Dimension Reduction of Images using Principal Component Analysis Algorithm

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@article{1702269,
      author = {Thet Thet Khaing, Phyu Sin Nyein, Myint Soe Khaing, Khaing Khaing Wai},
      title = {Dimension Reduction of Images using Principal Component Analysis Algorithm},
      journal = {Iconic Research And Engineering Journals},
      year = {2020},
      volume = {3},
      number = {11},
      pages = {39-42},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1702269.pdf},
      abstract = {Nowadays, the increasing volume of images is absolutely demanded in most of digital image processing and analysis. It is observed that high-resolution or high-dimensional images create many issues and challenges to deal with and, as a consequence, the compression of images has become an essential requirement in various applications related with images. In this paper, Principal Component Analysis (PCA), a dimensionality reduction algorithm, is applied in effectively compressing or reducing high-dimensionality of images.The main objective of the system is to show that applying PCA algorithm can efficiently perform not only in compressing of images but also in minimizing transmission time of images over the Internet. According to the experimental results, the transmission time of compressed images has achieved a significant improvement especially for the downloading activities via mobile devices.},
      keywords = {High-dimensional, High-resolution, Image Compression, Principal Component Analysis},
      month = {May},
  }