Approximate image quality measure in low-dimensional domain based on random projection

Frank Y. Shih, Yan Yu Fu

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Image Quality Measure (IQM) is used to automatically measure the degree of image artifacts such as blocking, ringing and blurring effects. It is calculated traditionally in the image spatial domain. In this paper, we present a new method of transforming an image into a low-dimensional domain based on random projection, so we can efficiently obtain the compatible IQM. From the transformed domain, we can calculate the Peak Signal-to-Noise Ratio (PSNR) and apply fuzzy logic to generate a Low-Dimensional Quality Index (LDQI). Experimental results show that the LDQI can approximate the IQM in the image spatial domain. We observe that the LDQI is suited for measuring the compression blur due to its relatively low distortion. The relative error is about 0.15 as the compression blur increases.

Original languageEnglish (US)
Pages (from-to)335-345
Number of pages11
JournalInternational Journal of Pattern Recognition and Artificial Intelligence
Volume22
Issue number2
DOIs
StatePublished - Mar 2008

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

Keywords

  • Image compression
  • Image quality measure
  • Random projection

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