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Video Deblocking Using Multipath Deep Neural Networks

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Technologies and Applications of Artificial Intelligence (TAAI 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 2075))

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Abstract

In this study, a video deblocking approach using multipath deep neural networks is proposed. The proposed approach contains temporal fusion subnet, variable-filter-size (VFS) subnet, and enhancement subnet. Video deblocking is performed via early fusion so that temporal correlations between adjacent video frames are employed. Based on the experimental results obtained in this study, in terms of two objective performance metrics and subjective evaluation, the performance of the proposed approach is better than those of four comparison approaches.

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Acknowledgements

This work was supported in part by National Science and Technology Council, Taiwan, Republic of China under grants MOST 111-2221-E-194-021 and NSTC 112-2221-E-194-030.

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Authors and Affiliations

  1. Department of Computer Science and Information Engineering, National Chung Cheng University, Chiayi, Taiwan, 621, Republic of China

    Ping-Peng Chou & Jin-Jang Leou

Authors
  1. Ping-Peng Chou
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  2. Jin-Jang Leou
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Corresponding author

Correspondence to Ping-Peng Chou .

Editor information

Editors and Affiliations

  1. National Yunlin University of Science and Technology, Douliu, Taiwan

    Chao-Yang Lee

  2. National Yunlin University of Science and Technology, Douliou, Taiwan

    Chun-Li Lin

  3. National Yunlin University of Science and Technology, Douliou, Taiwan

    Hsuan-Ting Chang

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

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Chou, PP., Leou, JJ. (2024). Video Deblocking Using Multipath Deep Neural Networks. In: Lee, CY., Lin, CL., Chang, HT. (eds) Technologies and Applications of Artificial Intelligence. TAAI 2023. Communications in Computer and Information Science, vol 2075. Springer, Singapore. https://doi.org/10.1007/978-981-97-1714-9_3

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  • DOI: https://doi.org/10.1007/978-981-97-1714-9_3

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-97-1713-2

  • Online ISBN: 978-981-97-1714-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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