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.

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

Autorentext

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.

Inhalt
Introduction.- Fundamental knowledges of machine learning.- Image features and feature processing.- Feature pooling by learning.- Metrics fusion.- Summary and remarks for future research.