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    Sparse representation for blind image quality assessment

    He, L. and Tao, D. and Li, Xuelong and Gao, X. (2012) Sparse representation for blind image quality assessment. In: UNSPECIFIED (ed.) IEEE Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE Computer Society, pp. 1146-1153. ISBN 9781467312264.

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    Abstract

    Blind image quality assessment (BIQA) is an important yet difficult task in image processing related applications. Existing algorithms for universal BIQA learn a mapping from features of an image to the corresponding subjective quality or divide the image into different distortions before mapping. Although these algorithms are promising, they face the following problems: (1) they require a large number of samples (pairs of distorted image and its subjective quality) to train a robust mapping; (2) they are sensitive to different datasets; and (3) they have to be retrained when new training samples are available. In this paper, we introduce a simple yet effective algorithm based upon the sparse representation of natural scene statistics (NSS) feature. It consists of three key steps: extracting NSS features in the wavelet domain, representing features via sparse coding, and weighting differential mean opinion scores by the sparse coding coefficients to obtain the final visual quality values. Thorough experiments on standard databases show that the proposed algorithm outperforms representative BIQA algorithms and some full-reference metrics.

    Metadata

    Item Type: Book Section
    School: Birkbeck Schools and Departments > School of Business, Economics & Informatics > Computer Science and Information Systems
    Depositing User: Sarah Hall
    Date Deposited: 06 Jun 2013 11:21
    Last Modified: 11 Oct 2016 15:27
    URI: http://eprints.bbk.ac.uk/id/eprint/7302

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