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E-raamat: Visual Quality Assessment by Machine Learning

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The book encompasses the state-of-the-art visual quality assessment (VQA) and learning based visual quality assessment (LB-VQA) by providing a comprehensive overview of the existing relevant methods. It delivers the readers the basic knowledge, systematic overview and new development of VQA. It also encompasses the preliminary knowledge of Machine Learning (ML) to VQA tasks and newly developed ML techniques for the purpose. Hence, firstly, it is particularly helpful to the beginner-readers (including research students) to enter into VQA field in general and LB-VQA one in particular. Secondly, new development in VQA and LB-VQA particularly are detailed in this book, which will give peer researchers and engineers new insights in VQA.
1 Introduction
1(22)
1.1 Subjective Visual Quality Assessment
2(1)
1.2 Image Quality Databases
3(2)
1.3 Video Quality Databases
5(2)
1.4 Classification of Objective Visual Quality Assessment
7(2)
1.4.1 Availability of Reference
8(1)
1.4.2 Methodology for Assessment
8(1)
1.5 Further Discussion on Major Existing Objective Visual Quality Metrics
9(4)
1.5.1 Model-Based Metrics
10(2)
1.5.2 Signal-Driven Metrics
12(1)
1.6 Evaluation Criteria for Assessing Metrics
13(1)
1.7 Emerging Machine Learning-Based Visual Quality Assessment
14(1)
1.8 Significance and Highlights of This Book
15(8)
References
17(6)
2 Fundamental Knowledge of Machine Learning
23(14)
2.1 Artificial Neural Networks
24(1)
2.2 Support Vector Machine
24(4)
2.2.1 Primal Form
26(1)
2.2.2 Dual Form
26(2)
2.3 Clustering
28(1)
2.4 Bayesian Networks
29(1)
2.5 Representation Learning
29(1)
2.6 Sparse Dictionary Learning
29(1)
2.7 AdaBoost
30(1)
2.8 Deep Learning
31(6)
2.8.1 Deep Neural Networks
31(2)
2.8.2 Deep Belief Network
33(1)
References
34(3)
3 Image Features and Feature Processing
37(30)
3.1 Types of Image Features
38(1)
3.2 Commonly Used Feature Detectors
38(4)
3.2.1 Laplacian of Gaussian (LoG)
38(1)
3.2.2 Difference of Gaussian (DoG)
39(2)
3.2.3 Gabor Filter Coefficients
41(1)
3.3 Commonly Used Feature Descriptors
42(6)
3.3.1 Scale-Invariant Feature Transform (SIFT)
43(4)
3.3.2 Speeded up Robust Features (SURF)
47(1)
3.3.3 A Global Scene Feature: GIST
48(1)
3.4 Feature Selection and Extraction
48(11)
3.4.1 Fisher Scores
49(1)
3.4.2 Generalized Fisher Scores
50(1)
3.4.3 Laplacian Scores
51(1)
3.4.4 mRMR Method
52(1)
3.4.5 Max-Dependency and Two-Stage Method
53(1)
3.4.6 Principal Component Analysis (PCA)
54(2)
3.4.7 Singular Vector Decomposition (SVD)
56(3)
3.5 Feature Learning
59(8)
3.5.1 K-Means Clustering
60(2)
3.5.2 Sparse Feature Learning
62(1)
References
63(4)
4 Feature Pooling by Learning
67(26)
4.1 Support Vector Machine Learning for Feature Pooling
68(3)
4.2 SVD-Based Feature and Feature Pooling Using SVM
71(4)
4.2.1 SVD-Based Feature Detection
71(2)
4.2.2 Combining Features into a Perceptual Quality Score
73(1)
4.2.3 Support Vector Regression (SVR)
73(2)
4.3 Rank Learning for Feature Pooling
75(18)
4.3.1 Categorical, Ordinal, and Interval Variables
75(1)
4.3.2 Numerical Rating and Pairwise Comparison
76(1)
4.3.3 Rank Learning Approaches
77(2)
4.3.4 Pairwise Rank Learning Image Quality Assessment (PRLIQA)
79(10)
References
89(4)
5 Metrics Fusion
93(30)
5.1 Multi-method Fusion (MMF)
94(4)
5.1.1 Support Vector Regression for Fusion
94(2)
5.1.2 MMF Scores
96(1)
5.1.3 Data Scaling and Cross-Validation
97(1)
5.1.4 Training
97(1)
5.1.5 Testing
97(1)
5.2 Context-Dependent MMF
98(4)
5.2.1 Context Definition
98(1)
5.2.2 Automatic Context Determination
99(1)
5.2.3 Fused IQA Method Selection
100(1)
5.2.4 Evaluation
101(1)
5.3 Image Quality Assessment Using ParaBoosting Ensemble
102(21)
5.3.1 Image Quality Scores (IQSs)
106(8)
5.3.2 Evaluation of IQSs, Training Strategy, and ParaBoosting Ensemble (PBE)
114(5)
5.3.3 Scorer Selection for PBE IQA
119(2)
References
121(2)
6 Summary and Remarks for Future Research
123
6.1 Summary
123(2)
6.2 Further Discussions on Future Possibilities
125(4)
6.2.1 3D Quality Assessment
126(1)
6.2.2 Joint Audiovisual Quality Metrics
126(2)
6.2.3 Computer-Generated Visual Signal Quality Assessment
128(1)
6.3 Final Remarks
129
References
129
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