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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...
Autores principales: | , , |
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Lenguaje: | eng |
Publicado: |
Springer
2015
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Materias: | |
Acceso en línea: | https://dx.doi.org/10.1007/978-981-287-468-9 http://cds.cern.ch/record/2020956 |
_version_ | 1780946867557761024 |
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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 |