A TRUNCATION METHOD FOR COMPUTING SLANT TRANSFORMS WITH APPLICATIONS TO IMAGE-PROCESSING

A truncation method for computing the Slant Transform is presented, Th e Slant Transform Truncation (STT) algorithm uses the divide and conqu er principle of hierarchical data structures to factorize coherent ima ge data into sparse subregions, Tn one dimension with a data array of size N = 2(n), the Truncation method takes time between O(N) and O(Nlo g(2) N), degenerating to the performance of the Fast Slant Transform ( FST) method in its worst case. In two dimensions, for a data array of size N x N, the one-dimensional Truncation method is applied to each r ow, then to each column of the array, to compute the transform in time between O(N-2) and O(N-2 log(2) N). Coherence is a fundamental charac teristic of digital images and so the Truncation method is superior to the FST method when computing Slant Transforms of digital images, Exp erimental results are presented to justify this assertion,