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    Fuzzy Regression for Perceptual Image Quality Assessment

    227735_155045_Kit_fuzzy_image_quality21_ue10_singlecolIEEEfuzzy8_ue4_5.pdf (333.6Kb)
    Access Status
    Open access
    Authors
    Chan, Kit Yan
    Engelke, U.
    Date
    2015
    Type
    Journal Article
    
    Metadata
    Show full item record
    Citation
    Chan, K.Y. and Engelke, U. 2015. Fuzzy Regression for Perceptual Image Quality Assessment. Engineering Applications of Artificial Intelligence. 43: pp. 102-110.
    Source Title
    Engineering Applications of Artificial Intelligence
    DOI
    10.1016/j.engappai.2015.04.007
    ISSN
    0952-1976
    School
    Department of Electrical and Computer Engineering
    URI
    http://hdl.handle.net/20.500.11937/11495
    Collection
    • Curtin Research Publications
    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.

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