An efficient no-reference metric for perceived blur

H. Liu, Junle Wang, J.A. Redi, P. Callet, Le, I.E.J. Heynderickx

    Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

    15 Citations (Scopus)


    This paper presents an efficient no-reference metric that quantifies perceived image quality induced by blur. Instead of explicitly simulating the human visual perception of blur, it calculates the local edge blur in a cost-effective way, and applies an adaptive neural network to empirically learn the highly nonlinear relationship between the local values and the overall image quality. Evaluation of the proposed metric using the LIVE blur database shows its high prediction accuracy at a largely reduced computational cost. To further validate the performance of the blur metric on its robustness against different image content, two additional quality perception experiments were conducted: one with highly textured natural images and one with images with an intentionally blurred background 1. Experimental results demonstrate that the proposed blur metric is promising for real-world applications both in terms of computational efficiency and practical reliability.
    Original languageEnglish
    Title of host publication2011 3rd European Workshop on Visual Information Processing (EUVIP), 4-6 July 2011, Paris
    Place of PublicationParis
    Publication statusPublished - 2011


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