Grayscale Digital Halftoning Using Optimization Techniques

Abstract

In this paper a complete outline of advanced Digital halftoning techniques is given. This paper explains halftoning from its definition, application of halftoning to different useful techniques which are improved to give a high signal to noise ratio image. Apart from signal to noise ratio another parameter which measures the similarity between two images is also shown in this paper. Additionally the drawback of each method and comparison of the SNR, and SSIM of all methods is also shown in this paper. Error diffusion technique using FS, Stucki and JJN filters is an efficient approach to halftoning. Its main drawback is that it undergoes linear distortion. This paper is completely describing the error diffusion method and the improvements made to error diffusion so as to get a well-defined and a visually pleasing halftone image. However an evolutionary algorithms called as particle swarm optimization and Genetic Algorithm are used to create filters for the image block wise and comparing with that of the image and then finally reconstructing the whole image. In these methods of PSO and GA the cost function is formulated based on the SSIM, average minority pixel distance and the string with the best cost function value is sorted using the Evolutionary algorithms. As the human eye acts as a spatial low pass filter the image which is to be halftoned is filtered through any visual model such as a HVS model and then undergoes through the process of evolutionary algorithms.