Performance Comparison of K-means and Rectangle Segmentation Algorithms in Compression of Color Images

Technology, lot of Images are transported in compressed data formats.The use of color in image processing is motivated by two Principal factors; First color is a powerful descriptor that often Simplifies object identification and extraction from a scene. Second, human can discerned thousands of color shades and Intensities, compared to about only two dozen shades of gray.The main objective of this paper is to compare performance based on quality measures towards the compression of color images using K-means and Rectangle segmentation algorithms. In this paper, color image compression using Rectangle Segmentation is proposed, in which adjacent pixel points satisfying consistency condition are viewed as the same image block. Also, without the restriction of square which abides to 2n, the image block can be rectangle which reduces the amount of block, and improves the compression ratio. The algorithm is tested On several color images and results are compared with other image compression technique like K-means in terms of performance metrics like PSNR, CR,Execution Time etc.The experimental resuls show the K-mean algorithm is better than Rectangle segmentation in terms of PSNR leads to good quality Image, effect of changing cluster size on image quality is noticed. Keywords-Color Image compression, Rectangle segmentation