Regression Wavelet Analysis for Progressive-Lossy-to-Lossless Coding of Remote-Sensing Data

Persistent Link:
http://hdl.handle.net/10150/623190
Title:
Regression Wavelet Analysis for Progressive-Lossy-to-Lossless Coding of Remote-Sensing Data
Author:
Amrani, Naoufal; Serra-Sagrista, Joan; Hernandez-Cabronero, Miguel; Marcellin, Michael
Affiliation:
Univ Arizona, Dept Elect & Comp Engn
Issue Date:
2016-03
Publisher:
IEEE
Citation:
N. Amrani, J. Serra-Sagristà, M. Hernández-Cabronero and M. Marcellin, "Regression Wavelet Analysis for Progressive-Lossy-to-Lossless Coding of Remote-Sensing Data," 2016 Data Compression Conference (DCC), Snowbird, UT, 2016, pp. 121-130. doi: 10.1109/DCC.2016.43
Journal:
2016 DATA COMPRESSION CONFERENCE (DCC)
Rights:
© 2016, IEEE
Collection Information:
This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.
Abstract:
Regression Wavelet Analysis (RWA) is a novel wavelet-based scheme for coding hyperspectral images that employs multiple regression analysis to exploit the relationships among spectral wavelet transformed components. The scheme is based on a pyramidal prediction, using different regression models, to increase the statistical independence in the wavelet domain For lossless coding, RWA has proven to be superior to other spectral transform like PCA and to the best and most recent coding standard in remote sensing, CCSDS-123.0. In this paper we show that RWA also allows progressive lossy-to-lossless (PLL) coding and that it attains a rate-distortion performance superior to those obtained with state-of-the-art schemes. To take into account the predictive significance of the spectral components, we propose a Prediction Weighting scheme for JPEG2000 that captures the contribution of each transformed component to the prediction process.
ISSN:
1068-0314
DOI:
10.1109/DCC.2016.43
Keywords:
Encoding; Discrete wavelet transforms; Principal component analysis; Wavelet analysis; Computational modeling
Version:
Final accepted manuscript
Sponsors:
This work has been partially supported by the Spanish Government (MINECO), by FEDER, by the Catalan Government and by Universitat Autonoma de Barcelona, under Grants ` TIN2015- 71126-R, TIN2012-38102-C03-03, 2014SGR-691, and UAB-PIF-472-03-1/2012.
Additional Links:
http://ieeexplore.ieee.org/document/7786156/

Full metadata record

DC FieldValue Language
dc.contributor.authorAmrani, Naoufalen
dc.contributor.authorSerra-Sagrista, Joanen
dc.contributor.authorHernandez-Cabronero, Miguelen
dc.contributor.authorMarcellin, Michaelen
dc.date.accessioned2017-04-21T01:02:53Z-
dc.date.available2017-04-21T01:02:53Z-
dc.date.issued2016-03-
dc.identifier.citationN. Amrani, J. Serra-Sagristà, M. Hernández-Cabronero and M. Marcellin, "Regression Wavelet Analysis for Progressive-Lossy-to-Lossless Coding of Remote-Sensing Data," 2016 Data Compression Conference (DCC), Snowbird, UT, 2016, pp. 121-130. doi: 10.1109/DCC.2016.43en
dc.identifier.issn1068-0314-
dc.identifier.doi10.1109/DCC.2016.43-
dc.identifier.urihttp://hdl.handle.net/10150/623190-
dc.description.abstractRegression Wavelet Analysis (RWA) is a novel wavelet-based scheme for coding hyperspectral images that employs multiple regression analysis to exploit the relationships among spectral wavelet transformed components. The scheme is based on a pyramidal prediction, using different regression models, to increase the statistical independence in the wavelet domain For lossless coding, RWA has proven to be superior to other spectral transform like PCA and to the best and most recent coding standard in remote sensing, CCSDS-123.0. In this paper we show that RWA also allows progressive lossy-to-lossless (PLL) coding and that it attains a rate-distortion performance superior to those obtained with state-of-the-art schemes. To take into account the predictive significance of the spectral components, we propose a Prediction Weighting scheme for JPEG2000 that captures the contribution of each transformed component to the prediction process.en
dc.description.sponsorshipThis work has been partially supported by the Spanish Government (MINECO), by FEDER, by the Catalan Government and by Universitat Autonoma de Barcelona, under Grants ` TIN2015- 71126-R, TIN2012-38102-C03-03, 2014SGR-691, and UAB-PIF-472-03-1/2012.en
dc.language.isoenen
dc.publisherIEEEen
dc.relation.urlhttp://ieeexplore.ieee.org/document/7786156/en
dc.rights© 2016, IEEEen
dc.subjectEncodingen
dc.subjectDiscrete wavelet transformsen
dc.subjectPrincipal component analysisen
dc.subjectWavelet analysisen
dc.subjectComputational modelingen
dc.titleRegression Wavelet Analysis for Progressive-Lossy-to-Lossless Coding of Remote-Sensing Dataen
dc.typeProceedingsen
dc.contributor.departmentUniv Arizona, Dept Elect & Comp Engnen
dc.identifier.journal2016 DATA COMPRESSION CONFERENCE (DCC)en
dc.description.collectioninformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.en
dc.eprint.versionFinal accepted manuscripten
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