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| No-Reference Blurred Image Quality Assessment Based on Gray Level Co-occurrence Matrix |
| SANG Qing-Bing,LI Chao-Feng,WU Xiao-Jun |
| School of IOT Engineering,Jiangnan University,Wuxi 214122 |
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Abstract No-reference image quality assessment becomes a research hotspot recently. Based on gray level co-occurrence matrix,a no-reference blurred image quality assessment method is proposed which uses phase congruency feature learning. Firstly,this method generates phase congruency maps of testing images by Log Gabor wavelet. Secondly,it calculates the features of phase congruency map,which are entropy,energy,contrast,correlation and homogeneity based on gray level co-occurrence matrix. Finally,it predicts no-reference blurred image quality score by support vector regression (SVR) model training and learning. The experimental results on 4 public databases show that the predicted scores of the proposed method are in agreement with the subjective score,and it obtains a better evaluation index.
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Received: 17 May 2012
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2013, Vol. 26 
