Titlebook: Visual Quality Assessment by Machine Learning; Long Xu,Weisi Lin,C.-C. Jay Kuo Book 2015 The Author(s) 2015 Feature Selection.Machine Lear

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Image Features and Feature Processing,em into basic processing and advanced processing categories, resulting in basic features and advanced features, respectively. In addition, feature learning is investigated to generate more efficient features for biological image-processing tasks. The feature selection and feature extraction techniqu

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Feature Pooling by Learning,which is a real-scale number. This process is called “pooling” in the literature which is a kind of function of linear or nonlinear form. For example, summing up all quadratic components of a feature vector would come up with a real number that may represent image quality for some scenarios. This ch

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Metrics Fusion, specific distortion types, some advanced features are trained to be as advanced image quality scorers (AIQSs). In addition, two statistical testing methods are employed to do scorer selection. Finally, a machine learning approach is adopted as a score fuser to combine all outputs from the selected

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Image Features and Feature Processing,nt. Regarding biological tasks of image processing, such as recognition, retrieval, tracking, and categorizing, such a method would be very uneconomical. The neighboring points are highly correlated with each other in natural images, so there exists a large amount of redundancies in natural images.

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Feature Pooling by Learning,e obtained by the aid of priori knowledge that people have gained; for example, the aforementioned basic and advantage features. There is also increasing interest in learning-based features which are co-trained along with the learning tasks. For example, the so-called “deep learning” techniques are

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Metrics Fusion,e performance always tops the performance ranking list on all subjective databases and for all distortions. The combination of multiple IQA metrics is expected to be better than each of them individually used. Two metric fusion frameworks are introduced in this chapter. The one introduces a multi-me

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Summary and Remarks for Future Research,became more and more popular. In this book, ML-based VQA and related issues have been extensively investigated. Chapters .–. present the fundamental knowledge of VQA and ML. In Chap. ., ML was exploited for image feature selection and image feature learning. Chapter . presents two ML-based framework

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2191-8112 n visual quality assessment.Includes a number of real-world 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 knowle