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.
[1] Ferzli R,Karam L J. A No-Reference Objective Image Sharpness Metric Based on the Notion of Just Noticeable Blur (JNB). IEEE Trans on Image Processing,2009,18(4): 717-728 [2］Hassen R, Wang Zhou,Salama M. No-Reference Image Sharpness Assessment Based on Local Phase Coherence Measurement // Proc of the IEEE International Conference on Acoustics,Speech and Signal Processing. Dallas,USA,2010: 2434-2437 [3] Chen M J,Bovik A C. No-Reference Image Blur Assessment Using Multiscale Gradient. EURASIP Journal on Image and Video Processing,2011,3: 1-11 [4] Li C,Yuan W,Bovik A C,et al. No-Reference Blur Index Using Blur Comparisons. Electronics Letters,2011,47(17): 962-963 [5] Ciancio A,da Costa A L N T. No-Reference Blur Assessment of Digital Pictures Based on Multi-Feature Classifiers. IEEE Trans on Image Processing,2011,20(1): 64-75 [6] Morrone M C,Owens R A. Feature Detection Rom Local Energy. Pattern Recognition Letters,1987,6(5): 303-313 [7] Venkatesh S,Owens R A. An Energy Feature Detection Scheme // Proc of the IEEE International Conference on Image Process. Singapore,Singapore,1989: 553-557 [8] Kovesi P. Image Features from Phase Congruency. Computer Vision Research,1999,1(3): 1-26 [9] Zhang Lin,Zhang Lei,Mou Xuanqin,et al. FSIM: A Feature Similarity Index for Image Quality Assessment. IEEE Trans on Image Processing,2011,20(8): 2378-2386 [10] Li Chaofeng,Bovik A C,Wu Xiaojun J. Blind Image Quality Assessment Using a General Regression Neural Network. IEEE Trans on Neural Networks,2011,22(5): 793-799 [11] Wang Zhou,Bovik A C,Sheikh H R,et al. Image Quality Assessment: From Error Visibility to Structural Similarity. IEEE Trans on Image Processing,2004,13(4): 600-612 [12] Haralick R M,Shanmugam K,Dinstein I. Textural Features for Image Classification. IEEE Trans on Systems,Man and Cybernetics,1973,3(6): 610-621 [13] Hralick R M. Statistical and Structural Approaches to Texture. Proceedings of IEEE,1979,67(5): 786-804 [14] Vapnik V N. The Nature of Statistical Learning Theory. New York,USA: Springer Verlag,2000 [15] Kale A,Sundaresan A,Rajagopalan A N,et al. Identification of Humans Using Gait. IEEE Trans on Image Processing,2004,13(9): 1163-1173
2013, Vol. 26 
