Objective image sharpness metric based on perceptual contrast

Shaoshu Gao, Yanjiang Wang, Weiqi Jin, Xiaodong Zhnag

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)

Abstract

Image sharpness is one of the common metrics for image quality assessment. Existing sharpness metrics have not given enough consideration to the human luminance masking effect. The root mean squared contrast model is improved by considering the human luminance masking effect. A perceptual contrast model is presented. A no-reference sharpness metric is established by averaging the perceptual contrast over the region of interest (contains image details, edges, and texture). IVC database is used to test the proposed sharpness metric. Experimental results show that, compared with four existing sharpness (blur) metrics, the proposed perceptual sharpness metric provides better predictions which more closely matches to the human visual perception, and has lower computational complexity. The sharpness metric is simple but effective.

Original languageEnglish
Pages (from-to)396-399
Number of pages4
JournalGuangxue Jishu/Optical Technique
Volume41
Issue number5
Publication statusPublished - 1 Sept 2015

Keywords

  • Human luminance masking effect
  • Image contrast
  • Image sharpness
  • Objective evaluation

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