Inter-Block Evaluation Method to Further Reduce Evaluation Numbers in GA-Based Image Halftoning Technique

摘要

In this paper we propose an inter-block evaluation method to further reduce evaluation numbers in GA-based image halftoning technique. We design the algorithm to avoid noise in the fitness function by evolving all image blocks concurrently, exploiting the inter-block correlation, and sharing information between neighbor image blocks. The effectiveness of the method when the population and image block size are reduced, and the configuration of selection and genetic operators are investigated in detail. Simulation results show that the proposed method can remarkably reduce the entire evaluation numbers to generate high quality bi-level halftone images by suppressing noise around block boundaries.