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Paper Abstract and Keywords
Presentation 2019-10-04 14:00
3D CG Image Quality Assessment Including Noise Removal Based on Sparse Dictionary Learning Coding
Norifumi Kawabata (Tokyo Univ. of Science) IMQ2019-6
Abstract (in Japanese) (See Japanese page) 
(in English) By appearing of high-definition and high-quality images, it comes to increase many chance to process image big data. If it is possible to process images quickly, it is very good, however, all people are not always in high quality environment. Therefore, it is important to discuss how to represent image data. There were many studies for image processing method using sparse coding which represented a part or overall of image data by combination as less as possible from component of dictionary to represent image data in advance. However, from a view of image quality, in case it is satisfied as requirement, there is not discussion whether it is possible or not to utilize sparse coding as meaning image representation. Therefore, we need to clarify these points. In this paper, first, we carried out dictionary learning to process the sparse coding in advance, and then, we added six types of noise for 3D CG images. Next, we processed noise removal based on sparse coding theory and dictionary learning. Before and after image processing, we discussed improvement of image quality evaluation value eventually by measuring PSNR.
Keyword (in Japanese) (See Japanese page) 
(in English) Sparse Coding / Dictionary Learning / Noise Addition / Noise Removal / Image Quality Assessment / / /  
Reference Info. IEICE Tech. Rep., vol. 119, no. 215, IMQ2019-6, pp. 1-10, Oct. 2019.
Paper # IMQ2019-6 
Date of Issue 2019-09-27 (IMQ) 
ISSN Print edition: ISSN 0913-5685    Online edition: ISSN 2432-6380
All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
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Conference Information
Committee IMQ  
Conference Date 2019-10-04 - 2019-10-04 
Place (in Japanese) (See Japanese page) 
Place (in English) Osaka University 
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To IMQ 
Conference Code 2019-10-IMQ 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) 3D CG Image Quality Assessment Including Noise Removal Based on Sparse Dictionary Learning Coding 
Sub Title (in English)  
Keyword(1) Sparse Coding  
Keyword(2) Dictionary Learning  
Keyword(3) Noise Addition  
Keyword(4) Noise Removal  
Keyword(5) Image Quality Assessment  
1st Author's Name Norifumi Kawabata  
1st Author's Affiliation Tokyo University of Science (Tokyo Univ. of Science)
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Speaker Author-1 
Date Time 2019-10-04 14:00:00 
Presentation Time 25 minutes 
Registration for IMQ 
Paper # IMQ2019-6 
Volume (vol) vol.119 
Number (no) no.215 
Page pp.1-10 
#Pages 10 
Date of Issue 2019-09-27 (IMQ) 

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