Digital halftoning based on K-means clustering

This work presents a method based on the image content for digital halftoning using K-means clustering theory. Our algorithm applies to both a printer model and a model for the human visual system (HVS). First, the gray image is partitioned into two, three and four regions using K-means image segmentation method. Next, Each clustering uses the least-squares model-based (Lsmb) algorithm to obtain halftone image. Finally, analysis and simulation results show that the proposed algorithm produces better gray-scale halftone image quality when we increase the number of clustering with a certain range. The value of PSNR for two partitions is almost the same as that of the Lsmb algorithm, but for three and four partitions that the proposed algorithm achieves consistently better values of PSNR than the Lsmb algorithm.