Circular-ELM for the reduced-reference assessment of perceived image quality

Neurocomputing

Volumn 102, Issue , 2013, Pages 78-89

  Decherchi, Sergio ^a   Gastaldo, Paolo ^b   Zunino, Rodolfo ^b   Cambria, Erik ^d   Redi, Judith ^c  

^a ISTITUTO ITALIANO DI TECNOLOGIA   (Italy)

^b UNIVERSITY OF GENOA   (Italy)

^c DELFT UNIVERSITY OF TECHNOLOGY   (Netherlands)

^d NATIONAL UNIVERSITY OF SINGAPORE   (Singapore)

Author keywords

Circular backpropagation; Extreme learning machine; Image quality assessment

Indexed keywords

CIRCULAR BACKPROPAGATION; CONTROL MECHANISM; ELECTRONIC MULTIMEDIA; ENABLING TECHNOLOGIES; EXTREME LEARNING MACHINE; HUMAN PERCEPTION; IMAGE QUALITY ASSESSMENT; INPUT VECTOR; MULTI LAYER PERCEPTRON; PERCEPTUAL MECHANISM; REDUCED REFERENCE; VIDEO SIGNAL; VISUAL QUALITIES; VISUAL QUALITY ASSESSMENT; VISUAL SIGNALS;

ARTIFICIAL INTELLIGENCE; BACKPROPAGATION; KNOWLEDGE ACQUISITION; LEARNING SYSTEMS; QUALITY CONTROL;

IMAGE QUALITY;

ARTICLE; ARTIFICIAL NEURAL NETWORK; BACK PROPAGATION; CIRCULAR BACK PROPAGATION; CIRCULAR EXTREME LEARNING MACHINE; COMPUTATIONAL INTELLIGENCE; COMPUTER MODEL; CONCEPTUAL FRAMEWORK; CONTROLLED STUDY; HUMAN; IMAGE DISPLAY; IMAGE PROCESSING; IMAGE QUALITY; INTELLIGENCE; LEARNING ALGORITHM; MACHINE LEARNING; MATHEMATICAL ANALYSIS; PRIORITY JOURNAL; QUALITY CONTROL; SIGNAL PROCESSING;

EID: 84870245922     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2011.12.050     Document Type: Article

References (40)

