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Early Exit or Not: Resource-Efficient Blind Quality Enhancement for Compressed Images

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Computer Vision – ECCV 2020 (ECCV 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12361))

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Abstract

Lossy image compression is pervasively conducted to save communication bandwidth, resulting in undesirable compression artifacts. Recently, extensive approaches have been proposed to reduce image compression artifacts at the decoder side; however, they require a series of architecture-identical models to process images with different quality, which are inefficient and resource-consuming. Besides, it is common in practice that compressed images are with unknown quality and it is intractable for existing approaches to select a suitable model for blind quality enhancement. In this paper, we propose a resource-efficient blind quality enhancement (RBQE) approach for compressed images. Specifically, our approach blindly and progressively enhances the quality of compressed images through a dynamic deep neural network (DNN), in which an early-exit strategy is embedded. Then, our approach can automatically decide to terminate or continue enhancement according to the assessed quality of enhanced images. Consequently, slight artifacts can be removed in a simpler and faster process, while the severe artifacts can be further removed in a more elaborate process. Extensive experiments demonstrate that our RBQE approach achieves state-of-the-art performance in terms of both blind quality enhancement and resource efficiency.

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Notes

  1. 1.

    HM16.5 is the latest HEVC reference software.

  2. 2.

    Note that the definition of FLOPs follows [15, 18], i.e., the number of multiply-adds.

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Acknowledgment

This work was supported by the NSFC under Project 61876013, Project 61922009, and Project 61573037.

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Authors and Affiliations

  1. School of Electronic and Information Engineering, Beihang University, Beijing, China

    Qunliang Xing, Mai Xu, Tianyi Li & Zhenyu Guan

  2. Hangzhou Innovation Institute of Beihang University, Hangzhou, China

    Mai Xu

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  1. Qunliang Xing
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  2. Mai Xu
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  3. Tianyi Li
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  4. Zhenyu Guan
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Corresponding author

Correspondence to Mai Xu .

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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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Xing, Q., Xu, M., Li, T., Guan, Z. (2020). Early Exit or Not: Resource-Efficient Blind Quality Enhancement for Compressed Images. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12361. Springer, Cham. https://doi.org/10.1007/978-3-030-58517-4_17

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  • DOI: https://doi.org/10.1007/978-3-030-58517-4_17

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