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Paper Abstract and Keywords
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
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
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 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)  
1st Author's Name Norifumi Kawabata  
1st Author's Affiliation Nagoya University (Nagoya Univ.)
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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 
Date of Issue 2018-05-18 (IMQ) 

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