Progressive vector quantization of multispectral image data using a massively parallel SIMD machine

Abstract

Progressive transmission (PT) using vector quantization (VQ) is called progressive vector quantization (PVQ) and is used for efficient telebrowsing and dissemination of multispectral image data via computer networks. Theoretically any compression technique can be used in PT mode. Here VQ is selected as the baseline compression technique because the VQ encoded images can be decoded by simple table lookup process so that the users are not burdened with computational problems for using compressed data. Codebook generation or training phase is the most critical part of VQ. Two different algorithms have been used for this purpose. The first of these is based on well-known Linde-Buzo-Gray (LBG) algorithm. The other one is based on self organizing feature maps (SOFM). Since both training and encoding are computationally intensive tasks, the authors have used MasPar, a SIMD machine for this purpose. The multispectral imagery obtained from Advanced Very High Resolution Radiometer (AVHRR) instrument images form the testbed. The results from these two VQ techniques have been compared in compression ratios for a given mean squared error (MSE). The number of bytes required to transmit the image data without loss using this progressive compression technique is usually less than the number of bytes required by standard unix compress algorithm. >