Abstract
Image compression is indispensable in many visual applications. Recently, learned image compression (LIC) using deep learning has surpassed traditional image codecs such as JPEG in terms of compression efficiency but at the cost of increased complexity. Thus, employing LIC in resource-limited environments is challenging. In this paper, we propose an LIC model using a look-up table (LUT) to effectively reduce the complexity. Specifically, we design an LUT replacing the entropy decoder by analyzing its input characteristics and accordingly developing a dynamic sampling method for determining the indices of the LUT. Experimental results show that the proposed method achieves better compression efficiency than traditional codecs with faster runtime than existing LIC models.
Original language | English |
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Title of host publication | Neural Information Processing - 30th International Conference, ICONIP 2023, Proceedings |
Editors | Biao Luo, Long Cheng, Zheng-Guang Wu, Hongyi Li, Chaojie Li |
Publisher | Springer Science and Business Media Deutschland GmbH |
Pages | 430-441 |
Number of pages | 12 |
ISBN (Print) | 9789819981472 |
DOIs | |
Publication status | Published - 2024 |
Event | 30th International Conference on Neural Information Processing, ICONIP 2023 - Changsha, China Duration: 2023 Nov 20 → 2023 Nov 23 |
Publication series
Name | Communications in Computer and Information Science |
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Volume | 1966 CCIS |
ISSN (Print) | 1865-0929 |
ISSN (Electronic) | 1865-0937 |
Conference
Conference | 30th International Conference on Neural Information Processing, ICONIP 2023 |
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Country/Territory | China |
City | Changsha |
Period | 23/11/20 → 23/11/23 |
Bibliographical note
Publisher Copyright:© 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
All Science Journal Classification (ASJC) codes
- General Computer Science
- General Mathematics