International Journal of Computer Science & Network Security

International Journal of Computer Science & Network Security (국제컴퓨터통신보호논문지학회)
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Image compression using K-mean clustering algorithm

  • Munshi, Amani (Department of Computer Science and Information System, Umm Al-Qura University) ;
  • Alshehri, Asma (Department of Computer Science and Information System, Umm Al-Qura University) ;
  • Alharbi, Bayan (Department of Computer Science and Information System, Umm Al-Qura University) ;
  • AlGhamdi, Eman (Department of Computer Science and Information System, Umm Al-Qura University) ;
  • Banajjar, Esraa (Department of Computer Science and Information System, Umm Al-Qura University) ;
  • Albogami, Meznah (Department of Computer Science and Information System, Umm Al-Qura University) ;
  • Alshanbari, Hanan S. (Department of Computer Science and Information System, Umm Al-Qura University)
  • Received : 2021.09.05
  • Published : 2021.09.30
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Abstract

With the development of communication networks, the processes of exchanging and transmitting information rapidly developed. As millions of images are sent via social media every day, also wireless sensor networks are now used in all applications to capture images such as those used in traffic lights, roads and malls. Therefore, there is a need to reduce the size of these images while maintaining an acceptable degree of quality. In this paper, we use Python software to apply K-mean Clustering algorithm to compress RGB images. The PSNR, MSE, and SSIM are utilized to measure the image quality after image compression. The results of compression reduced the image size to nearly half the size of the original images using k = 64. In the SSIM measure, the higher the K, the greater the similarity between the two images which is a good indicator to a significant reduction in image size. Our proposed compression technique powered by the K-Mean clustering algorithm is useful for compressing images and reducing the size of images.

Keywords

Acknowledgement

We would like to express our high regard to our families for their encouragement and inspiration supported us, and without which, we would not have come this far. Many thanks go to supervisor Dr. Hanan and our deep appreciation for continuous guidance and her prompt help and provide advice support to helped us finalize our project and offered deep insight into the study. Also, special thanks should be given to group friends that worked on this project for the kindness, cooperation, positive energy, constant motivational words and caring throughout the whole project.

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