5th International Mobile Multimedia Communications Conference

Research Article

Progressive Distributed Coding of Multispectral Images

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  • @INPROCEEDINGS{10.4108/ICST.MOBIMEDIA2009.7843,
        author={Jinrong  Zhang and Houqiang  Li and Chang Wen  Chen},
        title={Progressive Distributed Coding of Multispectral Images},
        proceedings={5th International Mobile Multimedia Communications Conference},
        publisher={ICST},
        proceedings_a={MOBIMEDIA},
        year={2010},
        month={5},
        keywords={Distributed coding progressive coding lossless compression multispectral images low complexity encoding},
        doi={10.4108/ICST.MOBIMEDIA2009.7843}
    }
    
  • Jinrong Zhang
    Houqiang Li
    Chang Wen Chen
    Year: 2010
    Progressive Distributed Coding of Multispectral Images
    MOBIMEDIA
    ICST
    DOI: 10.4108/ICST.MOBIMEDIA2009.7843
Jinrong Zhang1,*, Houqiang Li1,*, Chang Wen Chen2,*
  • 1: Univ. of Science and Tech. of China, Hefei, 230027, P. R. China. +86-551-3601340
  • 2: State Univ. of New York at Buffalo, Buffalo, NY, 14260, USA. +1-716-645-3180
*Contact email: jinrong@mail.ustc.edu.cn, lihq@ustc.edu.cn, chencw@buffalo.edu

Abstract

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.