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A universal full reference image quality metric based on a neural fusion approach

  • Université Paris 13
  • King Fahd University of Petroleum and Minerals

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

We present in this paper a new global Full-Reference (FR) image quality metric (IQM) based on the fusion of several conventional FR metrics using an ANN learning algorithm. The fusion is shown to result in improved performance compared to individual FR metrics. Indeed, existing FR metrics can provide excellent results for specific degradations but poor results for others. Here, we propose to overcome this limitation by first improving the performance of existing FR metrics across different degradations through a ranking process. Then, using an Artificial Neural Network, we fuse the best-performing measures into a single metric called Global Index Quality Metric (G-IQM). The experimental results using the TID 2008 image database demonstrate that this new G-IQM metric achieves consistent image quality evaluation results with subjective evaluation.

Original languageEnglish
Title of host publication2010 IEEE International Conference on Image Processing, ICIP 2010 - Proceedings
Pages2517-2520
Number of pages4
DOIs
StatePublished - 2010
Externally publishedYes
Event2010 17th IEEE International Conference on Image Processing, ICIP 2010 - Hong Kong, Hong Kong
Duration: 26 Sep 201029 Sep 2010

Publication series

NameProceedings - International Conference on Image Processing, ICIP
ISSN (Print)1522-4880

Conference

Conference2010 17th IEEE International Conference on Image Processing, ICIP 2010
Country/TerritoryHong Kong
CityHong Kong
Period26/09/1029/09/10

Keywords

  • Artifacts
  • Artificial neural networks
  • Image quality
  • Subjective scores

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