Progressive Distributed Coding of Multispectral Images

We present in this paper a novel distributed coding scheme for lossless and progressive compression of multispectral images. The main strategy of this new scheme is to remove data redundancies at the decoder in order to design a lightweight yet very efficient encoder suitable for onboard applications during the acquisition of multispectral image. A sequence of increasing resolution layers is encoded and transmitted successively until the original image can be losslessly reconstructed from all layers. We assume that the decoder with abundant resources is able to perform adaptive region-based predictor estimation to capture spatially varying spectral correlation with the knowledge of lower-resolution layers, thus generate high quality side information for decoding the higher-resolution layer. Progressive transmission enables the spectral correlation to be refined successively, resulting in gradually improved decoding performance of higher-resolution layers as more data are decoded. Simulations have been carried out to demonstrate that the proposed scheme, with innovative combination of low complexity encoding, lossless compression and progressive coding, can achieve competitive performance comparing with the state-of-the-art 3-D DPCM technique.