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| Presentation | 2018-05-25 14:15 Optimal Design and Coded Image Quality Assessment of the Multi-view and Super-resolution Images Based on Structure of Convolutional Neural Network Norifumi Kawabata (Nagoya Univ.)  IMQ2018-3 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | The image screen resolution by viewpoints is low, comparing to single-view images since there are many viewpoints for multi-view images. Therefore, the super-resolution image processing is often carried out in the case of presenting image. In the case of transforming from low to high resolution image, the number of output data is more than that of input data. From this, there are many studies for super-resolution processing method using neural network. Furthermore, we can come to approach on super-resolution processing based on deep learning by appearing deep learning tools. These performance are shown by applying the only deep learning theory for super-resolution processing. However, we consider that the optimal condition and design for super-resolution processing are achieved better by improving these algorithms and setting parameter appropriately. In this paper, first, we carried out experiments on optimal condition and design of super-resolution processing for the multi-view 3D images encoded and decoded by H.265/HEVC, focused on structure of convolutional neural network by using Chainer. And then, we assessed for the generated images quality objectively, and compare to each image. Finally, we discussed for experimental results. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Multi-view 3D Image / Super-resolution / Chainer / Convolutional Neural Network (CNN) / H.265/HEVC / Peak Signal to Noise Ratio (PSNR) / / | |
| Reference Info. | IEICE Tech. Rep., vol. 118, no. 65, IMQ2018-3, pp. 15-20, May 2018. | |
| Paper # | IMQ2018-3 | |
| Date of Issue | 2018-05-18 (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 | IMQ2018-3 |
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| Conference Information | |
| Committee | IMQ |
| Conference Date | 2018-05-25 - 2018-05-25 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Chibe Institute of Technology, Tsudanuma Campus |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Image Media Quality |
| Paper Information | |
| Registration To | IMQ |
| Conference Code | 2018-05-IMQ |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Optimal Design and Coded Image Quality Assessment of the Multi-view and Super-resolution Images Based on Structure of Convolutional Neural Network |
| Sub Title (in English) | |
| Keyword(1) | Multi-view 3D Image |
| Keyword(2) | Super-resolution |
| Keyword(3) | Chainer |
| Keyword(4) | Convolutional Neural Network (CNN) |
| Keyword(5) | H.265/HEVC |
| Keyword(6) | Peak Signal to Noise Ratio (PSNR) |
| Keyword(7) | |
| Keyword(8) | |
| 1st Author's Name | Norifumi Kawabata |
| 1st Author's Affiliation | Nagoya University (Nagoya Univ.) |
| 2nd Author's Name | |
| 2nd Author's Affiliation | () |
| 3rd Author's Name | |
| 3rd Author's Affiliation | () |
| 4th Author's Name | |
| 4th Author's Affiliation | () |
| 5th Author's Name | |
| 5th Author's Affiliation | () |
| 6th Author's Name | |
| 6th Author's Affiliation | () |
| Speaker | Author-1 |
| Date Time | 2018-05-25 14:15:00 |
| Presentation Time | 25 minutes |
| Registration for | IMQ |
| Paper # | IMQ2018-3 |
| Volume (vol) | vol.118 |
| Number (no) | no.65 |
| Page | pp.15-20 |
| #Pages | 6 |
| Date of Issue | 2018-05-18 (IMQ) |
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