1. Eskicioglu A.M., Fisher P.S. Image quality measures and their performance. IEEE Trans. Commun. 1995, 43(12):2959-2965.
2. Pappas T.N., Safranek R.J. Perceptual criteria for image quality evaluation. Handbook of Image and Video Processing 2000, Academic, New York. A. Bovik (Ed.).
3. Wang.Z., Sheikh H.R., Bovik A.C. Objective video quality assessment. The Handbook of Video Databases: Design and Applications 2003, CRC Press, Boca Raton, FL. B. Furth, O. Marques (Eds.).
4. Keelan B.W. Handbook of Image Quality 2002, Marcel Dekker, New York.
5. E. Cambria, A. Hussain, Sentic Album: content, concept and context based online personal photo management system, Cognitive Computation, , in press. http://dx.doi.org/10.1007/s12559-012-9145-4.
6. Gastaldo P., Zunino R. Neural networks for the no-reference assessment of perceived quality. J. Electron. Imag. 2005, 14(3). 033004-1-033004-11.
7. Babu R.V., Suresh S., Perkis A. No-reference JPEG image quality assessment using GAP-RBF. Signal Process. 2007, 87(6):1493-1503.
8. Le Callet P., Viard-Gaudin C., Barba D. A convolutional neural network approach for objective video quality assessment. IEEE Trans. Neural Networks 2006, 17(5):1316-1327.
9. Lin F.-H., Mersereau R.M. Rate-quality tradeoff MPEG video encoder. Signal Proc.: Image Commun. 1999, 14(4):297-309.
10. Lu W., Zeng K., Tao D., Yuan Y., Gao X. No-reference image quality assessment in contourlet domain. Neurocomputing 2010, 73(4-6):784-794.
11. Narwaria M., Lin W. Objective image quality assessment based on support vector regression. IEEE Trans. Neural Networks 2010, 21(3):515-519.
12. Redi J., Gastaldo P., Heynderickx I., Zunino R. Color distribution information for the reduced-reference assessment of perceived image quality. IEEE Trans. Circuits Syst. Video Technol. 2010, 20(12):1757-1769.
13. Huang G.-B., Wang D., Lan Y. Extreme learning machines: a survey. Int. J. Mach. Learn. Cybern. 2011, 2(2):107-122.
14. Huang G.-B., Chen L. Enhanced random search based incremental extreme learning machine. Neurocomputing 2008, 71:3460-3468.
15. Huang G.-B., Zhou H., Ding X., Zhang R. Extreme learning machine for regression and multiclass classification. IEEE Transactions on Systems, Man, and Cybernetics - Part B: Cybernetics 2012, 42(2):513-529.
16. Wang G., Zhao Y., Wang D. A protein secondary structure prediction framework based on the Extreme Learning Machine. Neurocomputing 2008, 72(1-3):262-268.
17. Y. Lan, Y.C. Soh, G.-B. Huang, Extreme Learning Machine based bacterial protein subcellular localization prediction, in: Proceedings of the IEEE International Joint Conference on Neural Networks, IJCNN 2008, Hong Kong, 2008, pp. 1859-1863.
18. Zhang R., Huang G.-B., Sundararajan N., Saratchandran P. Multicategory classification using an Extreme Learning Machine for microarray gene expression cancer diagnosis. IEEE/ACM Trans. Comput. Biol. Bioinform. 2007, 4(3):485-495.
19. Mohammed A.A., Minhas R., Jonathan Wu Q.M., Sid-Ahmed M.A. Human face recognition based on multidimensional PCA and extreme learning machine. Pattern Recognition 2011, 44(10-11):2588-2597.
20. Nizar A.H., Dong Z.Y., Wang Y. Power utility nontechnical loss analysis with Extreme Learning Machine Method. IEEE Trans. Power Syst. 2008, 23(3):946-955.
21. Decherchi S., Gastaldo P., Dahiya R.S., Valle M., Zunino R. Tactile data classification of contact materials using computational intelligence. IEEE Trans. Robotics 2011, 27(3):635-639.
22. Ridella S., Rovetta S., Zunino R. Circular back-propagation networks for classification. IEEE Trans. Neural Networks 1997, 8(1):84-97.
23. S. Saha, R. Vemuri, An analysis on the effect of image activity on lossy coding performance, in: Proceedings of the IEEE International Symposium on Circuits and Systems, vol. 3, Geneva, Switzerland, 2000, pp. 295-298.
24. T.M. Kusuma, H.-J. Zepernick, On perceptual objective quality metrics for in-service picture quality monitoring, in: Proceedings of the Third ATcrc Telecommunications and Networking Conference and Workshop, Melbourne, Australia, 2003.
25. Z. Wang, E.P. Simoncelli, Reduced-reference image quality assessment using a wavelet-domain natural image statistic model, in: Proceedings of the SPIE Human Vision and Electronic Imaging X, vol. 5666, San Jose, CA, 2005, pp. 149-159.
26. Wang Z., Wu G., Sheikh H.R., Simoncelli E.P., Yang E.H., Bovik A.C. Quality-aware images. IEEE Trans. Image Process. 2006, 15(6):1680-1689.
27. Carnec M., Le Callet P., Barba D. Objective quality assessment of color images based on a generic perceptual reduced reference. Signal Process.: Image Commun. 2008, 23(4):239-256.
28. Q. Li, Z. Wang, General-purpose reduced-reference image quality assessment based on perceptually and statistically motivated image representation, in: Proceedings of the IEEE International Conference on Image Processing, ICIP 2008, San Diego, CA, 2008.
29. J. Huang, S. Ravi Kumar, M. Mitra, W.J. Zhu, R. Zabih, Image indexing using color correlograms, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, CVPR'97, 1997, pp. 762-768.
30. H.R. Sheikh, Z. Wang, L. Cormack, A.C. Bovik: LIVE Image Quality Assessment Database at http://live.ece.utexas.edu/research/quality.
31. J. Redi, P. Gastaldo, R. Zunino, I. Heynderickx, Co-occurrence matrixes for the quality assessment of coded images, in: Proceedings of the International Conference on Artificial Neural Networks, ICANN'08, 2008.
32. J. Redi, P. Gastaldo, R. Zunino, I. Heynderickx, Reduced Reference Assessment of perceived quality by exploiting color information, in: Proceedings of the Fourth International Workshop on Video Processing and Quality Metrics for Consumer Electronics, VPQM'09, 2009.
33. Vogl T.P., Mangis J.K., Rigler A.K., Zink W.T., Alkon D.L. Accelerating the convergence of the back-propagation method. Biol. Cybern. 1988, 59(4-5):257-263.
34. Evgeniou T., Pontil M., Poggio T. Regularization networks and support vector machines. J. Adv. Comput. Math. 2000, 13(1):1-50.
35. M. Perrone, Improving Regression Estimates: Averaging Methods for Variance Reduction With Extension to General Convex Measure Optimization, Ph.D. Dissertation, Brown University, Providence, RI, 1993.
36. Geman S., Bienenstock E., Doursat R. Neural networks and the bias/variance dilemma. Neural Comput. 1992, 4(1):1-48.
37. Rumelhart D.E., McClelland J.L. Parallel Distributed Processing 1986, MIT Press, Cambridge, MA.
38. Bartlett P., Boucheron S., Lugosi G. Model selection and error estimation. Mach. Learn. 2011, 48(1-3):85-113.
39. Wang Z., Bovik A.C., Sheikh H.R., Simoncelli E.P. Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 2004, 13(4):600-612.
40. VQEG: Final report from the video quality experts group on the validation of objective models of video quality assessment, 2003, Available: http://www.vqeg.org.