Distributed Source Coding Techniques for Lossless Compression of Hyperspectral Images
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Research Article Distributed Source Coding Techniques for Lossless Compression of Hyperspectral Images Enrico Magli,1 Mauro Barni,2 Andrea Abrardo,2 and Marco Grangetto1 1 Center
for Multimedia Radio Communications (CERCOM), Department of Electronics, Politecnico di Torino, 24 Corso Duca degli Abruzzi, 10129 Torino, Italy 2 Dipartimento di Ingegneria dell’Informazione, Universit` a di Siena, Via Roma 56, 59100 Siena, Italy Received 10 February 2006; Revised 18 October 2006; Accepted 23 October 2006 Recommended by Yap-Peng Tan This paper deals with the application of distributed source coding (DSC) theory to remote sensing image compression. Although DSC exhibits a significant potential in many application fields, up till now the results obtained on real signals fall short of the theoretical bounds, and often impose additional system-level constraints. The objective of this paper is to assess the potential of DSC for lossless image compression carried out onboard a remote platform. We first provide a brief overview of DSC of correlated information sources. We then focus on onboard lossless image compression, and apply DSC techniques in order to reduce the complexity of the onboard encoder, at the expense of the decoder’s, by exploiting the correlation of different bands of a hyperspectral dataset. Specifically, we propose two different compression schemes, one based on powerful binary error-correcting codes employed as source codes, and one based on simpler multilevel coset codes. The performance of both schemes is evaluated on a few AVIRIS scenes, and is compared with other state-of-the-art 2D and 3D coders. Both schemes turn out to achieve competitive compression performance, and one of them also has reduced complexity. Based on these results, we highlight the main issues that are still to be solved to further improve the performance of DSC-based remote sensing systems. Copyright © 2007 Enrico Magli et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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INTRODUCTION
In recent years, distributed source coding (DSC) has received an increasing attention from the signal processing community as a new paradigm to code statistically dependent sources [1, 2]. DSC considers a situation in which two or more statistically dependent data sources must be encoded by two separate encoders that are not allowed to talk to each other, that is, each encoder sees only the output of one of the two sources; in the following we will use the terms “dependent” and “correlated” interchangeably. Data sources must be encoded by two separate encoders that are not allowed to talk to each other, that is, each encoder sees only the output of one of the two sources. Following the standard encoding paradigm, each source can be compressed losslessly, with a total rate no less than the sum of the two source entropies. This is clearly less efficient than an encoder that jointly compr
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