Stereoscopic Image Quality Assessment

Yong Ding received his B.S. and M.S. degrees from the School of Electronic Science & Applied Physics, Hefei University of Technology, P. R. China, in 1997 and 2000, respectively, and his Ph.D. from the College of Electronic Science & Engineering, Nanjing, P. R. China, in 2008. From 2000 to 2006, he was a Senior Engineer at the R & D Center of Hisense, and from 2006 to 2008 was a Senior Project Leader at the Architecture Design Department at Ominivision. He is currently a Professor at the Institute of VLSI Design at Zhejiang University, and is Vice-Director of the Engineering Research Center of Embedded Systems, Ministry of Education of China. He was a Visiting Scholar at the Laboratory of Mathematical Methods of Image Processing at Moscow Lomonosov State University, Russia, in 2011, and at Illinois Institute of Technology, USA, from 2015 to 2016. His research interests include objective image quality assessment, digital image/video processing and associated SoC architectures. At present, he is in charge of several projects supported by the Chinese government, including the National High Technology Program (863 programs), and a National Science and Technology Major Project, which focus on image quality assessment and video processing. Prof. Ding has authored more than 70 papers in journals in these fields, and has given several plenary or invited talks at international conferences. He holds more than 30 Chinese patents. He is an editor of the International Journal of Digital Content Technology and its Applications (JDCTA) and an invited reviewer for several leading international publishers. Guangming Sun is currently a postgraduate at the College of Information Science and Electronics, Zhejiang University, Hangzhou, P. R. China.

Introduction.- Basic of 2D Image Quality Assessment.- The Difference Between 2D IQA and 3D IQA.- Stereoscopic Image Quality Assessment Based on 2D IQA Models.- Stereoscopic Image Quality Assessment Based on Binocular Vision.- Learning Perceptual Quality of Stereopsis from Human Visual Properties.- Stereoscopic Image Quality Assessment Based on Deep Convolutional Neural Models.- Summary and Future Directions.