Learning a blind measure of perceptual image quality

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Volumn , Issue , 2011, Pages 305-312

  Tang, Huixuan ^a   Joshi, Neel ^b   Kapoor, Ashish ^b  

^a UNIVERSITY OF TORONTO   (Canada)

^b MICROSOFT RESEARCH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BENCHMARKING; COMPUTER VISION; CONTENT BASED RETRIEVAL; LEARNING SYSTEMS; QUALITY CONTROL;

BENCHMARK DATASETS; COMPUTER VISION APPLICATIONS; DEGRADATION PROCESS; IMAGE QUALITY MEASURE; LOW-LEVEL IMAGE FEATURES; PERCEPTUAL IMAGE QUALITY; QUALITY FEATURES; SUBJECTIVE IMAGE QUALITY;

IMAGE QUALITY;

EID: 80052870291     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2011.5995446     Document Type: Conference Paper

References (28)

1. C. C. Chang and C. J. Lin. LIBSVM: a library for support vector machines, 2001. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm.
2. C. Charrier, G. Lebrun, and O. Lezoray. A machine learning-based color image quality metric. In Color in Graphics, Imaging and Vision (CGIV), pages 251-256, 2006.
3. T. S. Cho, N. Joshi, C. L. Zitnick, S. B. Kang, R. Szeliski, and W. T. Freeman. A content-aware image prior. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2010.
4. R. Datta, D. Joshi, J. Li, and J. Z. Wang. Studying aesthetics in photographic images using a computational approach. In European Conference on Computer Vision (ECCV), volume 3, pages 288-301, 2006.
5. A. L. F. Cutzu, R. Hammoud. Estimating the photorealism of images: Distinguishing paintings from photographs. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), volume 2, page 305, 2003.
6. H. Farid and S. Lyu. Higher-order wavelet statistics and their application to digital forensics. In IEEE Workshop on Statistical Analysis in Computer Vision (in conjunction with CVPR), 2003.
7. S. Ghebreab, H. S. Scholte, V. A. F. Lamme, and A. W. M. Smeulders. A biologically plausible model for rapid natural scene identification. In Advances in Neural Information Processing Systems (NIPS), pages 1-9, 2009.
8. D. Glasner, S. Bagon, and M. Irani. Super-resolution from a single image. In IEEE International Conference on Computer Vision (ICCV), 2009.
9. N. Joshi, R. Szeliski, and D. Kriegman. Psf estimation using sharp edge prediction. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2009.
10. N. Joshi, C. L. Zitnick, R. Szeliski, and D. Kriegman. Image deblurring and denoising using color priors. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), 2009.
11. M. Jung, D. Léger, and M. Gazalet. Univariant assessment of the quality of images. Journal of Electronic Imaging, 11(3):354-364, 2002.
12. Y. Ke, X. Tang, and F. Jing. The design of high-level features for photo quality assessment. In IEEE Conference on Computer Vision and Pattern Recognition, volume 1, pages 419-426, 2006.
13. C. Li and A. C. Bovik. Content-partitioned structural similarity index for image quality assessment. Image Communication, 25(7):517-526, 2010.
14. J. Lubin. Sarnoff jnd vision model: Algorithm description and testing. Technical report, Sarnoff Corporation, 1997.
15. Y. Luo and X. Tang. Photo and video quality evaluation: focusing on the subject. In European Conference on Computer Vision (ECCV), pages 386-399, 2008.
16. S. Lyu and H. Farid. How realistic is photorealistic? IEEE Transactions on Signal Processing, 53(2):845-850, 2005.
17. A. K. Moorthy and A. C. Bovik. A two-step framework for constructing blind image quality indices. IEEE Signal Processing Letters, 17(5):513-516, 2010.
18. J. