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Visual quality assessment by machine...

Kuo, C.-C. Jay.

Visual quality assessment by machine learning

Record Type:	Electronic resources : Monograph/item
Title/Author:	Visual quality assessment by machine learningby Long Xu, Weisi Lin, C.-C. Jay Kuo.
Author:	Xu, Long.
other author:	Lin, Weisi.
Published:	Singapore :Springer Singapore :2015.
Description:	xiv, 132 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Machine learning.
Online resource:	http://dx.doi.org/10.1007/978-981-287-468-9
ISBN:	9789812874689 (electronic bk.)

Visual quality assessment by machine learning
Xu, Long.

Visual quality assessment by machine learning[electronic resource] /by Long Xu, Weisi Lin, C.-C. Jay Kuo. - Singapore :Springer Singapore :2015. - xiv, 132 p. :ill., digital ;24 cm. - SpringerBriefs in electrical and computer engineering, Signal processing,2191-8112. - SpringerBriefs in electrical and computer engineering.Signal processing..

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

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.

ISBN: 9789812874689 (electronic bk.)

Standard No.: 10.1007/978-981-287-468-9doiSubjects--Topical Terms:

188639
Machine learning.

LC Class. No.: Q325.5

Dewey Class. No.: 006.31

Visual quality assessment by machine learning
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505 0 $a Introduction -- Fundamental knowledges of machine learning -- Image features and feature processing -- Feature pooling by learning -- Metrics fusion -- Summary and remarks for future research.
520 $a 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.
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