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Content-Adaptive Optimization Framework for Universal Deep Image Compression

Koki TSUBOTA, Kiyoharu AIZAWA

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Summary :

While deep image compression performs better than traditional codecs like JPEG on natural images, it faces a challenge as a learning-based approach: compression performance drastically decreases for out-of-domain images. To investigate this problem, we introduce a novel task that we call universal deep image compression, which involves compressing images in arbitrary domains, such as natural images, line drawings, and comics. Furthermore, we propose a content-adaptive optimization framework to tackle this task. This framework adapts a pre-trained compression model to each target image during testing for addressing the domain gap between pre-training and testing. For each input image, we insert adapters into the decoder of the model and optimize the latent representation extracted by the encoder and the adapter parameters in terms of rate-distortion, with the adapter parameters transmitted per image. To achieve the evaluation of the proposed universal deep compression, we constructed a benchmark dataset containing uncompressed images of four domains: natural images, line drawings, comics, and vector arts. We compare our proposed method with non-adaptive and existing adaptive compression methods, and the results show that our method outperforms them. Our code and dataset are publicly available at https://github.com/kktsubota/universal-dic.

Publication
IEICE TRANSACTIONS on Information Vol.E107-D No.2 pp.201-211
Publication Date
2024/02/01
Publicized
2023/10/24
Online ISSN
1745-1361
DOI
10.1587/transinf.2023EDP7114
Type of Manuscript
PAPER
Category
Image Processing and Video Processing

Authors

Koki TSUBOTA
  The University of Tokyo
Kiyoharu AIZAWA
  The University of Tokyo

Keyword