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A dual-residual network for JPEG compression artifacts reduction

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Abstract

Current image compression techniques such as JPEG and WebP are widely applied in the age of information. However, lossy compression like JPEG in its nature will introduce visually annoying artifacts while saving Internet bandwidth and storage space. The artifacts such as blocking and ringing are especially sharp at low bitrates. In this paper, we propose a novel dual-residual network to reduce compression artifacts caused by lossy compression codecs. This network directly learns an end-to-end mapping between the distorted image processed by JPEG or other compression methods and the original image, which takes decompressed images with blocking artifacts as input and produces clearer images with less artifacts. Experiments results on the test dataset demonstrate the efficiency of the proposed model, especially at very low bitrates.

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Authors and Affiliations

  1. Department of Biomedical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, China

    Jianfei Li, Dongsheng Li, Chunxiao Chen & Qiang Yan

  2. Department of Measurement and Testing Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, China

    Xiong Lu

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  1. Jianfei Li
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  2. Dongsheng Li
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  3. Chunxiao Chen
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  4. Qiang Yan
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  5. Xiong Lu
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Correspondence to Chunxiao Chen.

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Li, J., Li, D., Chen, C. et al. A dual-residual network for JPEG compression artifacts reduction. SIViP 15, 485–491 (2021). https://doi.org/10.1007/s11760-020-01768-3

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  • DOI: https://doi.org/10.1007/s11760-020-01768-3

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