Lossless and near-lossless compression of hyperspectral images based on distributed source coding

This paper addresses the problem of the lossless and near-lossless compression of hyperspectral images and presents two efficient algorithms based on distributed source coding, which perform the lossless compression by means of multilevel scalar codes. The proposed algorithms are implemented on the...

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Bibliographic Details
Published in:Journal of visual communication and image representation Vol. 28; pp. 113 - 119
Main Authors: Nian, Yongjian, He, Mi, Wan, Jianwei
Format: Journal Article
Language:English
Published: Elsevier Inc 01.04.2015
Subjects:
Distributed source coding
Error resilience
Hyperspectral images
Linear prediction
Lossless compression
Low complexity
Near-lossless compression
Peak error
Hyperspectral images
Near-lossless compression
Linear prediction
Lossless compression
Peak error
Low complexity
Error resilience
Distributed source coding
ISSN:1047-3203, 1095-9076
Online Access:Get full text
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Summary:This paper addresses the problem of the lossless and near-lossless compression of hyperspectral images and presents two efficient algorithms based on distributed source coding, which perform the lossless compression by means of multilevel scalar codes. The proposed algorithms are implemented on the co-located blocks in the spectral orientation. A novel multiband spectral predictor is proposed to construct the side information of each block. The back-up side information is introduced for the second algorithm to recover the images when the original side information is corrupted by errors. The encoder only requires the transmission of the least significant bit (LSB) bit-planes to the decoder, and the number of bits is computed by the maximum error between the block and its side information. The proposed algorithms are also extended to near-lossless compression. The experimental results show that the proposed algorithms have a competitive compression performance with the existing distributed compression algorithms. Moreover, the proposed algorithms can provide low complexity and different degrees of error resilience, which is suitable for onboard compression.
ISSN:1047-3203
1095-9076
DOI:10.1016/j.jvcir.2014.06.008