<abstract xmlns="http://www.w3.org/1999/xhtml"><p> The advances in the development of imaging devices resulted in the need of an automatic quality evaluation of displayed visual content in a way that is consistent with human visual perception. In this paper, an approach to full-reference image quality assessment (IQA) is proposed, in which several IQA measures, representing different approaches to modelling human visual perception, are efficiently combined in order to produce objective quality evaluation of examined images, which is highly correlated with evaluation provided by human subjects. In the paper, an optimisation problem of selection of several IQA measures for creating a regression-based IQA hybrid measure, or a multimeasure, is defined and solved using a genetic algorithm. Experimental evaluation on four largest IQA benchmarks reveals that the multimeasures obtained using the proposed approach outperform state-of-the-art full-reference IQA techniques, including other recently developed fusion approaches.</p></abstract>

The advances in the development of imaging devices resulted in the need of an automatic quality evaluation of displayed visual content in a way that is consistent with human visual perception. In this paper, an approach to full-reference image quality assessment (IQA) is proposed, in which several IQA measures, representing different approaches to modelling human visual perception, are efficiently combined in order to produce objective quality evaluation of examined images, which is highly correlated with evaluation provided by human subjects. In the paper, an optimisation problem of selection of several IQA measures for creating a regression-based IQA hybrid measure, or a multimeasure, is defined and solved using a genetic algorithm. Experimental evaluation on four largest IQA benchmarks reveals that the multimeasures obtained using the proposed approach outperform state-of-the-art full-reference IQA techniques, including other recently developed fusion approaches.

A Regression-Based Family of Measures for Full-Reference Image Quality Assessment

Department of Computer and Control Engineering, Rzeszow University of Technology, W. Pola 2, 35-959 Rzeszow,

Measurement Science Review

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

The advances in the development of imaging devices resulted in the need of an automatic quality evaluation of displayed visual content in a way that is...

A Regression-Based Family of Measures for Full-Reference Image Quality Assessment

Online veröffentlicht: 13. Dez. 2016

Seitenbereich: 316 - 325

Eingereicht: 18. Aug. 2016

Akzeptiert: 28. Nov. 2016

DOI: https://doi.org/10.1515/msr-2016-0040

Schlüsselwörter
Image quality assessment, quality measurement, feature selection, multiple linear regression, genetic algorithm

© by Mariusz Oszust

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

A Regression-Based Family of Measures for Full-Reference Image Quality Assessment

Online veröffentlicht: 13. Dez. 2016

Seitenbereich: 316 - 325

Eingereicht: 18. Aug. 2016

Akzeptiert: 28. Nov. 2016

DOI: https://doi.org/10.1515/msr-2016-0040

SchlüsselwörterImage quality assessment, quality measurement, feature selection, multiple linear regression, genetic algorithm

© by Mariusz Oszust

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Schlüsselwörter
Image quality assessment, quality measurement, feature selection, multiple linear regression, genetic algorithm