© The Institution of Engineering and Technology
This work presents an application of the extreme learning machine (ELM) algorithm based on a single-hidden layer feedforward neural network for no-reference video quality assessment. The present research introduces an augmented version of ELM through simple stop criteria, which proved the effectiveness of the video quality assessment method. The authors present empirical studies using LIVE video data base show that the proposed method delivers accuracy (Pearson's correlation coefficient) and monotonicity (Spearman's correlation coefficient) with subjective scores against no-reference, Joint Photographic Experts Group No-Reference, metric and full-reference metrics, for instance, peak signal-to-noise ratio, structural similarity (SSIM) and multi-scale-SSIM indexes, and the proposed method is suitable for quality monitoring of video transmission and reception system.
References
-
-
1)
-
37. Engelke, U., Kusuma, M., Zepernick, H.J., et al: ‘Reduced-reference metric design for objective perceptual quality assessment in wireless imaging’, Image Commun., 2009, 24, (7), pp. 525–547.
-
2)
-
3)
-
18. Pradhan, M.K., Minz, S., Shrivastava, V.K.: ‘Fast active learning for hyperspectral image classification using extreme learning machine’, IET Image Process., 2019, 13, (4), pp. 549–555.
-
4)
-
8. Wang, Z., Bovik, A.: ‘Reduced- and no-reference image quality assessment’, IEEE Signal Process. Mag., 2011, 28, (6), pp. 29–40.
-
5)
-
10. Jiang, X., Meng, F., Xu, J., et al: ‘No-reference perceptual video quality measurement for high definition videos based on an artificial neural network’. Int. Conf. on Computer and Electrical Engineering (ICCEE'08), Phuket, Thailand, 2008, pp. 424–427.
-
6)
-
21. Ding, W., Tong, Y., Zhang, Q., et al: ‘Image and video quality assessment using neural network and SVM’, Tsinghua Sci. Technol., 2008, 13, (1), pp. 112–116.
-
7)
-
5. Wang, Z., Bovik, A.C., Sheikh, H.R., et al: ‘Image quality assessment: from error visibility to structural similarity’, IEEE Trans. Image Process., 2004, 13, (4), pp. 600–612.
-
8)
-
22. Liang, N.Y., Huang, G.B., Saratchandran, P., et al: ‘A fast and accurate online sequential learning algorithm for feedforward networks’, IEEE Trans. Neural Netw., 2006, 17, (6), pp. 1411–1423.
-
9)
-
15. Decherchi, S., Gastaldo, P., Zunino, R., et al: ‘Circular-ELM for the reduced-reference assessment of perceived image quality’, Neurocomputing, 2013, 102, pp. 78–89.
-
10)
-
6. Dostal, P., Krasula, L., Klima, M.: ‘Can state-of-the-art HVS-based objective image quality criteria be used for image reconstruction techniques based on RoI analysis?’, Opt. Photonics Digit. Technol. Multimedia Appl. II, 2012, 2012, 8436, pp. 84361K–84361K–12.
-
11)
-
31. Liu, T.J., Liu, K.H., Liu, H.H: ‘Temporal information assisted video quality metric for multimedia’. IEEE Int. Conf. on Multimedia and Expo (ICME'10), Singapore, 2010, pp. 697–701.
-
12)
-
7. Wang, Z., Sheikh, H.R., Bovik, A.C: ‘No-reference perceptual quality assessment of JPEG compressed images’. Proc. IEEE Int. Conf. on Image Processing (ICIP'02), Rochester, NY, USA, 2002, vol. 1, pp. I477I–477 –I–480.
-
13)
-
27. González, A.R., Ángel, G.C., Palacios, R.C., et al: ‘CAST: using neural networks to improve trading systems based on technical analysis by means of the RSI financial indicator’, Expert Syst. Appl., 2011, 38, (9), pp. 11489–11500.
-
14)
-
2. Wang, Z., Bovik, A.C.: ‘Modern image quality assessment’ (Morgan & Claypool, San Rafael, CA, 2006).
-
15)
-
12. Huang, G.B., Zhu, Q.Y., Siew, C.K.: ‘Extreme learning machine: theory and applications’, Neurocomputing, 2006, 70, (1–3), pp. 489–501.
-
16)
-
16. Ridella, S., Rovetta, S., Zunino, R.: ‘Circular backpropagation networks for classification’, IEEE Trans. Neural Netw., 1997, 8, (1), pp. 84–97.
