Regular Articles

Blue noise sampling method based on mixture distance

[+] Author Affiliations
Hongxing Qin

Chongqing Key Laboratory of Computational Intelligence, Chongqing 400065, China

Chongqing University of Posts and Telecommunications, Institute of Computer Science and Technology, ChongWen Road, Chongqing 400065, China

XiaoYang Hong

Chongqing Key Laboratory of Computational Intelligence, Chongqing 400065, China

Chongqing University of Posts and Telecommunications, Institute of Computer Science and Technology, ChongWen Road, Chongqing 400065, China

Bin Xiao

Chongqing Key Laboratory of Computational Intelligence, Chongqing 400065, China

Chongqing University of Posts and Telecommunications, Institute of Computer Science and Technology, ChongWen Road, Chongqing 400065, China

Shaoting Zhang

University of North Carolina at Charlotte, Department of Computer Science, Charlotte, North Carolina 28027, United States

Guoyin Wang

Chongqing Key Laboratory of Computational Intelligence, Chongqing 400065, China

Chongqing University of Posts and Telecommunications, Institute of Computer Science and Technology, ChongWen Road, Chongqing 400065, China

J. Electron. Imaging. 23(6), 063015 (Dec 16, 2014). doi:10.1117/1.JEI.23.6.063015
History: Received April 2, 2014; Accepted November 10, 2014
Text Size: A A A

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.

© 2014 SPIE and IS&T

Citation

Hongxing Qin ; XiaoYang Hong ; Bin Xiao ; Shaoting Zhang and Guoyin Wang
"Blue noise sampling method based on mixture distance", J. Electron. Imaging. 23(6), 063015 (Dec 16, 2014). ; http://dx.doi.org/10.1117/1.JEI.23.6.063015


Tables

Access This Article
Sign in or Create a personal account to Buy this article ($20 for members, $25 for non-members).

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging & repositioning the boxes below.

Related Book Chapters

Topic Collections

Advertisement
  • Don't have an account?
  • Subscribe to the SPIE Digital Library
  • Create a FREE account to sign up for Digital Library content alerts and gain access to institutional subscriptions remotely.
Access This Article
Sign in or Create a personal account to Buy this article ($20 for members, $25 for non-members).
Access This Proceeding
Sign in or Create a personal account to Buy this article ($15 for members, $18 for non-members).
Access This Chapter

Access to SPIE eBooks is limited to subscribing institutions and is not available as part of a personal subscription. Print or electronic versions of individual SPIE books may be purchased via SPIE.org.