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An efficient no-reference metric for perceived blur

Liu, Hantao ORCID:, Wang, Junle, Redi, Judith, Le Callet, Patrick and Heynderickx, Ingrid 2011. An efficient no-reference metric for perceived blur. Presented at: 3rd European Workshop on Visual Information Processing 2011, Paris, France, 4-6 July 2011. 3rd European Workshop on Visual Information Processing Proceedings. IEEE, p. 174. 10.1109/EuVIP.2011.6045525

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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 background1. Experimental results demonstrate that the proposed blur metric is promising for real-world applications both in terms of computational efficiency and practical reliability.

Item Type: Conference or Workshop Item (Paper)
Date Type: Published Online
Status: Published
Schools: Computer Science & Informatics
Publisher: IEEE
ISBN: 978-1-4577-0072-9
Last Modified: 25 Oct 2022 13:07

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