Authors:
- 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)
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Front Matter
About this book
Authors and Affiliations
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National Astronomical Observatories, Chinese Academy of Sciences, Beijing, China
Long Xu
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Nanyang Technological University, Singapore, Singapore
Weisi Lin
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University of Southern California, Los Angeles, USA
C.-C. Jay Kuo
Bibliographic Information
Book Title: Visual Quality Assessment by Machine Learning
Authors: Long Xu, Weisi Lin, C.-C. Jay Kuo
Series Title: SpringerBriefs in Electrical and Computer Engineering
DOI: https://doi.org/10.1007/978-981-287-468-9
Publisher: Springer Singapore
eBook Packages: Engineering, Engineering (R0)
Copyright Information: The Author(s) 2015
Softcover ISBN: 978-981-287-467-2Published: 27 May 2015
eBook ISBN: 978-981-287-468-9Published: 09 May 2015
Series ISSN: 2191-8112
Series E-ISSN: 2191-8120
Edition Number: 1
Number of Pages: XIV, 132
Number of Illustrations: 3 b/w illustrations, 16 illustrations in colour
Topics: Signal, Image and Speech Processing, Image Processing and Computer Vision, Computational Intelligence