Lossless region-based multispectral image compression

Abstract

We present a lossless coding scheme for multispectral images. The algorithm differs from classical lossless approaches of multispectral image coding in the fact that it is based on an independent coding of spectrally homogeneous regions. Regions that present a common multispectral signature are segmented. Then, spectral prediction is performed within these regions and finally spatial prediction removes the remaining correlation in the error images. This spatial prediction is also performed inside the regions by a region growing prediction algorithm that exploits the spatial correlation within region boundaries. The motivations of using a region-based approach are twofold: (i) to achieve better coding performance by adaptively exploiting spectral redundancies and (ii) to introduce region scalability functionality to multispectral coding, where regions of interest are coded differently according to user preferences. Possible multispectral segmentations are discussed and an ordering of the spectral bands before spectral prediction is proposed. Spectral prediction is explained, a clustering method that optimizes this prediction is presented, the spatial decorrelation step is discussed, and finally, simulation results are given.