Blind Image Quality Assessment on Gaussian Blur Images

Liping Wang, Chengyou Wang and Xiao Zhou
Volume: 13, No: 3, Page: 448 ~ 463, Year: 2017
Keywords: Blind Image Quality Assessment (BIQA), Gaussian Blur Image, Saliency Map, Structure Tensor, Structural Similarity (SSIM)
Full Text:

Multimedia is a ubiquitous and indispensable part of our daily life and learning such as audio, image, and video. Objective and subjective quality evaluations play an important role in various multimedia applications. Blind image quality assessment (BIQA) is used to indicate the perceptual quality of a distorted image, while its reference image is not considered and used. Blur is one of the common image distortions. In this paper, we propose a novel BIQA index for Gaussian blur distortion based on the fact that images with different blur degree will have different changes through the same blur. We describe this discrimination from three aspects: color, edge, and structure. For color, we adopt color histogram; for edge, we use edge intensity map, and saliency map is used as the weighting function to be consistent with human visual system (HVS); for structure, we use structure tensor and structural similarity (SSIM) index. Numerous experiments based on four benchmark databases show that our proposed index is highly consistent with the subjective quality assessment.

Article Statistics
Multiple requests among the same broswer session are counted as one view (or download).
If you mouse over a chart, a box will show the data point's value.

Cite this article
IEEE Style
L. Wang, C. Wang and X. Zhou, "Blind Image Quality Assessment on Gaussian Blur Images," Journal of Information Processing Systems, vol. 13, no. 3, pp. 448~463, 2017. DOI: 10.3745/JIPS.02.0059.

ACM Style
Liping Wang, Chengyou Wang, and Xiao Zhou. 2017. Blind Image Quality Assessment on Gaussian Blur Images, Journal of Information Processing Systems, 13, 3, (2017), 448~463. DOI: 10.3745/JIPS.02.0059.