
Visual Quality Assessment by Machine Learning
Springer (Publisher)
Published on 27. May 2015
Book
Paperback/Softback
XIV, 132 pages
978-981-287-467-2 (ISBN)
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.
More details
Series
Edition
2015 ed.
Language
English
Place of publication
Singapore
Singapore
Target group
Professional and scholarly
Research
Illustrations
16 farbige Abbildungen, 3 s/w Abbildungen
XIV, 132 p. 19 illus., 16 illus. in color.
Dimensions
Height: 235 mm
Width: 155 mm
Thickness: 9 mm
Weight
236 gr
ISBN-13
978-981-287-467-2 (9789812874672)
DOI
10.1007/978-981-287-468-9
Schweitzer Classification
Other editions
Additional editions

Long Xu | Weisi Lin | C.-C. Jay Kuo
Visual Quality Assessment by Machine Learning
E-Book
05/2015
1st Edition
Springer
€53.49
Available for download
Persons
Prof. Long Xu received his Ph.D. degree from the Institute of Computing Technology, Chinese Academy of Sciences (CAS) in 2009. He was selected into the 100-Talents Plan of CAS in 2014. From 2014 to 2022, he was with the National Astronomical Observatories, CAS. He is currently with both National Space Science Center, CAS and Peng Cheng Laboratory. His research interests include image/video processing, solar radio astronomy, wavelet, machine learning, and computer vision. He has published more than 100 academic papers, and a book "Visual quality assessment by machine learning" with Springer in 2015.
Prof. Yihua Yan received his Ph.D. degree from the Dalian University of Technology in 1990. He was a Foreign Research Fellow with the NAOJ (Japan) from 1995 to 1996, and an Alexander von Humboldt Fellow with the Astronomical Institute, Wurzburg University, Germany, from 1996 to 1997. He was the President of IAU Division E: Sun and Heliosphere from 2015 to 2018. He was the Director of the CAS Key Laboratory of Solar Activity (2008-2019), and the Director of Solar Physics Division (2013-2021), at NAOC. He is currently a Professor and a Chief Scientist, National Space Science Center, Chinese Academy of Sciences.
Dr. Xin Huang received the Ph.D. degree from Harbin Institute of Technology in 2010. He was an associate professor at Solar Activity Prediction Center, NAOC from 2013. Now, he is with the Space Environment Prediction Center, National Space Science Center, Chinese Academy of Sciences. His research interests include data mining, image processing and short-term solar activity forecasting. He has published more than 20 academic papers, including one of the top 1% most cited papers in IOP Publishing's astrophysics journals, published over the period of 2018-2020.
Content
Introduction.- Fundamental knowledges of machine learning.- Image features and feature processing.- Feature pooling by learning.- Metrics fusion.- Summary and remarks for future research.