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| Presentation | 2021-01-21 14:45 [Invited Talk] GAN-based Image Coding Methods for Maximizing Subjective Image Quality Shinobu Kudo (NTT)  IE2020-37 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | The increasing image resolution and the spread of IoT devices require more efficient video storage and transmission systems. Conventional image quality evaluation criteria such as peak signal-to-noise ratio and structural similarity are based on the difference of signal values. In order to improve the coding efficiency, a method based on a new evaluation criteria has been proposed, where that allows the data to be different from the original signal value if there is no subjective visual discomfort. In this paper, we introduce our generative adversarial networks (GAN)-based image coding methods for maximizing subjective image quality. |
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| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Image coding / Deep learning / Generative adversarial networks / Subjective image quality / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 120, no. 329, IE2020-37, pp. 9-13, Jan. 2021. | |
| Paper # | IE2020-37 | |
| Date of Issue | 2021-01-14 (IE) | |
| ISSN | 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 | IE2020-37 |
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| Conference Information | |
| Committee | IE |
| Conference Date | 2021-01-21 - 2021-01-21 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Online |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Image Processing, Image Coding, etc |
| Paper Information | |
| Registration To | IE |
| Conference Code | 2021-01-IE |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | GAN-based Image Coding Methods for Maximizing Subjective Image Quality |
| Sub Title (in English) | |
| Keyword(1) | Image coding |
| Keyword(2) | Deep learning |
| Keyword(3) | Generative adversarial networks |
| Keyword(4) | Subjective image quality |
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| 1st Author's Name | Shinobu Kudo |
| 1st Author's Affiliation | Nippon Telegraph and Telephone Corporation (NTT) |
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| Speaker | Author-1 |
| Date Time | 2021-01-21 14:45:00 |
| Presentation Time | 35 minutes |
| Registration for | IE |
| Paper # | IE2020-37 |
| Volume (vol) | vol.120 |
| Number (no) | no.329 |
| Page | pp.9-13 |
| #Pages | 5 |
| Date of Issue | 2021-01-14 (IE) |
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