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Summary and Remarks for Future Research

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Visual Quality Assessment by Machine Learning

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

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Abstract

There has been increasing interest in visual quality assessment (VQA) during recent years. Of all these VQA methods, machine learning (ML) based ones became more and more popular. In this book, ML-based VQA and related issues have been extensively investigated. Chapters 1–2 present the fundamental knowledge of VQA and ML. In Chap. 3, ML was exploited for image feature selection and image feature learning. Chapter 4 presents two ML-based frameworks for pooling image features of an image into a number score. In Chap. 5, two metric fusion frameworks designed to combine multiple existing metrics into a better one, were developed by the aid of ML tools.

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Authors and Affiliations

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

    Long Xu

  2. Nanyang Technological University, Singapore, Singapore

    Weisi Lin

  3. University of Southern California, Los Angeles, CA, USA

    C.-C. Jay Kuo

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  1. Long Xu
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  2. Weisi Lin
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  3. C.-C. Jay Kuo
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Correspondence to Long Xu .

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Xu, L., Lin, W., Kuo, CC.J. (2015). Summary and Remarks for Future Research. In: Visual Quality Assessment by Machine Learning. SpringerBriefs in Electrical and Computer Engineering(). Springer, Singapore. https://doi.org/10.1007/978-981-287-468-9_6

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  • DOI: https://doi.org/10.1007/978-981-287-468-9_6

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-287-467-2

  • Online ISBN: 978-981-287-468-9

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