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
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Long Xu Weisi Lin C.-C. Jay Kuo
Visual Quality Assessment by Machine Learning 123
SpringerBriefs in Electrical and Computer Engineering Signal Processing
Series editors Woon-Seng Gan, Singapore, Singapore C.-C. Jay Kuo, Los Angeles, USA Thomas Fang Zheng, Beijing, China Mauro Barni, Siena, Italy
More information about this series at http://www.springer.com/series/11560
Long Xu Weisi Lin C.-C. Jay Kuo •
•
Visual Quality Assessment by Machine Learning
123
Long Xu National Astronomical Observatories Chinese Academy of Sciences Beijing China
C.-C. Jay Kuo University of Southern California Los Angeles, CA USA
Weisi Lin Nanyang Technological University Singapore Singapore
ISSN 2191-8112 ISSN 2191-8120 (electronic) SpringerBriefs in Electrical and Computer Engineering ISSN 2196-4076 ISSN 2196-4084 (electronic) SpringerBriefs in Signal Processing ISBN 978-981-287-467-2 ISBN 978-981-287-468-9 (eBook) DOI 10.1007/978-981-287-468-9 Library of Congress Control Number: 2015935610 Springer Singapore Heidelberg New York Dordrecht London © The Author(s) 2015 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made. Printed on acid-free paper Springer Science+Business Media Singapore Pte Ltd. is part of Springer Science+Business Media (www.springer.com)
Preface
After visual signals (referring to image, video, graphics, and animation) are captured or generated, they undergo a variety of processings, including compression, enhancement, editing, retargeting, and transmission. These processes change the quality of visual signals. To measure the extent of such changes, visual quality assessment (VQA) has gained popularity as a hot research topic during the last decade. The psychological and physiological research results have been plugged into this research field to provide fundamental knowledge of the visual perception mechanism and theoretical support for developing VQA models. In addition, the newly developed computer vision, artificial intelligence, and machine learning techniques have b
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