Visual Quality Assessment by Machine Learning

  • Long Xu
  • Weisi Lin
  • C.-C. Jay Kuo

Part of the SpringerBriefs in Electrical and Computer Engineering book series (BRIEFSELECTRIC)

Also part of the SpringerBriefs in Signal Processing book sub series (BRIEFSSIGNAL)

Table of contents

  1. Front Matter
    Pages i-xiv
  2. Long Xu, Weisi Lin, C.-C. Jay Kuo
    Pages 1-22
  3. Long Xu, Weisi Lin, C.-C. Jay Kuo
    Pages 23-35
  4. Long Xu, Weisi Lin, C.-C. Jay Kuo
    Pages 37-65
  5. Long Xu, Weisi Lin, C.-C. Jay Kuo
    Pages 67-91
  6. Long Xu, Weisi Lin, C.-C. Jay Kuo
    Pages 93-122
  7. Long Xu, Weisi Lin, C.-C. Jay Kuo
    Pages 123-132

About this book


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.


Feature Selection Machine Learning Rank Learning Support Vector Learning Visual Quality Assessment (VQA)

Authors and affiliations

  • Long Xu
    • 1
  • Weisi Lin
    • 2
  • C.-C. Jay Kuo
    • 3
  1. 1.National Astronomical ObservatoriesChinese Academy of SciencesBeijingChina
  2. 2.Nanyang Technological UniversitySingaporeSingapore
  3. 3.University of Southern CaliforniaLos AngelesUSA

Bibliographic information

  • DOI
  • Copyright Information The Author(s) 2015
  • Publisher Name Springer, Singapore
  • eBook Packages Engineering
  • Print ISBN 978-981-287-467-2
  • Online ISBN 978-981-287-468-9
  • Series Print ISSN 2191-8112
  • Series Online ISSN 2191-8120
  • Buy this book on publisher's site
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