| Peer-Reviewed

An Enhanced Satellite Image Compression Using Hybrid (DWT, DCT and SVD) Algorithm

Received: 29 January 2021     Accepted: 14 February 2021     Published: 27 February 2021
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Abstract

Storing images consumes a lot of storage space due to the large number of bits used to represent them. These bits are comprised of pixels that make up the image. These heavy images are also very difficult to be transmitted over channels due to their great size. Compression involves the reduction of the amount of bits used in representing an image and consequently reducing the size of that image without losing any detail from the image. There are so many image compression techniques used to achieve this feat, but they have drawbacks such as lack of a model that can compress a satellite image, lack of adaptive reversible techniques for compression and inability to compress complex images such as satellite images. This work, proposed an hybrid Discrete Wavelet Transform, Discrete Cosine Transform and Singular Value Decomposition (DCT-DWT-SVD)-based techniques for satellite image compression. The algorithms were combined to breakdown the images into blocks/matrices and assign certain values to them depending on the concentration of colour bits around the region. The areas with higher bits are reduced and compression is achieved. A hybrid methodology of Agile and Waterfall model was used in this approach. The model was implemented using MATLAB and satellite images gotten from a public repository. The Compression ratio was 0.9990 and 0.9941 for the two images compressed which shows high and efficient compression. The Mean Square Error (MSE) was 2.51 which is low. This study will be beneficial to remote sensor companies, Graphic designers and the research community.

Published in American Journal of Computer Science and Technology (Volume 4, Issue 1)
DOI 10.11648/j.ajcst.20210401.11
Page(s) 1-10
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2021. Published by Science Publishing Group

Keywords

Satellite, Image, Image Compression, Singular Value Decomposition, Image Transform

References
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Cite This Article
  • APA Style

    Moko Anasuodei, Onuodu Friday Eleonu. (2021). An Enhanced Satellite Image Compression Using Hybrid (DWT, DCT and SVD) Algorithm. American Journal of Computer Science and Technology, 4(1), 1-10. https://doi.org/10.11648/j.ajcst.20210401.11

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    ACS Style

    Moko Anasuodei; Onuodu Friday Eleonu. An Enhanced Satellite Image Compression Using Hybrid (DWT, DCT and SVD) Algorithm. Am. J. Comput. Sci. Technol. 2021, 4(1), 1-10. doi: 10.11648/j.ajcst.20210401.11

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    AMA Style

    Moko Anasuodei, Onuodu Friday Eleonu. An Enhanced Satellite Image Compression Using Hybrid (DWT, DCT and SVD) Algorithm. Am J Comput Sci Technol. 2021;4(1):1-10. doi: 10.11648/j.ajcst.20210401.11

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  • @article{10.11648/j.ajcst.20210401.11,
      author = {Moko Anasuodei and Onuodu Friday Eleonu},
      title = {An Enhanced Satellite Image Compression Using Hybrid (DWT, DCT and SVD) Algorithm},
      journal = {American Journal of Computer Science and Technology},
      volume = {4},
      number = {1},
      pages = {1-10},
      doi = {10.11648/j.ajcst.20210401.11},
      url = {https://doi.org/10.11648/j.ajcst.20210401.11},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajcst.20210401.11},
      abstract = {Storing images consumes a lot of storage space due to the large number of bits used to represent them. These bits are comprised of pixels that make up the image. These heavy images are also very difficult to be transmitted over channels due to their great size. Compression involves the reduction of the amount of bits used in representing an image and consequently reducing the size of that image without losing any detail from the image. There are so many image compression techniques used to achieve this feat, but they have drawbacks such as lack of a model that can compress a satellite image, lack of adaptive reversible techniques for compression and inability to compress complex images such as satellite images. This work, proposed an hybrid Discrete Wavelet Transform, Discrete Cosine Transform and Singular Value Decomposition (DCT-DWT-SVD)-based techniques for satellite image compression. The algorithms were combined to breakdown the images into blocks/matrices and assign certain values to them depending on the concentration of colour bits around the region. The areas with higher bits are reduced and compression is achieved. A hybrid methodology of Agile and Waterfall model was used in this approach. The model was implemented using MATLAB and satellite images gotten from a public repository. The Compression ratio was 0.9990 and 0.9941 for the two images compressed which shows high and efficient compression. The Mean Square Error (MSE) was 2.51 which is low. This study will be beneficial to remote sensor companies, Graphic designers and the research community.},
     year = {2021}
    }
    

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    AU  - Moko Anasuodei
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    JF  - American Journal of Computer Science and Technology
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    AB  - Storing images consumes a lot of storage space due to the large number of bits used to represent them. These bits are comprised of pixels that make up the image. These heavy images are also very difficult to be transmitted over channels due to their great size. Compression involves the reduction of the amount of bits used in representing an image and consequently reducing the size of that image without losing any detail from the image. There are so many image compression techniques used to achieve this feat, but they have drawbacks such as lack of a model that can compress a satellite image, lack of adaptive reversible techniques for compression and inability to compress complex images such as satellite images. This work, proposed an hybrid Discrete Wavelet Transform, Discrete Cosine Transform and Singular Value Decomposition (DCT-DWT-SVD)-based techniques for satellite image compression. The algorithms were combined to breakdown the images into blocks/matrices and assign certain values to them depending on the concentration of colour bits around the region. The areas with higher bits are reduced and compression is achieved. A hybrid methodology of Agile and Waterfall model was used in this approach. The model was implemented using MATLAB and satellite images gotten from a public repository. The Compression ratio was 0.9990 and 0.9941 for the two images compressed which shows high and efficient compression. The Mean Square Error (MSE) was 2.51 which is low. This study will be beneficial to remote sensor companies, Graphic designers and the research community.
    VL  - 4
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Author Information
  • Department of Computer Science and informatics, Federal University Otuoke, Otuoke, Nigeria

  • Department of Computer Science, University of Port-Harcourt, Port-Harcourt, Nigeria

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