Image Quality Self-adaptive Assessment Based on Visual Salience Distortion
Feng Ming-kun①② Zhao Sheng-mei① Xing Chao①
①(Institute of Signal Processing and Transmission, Nanjing University of Posts and Telecommunications,
Nanjing 210003, China) ②(School of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China)
The Structural SIMilarity (SSIM) algorithm of image quality assessment does not take into account the characteristics of multi-channel resolutions of human vision, it is also not consistent with subjective human evaluation for high level distortions. A Visual Salience Adaptive Pooling (VSAP) strategy of image quality assessment is proposed based on visual multi-scale and multi-orientation of log-Gabor transformation. Firstly, the visual characteristics of image on the high, medium, and low frequency are extracted by the log-Gabor transformation. Then the visual similarity scores based on visual scales and visual orientations of log-Gabor are calculated, accordingly, the visual distortion levels of image are calculated iteratively with the visual multi- resolution threshold. Finally, a strategy of image quality assessment is proposed with adaptive pooling similarity scores to distortion scores. The experimental results show that objective assessments of VSAP for different types of distortion hold higher correlation with subjective assessment. More importantly, the overall assessment performance of the Spearman Rank-Order Correlation Coefficient (SROCC), Correlation Coefficient (CC) and Root Mean Square Error (RMSE) for different levels of distortion is more consistent with subjective scores and superior to other methods.
Jiang Gang-yi, Huang Da-jiang, Wang Xu, et al.. Overview on image quality assessment methods[J]. Journal of Electronics & Information Technology, 2010, 32(1): 219-226.
Zhang Fei-yan, Xie Wei, Chen Rong-yuan, et al.. Compression image quality assessment based on human visual weight and singular value decomposition[J]. Journal of Electronics & Information Technology, 2010, 32(5): 1061-1065.
Wang Xiang and Ding Yong. Full reference image quality assessment based on Gabor filter[J]. Journal of Zhejiang University(Engineering Science), 2013, 47(3): 422-430.
Mi Zeng-zhen. Image quality evaluation method based on frequency and direction weighted to CSF in wavelet domain[J]. Acta Electronica Sinica, 2014, 42(7): 1273-1276.
[5]
Yalman Y. Histogram based perceptual quality assessment method for color images[J]. Computer Standards & Interfaces, 2014, 36(6): 899-908.
[6]
Daly S. The visible different predictor: an algorithm for the assessment of images fidelity[C]. Digital Images and Human Vision Conference, Cambridge, England, 1993: 179-206.
[7]
Lubin J. A visual discrimination model for images system design and evaluation[C]. Proceedings of the Conference on Visual Models for Target Detection and Recognition, Singapore City, Singapore, 1995: 207-220.
[8]
Safranek R J and Johnston J D. A perceptually tuned sub-band image coder with image dependent quantization and post-quantization data compression[C]. Proceedings of the IEEE International Conference on Acoust, Speech and Signal Processing, Glasgow, UK, 1989: 1945-1948.
[9]
Watson A B. DCT quantization matrices visually optimized for individual images[C]. Proceedings of the SPIE Human vision, Visual Processing, and Digital Display IV, Washington, USA, 1993: 202-216.
[10]
Teo P C and Heeger D J. Perceptual image distortion[C]. SPIE International Conference on Image Processing, Texas, USA, 1994: 982-986.
[11]
Wang Zhou, Bovik A C, Sheikh H R, et al.. Image quality assessment: from error visibility to structural similarity[J]. IEEE Transactions on Image Processing, 2004, 13(4): 600-612.
[12]
Sheikh H R, Bovik A C, and Veciana G D. An information fidelity criterion for image quality assessment using natural scene statistics[J]. IEEE Transactions on Image Processing, 2005, 14(12): 2117-2128.
[13]
Aleksandr S D, Alexander G, and Eskicioglu A M. An SVD-based grayscale image quality measure for local and global assessment[J]. IEEE Transactions on Image Processing, 2006, 15(2): 422-429.
[14]
Venkata N D, Kite T D, Bovik A C, et al.. Image quality assessment based on degradation model[J]. IEEE Transactions on Image Processing, 2000, 9(4): 636-650.
[15]
Wang Zhou, Simoncelli E P, and Bovik A C. Multi-scale structural similarity for image quality assessment[C]. Proceedings of the 37th IEEE Asilomar Conference on Signals, Systems and Computers, Pacific Grove, Canada, 2002(2): 1398-1402.
[16]
Zhang Lin, Zhang Lei, Mou Xuanqin, et al.. FSIM: a feature similarity index for image quality assessment[J]. IEEE Transactions on Image Processing, 2011, 20(8): 2378-2386.
[17]
Ding Yong, Wang Shao-ze, and Zhang Dong. Full-reference image quality assessment using statistical local correlation [J]. Electronics Letters, 2014, 50(2): 79-81.
[18]
Hu An-zhou, Zhang Rong, Yin Dong, et al.. Image quality assessment using a SVD-based structural projection[J]. Signal Processing: Image Communication, 2014, 29(3): 293-302.
[19]
Zhang Lin, Shen Ying, and Li Hong-yu. VSI: a visual saliency-induced index for perceptual image quality assessment[J]. IEEE Transactions on Image Processing, 2014, 23(10): 4270-4281.
[20]
Chang Hua-wen, Yang Hua, Gan Yong, et al.. Sparse feature fidelity for perceptual image quality assessment[J]. IEEE Transactions on Image Processing, 2013, 22(10): 4007-4018.
[21]
Larson E C and Chandler D M. Most apparent distortion: full-reference image quality assessment and the role of strategy[J]. Journal of Electronic Imaging, 2010, 19(1): 011006-1-011006-21.
[22]
Wandell B A. Foundations of Vision[M]. Stanford: Sinauer Associates, 1995: 277-284.
[23]
Wang Zhou, Lu L G, and Bovik A C. Foveation scalablevideo coding with automatic fixation selection[J]. IEEE Transactions on Image Processing, 2003, 12(2): 243-254.
