Compression of multispectral images by address-predictive vector quantization

Abstract Multispectral images are formed by a large number of component images of a single subject taken in different spectral windows. They are often represented by tens or even hundreds of Mbits of data and huge resources are required to transmit and store them, making some form of data compression necessary. To obtain a high compression efficiency, exploiting both the spatial and the spectral dependency, we propose two coding schemes based on vector quantization and address prediction, one more suited to the case of strong spectral dependence, and the other preferable in the case of strong spatial dependence. The performances of the proposed techniques are assessed by means of numerical experiments and compared to those of other techniques known in the literature. It turns out that for compression ratios on the order of 25:1 the reconstructed images are almost undistinguishable from the original ones, and that a good image quality is still achieved for ratios as high as 40:1.