Visual Information Representation Using Embedded Block with Significance Selecting Model

Abstract

In this paper, we propose a new scheme based on contourlet to represent the visual information from the aspect of magnitude and orientation. Based on the detail statistics analysis of the individual, joint behaviors and correlations of contourlet coefficients of natural images across scales, positions and directions, it reveals strong local dependencies and clustering when the coefficients are at low amplitude. According to these fundamental findings, a novel embedded block with significance selecting model is developed to present the transformed coefficients. Experimental results demonstrate that our proposed representation is efficient. It is comparable to the wavelet coder in terms of the PSNR metric, and visually superior to the wavelet coder for the images with detailed texture, which is more fit for the Human Visual System.