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Distributed Coding and Transmission Scheme for Wireless Communication of Satellite Images

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Computer and Information Science (ICIS 2017)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 719))

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Abstract

In this chapter, we propose a novel coding and transmission scheme for satellite images broadcasting. First, we use a 2D-DWT to divide full-size satellite image band into four sub-bands; three of them are the details and a small version of image band in LL sub-band. Second, our scheme utilizes coset coding based on distributed source coding (DSC) for the LL sub-band to achieve high compression efficiency and a low encoding complexity. After that, without syndrome coding, the transmission power is directly allocated to band details and coset values according to their distributions and magnitudes without forward error correction (FEC). Finally, these data are transformed by Hadamard matrix and transmitted over a dense constellation. Experiments on satellite images demonstrate that the proposed scheme improve the average image quality by 2.28 dB, 2.83 dB and 3.66 dB over LineCast, SoftCast-3D and SoftCast-2D, respectively, and it achieves up to 6.26 dB gain over JPEG2000 with FEC.

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Authors and Affiliations

  1. Department of Computer Science, Harbin Institute of Technology, Harbin, 150001, China

    Ahmed Hagag, Ibrahim Omara, Souleyman Chaib & Xiaopeng Fan

  2. Faculty of Electronic Engineering, Department of Electronics and Electrical Communications, Menoufia University, Menouf, 32952, Egypt

    Fathi E. Abd El-Samie

Authors
  1. Ahmed Hagag
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  2. Ibrahim Omara
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  3. Souleyman Chaib
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  4. Xiaopeng Fan
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  5. Fathi E. Abd El-Samie
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Corresponding author

Correspondence to Ahmed Hagag .

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Editors and Affiliations

  1. Software Engineering and Information Technology Institute, Central Michigan University, Mt. Pleasant, MI, USA

    Roger Lee

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Hagag, A., Omara, I., Chaib, S., Fan, X., Abd El-Samie, F.E. (2018). Distributed Coding and Transmission Scheme for Wireless Communication of Satellite Images. In: Lee, R. (eds) Computer and Information Science. ICIS 2017. Studies in Computational Intelligence, vol 719. Springer, Cham. https://doi.org/10.1007/978-3-319-60170-0_3

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  • DOI: https://doi.org/10.1007/978-3-319-60170-0_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-60169-4

  • Online ISBN: 978-3-319-60170-0

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