Blue noise sampling method based on mixture distance

Hongxing Qin, XiaoYang Hong, Bin Xiao, Shaoting Zhang, Guo-Yin Wang

Abstract: Blue noise sampling is a core component for a large number of computer graphic applications such as imaging, modeling, animation, and rendering. However, most existing methods are concentrated on preserving spatial domain properties like density and anisotropy, while ignoring feature preserving. In order to solve the problem, we present a new distance metric called mixture distance for blue noise sampling, which is a combination of geodesic and feature distances. Based on mixture distance, the blue noise property and features can be preserved by controlling the ratio of the geodesic distance to the feature distance. With the intention of meeting different requirements from various applications, an adaptive adjustment for parameters is also proposed to achieve a balance between the preservation of features and spatial properties. Finally, implementation on a graphic processing unit is introduced to improve the efficiency of computation. The efficacy of the method is demonstrated by the results of image stippling, surface sampling, and remeshing.