Color image coding using block truncation and vector quantization

摘要

In this paper, we describe an adaptive coding method for color images of natural scenes. It is based on the block truncation coding (BTC) and vector quantization (VQ) methods which attempt to retain important visual characteristics of an image without discarding any important details. The proposed algorithm is an iterative procedure developed by extending the within group variance and the information distance measurements to color images. It attempts to minimize one of these two measurements within m by m local windows so that the selected criterion results in the best compression rate for a given color image. This adaptive operation of the algorithm makes it particularly suitable for unsupervised parallel implementation. Once the window size is determined for an input image, then subimages within such windows are divided into two color classes using least- mean square (LMS) algorithm. Each color cluster within a window is represented by its mean color vector. A linear vector quantizer is then used to further compress the coded outputs of local windows to achieve the lowest compression rate for the input image. This results in lower bit rates (as low as 1.0 bit per pixel for the R, G, B color images used in the experiments) and reconstruction errors (as low as 7.0%) with some perceivable errors.