JPEG 2000: Image Compression Fundamentals, Standards and Practice
JPEG 2000: Image Compression Fundamentals, Standards and Practice
JPEG Still Image Data Compression Standard
JPEG Still Image Data Compression Standard
An Efficient k-Means Clustering Algorithm: Analysis and Implementation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient wavelet-based predictive Slepian-Wolf coding for hyperspectral imagery
Signal Processing - Special section: Distributed source coding
Distributed source coding techniques for lossless compression of hyperspectral images
EURASIP Journal on Applied Signal Processing
Region-Based Transform Coding of Multispectral Images
IEEE Transactions on Image Processing
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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 explore data redundancies at the decoder in order to design a lightweight yet very efficient encoder suitable for onboard applications during 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 high complexity state-of-the-art 3-D DPCM technique.