Journal of Computer Applications ›› 2017, Vol. 37 ›› Issue (10): 2871-2874.DOI: 10.11772/j.issn.1001-9081.2017.10.2871

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Improvement of sub-pixel morphological anti-aliasing algorithm

LIU Jingrong1, DU Huimin1, DU Qinqin2   

  1. 1. School of Electronic Engineering, Xi'an University of Posts and Telecommunications, Xi'an Shaanxi 710061, China;
    2. School of Computer Science & Technology, Xi'an University of Posts and Telecommunications, Xi'an Shaanxi 710061, China
  • Received:2017-04-06 Revised:2017-06-12 Online:2017-10-10 Published:2017-10-16
  • Supported by:
    This work is partially supported by the National Natural Science Foundation of China (61136002);the Science and Technology Development Programmer of Xi'an (CXY 1440(10)).


刘镜荣1, 杜慧敏1, 杜琴琴2   

  1. 1. 西安邮电大学 电子工程学院, 西安 710061;
    2. 西安邮电大学 计算机学院, 西安 710061
  • 通讯作者: 杜慧敏(1966-),女,山东潍坊人,教授,博士,CCF会员,主要研究方向:计算机体系结构、计算机图形学,
  • 作者简介:刘镜荣(1992-),男,陕西西安人,硕士研究生,主要研究方向:计算机图形学、图形图像处理;杜慧敏(1966-),女,山东潍坊人,教授,博士,CCF会员,主要研究方向:计算机体系结构、计算机图形学;杜琴琴(1989-),女,陕西西安人,硕士研究生,主要研究方向:计算机体系结构.
  • 基金资助:

Abstract: Since Sub-pixel Morphological Anti-Aliasing (SMAA) algorithm extracts images with less contour and needs larger storage, an improved algorithm for SMAA was presented.In the improved algorithm, the multiplication of luminance of a pixel and an adjustable factor was regarded as a dynamic threshold, which was used to decide whether the pixel is a boundary pixel. Compared with fixed threshold for boundary decision in SMAA, the dynamic threshold is stricter for deciding whether a pixel is a boundary one, so the presented algorithm can extract more boundaries. Based on the analysis of different morphological models and used storage, some redundant storages were merged so as to reduce the size of memory. The algorithm was implemented by Microsoft DirectX SDK and HLSL under Windows 7. The experimental results show that the proposed algorithm can extract clearer boundaries and the size of the memory is reduced by 51.93%.

Key words: morphology, anti-aliasing, pixel luminance, adjustable factor, threshold value

摘要: 针对子像素形态学反走样(SMAA)算法提取图像轮廓信息少和存储空间较大的问题,提出一种改进的形态学反走样算法。该算法用一个像素的亮度与调整因子的乘积作为动态阈值,来判定该像素是否为轮廓条件。与SMAA利用固定阈值判定轮廓相比,动态阈值严格限制了轮廓的判断条件,因此改进算法可以提取出更多的轮廓信息。同时,在分析SMAA存储形态模式的基础上,合并了不同模式但是面积计算和混合方式相同的存储,能有效地减少面积纹理的存储面积。在Windows 7操作系统下,用Microsoft DirectX SDK和HLSL着色语言实现了所改进的算法。实验结果表明:相对于SMAA算法,改进后算法可以提取更多更清晰的轮廓线,存储减少了51.93%。

关键词: 形态学, 反走样, 像素亮度, 调整因子, 阈值

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