Multilevel split regression wavelet analysis for lossless compression of remote sensing data

Spectral redundancy is a key element to be exploited in compression of remote sensing data. Combined with an entropy encoder, it can achieve competitive lossless coding performance. One of the latest techniques to decorrelate the spectral signal is the regression wavelet analysis (RWA). RWA applies...

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Autores: Álvarez Cortés, Sara|||0000-0003-3244-8080, Bartrina-Rapesta, Joan|||0000-0002-1551-3680, Serra-Sagristà, Joan|||0000-0003-4729-9292
Tipo de recurso: artículo
Fecha de publicación:2018
País:España
Institución:Universitat Autònoma de Barcelona
Repositorio:Dipòsit Digital de Documents de la UAB
Idioma:inglés
OAI Identifier:oai:ddd.uab.cat:200081
Acceso en línea:https://ddd.uab.cat/record/200081
https://dx.doi.org/urn:doi:10.1109/LGRS.2018.2850938
Access Level:acceso abierto
Palabra clave:Lossless coding
Predictive coding
Spectral decorrelation
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spelling Multilevel split regression wavelet analysis for lossless compression of remote sensing dataÁlvarez Cortés, Sara|||0000-0003-3244-8080Bartrina-Rapesta, Joan|||0000-0002-1551-3680Serra-Sagristà, Joan|||0000-0003-4729-9292Lossless codingPredictive codingSpectral decorrelationSpectral redundancy is a key element to be exploited in compression of remote sensing data. Combined with an entropy encoder, it can achieve competitive lossless coding performance. One of the latest techniques to decorrelate the spectral signal is the regression wavelet analysis (RWA). RWA applies a wavelet transform in the spectral domain and estimates the detail coeffi- cients through the approximation coefficients using linear regres- sion. RWA was originally coupled with JPEG 2000. This letter introduces a novel coding approach, where RWA is coupled with the predictor of CCSDS-123.0-B-1 standard and a lightweight contextual arithmetic coder. In addition, we also propose a smart strategy to select the number of RWA decomposition levels that maximize the coding performance. Experimental results indicate that, on average, the obtained coding gains vary between 0.1 and 1.35 bits-per-pixel-per-component compared with the other state- of-the-art coding techniques. 22018-01-0120182018-01-01Articlehttp://purl.org/coar/resource_type/c_6501AMhttp://purl.org/coar/version/c_ab4af688f83e57aainfo:eu-repo/semantics/articleapplication/pdfhttps://ddd.uab.cat/record/200081https://dx.doi.org/urn:doi:10.1109/LGRS.2018.2850938reponame:Dipòsit Digital de Documents de la UABinstname:Universitat Autònoma de BarcelonaInglésengMinisterio de Economía y Competitividad https://doi.org/10.13039/501100003329 TIN2015-71126-RAgència de Gestió d'Ajuts Universitaris i de Recerca https://doi.org/10.13039/501100003030 2017/SGR-463open accesshttp://purl.org/coar/access_right/c_abf2Aquest material està protegit per drets d'autor i/o drets afins. Podeu utilitzar aquest material en funció del que permet la legislació de drets d'autor i drets afins d'aplicació al vostre cas. Per a d'altres usos heu d'obtenir permís del(s) titular(s) de drets.https://rightsstatements.org/vocab/InC/1.0/info:eu-repo/semantics/openAccessoai:ddd.uab.cat:2000812026-06-06T12:50:31Z
dc.title.none.fl_str_mv Multilevel split regression wavelet analysis for lossless compression of remote sensing data
title Multilevel split regression wavelet analysis for lossless compression of remote sensing data
spellingShingle Multilevel split regression wavelet analysis for lossless compression of remote sensing data
Álvarez Cortés, Sara|||0000-0003-3244-8080
Lossless coding
Predictive coding
Spectral decorrelation
title_short Multilevel split regression wavelet analysis for lossless compression of remote sensing data
title_full Multilevel split regression wavelet analysis for lossless compression of remote sensing data
title_fullStr Multilevel split regression wavelet analysis for lossless compression of remote sensing data
title_full_unstemmed Multilevel split regression wavelet analysis for lossless compression of remote sensing data
title_sort Multilevel split regression wavelet analysis for lossless compression of remote sensing data
dc.creator.none.fl_str_mv Álvarez Cortés, Sara|||0000-0003-3244-8080
Bartrina-Rapesta, Joan|||0000-0002-1551-3680
Serra-Sagristà, Joan|||0000-0003-4729-9292
author Álvarez Cortés, Sara|||0000-0003-3244-8080
author_facet Álvarez Cortés, Sara|||0000-0003-3244-8080
Bartrina-Rapesta, Joan|||0000-0002-1551-3680
Serra-Sagristà, Joan|||0000-0003-4729-9292
author_role author
author2 Bartrina-Rapesta, Joan|||0000-0002-1551-3680
Serra-Sagristà, Joan|||0000-0003-4729-9292
author2_role author
author
dc.subject.none.fl_str_mv Lossless coding
Predictive coding
Spectral decorrelation
topic Lossless coding
Predictive coding
Spectral decorrelation
description Spectral redundancy is a key element to be exploited in compression of remote sensing data. Combined with an entropy encoder, it can achieve competitive lossless coding performance. One of the latest techniques to decorrelate the spectral signal is the regression wavelet analysis (RWA). RWA applies a wavelet transform in the spectral domain and estimates the detail coeffi- cients through the approximation coefficients using linear regres- sion. RWA was originally coupled with JPEG 2000. This letter introduces a novel coding approach, where RWA is coupled with the predictor of CCSDS-123.0-B-1 standard and a lightweight contextual arithmetic coder. In addition, we also propose a smart strategy to select the number of RWA decomposition levels that maximize the coding performance. Experimental results indicate that, on average, the obtained coding gains vary between 0.1 and 1.35 bits-per-pixel-per-component compared with the other state- of-the-art coding techniques.
publishDate 2018
dc.date.none.fl_str_mv 2
2018-01-01
2018
2018-01-01
dc.type.none.fl_str_mv Article
http://purl.org/coar/resource_type/c_6501
AM
http://purl.org/coar/version/c_ab4af688f83e57aa
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
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dc.identifier.none.fl_str_mv https://ddd.uab.cat/record/200081
https://dx.doi.org/urn:doi:10.1109/LGRS.2018.2850938
url https://ddd.uab.cat/record/200081
https://dx.doi.org/urn:doi:10.1109/LGRS.2018.2850938
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv Ministerio de Economía y Competitividad https://doi.org/10.13039/501100003329 TIN2015-71126-R
Agència de Gestió d'Ajuts Universitaris i de Recerca https://doi.org/10.13039/501100003030 2017/SGR-463
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
https://rightsstatements.org/vocab/InC/1.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
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eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Dipòsit Digital de Documents de la UAB
instname:Universitat Autònoma de Barcelona
instname_str Universitat Autònoma de Barcelona
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collection Dipòsit Digital de Documents de la UAB
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