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A novel no-reference image quality assessment metric based on statistical independence

  • Xi'an Jiaotong University
  • Shenzhen University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

4 Scopus citations

Abstract

No-reference image quality assessment (NR IQA) has wide applicability to many problems. This paper focuses on the mechanism of divisive normalization transform (DNT) which simulates the behavior of visual cortex neurons to extract the independent components of natural images, analyzes the difference between the statistics of neighboring DNT coefficients of the images of a variety of distortion, and proposes a novel solution for NR IQA metric design. We demonstrate that measuring the statistical independence between neighboring DNT coefficients could provide features useful for quality assessment. The performance of the proposed method is quite satisfactory when it was tested on the popular LIVE, CSIQ and TID2008 databases. The experimental results are fairly competitive with the existing NR IQA metrics.

Original languageEnglish
Title of host publication2012 IEEE Visual Communications and Image Processing, VCIP 2012
DOIs
StatePublished - 2012
Event2012 IEEE Visual Communications and Image Processing, VCIP 2012 - San Diego, CA, United States
Duration: 27 Nov 2012 → 30 Nov 2012

Publication series

Name2012 IEEE Visual Communications and Image Processing, VCIP 2012

Conference

Conference2012 IEEE Visual Communications and Image Processing, VCIP 2012
Country/TerritoryUnited States
CitySan Diego, CA
Period27/11/12 → 30/11/12

Keywords

  • Image quality assessment
  • divisive normalization transform
  • no-reference
  • statistical independence
  • support vector regression

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