Quality Assessment of Visual Content

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.

本書為讀者提供了對圖像品質評估技術的全面回顧，特別是在屏幕內容圖像、3D合成圖像、聲納圖像、增強圖像、光場圖像、虛擬實境圖像和超解析圖像等應用方面。它涵蓋了結構變異分析、稀疏參考信息、多尺度自然場景統計分析、任務和視覺感知、輪廓退化測量、空間角度測量、局部和全局評估指標等主題。本書中的所有圖像品質評估算法在效率和性能方面都優於其他圖像品質評估算法，並且這些方法的性能可以通過對真實圖像的實驗結果來證明。在此基礎上，對相關領域感興趣的人可以使用通過這些品質評估算法獲得的結果進一步進行圖像處理。

本書的目標是方便來自光學、電子、數學、攝影技術和計算技術等各個學科的工程師和科學家使用這些圖像品質評估算法。本書可以作為對圖像品質評估技術感興趣的研究生的參考書，對實踐這些方法的前沿研究人員的參考書，以及在這個領域工作或進行相關應用開發的領域專家的參考書。

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.

柯谷是北京工業大學的電子工程教授。他的研究興趣包括影像處理、影像品質評估、環境感知和機器學習。他在影像處理領域發表了大量論文，並於2016年獲得了IEEE多媒體交易的最佳論文獎、IEEE國際多媒體和博覽會的最佳學生論文獎，以及2016年中國電子學會的優秀博士論文獎。目前，他是《信號處理影像通訊》、《顯示器》、《熵》、《IEEE ACCESS》和《IET影像處理》的副編輯，並且是20多個頂級SCI期刊的審稿人。

劉紅艷於2021年在北京工業大學獲得自動化學士學位，目前正在北京工業大學攻讀博士學位。她的研究專注於環境感知和機器學習。

周成旭是遼寧工業大學的副教授，目前正在北京工業大學攻讀博士學位。她的研究專注於影像處理、影像品質評估和機器學習。