Selective sampling and optimal filtering for subpixel-based image down-sampling

Sung Ho Chae, Sung Tae Kim, Joon Yeon Kim, Cheol Hwan Yoo, Sung Jea Ko

Research output: Contribution to journalArticlepeer-review

Abstract

Subpixel-based image down-sampling has been widely used to improve the apparent resolution of down-sampled images on display. However, previous subpixel rendering methods often introduce distortions, such as aliasing and color-fringing. This study proposes a novel subpixel rendering method that uses selective sampling and optimal filtering. We first generalize the previous frequency domain analysis results indicating the relationships between various down-sampling patterns and the aliasing artifact. Based on this generalized analysis, a subpixel-based down-sampling pattern for each image is selectively determined by utilizing the edge distribution of the image. Moreover, we investigate the origin of the color-fringing artifact in the frequency domain. Optimal spatial filters that can effectively remove distortions caused by the selected down-sampling pattern are designed via frequency domain analyses of aliasing and color-fringing. The experimental results show that the proposed method is not only robust to the aliasing and color-fringing artifacts but also outperforms the existing ones in terms of information preservation.

Original languageEnglish
Article number8819919
Pages (from-to)124096-124105
Number of pages10
JournalIEEE Access
Volume7
DOIs
Publication statusPublished - 2019

Keywords

  • Aliasing
  • Color-fringing
  • Frequency domain analysis
  • Image down-sampling
  • Optimal filtering
  • Selective sampling
  • Subpixel rendering

ASJC Scopus subject areas

  • Computer Science(all)
  • Materials Science(all)
  • Engineering(all)

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