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Combined no-reference IQA metric and its performance analysis

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

Details

Original languageEnglish
Title of host publicationImage Processing: Algorithms and Systems XVII
DOIs
Publication statusPublished - 13 Jan 2019
Publication typeA4 Article in a conference publication
Event17th Image Processing: Algorithms and Systems Conference, IPAS 2019 - Burlingame, United States
Duration: 13 Jan 201917 Jan 2019

Publication series

NameIS and T International Symposium on Electronic Imaging Science and Technology

Conference

Conference17th Image Processing: Algorithms and Systems Conference, IPAS 2019
CountryUnited States
CityBurlingame
Period13/01/1917/01/19

Abstract

The problem of increasing efficiency of blind image quality assessment is considered. No-reference image quality metrics both independently and as components of complex image processing systems are employed in various application areas where images are the main carriers of information. Meanwhile, existing no-reference metrics have a significant drawback characterized by a low adequacy to image perception by human visual system (HVS). Many well-known no-reference metrics are analyzed in our paper for several image databases. A method of combining several no-reference metrics based on artificial neural networks is proposed based on multi-database verification approach. The effectiveness of the proposed approach is confirmed by extensive experiments.

Keywords

  • Combined metrics, Full-reference metrics, Image visual quality assessment, Robust metrics