Weighted Least-squares Color Digital Halftoning

In this paper, we propose a weighted least-squares based color halftoning model from human vision system(HVS) model and an efficient iterative strategy using color image statistical information. The statistics of the mean and variance of the color pixel of each clustering are calculated. The energy function is constructed using the weighted least squares method, which the reciprocal of the variance of the segmented regions are referred to as the weighting operator. Starting with an initial halftone.The analysis and simulation results show that the proposed algorithm produces better color halftone image quality. A performance measure for halftone images is used to evaluate our algorithm. The value of MSEv and PSNR for the partitions regions that the proposed algorithm achieves consistently better values of MSEv and PSNR than the model-based color halftoning algorithm. Introduction The goal of digital color halftoning is to create the perception of a continuous-tone color image using the limited spatio-colorful discriminability of the human visual system.The algorithm for digital black and white halftoning methods can be categorized into three classes, which included error diffusion[1,2], dithering with blue noise[3], and direct binary search[4]. Color Halftoning is a method for creating the illusion of continuous tone output with a low-bit device. Effective digital color halftoning can substantially improve the quality of rendered images at minimal cost. Flohr et al. used the total squared error in a luminance/chrominance-based space as the metric for their model-based color halftoning algorithm[5] . A Ufuk exploited the differences in how the human viewers respond to luminance and chrominance information and use the total squared error[6]. Image Segmentation Using Fuzzy c-means Clustering In fuzzy clustering, every point has a degree of belonging to clusters, as in fuzzy logic, rather than belonging completely to just one cluster. Thus, points on the edge of a cluster, may be in the cluster to a lesser degree than points in the center of cluster. An overview and comparison of different fuzzy clustering algorithms is available.[7] Any point x has a set of coefficients giving the degree of being in the kth cluster wk(x). With fuzzy c-means, the centroid of a cluster is the mean of all points, weighted by their degree of belonging to the cluster: ( ) ( ) m k x k m k x w x x c w x = ∑ ∑ (1) The degree of belonging, wk(x), is related inversely to the distance from x to the cluster center as calculated on the previous pass. It also depends on a parameter m that controls how much weight is given to the closest center. The fuzzy c-means algorithm is very similar to the k-means algorithm by choose a number of clusters[8]. International Conference on Intelligent Systems Research and Mechatronics Engineering (ISRME 2015) © 2015. The authors Published by Atlantis Press 84 Weighted Least-squares Color Halftoning In this paper, we use the Flohr HVS model to design an efficient color halftoning algorithm. The luminance spatial frequency response model ( , ) y Y H u v − −