A convolutional neural network approach for objective video quality assessment

IEEE Transactions on Neural Networks

Volumn 17, Issue 5, 2006, Pages 1316-1327

  Le Callet, Patrick ^a,b   Viard Gaudin, Christian ^b   Barba, Dominique ^a,b  

^a IEEE   (France)

^b INSTITUT DE RECHERCHE EN COMMUNICATIONS ET CYBERNÉTIQUE DE NANTES   (France)

Author keywords

Convolutional neural network (CNN); MPEG 2; Temporal pooling; Video quality assessment

Indexed keywords

CONVOLUTIONAL NEURAL NETWORK; MPEG-2; TEMPORAL POOLING; VIDEO QUALITY ASSESSMENT;

CORRELATION THEORY; IMAGE COMPRESSION; IMAGE QUALITY; PROBLEM SOLVING; VIDEO SIGNAL PROCESSING;

NEURAL NETWORKS;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; ARTIFICIAL NEURAL NETWORK; AUTOMATED PATTERN RECOGNITION; COMPUTER ASSISTED DIAGNOSIS; EVALUATION; IMAGE ENHANCEMENT; METHODOLOGY; PHOTOGRAPHY; SIGNAL PROCESSING; VIDEORECORDING;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; NEURAL NETWORKS (COMPUTER); PATTERN RECOGNITION, AUTOMATED; PHOTOGRAPHY; SIGNAL PROCESSING, COMPUTER-ASSISTED; VIDEO RECORDING;

EID: 33750135479     PISSN: 10459227     EISSN: None     Source Type: Journal    
DOI: 10.1109/TNN.2006.879766     Document Type: Article

References (40)

