商品描述
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.
商品描述(中文翻譯)
本書為讀者提供了影像品質評估技術的全面回顧，特別是針對螢幕內容影像、3D合成影像、聲納影像、增強影像、光場影像、虛擬實境影像以及超解析度影像的應用。內容涵蓋結構變化分析、稀疏參考資訊、多尺度自然場景統計分析、任務與視覺感知、輪廓退化測量、空間角度測量、本地與全局評估指標等主題。本書中的所有影像品質評估演算法相較於其他影像品質評估演算法具有高效率和更佳的性能，這些方法的性能可透過對真實影像的實驗結果來證明。在此基礎上，對相關領域感興趣的人士可以利用這些品質評估演算法所獲得的結果進行進一步的影像處理。
本書的目標是促進來自光學、電子學、數學、攝影技術及計算技術等各個學科的工程師和科學家使用這些影像品質評估演算法。本書可作為對影像品質評估技術感興趣的研究生、實踐這些方法的一線研究人員，以及在此領域工作或進行相關應用開發的專家們的參考資料。
作者簡介
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.
作者簡介(中文翻譯)
柯谷是北京科技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的研究，並獲得了IEEE多媒體期刊的最佳論文獎、2016年IEEE國際多媒體與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，他擔任《信號處理影像通信》、《顯示器》、《熵》、《IEEE ACCESS》和《IET影像處理》的副編輯，並且是20多本頂尖SCI期刊的審稿人。
劉紅燕於2021年在北京科技大學獲得自動化學士學位。她目前正在北京科技大學攻讀博士學位。她的研究專注於環境感知和機器學習。
周成旭是遼寧科技大學的副教授。她目前正在北京科技大學攻讀博士學位。她的研究專注於影像處理、影像品質評估和機器學習。
