Visual Quality Assessment by Machine Learning

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.

List of contents

Introduction.- Fundamental knowledges of machine learning.- Image features and feature processing.- Feature pooling by learning.- Metrics fusion.- Summary and remarks for future research.

Summary

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.

Product details

• Authors: C -C Jay Kuo, C. -C. Jay Kuo, C.-C. Jay Kuo, Weis Lin, Weisi Lin, Lon Xu, Long Xu
• Publisher: Springer, Berlin
• Languages: English
• Product format: Paperback / Softback
• Released: 30.06.2015
• EAN: 9789812874672
• ISBN: 978-981-287-467-2
• No. of pages: 132
• Dimensions: 157 mm x 236 mm x 7 mm
• Weight: 236 g
• Illustrations: XIV, 132 p. 19 illus., 16 illus. in color.
• Series: SpringerBriefs in Electrical and Computer Engineering; SpringerBriefs in Electrical and Computer Engineering / SpringerBriefs in Signal Processing; SpringerBriefs in Signal Processing; SpringerBriefs in Electrical and Computer Engineering; SpringerBriefs in Signal Processing
• Subject: Natural sciences, medicine, IT, technology > Technology > Electronics, electrical engineering, communications engineering