-
17)
-
26. Panchal, G., Ganatra, A., Kosta, Y.P., et al: ‘Behaviour analysis of multilayer perceptrons with multiple hidden neurons and hidden layers’, Int. J. Comput. Theory Eng., 2011, 3, (2), pp. 332–337.
-
18)
-
19)
-
20)
-
13. Chen, Z.X., Zhu, H.Y., Wang, Y.G.: ‘A modified extreme learning machine with sigmoidal activation functions’, Neural Comput. Appl., 2013, 22, pp. 541–550.
-
21)
-
28. Mao, W., Tian, M., Cao, X., et al: ‘Model selection of extreme learning machine based on multi-objective optimization’, Neural Comput. Appl., 2013, pp. 22, 521–529.
-
22)
-
11. Choe, J., Lee, K., Lee, C., et al: ‘No-reference video quality measurement using neural networks’. 16th Int. Conf. on Digital Signal Processing, Santorini-Hellas, Greece, 2009, pp. 1–4.
-
23)
-
24. Golub, G.H., Reinsch, C.: ‘Singular value decomposition and least squares solutions’, Numer. Math., 1970, 14, pp. 403–420.
-
24)
-
3. Lin, W., Jay Kuo, C.C.: ‘Perceptual visual quality metrics: a survey’, J. Vis. Commun. Image Represent., 2011, 22, (4), pp. 297–312.
-
25)
-
30. Seshadrinathan, K., Soundararajan, R., Bovik, A.C., et al: ‘Study of subjective and objective quality assessment of video’, IEEE Trans. Image Process., 2010, 19, (6), pp. 1427–1441.
-
26)
-
29. Spiegel, M.R., Stephens, L.J.: ‘Theory and problems of statistics (Schaum's outline series)’ (McGraw-Hill, USA, 1998, 3rd edn.).
-
27)
-
25. Lu, S., Zhang, G., Wang, X.: ‘A rank reduced matrix method in extreme learning machine’, in Wang, J., Yen, G., Polycarpou, M (Eds.): ‘Advances in neural networks (ISNN’12), (), (Springer, Berlin, Heidelberg, 2012), pp. 72–79.
-
28)
-
23. Golub, G., Kahan, W.: ‘Calculating the singular values and pseudo-inverse of a matrix’, J. Soc. Ind. Appl. Math. B, Numer. Anal., 1965, 2, pp. 205–224.
-
29)
-
17. Po, L., Liu, M., Yuen, W.Y.F., et al: ‘A novel patch variance biased convolutional neural network for no-reference image quality assessment’, IEEE Trans. Circuits Syst. Video Technol., 2019, 29, (4), pp. 1223–1229.
-
30)
-
4. Li, C., Yuan, W., Bovik, A.C., et al: ‘No-reference blur index using blur comparisons’, Electron. Lett., 2011, 47, (17), p. 962.
-
31)
-
39. Tukey, J.W.: ‘Exploratory data analysis’ (Addison-Wesley Publishing Company, New York, 1977).
-
32)
-
33)
-
34)
-
40. Wang, Z., Simoncelli, E.P., Bovik, A.C: ‘Multiscale structural similarity for image quality assessment’. Thirty-Seventh Asilomar Conf. on Signals, Systems & Computers, Pacific Grove, CA, USA, 2003, vol. 2, pp. 1398–1402.
-
35)
-
19. Banerjee, K.S., Rao, C.R., Mitra, S.K.: ‘Generalized inverse of matrices and its applications’, Technometrics, 1973, 15, (1), p. 197.
-
36)
-
32. Parker, J.R.: ‘Algorithms for image processing and computer vision’ (John Wiley & Sons, Inc., New York, NY, USA, 1997).
-
37)
-
38)
-
14. Suresh, S., Venkatesh Babu, R., Kim, H.J.: ‘No-reference image quality assessment using modified extreme learning machine classifier’, Appl. Soft Comput., 2009, 9, (2), pp. 541–552.
-
39)
-
9. Babu, R.V., Suresh, S., Perkis, A.: ‘No-reference JPEG-image quality assessment using GAP-RBF’, Signal Process., 2007, 87, (6), pp. 1493–1503.
-
40)
-
1. Winkler, S.: ‘Issues in vision modeling for perceptual video quality assessment’, Signal Process., 1999, 78, (2), pp. 231–252.
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