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Communication Dans Un Congrès Année : 2011

AN EFFICIENT NO-REFERENCE METRIC FOR PERCEIVED BLUR

Résumé

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 . Experimental results demonstrate that the proposed blur metric is promising for real-world applications both in terms of computational efficiency and practical reliability.
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Dates et versions

hal-00606007 , version 1 (02-11-2011)

Identifiants

  • HAL Id : hal-00606007 , version 1

Citer

Hantao Liu, Junle Wang, Judith Redi, Patrick Le Callet, Heynderickx Ingrid. AN EFFICIENT NO-REFERENCE METRIC FOR PERCEIVED BLUR. 2011 3rd European Workshop on Visual Information Processing (EUVIP), Jul 2011, Paris, France. pp.6. ⟨hal-00606007⟩
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