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Visual quality assessment by machine learning

The book encompasses the state-of-the-art visual quality assessment (VQA) and learning based visual quality assessment (LB-VQA) by providing a comprehensive overview of the existing relevant methods. It delivers the readers the basic knowledge, systematic overview and new development of VQA. It also...

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Detalles Bibliográficos
Autores principales: Xu, Long, Lin, Weisi, Kuo, C -C Jay
Lenguaje:eng
Publicado: Springer 2015
Materias:
Acceso en línea:https://dx.doi.org/10.1007/978-981-287-468-9
http://cds.cern.ch/record/2020956
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author Xu, Long
Lin, Weisi
Kuo, C -C Jay
author_facet Xu, Long
Lin, Weisi
Kuo, C -C Jay
author_sort Xu, Long
collection CERN
description The book encompasses the state-of-the-art visual quality assessment (VQA) and learning based visual quality assessment (LB-VQA) by providing a comprehensive overview of the existing relevant methods. It delivers the readers the basic knowledge, systematic overview and new development of VQA. It also encompasses the preliminary knowledge of Machine Learning (ML) to VQA tasks and newly developed ML techniques for the purpose. Hence, firstly, it is particularly helpful to the beginner-readers (including research students) to enter into VQA field in general and LB-VQA one in particular. Secondly, new development in VQA and LB-VQA particularly are detailed in this book, which will give peer researchers and engineers new insights in VQA.
id cern-2020956
institution Organización Europea para la Investigación Nuclear
language eng
publishDate 2015
publisher Springer
record_format invenio
spelling cern-20209562021-04-21T20:17:02Zdoi:10.1007/978-981-287-468-9http://cds.cern.ch/record/2020956engXu, LongLin, WeisiKuo, C -C JayVisual quality assessment by machine learningEngineeringThe book encompasses the state-of-the-art visual quality assessment (VQA) and learning based visual quality assessment (LB-VQA) by providing a comprehensive overview of the existing relevant methods. It delivers the readers the basic knowledge, systematic overview and new development of VQA. It also encompasses the preliminary knowledge of Machine Learning (ML) to VQA tasks and newly developed ML techniques for the purpose. Hence, firstly, it is particularly helpful to the beginner-readers (including research students) to enter into VQA field in general and LB-VQA one in particular. Secondly, new development in VQA and LB-VQA particularly are detailed in this book, which will give peer researchers and engineers new insights in VQA.Springeroai:cds.cern.ch:20209562015
spellingShingle Engineering
Xu, Long
Lin, Weisi
Kuo, C -C Jay
Visual quality assessment by machine learning
title Visual quality assessment by machine learning
title_full Visual quality assessment by machine learning
title_fullStr Visual quality assessment by machine learning
title_full_unstemmed Visual quality assessment by machine learning
title_short Visual quality assessment by machine learning
title_sort visual quality assessment by machine learning
topic Engineering
url https://dx.doi.org/10.1007/978-981-287-468-9
http://cds.cern.ch/record/2020956
work_keys_str_mv AT xulong visualqualityassessmentbymachinelearning
AT linweisi visualqualityassessmentbymachinelearning
AT kuoccjay visualqualityassessmentbymachinelearning