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Color quantization is a way to contribute to the reduction of the storage while maintaining image quality. Color quantization reduces the number of distinct colors used in an image with the intention that the new image should be as visually similar as possible to the original image. Therefore, K-Means++ Clustering algorithm is proposed to analyze an image’s color distribution and produce the color palette for transforming to a quantized image. K-Means++ is an amendment to the K-Means algorithm in order to surmount the arbitrary selection of the initial cluster cente r.  In this paper, the tests were conducted on several images with numbers of K value: 32, 64, 128, and 256. The results show that K-Means++ algorithm yields higher PSNR values and lower file size   with   considerable   PSNR and file size percentage decreasescompared to K-Means algorithm  ; 2.58% and 1.05%.

Quality and size assessment of quantized images using K-Means++ clustering