Image quality measurement using sparse extreme learning machine classifier

9th International Conference on Control, Automation, Robotics and Vision, 2006, ICARCV '06

Volumn , Issue , 2006, Pages

  Suresh, S ^a   Babu, Venkatesh ^a   Sundararajan, N ^a  

^a NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

Author keywords

Classification; Extreme learning machine; Image quality; JPEG; Neural network

Indexed keywords

CLASSIFICATION (OF INFORMATION); COMPUTATION THEORY; IMAGE CODING; LEARNING SYSTEMS; NEURAL NETWORKS; PARAMETER ESTIMATION;

EXTREME LEARNING MACHINES; JPEG; POSTERIOR PROBABILITY; VISUAL QUALITY;

IMAGE QUALITY;

EID: 34547204577     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICARCV.2006.345467     Document Type: Conference Paper

References (17)

1. Z. Wang, H. R. Sheikh, and A. C. Bovik, "No-reference perceptual quality assessment of JPEG compressed images," in Proc. ICIP'02, September 2002. vol. 1, pp. 477-480.
2. S. A. Karunasekera and N.G. Kingsbury. "A distortion measure for blocking artifacts in images based on human visual sensitivity," IEEE Transactions on Image Processing, vol. 4, no. 6, pp. 713-724, June 1995.
3. H. R. Wu and M. Yuen, "A generalized block-edge impairment metric for video coding," IEEE Signal Processing Letters, vol. 70, no. 3, pp. 247-278, November 1998.
4. S. Suthaharan, "A perceptually significant block-edge impairment metric for digital video coding," in Proc. ICASSP'2003, Hong Kong, 2003, vol. 3, pp. 681-684.
5. W. Gao, C. Mermer, and Y. Kim, "A de-blocking algorithm and a blockiness metric for highly compressed images." IEEE Transactions on Circuits and Systems for Video Technology, vol. 12, no. 12, pp. 1150-1159, December 2002.
6. Video Quality Experts Group (VQEG). "website: http://www.vqeg.org, "
7. JPEG official site, "http://www.jpeg.org/," .
8. Lydia Meesters and Jean-Bernard Maliens, "A single-ended blockiness measure for JPEG-coded images," Signal Processing, vol. 82, no. 3, pp. 369-387. March 2002.
9. G.-B. Huang. "Learning capability and storage capacity of two-hidden layer feedforward networks," IEEE Trans. on Neural Networks, vol. 14, no. 2, pp. 274-281, 2003.
10. G.-B. Huang, Q. Y. Zhu, and C. K. Siew, "Extreme learning machine: Theory and applications," Neurocomputing, 2006.
11. Venkatesh Babu and Andrew Perkis, "An hvs-based no-reference perceptual quality assessment of jpeg coded images using neural networks," in Proc. Int. Conf. on Image Processing (ICIP'05). Genova, Italy, September 2005. vol. 1, pp. 433-436.
12. H. R. Sheikh. Z. Wang. L. Cormack, and A. C Bovik, "Live image quality assessment database," http://live.ece.utexas.edu/research/quality.
13. L. Breiman. "Heuristics of instability and stabilization in model selection," Annals of Statistics, vol. 24. pp. 2350-2383, 1996.
14. R. Rojas, "A short proof of the posterior probability property of classifier neural networks," Neuml Computation, vol. 8. no. 2, pp. 41-43, 1996.
15. Z. Wang and A. C. Bovik and L. Lu , "Why is image quality assessment so difficult?." in Proc. ICASSP'02. 2002, vol. 4, pp. IV-3313- IV-3316.
16. Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli. "Image quality assessment: From error visibility to structural similarity," IEEE Trans. Image Processing, vol. 3, no. 4, pp. 600-612, 2004.
17. Z. Wang. A. C. Bovik, and E. P. Simoncelli, "A universal image quality index," IEEE Signal Processing Letters, vol. 9, no. 3, pp. 81-84, 2002.