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Paper Abstract and Keywords
Presentation 2016-05-20 15:40
Multi-hypothesis Pixel Predictor using Cartesian Genetic Programming
Yiding Zhao, Seishi Takamura (NTT) SIP2016-33 IE2016-33 PRMU2016-33 MI2016-33
Abstract (in Japanese) (See Japanese page) 
(in English) In lossless image coding, pixel prediction plays an important role in utilizing adjacent pixel values to reduce residual information thus further achieving less entropy. Recent work has also explored the concurrent evolution of both pixel prediction and context modeling with the purpose of investigating the evolved pair. However, it is rare among current studies regarding whether a single equation of predictor can generalize the image specific characteristics. In the light of this, we pro-pose a system that allows of choosing one prediction value from among multiple prediction values for each pixel element. We evolve a combination of predictor, predictor selector and context modeling using Cartesian Genetic Programming (CGP), a directed cyclic graph based self-modified method. Experimental results show up to 3.8% less entropy against one of the best conventional compression methods, CALIC. Besides this, we also found that the number of predictors in use as well as abun-dance of image characteristics have affected evolution performance of our model.
Keyword (in Japanese) (See Japanese page) 
(in English) lossless image coding / genetic programming / / / / / /  
Reference Info. IEICE Tech. Rep., vol. 116, no. 37, IE2016-33, pp. 173-178, May 2016.
Paper # IE2016-33 
Date of Issue 2016-05-12 (SIP, IE, PRMU, MI) 
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 SIP2016-33 IE2016-33 PRMU2016-33 MI2016-33

Conference Information
Committee PRMU IE MI SIP  
Conference Date 2016-05-19 - 2016-05-20 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
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Paper Information
Registration To IE 
Conference Code 2016-05-PRMU-IE-MI-SIP 
Language English 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Multi-hypothesis Pixel Predictor using Cartesian Genetic Programming 
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Keyword(1) lossless image coding  
Keyword(2) genetic programming  
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1st Author's Name Yiding Zhao  
1st Author's Affiliation Nippon Telegraph and Telephone Corporation (NTT)
2nd Author's Name Seishi Takamura  
2nd Author's Affiliation Nippon Telegraph and Telephone Corporation (NTT)
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Speaker Author-1 
Date Time 2016-05-20 15:40:00 
Presentation Time 30 minutes 
Registration for IE 
Paper # SIP2016-33, IE2016-33, PRMU2016-33, MI2016-33 
Volume (vol) vol.116 
Number (no) no.36(SIP), no.37(IE), no.38(PRMU), no.39(MI) 
Page pp.173-178 
#Pages 6 
Date of Issue 2016-05-12 (SIP, IE, PRMU, MI) 

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