© The Institution of Engineering and Technology
Image quality assessment is of fundamental importance for various image processing applications. A novel method is presented in which the joint occurrences of statistical local representation by log-Gabor filters and texture analysis by local tetra patterns and histograms of colour are considered as quality-aware features. Then the dissimilarities of these features between the distorted and reference images are quantified and mapped into quality score prediction by utilising a support vector regression. Extensive experiments on LIVE, CSIQ and TID databases show that the proposed method is remarkably consistent with human perception and outperforms many state-of-the-art methods, and also it is robust across different distortion types and different databases.
References
-
-
1)
-
26. Sheikh, H.R., Bovik, A.C.: ‘Image information and visual quality image processing’, IEEE Trans. Image Process., 2006, 15, pp. 430–444 (doi: 10.1109/TIP.2005.859378).
-
2)
-
4. Murala, S., Maheshwari, R., Balasubramanian, R.: ‘Local tetra patterns: a new feature descriptor for content-based image retrieval’, IEEE Trans. Image Process., 2012, 21, (5), pp. 2874–2886 (doi: 10.1109/TIP.2012.2188809).
-
3)
-
8. Ponomarenko, N., Ieremeiev, O., Lukin, V., Egiazarian, K., Jin, L., Astola, J., Vozel, B., Chehdi, K., Carli, M., Battisti, F., Kuo, C.-C.J.: ‘Image database TID2013: peculiarities, results and perspectives’, Signal Process., Image Commun., 2015, 30, (1), pp. 57–77 (doi: 10.1016/j.image.2014.10.009).
-
4)
-
4. Oszust, M.: ‘Full-reference image quality assessment with linear combination of genetically selected quality measures’, Public Libr. Sci. ONE, 2016, 11, (6), pp. 0158333.
-
5)
-
3. Zhao, Y., Ding, Y., Zhao, X.Y.: ‘Image quality assessment based on complementary local feature extraction and quantification’, Electron. Lett., 2016, 52, (22), pp. 1849–1851 (doi: 10.1049/el.2016.1328).
-
6)
-
25. Zhang, L., Zhang, L., Mou, X., et al: ‘FSIM: a feature similarity index for image quality assessment’, IEEE Trans. Image Process., 2011, 20, (8), pp. 2378–2386 (doi: 10.1109/TIP.2011.2109730).
-
7)
-
1. Zhang, L., Shen, Y., Li, H.: ‘VSI: A visual saliency-induced index for perceptual image quality assessment’, Trans. Image Process., 2014, 23, (10), pp. 4270–4281 (doi: 10.1109/TIP.2014.2346028).
-
8)
-
7. Ponomarenko, N., Egiazarian, K.: .
-
9)
-
2. Chang, H.W., Zhang, Q.W., Wu, Q.Q., Gan, Y.: ‘Perceptual image quality assessment by independent feature detector’, Neurocomputing, 2015, 151, pp. 1142–1152 (doi: 10.1016/j.neucom.2014.04.081).
-
10)
-
6. Sheikh, H.R., Wang, Z., Cormack, L., et al: .
-
11)
-
9. Larson, E.C., Chandler, D.M.: .
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