FEATURE PREDICTIVE VECTOR QUANTIZATION OF MULTISPECTRAL IMAGES

Multispectral satellite images of the Earth consist of sets of images obtained by sensing electromagnetic radiation in different spectral bands for each geographical region. A new compression method for such data sets is proposed where a small subset of image bands is initially vector quantized. The remaining bands are predicted from the quantized images. Two different types of predictors are examined, an affine predictor and a new nonlinear predictor. The residual (error) images are encoded at a second stage based on the magnitude of the errors. This scheme simultaneously exploits both spatial and spectral correlation inherent in multispectral images. Simulation results on an image set from the Thematic Mapper with seven spectral bands provide a comparison of the affine predictor with the nonlinear predictor. It is shown that the nonlinear predictor provides significantly improved performance compared to the affine predictor. Image compression ratios between 15 and 25 are achieved with remarkably good image quality.