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

Moko Anasuodei; Onuodu Friday Eleonu

doi:doi:10.11648/j.ajcst.20210401.11

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

Moko Anasuodei, Onuodu Friday Eleonu

Published in American Journal of Computer Science and Technology (Volume 4, Issue 1)

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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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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TY - JOUR
T1 - An Enhanced Satellite Image Compression Using Hybrid (DWT, DCT and SVD) Algorithm
AU - Moko Anasuodei
AU - Onuodu Friday Eleonu
Y1 - 2021/02/27
PY - 2021
N1 - https://doi.org/10.11648/j.ajcst.20210401.11
DO - 10.11648/j.ajcst.20210401.11
T2 - American Journal of Computer Science and Technology
JF - American Journal of Computer Science and Technology
JO - American Journal of Computer Science and Technology
SP - 1
EP - 10
PB - Science Publishing Group
SN - 2640-012X
UR - https://doi.org/10.11648/j.ajcst.20210401.11
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
IS - 1
ER -

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Author Information

Moko Anasuodei

Department of Computer Science and informatics, Federal University Otuoke, Otuoke, Nigeria
Onuodu Friday Eleonu

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

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Plain Text BibTeX RIS

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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