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Visual Quality Assessment by Machine Learning

  • Book
  • © 2015

Overview

Authors:
  1. Long Xu

    1. National Astronomical Observatories, Chinese Academy of Sciences, Beijing, China

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    You can also search for this author in PubMed   Google Scholar

  2. Weisi Lin

    1. Nanyang Technological University, Singapore, Singapore

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    You can also search for this author in PubMed   Google Scholar

  3. C.-C. Jay Kuo

    1. University of Southern California, Los Angeles, USA

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    You can also search for this author in PubMed   Google Scholar

  • Presents the emerging techniques of learning based visual quality assessment
  • Highlights machine learning techniques and their applications in visual quality assessment
  • Includes a number of real-world examples that readers can implement in their own work
  • Includes supplementary material: sn.pub/extras

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

Part of the book sub series: SpringerBriefs in Signal Processing (BRIEFSSIGNAL)

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Table of contents (6 chapters)

  1. Front Matter

    Pages i-xiv
    Download chapter PDF

  2. Introduction

    • Long Xu, Weisi Lin, C.-C. Jay Kuo
    Pages 1-22

  3. Fundamental Knowledge of Machine Learning

    • Long Xu, Weisi Lin, C.-C. Jay Kuo
    Pages 23-35

  4. Image Features and Feature Processing

    • Long Xu, Weisi Lin, C.-C. Jay Kuo
    Pages 37-65

  5. Feature Pooling by Learning

    • Long Xu, Weisi Lin, C.-C. Jay Kuo
    Pages 67-91

  6. Metrics Fusion

    • Long Xu, Weisi Lin, C.-C. Jay Kuo
    Pages 93-122

  7. Summary and Remarks for Future Research

    • Long Xu, Weisi Lin, C.-C. Jay Kuo
    Pages 123-132
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Keywords

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.

Authors and Affiliations

  • National Astronomical Observatories, Chinese Academy of Sciences, Beijing, China

    Long Xu

  • Nanyang Technological University, Singapore, Singapore

    Weisi Lin

  • University of Southern California, Los Angeles, USA

    C.-C. Jay Kuo

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