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    A novel metric based on mca for image quality

    He, L. and Lu, W. and Gao, X. and Tao, D. and Li, Xuelong (2011) A novel metric based on mca for image quality. International Journal of Wavelets, Multiresolution and Information Processing 09 (05), pp. 743-757. ISSN 0219-6913.

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    Abstract

    Considering that the Human Visual System (HVS) has different perceptual characteristics for different morphological components, a novel image quality metric is proposed by incorporating Morphological Component Analysis (MCA) and HVS, which is capable of assessing the image with different kinds of distortion. Firstly, reference and distorted images are decomposed into linearly combined texture and cartoon components by MCA respectively. Then these components are turned into perceptual features by Just Noticeable Difference (JND) which integrates masking features, luminance adaptation and Contrast Sensitive Function (CSF). Finally, the discrimination between reference and distorted images perceptual features is quantified using a pooling strategy before the final image quality is obtained. Experimental results demonstrate that the performance of the proposed prevails over some existing methods on LIVE database II.

    Metadata

    Item Type: Article
    Keyword(s) / Subject(s): Image quality assessment, morphological component analysis, human visual system, just noticeable difference
    School: Birkbeck Schools and Departments > School of Business, Economics & Informatics > Computer Science and Information Systems
    Depositing User: Sarah Hall
    Date Deposited: 07 Jun 2013 14:15
    Last Modified: 11 Oct 2016 15:27
    URI: http://eprints.bbk.ac.uk/id/eprint/7411

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