Vol. 14, No. 10, October 31, 2020
10.3837/tiis.2020.10.008,
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Abstract
Blur is an important type of image distortion. How to evaluate the quality of blurred image
accurately and efficiently is a research hotspot in the field of image processing in recent years.
Inspired by the multi-scale perceptual characteristics of the human visual system (HVS), this
paper presents a no-reference image blur/sharpness assessment method based on multi-scale
local features in the spatial domain. First, considering various content has different sensitivity
to blur distortion, the image is divided into smooth, edge, and texture regions in blocks. Then,
the Gaussian scale space of the image is constructed, and the categorized contrast features
between the original image and the Gaussian scale space images are calculated to express the
blur degree of different image contents. To simulate the impact of viewing distance on blur
distortion, the distribution characteristics of local maximum gradient of multi-resolution
images were also calculated in the spatial domain. Finally, the image blur assessment model is
obtained by fusing all features and learning the mapping from features to quality scores by
support vector regression (SVR). Performance of the proposed method is evaluated on four
synthetically blurred databases and one real blurred database. The experimental results
demonstrate that our method can produce quality scores more consistent with subjective
evaluations than other methods, especially for real burred images.
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Cite this article
[IEEE Style]
C. Sun, Z. Cui, Z. Gan, F. Liu, "No-reference Image Blur Assessment Based on Multi-scale Spatial Local Features," KSII Transactions on Internet and Information Systems, vol. 14, no. 10, pp. 4060-4079, 2020. DOI: 10.3837/tiis.2020.10.008.
[ACM Style]
Chenchen Sun, Ziguan Cui, Zongliang Gan, and Feng Liu. 2020. No-reference Image Blur Assessment Based on Multi-scale Spatial Local Features. KSII Transactions on Internet and Information Systems, 14, 10, (2020), 4060-4079. DOI: 10.3837/tiis.2020.10.008.
[BibTeX Style]
@article{tiis:23920, title="No-reference Image Blur Assessment Based on Multi-scale Spatial Local Features", author="Chenchen Sun and Ziguan Cui and Zongliang Gan and Feng Liu and ", journal="KSII Transactions on Internet and Information Systems", DOI={10.3837/tiis.2020.10.008}, volume={14}, number={10}, year="2020", month={October}, pages={4060-4079}}