Compression of SAR and ultrasound imagery using texture models

Abstract

Summary form only given. This paper addresses an approach for handling SAR and US images with different statistical properties. The approach is based on a image-structure/speckle-texture decomposition. The image model in this case views an image X(i,j) as the combination of two components: an image structure S(i,j) and a speckle texture T(i,j). An octave-band subband decomposition is performed on the data and the structure is separated from the speckle by applying soft-thresholding to the high frequency subband coefficients. The coefficients remaining after the operation are used to synthesize S(i,j) while the complement set of coefficients is a representation of T(i,j). Once the two components are obtained, they are coded separately. S(i,j) has a low frequency characteristic similar to natural images and is suitable for conventional compression techniques. In the proposed algorithm we use a quadtree coder for S(i,j). The speckle component is parametrized using a texture model. Two texture models have been tested: a 2D-AR model and the pyramid-based algorithm proposed by Heeger and Bergen. For the latter, a compact parametrization of the texture is achieved by modeling the histograms of T(i,j) and its pyramid subbands as generalized Gaussians. The synthesized speckle is visually similar to the original for both models. The image is reconstructed by adding together the decoded structure and the synthesized speckle. The subjective quality gains obtained from the proposed approach are evident. We performed a subjective test, which followed the CCIR recommendation 500-4 for image quality assessment. Several codecs were included in the tests.