No-reference Video Quality Assessment and Applications

Zhu, Kongfeng

No-reference Video Quality Assessment and Applications

Dateien

Zhu_289206.pdfGröße: 6.67 MBDownloads: 567

Datum

2014

Open Access-Veröffentlichung

Open Access Green

Sammlungen

Informatik und Informationswissenschaft

Publikationstyp

Dissertation

Publikationsstatus

Published

Zusammenfassung

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.

Fachgebiet (DDC)

004 Informatik

Zitieren

ISO 690

ZHU, Kongfeng, 2014. No-reference Video Quality Assessment and Applications [Dissertation]. Konstanz: University of Konstanz

BibTex

@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>

Prüfungsdatum der Dissertation

July 17, 2014

Universitätsbibliographie

Nein