<?xml version="1.0" encoding="UTF-8"?><?xml-stylesheet type="text/xsl" href="static/style.xsl"?><OAI-PMH xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/ http://www.openarchives.org/OAI/2.0/OAI-PMH.xsd"><responseDate>2026-04-13T04:13:05Z</responseDate><request verb="GetRecord" identifier="oai:www.recercat.cat:2072/438120" metadataPrefix="oai_dc">https://recercat.cat/oai/request</request><GetRecord><record><header><identifier>oai:recercat.cat:2072/438120</identifier><datestamp>2026-03-21T02:33:34Z</datestamp><setSpec>com_2072_98</setSpec><setSpec>col_2072_378192</setSpec></header><metadata><oai_dc:dc xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:doc="http://www.lyncode.com/xoai" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
   <dc:title>Regression Wavelet Analysis for Near-Lossless Remote Sensing Data Compression</dc:title>
   <dc:creator>Álvarez Cortés, Sara</dc:creator>
   <dc:creator>Serra-Sagristà, Joan</dc:creator>
   <dc:creator>Bartrina-Rapesta, Joan</dc:creator>
   <dc:creator>Marcellin, Michael W.</dc:creator>
   <dc:subject>Lossless and near-lossless compression</dc:subject>
   <dc:subject>Pyramidal multiresolution scheme</dc:subject>
   <dc:subject>Regression wavelet analysis</dc:subject>
   <dc:subject>Remote sensing data compression</dc:subject>
   <dc:description>Altres ajuts: Universitat Autònoma de Barcelona under Grant UAB-PIF-472/2015</dc:description>
   <dc:description>Regression wavelet analysis (RWA) is one of the current state-of-the-art lossless compression techniques for remote sensing data. This article presents the first regression-based near-lossless compression method. It is built upon RWA, a quantizer, and a feedback loop to compensate the quantization error. Our near-lossless RWA (NLRWA) proposal can be followed by any entropy coding technique. Here, the NLRWA is coupled with a bitplane-based coder that supports progressive decoding. This successfully enables gradual quality refinement and lossless and near-lossless recovery. A smart strategy for selecting the NLRWA quantization steps is also included. Experimental results show that the proposed scheme outperforms the state-of-the-art lossless and the near-lossless compression methods in terms of compression ratios and quality retrieval.</dc:description>
   <dc:date>2020</dc:date>
   <dc:type>Article</dc:type>
   <dc:identifier>https://ddd.uab.cat/record/215773</dc:identifier>
   <dc:identifier>urn:10.1109/TGRS.2019.2940553</dc:identifier>
   <dc:identifier>urn:oai:ddd.uab.cat:215773</dc:identifier>
   <dc:identifier>urn:articleid:15580644v58n2p790</dc:identifier>
   <dc:identifier>urn:scopus_id:85078744720</dc:identifier>
   <dc:identifier>urn:wos_id:000510710600003</dc:identifier>
   <dc:identifier>urn:oai:egreta.uab.cat:publications/1f87fc69-ed05-4dad-96f5-0d77d59ad5dd</dc:identifier>
   <dc:language>eng</dc:language>
   <dc:relation>Agencia Estatal de Investigación RTI2018-095287-B-I00</dc:relation>
   <dc:relation>Agència de Gestió d'Ajuts Universitaris i de Recerca 2017/SGR-463</dc:relation>
   <dc:relation>IEEE transactions on geoscience and remote sensing ; Vol. 58, Issue 2 (February 2020), p. 790-798</dc:relation>
   <dc:rights>open access</dc:rights>
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   <dc:rights>https://rightsstatements.org/vocab/InC/1.0/</dc:rights>
   <dc:format>application/pdf</dc:format>
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