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Symmetry 2018, 10(8), 304; https://doi.org/10.3390/sym10080304

No-Reference Image Blur Assessment Based on Response Function of Singular Values

1
School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, China
2
Department of Computer Science, University of Warwick, Coventry CV4 7AL, UK
*
Author to whom correspondence should be addressed.
Received: 26 June 2018 / Revised: 21 July 2018 / Accepted: 23 July 2018 / Published: 1 August 2018
(This article belongs to the Special Issue Information Technology and Its Applications 2018)
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Abstract

Blur is an important factor affecting the image quality. This paper presents an efficient no-reference (NR) image blur assessment method based on a response function of singular values. For an image, the grayscale image is computed to the acquire spatial information. In the meantime, the gradient map is computed to acquire the shape information, and the saliency map can be obtained by using scale-invariant feature transform (SIFT). Then, the grayscale image, the gradient map, and the saliency map are divided into blocks of the same size. The blocks of the gradient map are converted into discrete cosine transform (DCT) coefficients, from which the response function of singular values (RFSV) are generated. The sum of the RFSV are then utilized to characterize the image blur. The variance of the grayscale image and the DCT domain entropy of the gradient map are used to reduce the impact of the image content. The SIFT-dependent weights are calculated in the saliency map, which are assigned to the image blocks. Finally, the blur score is the normalized sum of the RFSV. Extensive experiments are conducted on four synthetic databases and two real blur databases. The experimental results indicate that the blur scores produced by our method are highly correlated with the subjective evaluations. Furthermore, the proposed method is superior to six state-of-the-art methods. View Full-Text
Keywords: image blur assessment; gradient; no-reference; visual saliency; DCT domain entropy image blur assessment; gradient; no-reference; visual saliency; DCT domain entropy
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Zhang, S.; Li, P.; Xu, X.; Li, L.; Chang, C.-C. No-Reference Image Blur Assessment Based on Response Function of Singular Values. Symmetry 2018, 10, 304.

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