Lossless compression of multispectral images based on a bidirectional spectral prediction

When multispectral images are being losslessly compressed, if the inter-band correlation of the data is weak, as it usually occurs for data with few and sparse spectral bands, a 3D prediction may lead to negligible coding benefits. In this case, advantage may be taken from a bidirectional spectral prediction, in which once the (k - 1)st band is available, first the kth band is skipped and the (k + 1)st band is predicted from the (k - 1)st one; then, both these two bands are used to predict the kth band in a spatially causal but spectrally noncausal fashion. Starting from an extremely sophisticated and effective scheme for multispectral prediction based on fuzzy-logic concepts, the causal and noncausal 3D prediction strategies are compared and discussed varying with the spectral correlations of the data. Experiments on Landsat TM data show that a certain gain in bit rate can be obtained at no additional cost, by simply devising a scan order in which some bands are preliminarily skipped and then bidirectionally predicted.