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| 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 |
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| 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 | |
| Copyright and reproduction |
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) | |
| Download PDF | IMQ2019-6 |
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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 |
| Keyword(6) | |
| Keyword(7) | |
| Keyword(8) | |
| 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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