1. International Telecommunications Union (ITU), Methodology for the Subjective Assessment of the Quality of Television Pictures Geneva, Switzerland, Recommendation BT.500, 1999 [Online]. Available: http://www.itu.int/
2. J. Baina and G. Goudezeune, "Equipment and strategies for service quality monitoring for digital television networks," Soc. Motion Picture and Television Engineers (SMPTE) J., pp. 108-632, 1999.
3. S. Wolf and M. H. Pinson, "Spatio-temporal distortion metrics for in-service quality monitoring of any digital video system," Proc. SPIE, vol. 3845, pp. 266-277, 1999.
4. P. Le Callet and D. Barba, "Image quality assessment: from site errors to a global appreciation of quality," in Proc. Picture Coding Symp., Seoul, Korea, 2001, pp. 105-108.
5. M. Miyahara, K. Kotani, and V. R. Algazi, "Objective picture quality scale (PQS) for image coding," IEEE Trans. Commun., vol. 46, no. 9, pp. 1215-1225, Sep. 1998.
6. H. R. Wu and M. Yuen, "A generalize block-edge impairment metric for video coding," IEEE Signal Process. Lett., vol. 4, no. 11, pp. 317-320, Nov. 1997.
7. S. A. Karunasekera and N. G. Kingsbury, "A distortion measure for blocking artifacts in image based on human visual sensitivity," IEEE Trans. Image Process., vol. 4, no. 6, pp. 713-724, Jun. 1995.
8. Y. Horita, J. Ohnishi, and T. Murai, "Quality evaluation model for coded JPEG2000 still pictures," in Proc. Picture Coding Symp., 2001, pp. 125-128.
9. D. S. Turaga, Y. Chen, and J. Caviedes, "No reference PSNR estimation for compressed pictures," Signal Process.: Image Commun., vol. 19, pp. 173-184, 2004.
10. S. Winkler, "Issues in vision modeling for perceptual video quality assessment," Signal Process., vol. 78, no. 2, pp. 231-252, 1999.
11. M. D'Zumra, T. J. Shen, W. Wu, H. Chen, and M. Vassiliou, "Contrast gain control for color image quality," Proc. SPIE, vol. 3299, pp. 194-201, 1998.
12. A. B. Watson, J. Hu, and J. F. McGowan, "III, DVQ: a digital video quality metric based on human vision," J. Electr. Imag., vol. 10, no. 1, pp. 20-29, 2001.
13. M. Carnec, P. Lecallet, and D. Barba, "A new method for perceptual quality assessment of compressed images with reduced reference," in Proc. SPIE Visual Commun. Image Process., Lugano, Switzerland, 2003, pp. 1582-1593.
14. Z. Wang, L. Lu, and A. C. Bovik, "Video quality assessment based on structural distortion measurement," Signal Process.: Image Commun., vol. 19, pp. 121-132, 2004.
15. K. T. Tan, M. Ghanbari, and D. E. Pearson, "An objective measurement tool for MPEG video quality," Signal Process., vol. 70, pp. 279-294, 1998.
16. M. A. Masry and S. S. Hemami, "A metric for continuous quality evaluation of compressed video with severe distortions," Signal Process.: Image Commun., vol. 19, pp. 133-146, 2004.
17. S. Yao, W. Lin, Z. Lu, E. Ong, and X. Yang, "Video quality assessment using neural network based on multi-feature extraction," in Proc. SPIE Visual Commun. Image Process., T. Ebrahimi and T. Sikora, Eds., 2003, vol. 5150, pp. 604-612.
18. P. Gastaldo, S. Rovetta, and R. Zunino, "Objective quality assessment of MPEG-2 video streams by using CBP neural networks," IEEE Trans. Neural Netw., vol. 13, no. 4, pp. 939-947, Jul. 2002.
19. P. Le Callet and D. Barba, "Perceptual color image qualify metric using adequate error pooling for coding scheme evaluation," presented at the SPIE Human Vis. Electron. Imaaing Conf., San Jose, CA, Jan. 21-24, 2002.
20. P. Le Callet and D. Barba, "Robust approach for color image quality assessment," in Proc. SPIE Vis. Commun. Image Process. (VCIP), Lugano, Switzerland, 2003, pp. 1573-1581.
21. M. Carnec, P. Le Callet, and D. Barba, "An image quality assessment method based on perception of structural information," presented at the Int. Conf. Image Process. (ICIP), Barcelona, Spain, Sep. 14-18, 2003.
22. D. R. Williams, J. Krauskopf, and D. W. Heeley, "Cardinal directions of color space," Vision Res., vol. 22, pp. 1123-1131, 1982.
23. P. Le Callet, A. Saadane, and D. Barba, "Orientation selectivity of opponent-colour channels," in Supplement Eur. Conf. Vis. Perception, Trieste, Italy, Aug. 22-26, 1999, vol. 28, p. 67.
24. R. Aldridge and D. Pearson, "A calibration method for continuous video quality (SSCQE) measurements," Signal Process.: Image Commun., vol. 16, no. 3, pp. 321-332, 2000.
25. D. Melcher and S. Wolf, Objective Measures for Detecting Digital Tiling Document Number: T1A1.5/95-104, 1995 [Online]. Available: http://www.its. bldrdoc.gov
26. A. A. Webster, C. T. Jones, M. H. Pinson, S. D. Voran, and S. Wolf, "An objective video quality assessment system based on human perception," Proc. SPIE, vol. 1913, pp. 15-26, 1993.
27. T. Yamashita, M. Kameda, and M. Miyahara, "An objective picture quality scale for video images (PQSvideo) - definition of distortion factors," Proc. SPIE, Visual Commun. Image Process., vol. 4067, pp. 801-809, 2000.
28. S. Winkler, A. Sharma, and D. McNally, "Perceptual video quality and blockiness metrics for multimedia streaming applications," in Proc. 4th Int. Symp. Wireless Personal Multimedia Commun., Aalborg, Denmark, Sep. 9-12, 2001, pp. 553-556, (invited paper).
29. Z. Wang, A. Bovik, and B. Evans, "Blind measurement of blocking artefact in images," in Proc. Int. Conf. Image Process. (ICIP), Vancouver, BC, Canada, 2000, vol. 3, pp. 981-984.
30. M. Egmont-Petersena, D. de Ridder, and H. Handelsc, "Image processing with neural networks - a review," Pattern Recog., vol. 35, pp. 2279-2301, 2002.
31. Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, "Gradient-based learning applied to document recognition," Proc. IEEE, vol. 86, no. 11, pp. 2278-2324, Nov. 1998.
32. C. Garcia and M. Delakis, "Convolutional face finder: a neural architecture for fast and robust face detection," IEEE Trans. Pattern Anal. Mach. Intell., vol. 26, no. 11, pp. 1408-1423, Nov. 2004.
33. A. Waibel, T. Hanazawa, G. Hinton, K. Shikan, and K. Lang, "Phoneme recognition using time-delay neural networks," IEEE Trans. Acoust., Speech, Signal Process., vol. 37, no. 3, pp. 328-339, Mar. 1989.
34. M. Schenkel, I. Guyon, and D. Henderson, "On-line cursive script recognition using time delay neural networks and hidden Markov models," Mach. Vis. Appl., vol. 8, no. 4, 1995.
35. D. Hubel and T. Wiesel, "Receptive fields, binocular interaction and functional architecture in the cat's visual cortex," J. Physiol., vol. 160, pp. 106-154, 1962.
36. K. Fukushima, "Cognitron: a self-organizing multilayered neural network," Biol. Cybern., vol. 20, pp. 121-136, 1975.
37. M. Mozer, "The perception of multiple objects: a connectionist approach," in Connectionism in Perspective. Cambridge, MA: MIT Press-Bradford Books, 1991.
38. C. M. Bishop, Neural Nertworks for Pattern Recognition. London, U.K.: Oxford Univ. Press, 1995, pp. 116-161, ISBN 0-19-853849-9.
39. A. B. Watson and J. A. Soloman, "A model of visual contrast gain control and pattern masking," J. Opt. Soc, America A, vol. 14, no. 9, pp. 2379-2391, 1997.
40. E. Poisson, C. Viard-Gaudin, and P. M. Lallican, "Multi-modular architecture based on convolutional neural networks for online handwritten character recognition," in Proc. Int. Conf. Neural Inf. Process. (ICONIP), Singapore, 2002.