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Article Details

Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

Discrete Cosine Transformation based Image Data Compression Considering Image Restoration

Author 1: Kohei Arai

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Digital Object Identifier (DOI) : 10.14569/IJACSA.2020.0110616

Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 11 Issue 6, 2020.

  • Abstract and Keywords
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Abstract: Discrete Cosine Transformation (DCT) based image data compression considering image restoration is proposed. An image data compression method based on the compression (DCT) featuring an image restoration method is proposed. DCT image compression is widely used and has four major image defects. In order to reduce the noise and distortions, the proposed method expresses a set of parameters for the assumed distortion model based on an image restoration method. The results from the experiment with Landsat TM (Thematic Mapper) data of Saga show a good image compression performance of compression factor and image quality, namely, the proposed method achieved 25% of improvement of the compression factor compared to the existing method of DCT with almost comparable image quality between both methods.

Keywords: Discrete Cosine Transformation; data compression; image restoration; Landsat TM

Kohei Arai, “Discrete Cosine Transformation based Image Data Compression Considering Image Restoration” International Journal of Advanced Computer Science and Applications(IJACSA), 11(6), 2020. http://dx.doi.org/10.14569/IJACSA.2020.0110616

@article{Arai2020,
title = {Discrete Cosine Transformation based Image Data Compression Considering Image Restoration},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2020.0110616},
url = {http://dx.doi.org/10.14569/IJACSA.2020.0110616},
year = {2020},
publisher = {The Science and Information Organization},
volume = {11},
number = {6},
author = {Kohei Arai}
}


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