<?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>2026-04-05T12:58:04Z</responseDate><request verb="GetRecord" identifier="oai:www.recercat.cat:2099.1/7980" metadataPrefix="qdc">https://recercat.cat/oai/request</request><GetRecord><record><header><identifier>oai:recercat.cat:2099.1/7980</identifier><datestamp>2025-07-23T03:14:37Z</datestamp><setSpec>com_2072_1033</setSpec><setSpec>col_2072_452951</setSpec></header><metadata><qdc:qualifieddc xmlns:qdc="http://dspace.org/qualifieddc/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:dcterms="http://purl.org/dc/terms/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:doc="http://www.lyncode.com/xoai" xsi:schemaLocation="http://purl.org/dc/elements/1.1/ http://dublincore.org/schemas/xmls/qdc/2006/01/06/dc.xsd http://purl.org/dc/terms/ http://dublincore.org/schemas/xmls/qdc/2006/01/06/dcterms.xsd http://dspace.org/qualifieddc/ http://www.ukoln.ac.uk/metadata/dcmi/xmlschema/qualifieddc.xsd">
   <dc:title>Entropy Coding for Image Compression Based on Generalized Lifting and SPECK</dc:title>
   <dc:creator>Alameda i Pineda, Xavier</dc:creator>
   <dc:subject>Àrees temàtiques de la UPC::So, imatge i multimèdia::Creació multimèdia::Imatge digital</dc:subject>
   <dc:subject>Video compression</dc:subject>
   <dc:subject>Imatges Compressió (Informàtica)</dc:subject>
   <dcterms:abstract>Image source coding became very important in the 80's. There were some key factors that helped to that&#xd;
data (image) compression revolution. First, the idea of digital image: an image composed by a set of  niteprecision&#xd;
coe cients placed over a  nite and discrete grid. To represent the image one could scan its coe cients&#xd;
and concatenate the coe cients binary representation. This strategy does not take into account any of the&#xd;
characteristics of the input signal (the image). Moreover it is not an e cient representation of the image and&#xd;
compression techniques are therefore necessary. Second, researchers had started to develop new techniques and&#xd;
softwares in the  eld of text compression and generic data coding some years ago (see [10, 32]). Third, the&#xd;
presence of digital image processing in the market increased due to its simplicity, performance and results.&#xd;
Those are, under my opinion, the key factors that promoted image entropy coding at the research level in the&#xd;
80's.&#xd;
On the other hand, this data (image) compression revolution helped to trigger another spectacular growth: the&#xd;
number of plain Internet users. A large amount of people started to share information: plain and formatted&#xd;
text, photo, music, video,... These information exchanges required better-performed techniques in terms of ratedistortion&#xd;
characteristics. User's needs increased and researchers, developers and producers had to upgrade the&#xd;
system's quality and capacity. This Internet growth, in the 1990s, would not have been possible without the&#xd;
previous image compression systems quality rise.</dcterms:abstract>
   <dcterms:issued>2009-09</dcterms:issued>
   <dc:type>Master thesis (pre-Bologna period)</dc:type>
   <dc:rights>http://creativecommons.org/licenses/by-nc-nd/3.0/es/</dc:rights>
   <dc:rights>Open Access</dc:rights>
   <dc:rights>Attribution-NonCommercial-NoDerivs 3.0 Spain</dc:rights>
   <dc:publisher>Universitat Politècnica de Catalunya</dc:publisher>
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