基于信息理論的圖像相似度測(cè)量方法
發(fā)布時(shí)間:2021-01-20 09:15
Image similarity or distortion assessment is fundamental to a broad range of applications throughout the field of image processing and machine vision. Many existing image similarity measures have been proposed to handle specific types of image distortions. Also, there are methods such as the classical structural similarity (SSIM) index that are applicable to a wider range of applications. Most of existing image similarity measures are based on statistical approaches. Image similarity measures th...
【文章來(lái)源】:華中師范大學(xué)湖北省 211工程院校 教育部直屬院校
【文章頁(yè)數(shù)】:114 頁(yè)
【學(xué)位級(jí)別】:博士
【文章目錄】:
Abstract
1 Introduction
1.0 Overview
1.1 Motivations
1.2 Objective
1.3 Contributions
1.4 Thesis Organization
2 Literature survey
2.1 Image Quality Assessment
2.2 Structural(statistical)Similarity Based Image Quality Assessment
2.2.1 The Structural Similarity(SSIM)Index
2.2.1.1 Multi-Scale SSIM
2.2.1.2 Complex Wavelet-SSIM
2.3 Information Theoretic Similarity Based Image Quality Assessment
2.3.1 Information Entropy and Uncertainty
2.3.2 Joint entropy
2.3.3 Mutual Information
2.3.4 Joint Histogram
2.4 An Application of Similarity:Face Recognition
2.4.1 Face Datasets
2.4.1.1 FERET
2.4.1.2 ORL
2.4.1.3 LFW
2.4.1.4 MOBIO
3 High-Performance Information-Theoretic Image Similarity Measures
3.0 Overview
3.1 First Proposed Framework(HSSIM)Based on Joint Histogram
3.1.1 The Test Environment of (HSSIM)
3.2 Second Proposed Framework (ISSIM) For Face Recognition Based on Joint Histogram
3.2.1 Recognition Confidence
3.2.2 Image Database
4 Results and Discussion
4.1 Tests on HSSIM with Image Quality Database
4.1.1 Performance of HSSIM under Gaussian Noise
4.1.2 Effects of Analysis Parameters
4.2 Tests ISSIM with Face Image Database
4.2.1 Testing
5 Conclusion and Future Work
5.1 Conclusion
5.2 Future work
References
List of Abbreviations & Symbols
Publications Associated with This work
ACKNOWLEDGEMENTS
Dedication
本文編號(hào):2988782
【文章來(lái)源】:華中師范大學(xué)湖北省 211工程院校 教育部直屬院校
【文章頁(yè)數(shù)】:114 頁(yè)
【學(xué)位級(jí)別】:博士
【文章目錄】:
Abstract
1 Introduction
1.0 Overview
1.1 Motivations
1.2 Objective
1.3 Contributions
1.4 Thesis Organization
2 Literature survey
2.1 Image Quality Assessment
2.2 Structural(statistical)Similarity Based Image Quality Assessment
2.2.1 The Structural Similarity(SSIM)Index
2.2.1.1 Multi-Scale SSIM
2.2.1.2 Complex Wavelet-SSIM
2.3 Information Theoretic Similarity Based Image Quality Assessment
2.3.1 Information Entropy and Uncertainty
2.3.2 Joint entropy
2.3.3 Mutual Information
2.3.4 Joint Histogram
2.4 An Application of Similarity:Face Recognition
2.4.1 Face Datasets
2.4.1.1 FERET
2.4.1.2 ORL
2.4.1.3 LFW
2.4.1.4 MOBIO
3 High-Performance Information-Theoretic Image Similarity Measures
3.0 Overview
3.1 First Proposed Framework(HSSIM)Based on Joint Histogram
3.1.1 The Test Environment of (HSSIM)
3.2 Second Proposed Framework (ISSIM) For Face Recognition Based on Joint Histogram
3.2.1 Recognition Confidence
3.2.2 Image Database
4 Results and Discussion
4.1 Tests on HSSIM with Image Quality Database
4.1.1 Performance of HSSIM under Gaussian Noise
4.1.2 Effects of Analysis Parameters
4.2 Tests ISSIM with Face Image Database
4.2.1 Testing
5 Conclusion and Future Work
5.1 Conclusion
5.2 Future work
References
List of Abbreviations & Symbols
Publications Associated with This work
ACKNOWLEDGEMENTS
Dedication
本文編號(hào):2988782
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