Quality Assessment of Visual Content (eBook)

This book provides readers with a comprehensive review of image quality assessment technology, particularly applications on screen content images, 3D-synthesized images, sonar images, enhanced images, light-field images, VR images, and super-resolution images. It covers topics containing structural variation analysis, sparse reference information, multiscale natural scene statistical analysis, task and visual perception, contour degradation measurement, spatial angular measurement, local and global assessment metrics, and more. All of the image quality assessment algorithms of this book have a high efficiency with better performance compared to other image quality assessment algorithms, and the performance of these approaches mentioned above can be demonstrated by the results of experiments on real-world images. On the basis of this, those interested in relevant fields can use the results obtained through these quality assessment algorithms for further image processing.

 

The goal of this book is to facilitate the use of these image quality assessment algorithms by engineers and scientists from various disciplines, such as optics, electronics, math, photography techniques and computation techniques. The book can serve as a reference for graduate students who are interested in image quality assessment techniques, for front-line researchers practicing these methods, and for domain experts working in this area or conducting related application development.

Ke Gu is a professor of electronic engineering at Beijing University of Technology. His research interests include image processing, image quality assessment, environmental perception and machine learning. He has published intensively in the domain of image processing and then received the Best Paper Award from the IEEE Transactions on Multimedia, the Best Student Paper Award at the IEEE International Conference on Multimedia and Expo in 2016, and the Excellent Ph.D. Thesis Award from the Chinese Institute of Electronics in 2016. Currently, he is an Associate Editor for Signal Processing Image Communication, Displays, Entropy, IEEE ACCESS and IET Image Processing, and he is a Reviewer for more than 20 top SCI journals.

Hongyan Liu received the B.S. degree in Automation at Beijing University of Technology in 2021. She is currently working toward the Ph.D. degree at Beijing University of Technology. Her research focuses on environmental perception and machine learning.

Chengxu Zhou is an associate professor at Liaoning University of Technology. She is currently working toward the Ph.D. degree at Beijing University of Technology. Her research focuses on image processing, image quality assessment and machine learning.

---

This book provides readers with a comprehensive review of image quality assessment technology, particularly applications on screen content images, 3D-synthesized images, sonar images, enhanced images, light-field images, VR images, and super-resolution images. It covers topics containing structural variation analysis, sparse reference information, multiscale natural scene statistical analysis, task and visual perception, contour degradation measurement, spatial angular measurement, local and global assessment metrics, and more. All of the image quality assessment algorithms of this book have a high efficiency with better performance compared to other image quality assessment algorithms, and the performance of these approaches mentioned above can be demonstrated by the results of experiments on real-world images. On the basis of this, those interested in relevant fields can use the results obtained through these quality assessment algorithms for further image processing. The goal of this book is to facilitate the use of these image quality assessment algorithms by engineers and scientists from various disciplines, such as optics, electronics, math, photography techniques and computation techniques. The book can serve as a reference for graduate students who are interested in image quality assessment techniques, for front-line researchers practicing these methods, and for domain experts working in this area or conducting related application development.