Progressive lossy-to-lossless compression of DNA microarray images

The analysis techniques applied to DNA microarray images are under active development. As new techniques become available, it will be useful to apply them to existing microarray images to obtain more accurate results. The compression of these images can be a useful tool to alleviate the costs associ...

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Autores: Hernández-Cabronero, Miguel|||0000-0001-9301-4337, Blanes Garcia, Ian|||0000-0001-8939-1666, Pinho, Armando J., Marcellin, Michael W.|||0000-0001-9606-134X, Serra-Sagristà, Joan|||0000-0003-4729-9292
Formato: artículo
Fecha de publicación:2016
País:España
Recursos:Universitat Autònoma de Barcelona
Repositorio:Dipòsit Digital de Documents de la UAB
Idioma:inglés
OAI Identifier:oai:ddd.uab.cat:168571
Acesso em linha:https://ddd.uab.cat/record/168571
https://dx.doi.org/urn:doi:10.1109/LSP.2016.2547893
Access Level:acceso abierto
Palavra-chave:DNA microarray images
Image compression
Quantization
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spelling Progressive lossy-to-lossless compression of DNA microarray imagesHernández-Cabronero, Miguel|||0000-0001-9301-4337Blanes Garcia, Ian|||0000-0001-8939-1666Pinho, Armando J.Marcellin, Michael W.|||0000-0001-9606-134XSerra-Sagristà, Joan|||0000-0003-4729-9292DNA microarray imagesImage compressionQuantizationThe analysis techniques applied to DNA microarray images are under active development. As new techniques become available, it will be useful to apply them to existing microarray images to obtain more accurate results. The compression of these images can be a useful tool to alleviate the costs associated to their storage and transmission. The recently proposed Relative Quantizer (RQ) coder provides the most competitive lossy compression ratios while introducing only acceptable changes in the images. However, images compressed with the RQ coder can only be reconstructed with a limited quality, determined before compression. In this work, a progressive lossy-to-lossless scheme is presented to solve this problem. First, the regular structure of the RQ intervals is exploited to define a lossy-to-lossless coding algorithm called the Progressive RQ (PRQ) coder. Second, an enhanced version that prioritizes a region of interest, called the PRQ-region of interest (ROI) coder, is described. Experiments indicate that the PRQ coder offers progressivity with lossless and lossy coding performance almost identical to the best techniques in the literature, none of which is progressive. In turn, the PRQ-ROI exhibits very similar lossless coding results with better rate-distortion performance than both the RQ and PRQ coders. 22016-01-0120162016-01-01Articlehttp://purl.org/coar/resource_type/c_6501AMhttp://purl.org/coar/version/c_ab4af688f83e57aainfo:eu-repo/semantics/articleapplication/pdfhttps://ddd.uab.cat/record/168571https://dx.doi.org/urn:doi:10.1109/LSP.2016.2547893reponame:Dipòsit Digital de Documents de la UABinstname:Universitat Autònoma de BarcelonaInglésengMinisterio de Economía y Competitividad https://doi.org/10.13039/501100003329 FPU/AP2010-0172Agència de Gestió d'Ajuts Universitaris i de Recerca https://doi.org/10.13039/501100003030 2014/SGR-691Ministerio de Economía y Competitividad https://doi.org/10.13039/501100003329 TIN2012-38102-C03-03open 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:1685712026-06-06T12:50:31Z
dc.title.none.fl_str_mv Progressive lossy-to-lossless compression of DNA microarray images
title Progressive lossy-to-lossless compression of DNA microarray images
spellingShingle Progressive lossy-to-lossless compression of DNA microarray images
Hernández-Cabronero, Miguel|||0000-0001-9301-4337
DNA microarray images
Image compression
Quantization
title_short Progressive lossy-to-lossless compression of DNA microarray images
title_full Progressive lossy-to-lossless compression of DNA microarray images
title_fullStr Progressive lossy-to-lossless compression of DNA microarray images
title_full_unstemmed Progressive lossy-to-lossless compression of DNA microarray images
title_sort Progressive lossy-to-lossless compression of DNA microarray images
dc.creator.none.fl_str_mv Hernández-Cabronero, Miguel|||0000-0001-9301-4337
Blanes Garcia, Ian|||0000-0001-8939-1666
Pinho, Armando J.
Marcellin, Michael W.|||0000-0001-9606-134X
Serra-Sagristà, Joan|||0000-0003-4729-9292
author Hernández-Cabronero, Miguel|||0000-0001-9301-4337
author_facet Hernández-Cabronero, Miguel|||0000-0001-9301-4337
Blanes Garcia, Ian|||0000-0001-8939-1666
Pinho, Armando J.
Marcellin, Michael W.|||0000-0001-9606-134X
Serra-Sagristà, Joan|||0000-0003-4729-9292
author_role author
author2 Blanes Garcia, Ian|||0000-0001-8939-1666
Pinho, Armando J.
Marcellin, Michael W.|||0000-0001-9606-134X
Serra-Sagristà, Joan|||0000-0003-4729-9292
author2_role author
author
author
author
dc.subject.none.fl_str_mv DNA microarray images
Image compression
Quantization
topic DNA microarray images
Image compression
Quantization
description The analysis techniques applied to DNA microarray images are under active development. As new techniques become available, it will be useful to apply them to existing microarray images to obtain more accurate results. The compression of these images can be a useful tool to alleviate the costs associated to their storage and transmission. The recently proposed Relative Quantizer (RQ) coder provides the most competitive lossy compression ratios while introducing only acceptable changes in the images. However, images compressed with the RQ coder can only be reconstructed with a limited quality, determined before compression. In this work, a progressive lossy-to-lossless scheme is presented to solve this problem. First, the regular structure of the RQ intervals is exploited to define a lossy-to-lossless coding algorithm called the Progressive RQ (PRQ) coder. Second, an enhanced version that prioritizes a region of interest, called the PRQ-region of interest (ROI) coder, is described. Experiments indicate that the PRQ coder offers progressivity with lossless and lossy coding performance almost identical to the best techniques in the literature, none of which is progressive. In turn, the PRQ-ROI exhibits very similar lossless coding results with better rate-distortion performance than both the RQ and PRQ coders.
publishDate 2016
dc.date.none.fl_str_mv 2
2016-01-01
2016
2016-01-01
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
format article
dc.identifier.none.fl_str_mv https://ddd.uab.cat/record/168571
https://dx.doi.org/urn:doi:10.1109/LSP.2016.2547893
url https://ddd.uab.cat/record/168571
https://dx.doi.org/urn:doi:10.1109/LSP.2016.2547893
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 FPU/AP2010-0172
Agència de Gestió d'Ajuts Universitaris i de Recerca https://doi.org/10.13039/501100003030 2014/SGR-691
Ministerio de Economía y Competitividad https://doi.org/10.13039/501100003329 TIN2012-38102-C03-03
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
http://purl.org/coar/access_right/c_abf2
https://rightsstatements.org/vocab/InC/1.0/
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
reponame_str Dipòsit Digital de Documents de la UAB
collection Dipòsit Digital de Documents de la UAB
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,300719

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