Visual Quality Assessment by Machine Learning

✍ Scribed by Long Xu, Weisi Lin, C.-C. Jay Kuo (auth.)

• Publisher: Springer-Verlag Singapur
• Year: 2015
• Tongue: English
• Leaves: 142
• Series: SpringerBriefs in Electrical and Computer Engineering
• Edition: 1
• Category: Library

✦ Synopsis

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.

✦ Table of Contents

Front Matter....Pages i-xiv
Introduction....Pages 1-22
Fundamental Knowledge of Machine Learning....Pages 23-35
Image Features and Feature Processing....Pages 37-65
Feature Pooling by Learning....Pages 67-91
Metrics Fusion....Pages 93-122
Summary and Remarks for Future Research....Pages 123-132

✦ Subjects

Signal, Image and Speech Processing; Image Processing and Computer Vision; Computational Intelligence