No-reference Video Quality Assessment and Applications
No-reference Video Quality Assessment and Applications
Loading...
Date
2014
Authors
Editors
Journal ISSN
Electronic ISSN
ISBN
Bibliographical data
Publisher
Series
URI (citable link)
International patent number
Link to the license
EU project number
Project
Open Access publication
Collections
Title in another language
Publication type
Dissertation
Publication status
Published in
Abstract
With more and more visual signals being received by human observers, an important aspect of the quality of experience of such stimuli is the perceived visual quality. In this thesis, new techiques to assess this perceived visual quality of natural videos without a pristine reference video, referred to as no-reference video quality assessment (NR-VQA), are presented, in order to evaluate the performance of existing devices for video capturing or video compression. These techniques adopt a two-stage NR-VQA framework, in which the two stages are distortion measurement and quality prediction. Three NR-VQA metrics are designed to evaluate the performance of video imaging systems, while two computational NR-VQA models are proposed to assess the quality of compressed videos. An optimizing strategy is also designed for feature pooling and prediction models of NR-VQA algorithms.
Summary in another language
Subject (DDC)
004 Computer Science
Keywords
Conference
Review
undefined / . - undefined, undefined. - (undefined; undefined)
Cite This
ISO 690
ZHU, Kongfeng, 2014. No-reference Video Quality Assessment and Applications [Dissertation]. Konstanz: University of KonstanzBibTex
@phdthesis{Zhu2014Noref-28920, year={2014}, title={No-reference Video Quality Assessment and Applications}, author={Zhu, Kongfeng}, address={Konstanz}, school={Universität Konstanz} }
RDF
<rdf:RDF xmlns:dcterms="http://purl.org/dc/terms/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:bibo="http://purl.org/ontology/bibo/" xmlns:dspace="http://digital-repositories.org/ontologies/dspace/0.1.0#" xmlns:foaf="http://xmlns.com/foaf/0.1/" xmlns:void="http://rdfs.org/ns/void#" xmlns:xsd="http://www.w3.org/2001/XMLSchema#" > <rdf:Description rdf:about="https://kops.uni-konstanz.de/server/rdf/resource/123456789/28920"> <dcterms:hasPart rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/28920/1/Zhu_289206.pdf"/> <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2014-09-04T12:52:27Z</dc:date> <dc:contributor>Zhu, Kongfeng</dc:contributor> <dcterms:issued>2014</dcterms:issued> <dcterms:title>No-reference Video Quality Assessment and Applications</dcterms:title> <dc:language>eng</dc:language> <dc:creator>Zhu, Kongfeng</dc:creator> <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2014-09-04T12:52:27Z</dcterms:available> <dspace:hasBitstream rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/28920/1/Zhu_289206.pdf"/> <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/> <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/> <bibo:uri rdf:resource="http://kops.uni-konstanz.de/handle/123456789/28920"/> <foaf:homepage rdf:resource="http://localhost:8080/"/> <dc:rights>terms-of-use</dc:rights> <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/> <dcterms:rights rdf:resource="https://rightsstatements.org/page/InC/1.0/"/> <dcterms:abstract xml:lang="eng">With more and more visual signals being received by human observers, an important aspect of the quality of experience of such stimuli is the perceived visual quality. In this thesis, new techiques to assess this perceived visual quality of natural videos without a pristine reference video, referred to as no-reference video quality assessment (NR-VQA), are presented, in order to evaluate the performance of existing devices for video capturing or video compression. These techniques adopt a two-stage NR-VQA framework, in which the two stages are distortion measurement and quality prediction. Three NR-VQA metrics are designed to evaluate the performance of video imaging systems, while two computational NR-VQA models are proposed to assess the quality of compressed videos. An optimizing strategy is also designed for feature pooling and prediction models of NR-VQA algorithms.</dcterms:abstract> </rdf:Description> </rdf:RDF>
Internal note
xmlui.Submission.submit.DescribeStep.inputForms.label.kops_note_fromSubmitter
Examination date of dissertation
July 17, 2014
Method of financing
Comment on publication
Alliance license
Corresponding Authors der Uni Konstanz vorhanden
International Co-Authors
Bibliography of Konstanz
No