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Paper Abstract and Keywords
Presentation 2022-02-21 13:15
Towards Universal Deep Image Compression
Koki Tsubota (UTokyo), Hiroaki Akutsu (Hitachi), Kiyoharu Aizawa (UTokyo)
Abstract (in Japanese) (See Japanese page) 
(in English) In this paper, we investigate deep image compression towards universal usage. In image compression, it is desirable to be able to compress not only natural images but also images in a wide range of domains such as processed photographs, line drawings, and illustrations. However, deep image compression has been generally studied only for natural images and little has been studied for non-natural images. In this study, we first validate the existing deep image compression models using a dataset consisting of multiple domains. Then, we train a compression model on multiple domains and examine the performance on the training domains and unseen domains during training. This method is a baseline method for domain generalization and multi-domain learning. In experiments, we show that deep image compression methods trained on natural images achieve lower performance than traditional methods, especially at higher rates. We also show that while the average performance across multiple domains is higher when training on multiple domains than when training on a single domain, the best performance in each domain is achieved when training on only the evaluation domain.
Keyword (in Japanese) (See Japanese page) 
(in English) image compression / deep neural networks / domain generalization / multi-domain learning / / / /  
Reference Info. ITE Tech. Rep.
Paper #  
Date of Issue  
ISSN Print edition: ISSN 1342-6893  Online edition: ISSN 2424-1970
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Conference Information
Committee AIT ME MMS IEICE-IE IEICE-ITS  
Conference Date 2022-02-21 - 2022-02-22 
Place (in Japanese) (See Japanese page) 
Place (in English) online 
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Topics (in English)  
Paper Information
Registration To IEICE-IE 
Conference Code 2022-02-IE-ITS-AIT-ME-MMS 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Towards Universal Deep Image Compression 
Sub Title (in English)  
Keyword(1) image compression  
Keyword(2) deep neural networks  
Keyword(3) domain generalization  
Keyword(4) multi-domain learning  
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1st Author's Name Koki Tsubota  
1st Author's Affiliation The University of Tokyo (UTokyo)
2nd Author's Name Hiroaki Akutsu  
2nd Author's Affiliation Hitachi, Ltd. (Hitachi)
3rd Author's Name Kiyoharu Aizawa  
3rd Author's Affiliation The University of Tokyo (UTokyo)
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Speaker
Date Time 2022-02-21 13:15:00 
Presentation Time 15 
Registration for IEICE-IE 
Paper #  
Volume (vol) ITE-46 
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#Pages ITE- 
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