Unsupervised blind image quality assessment based on joint structure and natural scene statistics features |
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Affiliation: | 1. Ming Chuan University, Department of Electronic Engineering, No. 5, Deming Rd., Taoyuan City 33348, Taiwan;2. National Central University, Department of Communication Engineering, No. 300, Jhongda Rd., Taoyuan City 32001, Taiwan |
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Abstract: | Compared with the widely used supervised blind image quality assessment (BIQA) models, unsupervised BIQA models require little prior knowledge for calculating the objective quality scores of distorted images. In this paper, we propose an unsupervised BIQA method that aims to achieve both good performance and generalization capability with low computational complexity. Carefully selected and extensive structure and natural scene statistics (NSS) features can better represent image quality. First, we employ phase congruency (PC) and finely selected gradient magnitude map and Laplacian of Gaussian response (GM-LOG) features to represent image structure information. Second, we calculate the local mean-subtracted and contrast-normalized (MSCN) coefficients and the Karhunen–Loéve transform (KLT) coefficients to represent the naturalness of the distorted images. Last, multivariate Gaussian (MVG) model with joint features extracted from both the pristine images and the distorted images is adopted to calculate the objective image quality. Extensive experiments conducted on nine IQA databases demonstrate that the proposed method achieves better performance than the state-of-the-art BIQA methods. |
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Keywords: | Blind image quality assessment Structure information Natural scene statistics Karhunen–Loéve transform |
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