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Plenoptic Image Compression via Simplified Subaperture Projection

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Advances in Multimedia Information Processing – PCM 2018 (PCM 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11165))

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Abstract

In this paper, a generalized encoding architecture is proposed for compressing plenoptic images to reduce the redundancy among the subaperture images. Based on the homography analysis, simplification of subaperture projection is performed due to high correlations among adjacent views. Then, a pseudo-video sequence consisting of central subaperture images and a sequence consisting of residual images between the central image and the adjacent images are generated by the proposed partitioning and reordering methods. They are compressed by temporal coding tools in HEVC. The experimental results demonstrate that the proposed method outperforms state-of-the-art pseudo-video compression methods by an average of 25.3%/27.4%/21.7%/36.7%/5.3% bitrate saving with comparable/lower computational complexity.

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Acknowledgement

This work was supported in part by Shenzhen project JCYJ20170307153135771 and Foundation of Science and Technology Department of Sichuan Province 2017JZ0032c, China.

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Authors and Affiliations

  1. Graduate School at Shenzhen, Tsinghua University, Beijing, China

    Haixu Han, Jin Xin & Qionghai Dai

Authors
  1. Haixu Han
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  2. Jin Xin
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  3. Qionghai Dai
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Corresponding authors

Correspondence to Haixu Han or Jin Xin .

Editor information

Editors and Affiliations

  1. Hefei University of Technology, Hefei, China

    Richang Hong

  2. National Chiao Tung University, Hsinchu, Taiwan

    Wen-Huang Cheng

  3. University of Tokyo, Tokyo, Japan

    Toshihiko Yamasaki

  4. Hefei University of Technology, Hefei, China

    Meng Wang

  5. City University of Hong Kong, Hong Kong, Hong Kong

    Chong-Wah Ngo

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Han, H., Xin, J., Dai, Q. (2018). Plenoptic Image Compression via Simplified Subaperture Projection. In: Hong, R., Cheng, WH., Yamasaki, T., Wang, M., Ngo, CW. (eds) Advances in Multimedia Information Processing – PCM 2018. PCM 2018. Lecture Notes in Computer Science(), vol 11165. Springer, Cham. https://doi.org/10.1007/978-3-030-00767-6_26

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  • DOI: https://doi.org/10.1007/978-3-030-00767-6_26

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-00766-9

  • Online ISBN: 978-3-030-00767-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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