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Image compression using K-mean clustering algorithm


Amani Munshi , Asma Alshehri , Bayan Alharbi, Eman AlGhamdi, Esraa Banajjar, Meznah Albogami, Hanan S. Alshanbari


Vol. 21  No. 9  pp. 275-280


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.


Types of images, RGB Images, Image Compression, K-mean Clustering, Peak Signal-to-Noise Ratio (PSNR), Mean Square Error (MSE), Structural Similarity Index Measure (SSIM).