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Fuzzy regression for perceptual image quality assessment

Citation

Chan, KY and Engelke, U, Fuzzy regression for perceptual image quality assessment, Engineering Applications of Artificial Intelligence, 43 pp. 102-110. ISSN 0952-1976 (2015) [Refereed Article]

Copyright Statement

Copyright 2015 Elsevier Ltd.

DOI: doi:10.1016/j.engappai.2015.04.007

Abstract

Subjective image quality assessment (IQA) is fundamentally important in various image processing applications such as image/video compression and image reconstruction, since it directly indicates the actual human perception of an image. However, fuzziness due to human judgment is neglected in current methodologies for predicting subjective IQA, where the fuzziness indicates assessment uncertainty. In this article, we propose a fuzzy regression method that accounts for fuzziness introduced through human judgment and the limitations of widely-used psychometric quality scales. We demonstrate how fuzzy regression models provide fuzziness information regarding subjective IQA. We benchmark the fuzzy regression method against the commonly used explicit modeling method for subjective IQA namely statistical regression by considering three real situations involving subjective image quality experiments where: (a) the number of participants is insufficient; (b) an insufficient amount of data is used for modelling; and (c) variant fuzziness is caused by human judgment. Results indicate that fuzzy regression models achieve more effective data fitting and better generalization capability when predicting subjective IQA under different types and levels of image distortion.

Item Details

Item Type:Refereed Article
Keywords:fuzzy regression, mean opinion scores, MOS, subjective image quality assessment, objective image quality metric
Research Division:Information and Computing Sciences
Research Group:Artificial Intelligence and Image Processing
Research Field:Image Processing
Objective Division:Expanding Knowledge
Objective Group:Expanding Knowledge
Objective Field:Expanding Knowledge in the Information and Computing Sciences
Author:Engelke, U (Dr Ulrich Engelke)
ID Code:117317
Year Published:2015
Web of Science® Times Cited:4
Deposited By:Engineering
Deposited On:2017-06-07
Last Modified:2017-10-16
Downloads:0

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