repeated row which introduce over sampling of data by a factor of 2(2). Two rows go into the multifilter bank. For data compression , where one is trying to find compact transform representation for a dataset[3], it is imperative to find critically sampled multiwavelet transform schemes which this paper focus on finding a simple and easy to follow algorithm for its computation. One famous multiwavelet filter used here is the GHM filter proposed by Geronimo. The GHM basis offers a combination of orthogonally, symmetry, and compact support, which can not be achieved by any scalar wavelet basis [4]. Using a computer program for the proposed method, an example test on tiger image is verified which shows image properties after a single level decomposition and the reconstructed image after reconstruction