Facebook pixel tracking

The Science and Information (SAI) Organization publishes open-access peer-reviewed journals in computer science and artificial intelligence.

Contact Info
Website thesai.org
Follow Us
Contact Info
Follow Us
Research Article | Open Access |

Discrete Cosine Transformation based Image Data Compression Considering Image Restoration

Author 1: Kohei Arai
International Journal of Advanced Computer Science and Applications (IJACSA) · Vol. 11, No. 6 · Published 2020

DOI: https://doi.org/10.14569/IJACSA.2020.0110616

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

How to Cite this Article

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

Arai, Kohei. "Discrete Cosine Transformation based Image Data Compression Considering Image Restoration." International Journal of Advanced Computer Science and Applications, vol. 11, no. 6, 2020, https://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},
  volume    = {11},
  number    = {6},
  year      = {2020},
  publisher = {The Science and Information Organization},
  author    = {Kohei Arai},
  doi       = {10.14569/IJACSA.2020.0110616},
  url       = {https://doi.org/10.14569/IJACSA.2020.0110616}
}

Open Access — licensed under a Creative Commons Attribution 4.0 International License. Unrestricted use, distribution, and reproduction in any medium, even commercially, as long as the original work is properly cited.