Multi-class error-diffusion with blue-noise property

Existing researches on error-diffusion mainly focus on sampling over a single channel of input signal. But there are cases where multiple channels of signal need to be sampled simultaneously while keeping their blue-noise property for each individual channel as well as their superimposition. To solve this problem, we propose a novel discrete sampling algorithm called Multi-Class Error-Diffusion (MCED). The algorithm couples multiple processes of error-diffusion to maintain a sampling output with blue-noise distribution. The correlation among the classes are considered and a threshold displacement is introduced into each process of error-diffusion for solving the sampling conflicts. To minimize the destruction to the blue-noise property, an optimization method is used to find a set of optimal key threshold displacements. Experiments demonstrate that our MCED algorithm is able to generate satisfactory multi-class sampling output. Several application cases including color image halftoning and vectorization are also explored. ? Copyright 2016 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved.EI28-3