Divergence Parametric Smoothing in Image Compression Algorithms

Abstract

The paper elaborates on methods of digital image compression. The focus is on the compression method that represents a raster image as a set of multiply thinned sub-images. Sub-images are processed consecutively to generate special reference images. The difference between the synthesized reference image and original sub-image forms a divergence array. The algorithm introduces a discrete error into the divergence array to provide the actual bit-depth reduction. However, the introduction of the error inevitably impairs the quality of the decompressed image. The aim is to make sure that the parametric smoothing of divergence arrays can lessen this quality impairment without changing the bit depth reduction originally provided by the method. Numerical experiments on real digital images are carried out to prove that the use of parametric smoothing improves noticeably the efficiency of the image compression method under discussion.