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LUT-LIC: Look-Up Table-Assisted Learned Image Compression

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Neural Information Processing (ICONIP 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1966))

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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.

This work was supported in part by the Ministry of Trade, Industry and Energy (MOTIE) under Grant P0014268, and in part by the NRF grant funded by the Korea government (MSIT) (2021R1A2C2011474).

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Authors and Affiliations

  1. Yonsei University, Seoul, Republic of Korea

    SeungEun Yu & Jong-Seok Lee

Authors
  1. SeungEun Yu
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  2. Jong-Seok Lee
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Corresponding author

Correspondence to Jong-Seok Lee .

Editor information

Editors and Affiliations

  1. School of Automation, Central South University, Changsha, China

    Biao Luo

  2. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Long Cheng

  3. Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou, China

    Zheng-Guang Wu

  4. School of Automation, Guangdong University of Technology, Guangzhou, China

    Hongyi Li

  5. School of Electrical Engineering and Telecommunications, UNSW Sydney, Sydney, NSW, Australia

    Chaojie Li

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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Yu, S., Lee, JS. (2024). LUT-LIC: Look-Up Table-Assisted Learned Image Compression. In: Luo, B., Cheng, L., Wu, ZG., Li, H., Li, C. (eds) Neural Information Processing. ICONIP 2023. Communications in Computer and Information Science, vol 1966. Springer, Singapore. https://doi.org/10.1007/978-981-99-8148-9_34

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  • DOI: https://doi.org/10.1007/978-981-99-8148-9_34

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-8147-2

  • Online ISBN: 978-981-99-8148-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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