High fidelity adaptive vector quantization at very low bit rates for progressive transmission of radiographic images

Abstract

An adaptive vector quantizer (VQ) using a clustering technique known as adaptive fuzzy leader clustering (AFLC) that is similar in concept to deterministic annealing (DA) for VQ codebook design has been developed. This vector quantizer, AFLC-VQ, has been designed to vector quantize wavelet decomposed sub images with optimal bit allocation. The high-resolution sub images at each level have been statistically analyzed to conform to generalized Gaussian probability distributions by selecting the optimal number of filter taps. The adaptive characteristics of AFLC-VQ result from AFLC, an algorithm that uses self-organizing neural networks with fuzzy membership values of the input samples for upgrading the cluster centroids based on well known optimization criteria. By gen- erating codebooks containing codewords of varying bits, AFLC-VQ is capable of compressing large color/monochrome medical images at extremely low bit rates (0.1 bpp and less) and yet yielding high fidelity reconstructed images. The quality of the reconstructed im- ages formed by AFLC-VQ has been compared with JPEG and EZW, the standard and the well known wavelet based compression tech- nique (using scalar quantization), respectively, in terms of statistical performance criteria as well as visual perception. AFLC-VQ exhibits much better performance than the above techniques. JPEG and EZW were chosen as comparative benchmarks since these have been used in radiographic image compression. The superior perfor- mance of AFLC-VQ over LBG-VQ has been reported in earlier pa- pers. © 1999 SPIE and IS&T. (S1017-9909(99)01301-X)