Reduced reference image quality assessment via sub-image similarity based redundancy measurement

Xuanqin Mou, Wufeng Xue, Lei Zhang

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

4 Citations (Scopus)

Abstract

The reduced reference (RR) image quality assessment (IQA) has been attracting much attention from researchers for its loyalty to human perception and flexibility in practice. A promising RR metric should be able to predict the perceptual quality of an image accurately while using as few features as possible. In this paper, a novel RR metric is presented, whose novelty lies in two aspects. Firstly, it measures the image redundancy by calculating the so-called Sub-image Similarity (SIS), and the image quality is measured by comparing the SIS between the reference image and the test image. Secondly, the SIS is computed by the ratios of NSE (Non-shift Edge) between pairs of sub-images. Experiments on two IQA databases (i.e. LIVE and CSIQ databases) show that by using only 6 features, the proposed metric can work very well with high correlations between the subjective and objective scores. In particular, it works consistently well across all the distortion types.

Original languageEnglish
Title of host publicationProceedings of SPIE-IS and T Electronic Imaging - Human Vision and Electronic Imaging XVII
DOIs
Publication statusPublished - 11 Apr 2012
EventHuman Vision and Electronic Imaging XVII - Burlingame, CA, United States
Duration: 23 Jan 201226 Jan 2012

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume8291
ISSN (Print)0277-786X

Conference

ConferenceHuman Vision and Electronic Imaging XVII
CountryUnited States
CityBurlingame, CA
Period23/01/1226/01/12

Keywords

  • image quality assessment (IQA)
  • NSE
  • reduced reference
  • redundancy measurement
  • sub-image similarity

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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