Image coding using generalized optimal subband decomposition and vector quantization

Abstract

This paper presents a novel image coding scheme based on optimal filter design for subband coding followed by vector quantization. A generalized perfect reconstruction subband filter system is proposed in which constraints on filter coefficients have been removed so that optimal filter bank which is more effective for subband coding can be designed. A filter design criteria based on an upper bound on the entropy of the decomposed subimage is proposed. This upper bound is obtained by assuming that the probability density function of the pixel values of the image is Gaussian. An explicit function of this bound in terms of the filter response is derived. The optimal filter is designed by minimization of the bound with respect to the filter response using the steepest descent algorithm. The decomposed subimages are coded using vector quantization with varying bit allocation strategy, followed by lossless coding for further data compression. A significant compression ratio of 42:1 with image quality superior to that by JPEG is achieved. Computer simulation results using real images are presented and compared with other existing algorithms.