A model-based approach to multispectral image coding

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Abstract
A theory and specific methods for performing optimal transform coding of multispectral images are developed. The theory is based on the assumption that a multispectral image may be modeled as a set of jointly stationary Gaussian random processes. Therefore, the methods may be applied to any multilayer data set which meets this assumption. It is demonstrated that a coding scheme consisting of a frequency transform within each layer followed by a separate KL (Karhunen-Loeve) transform across the layers at each spatial frequency is asymptotically optimal as the block size becomes large. Two simplifications of this method are also asymptotically optimal if the data can be assumed to satisfy additional constraints. The proposed coding techniques are then implemented using subband filtering methods, and the various algorithms are tested on multispectral images to determine their relative performance characteristics. For the real multispectral images tested, the RSM (real subbands with multiple KL transforms) algorithm gives the best coding performance, with a computational cost only slightly higher than that of the RSS (real subbands with single KL transform) method.

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