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.
Author(s): Ke Gu, Hongyan Liu, Chengxu Zhou
Series: Advances in Computer Vision and Pattern Recognition
Publisher: Springer
Year: 2022
Language: English
Pages: 255
City: Singapore
Preface
Contents
Acronyms
1 Introduction
1.1 Quality Assessment of Traditional Images
1.2 Quality Assessment of Screen Content Images
1.3 Quality Assessment of 3D-Synthesized Images
1.4 Quality Assessment of Sonar Images
1.5 Quality Assessment of Enhanced Images
1.6 Quality Assessment of Light-Field Images
1.7 Quality Assessment of Virtual Reality Images
1.8 Quality Assessment of Super-Resolution Images
References
2 Quality Assessment of Screen Content Images
2.1 Introduction
2.2 Methodology
2.2.1 Full-Reference QA of Screen Content Images
2.2.2 Reduced-Reference QA of Screen Content Images
2.2.3 No-Reference QA of Screen Content Images
2.3 Comparison and Analysis of Algorithm Performance
2.3.1 Testing Database
2.3.2 Performance Comparison and Analysis
2.4 Conclusion
References
3 Quality Assessment of 3D-Synthesized Images
3.1 Introduction
3.2 Methodology
3.2.1 NSS-Based NR 3D-Synthesized Image QA
3.2.2 Transform Domain-Based NR 3D-Synthesized Image QA
3.2.3 Structure Variation-Based NR 3D-Synthesized Image QA
3.3 Comparison and Analysis of Algorithm Performance
3.3.1 DIBR-Synthesized Image Database
3.3.2 Performance Comparison and Analysis
3.4 Conclusion
References
4 Quality Assessment of Sonar Images
4.1 Introduction
4.2 Methodology
4.2.1 Full-Reference QA of Sonar Images
4.2.2 Semi-Reference QA of Sonar Images
4.2.3 Partial-Reference QA of Sonar Images
4.2.4 No-Reference QA of Sonar Images
4.3 Comparison and Analysis of Algorithm Performance
4.3.1 The Sonar Image Database
4.3.2 Performance Comparison and Analysis
4.4 Conclusion
References
5 Quality Assessment of Enhanced Images
5.1 Introduction
5.2 Methodology
5.2.1 Database Set-Up
5.2.2 Objective QA of Enhanced Images
5.2.3 Enhanced Image QA Based on the Enhancement Technology
5.3 Comparison and Analysis of Algorithm Performance
5.3.1 CCID 2014 Database
5.3.2 Performance Comparison and Analysis
5.4 Conclusion
References
6 Quality Assessment of Light-Field Image
6.1 Introduction
6.2 Methodology
6.2.1 FR QA of LF Images
6.2.2 RR QA of LF Images
6.2.3 NR LF Image QA Based on Spatial-Angular Measurement
6.2.4 Tensor Oriented NR LF Image QA
6.3 Comparison and Analysis of Algorithm Performance
6.3.1 Elaborated SMART Database
6.3.2 Performance Comparison and Analysis
6.4 Conclusion
References
7 Quality Assessment of Virtual Reality Images
7.1 Introduction
7.2 Methodology
7.2.1 Subjective QA of VR Images
7.2.2 Objective QA of VR Images
7.2.3 Subjective-Objective QA of VR Images
7.2.4 Cross-Reference Stitching QA
7.3 Comparison and Analysis of Algorithm Performance
7.3.1 Performance Comparison and Analysis
7.4 Conclusion
References
8 Quality Assessment of Super-Resolution Images
8.1 Introduction
8.2 Methodology
8.2.1 Creation of the QA Database for SR Image
8.2.2 QA of SR Image Based on Deep Learning
8.2.3 Natural Statistics-Based SR Image QA
8.3 Comparison and Analysis of Algorithm Performance
8.3.1 Performance Comparison and Analysis
8.4 Conclusion
References
