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Paper Abstract and Keywords
Presentation 2023-06-03 14:30
Video Encoding Scheme Using Autoencoder with Learning Decoded Image of Previous Frame
Koki Inoue, Atsushi Koike (Seikei Univ)
Abstract (in Japanese) (See Japanese page) 
(in English) In recent years, research on image processing using deep learning has been active, and an image coding method using neural networks has been proposed. In this paper, we propose a video coding method using an auto encoder. The encoding performance is improved by learning the decoded image of the previous frame during encoding, and video coding is performed. In this study, we conducted computer experiments using 4K video images to compare and evaluate two methods, one using a new autoencoder when learning decoded images and the other using a learned autoencoder. Compared to coding with a learned model, this method can improve the image quality with each successive frame.
Keyword (in Japanese) (See Japanese page) 
(in English) Neural network / Image processing / Video coding / / / / /  
Reference Info. ITE Tech. Rep., vol. 47, pp. 27-30, June 2023.
Paper #  
Date of Issue 2023-05-27 (AIT) 
ISSN Online edition: ISSN 2424-1970
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Conference Information
Committee IIEEJ AIT  
Conference Date 2023-06-03 - 2023-06-04 
Place (in Japanese) (See Japanese page) 
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To IIEEJ 
Conference Code 2023-06-IIEEJ-AIT 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Video Encoding Scheme Using Autoencoder with Learning Decoded Image of Previous Frame 
Sub Title (in English)  
Keyword(1) Neural network  
Keyword(2) Image processing  
Keyword(3) Video coding  
1st Author's Name Koki Inoue  
1st Author's Affiliation Seikei University (Seikei Univ)
2nd Author's Name Atsushi Koike  
2nd Author's Affiliation Seikei University (Seikei Univ)
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Speaker Author-1 
Date Time 2023-06-03 14:30:00 
Presentation Time 20 minutes 
Registration for IIEEJ 
Paper # AIT2023-131 
Volume (vol) vol.47 
Number (no) no.16 
Page pp.27-30 
Date of Issue 2023-05-27 (AIT) 

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