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European Conference on Computer Vision

ECCV 2020: Computer Vision – ECCV 2020 pp 309–324Cite as

Task-Aware Quantization Network for JPEG Image Compression

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12365))

Abstract

We propose to learn a deep neural network for JPEG image compression, which predicts image-specific optimized quantization tables fully compatible with the standard JPEG encoder and decoder. Moreover, our approach provides the capability to learn task-specific quantization tables in a principled way by adjusting the objective function of the network. The main challenge to realize this idea is that there exist non-differentiable components in the encoder such as run-length encoding and Huffman coding and it is not straightforward to predict the probability distribution of the quantized image representations. We address these issues by learning a differentiable loss function that approximates bitrates using simple network blocks—two MLPs and an LSTM. We evaluate the proposed algorithm using multiple task-specific losses—two for semantic image understanding and another two for conventional image compression—and demonstrate the effectiveness of our approach to the individual tasks.

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Notes

  1. 1.

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Acknowledgments

This work was partly supported by Kakao and Kakao Brain Corporation, and IITP grant funded by the Korea government (MSIT) (2016-0-00563, 2017-0-01779). We also thank Hyeonwoo Noh for fruitful discussions.

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Authors and Affiliations

  1. Department of ECE and ASRI, Seoul National University, Seoul, Korea

    Jinyoung Choi & Bohyung Han

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  1. Jinyoung Choi
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  2. Bohyung Han
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Correspondence to Bohyung Han .

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Editors and Affiliations

  1. University of Oxford, Oxford, UK

    Andrea Vedaldi

  2. Graz University of Technology, Graz, Austria

    Horst Bischof

  3. University of Freiburg, Freiburg im Breisgau, Germany

    Thomas Brox

  4. University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Jan-Michael Frahm

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Choi, J., Han, B. (2020). Task-Aware Quantization Network for JPEG Image Compression. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12365. Springer, Cham. https://doi.org/10.1007/978-3-030-58565-5_19

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  • DOI: https://doi.org/10.1007/978-3-030-58565-5_19

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-58564-8

  • Online ISBN: 978-3-030-58565-5

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