Wavelet Based Contourlet Transform for Image Compression

Wavelet transforms are not capable of reconstructing curved images perfectly, hence we go for this new concept, called Contourlet Transform. It is a multiresolution and directional decomposition of a signal using a combination of Laplacian Pyramid (LP) and a Directional Filter Bank (DFB). The Contourlet transform has good approximation properties for smooth 2D functions and finds a direct discrete-space construction, and is therefore computationally efficient. Recently introduced the wavelet-based contourlet transform (WBCT), it is a non-redundant version of the contourlet transform, and appropriately used this transform for image coding. In this work, we start with a discretedomain construction and then investigate its convergence to an expansion in the continuous-domain. This construction results in a flexible multiresolution, local, and directional image expansion using contour segments, and thus it is named the contourlet transform. Furthermore, a precise link between the developed filter bank and the associated continuous – domain contourlet expansion via a directional multiresolution analysis framework is established. Our simulation results also show that this new coding approach is competitive to the wavelet coder and is visually superior to the wavelet coder for the mentioned images. The original Contourlet Transform has high redundancy, in order to reduce that we have used wavelet transform followed by Directional Filter Banks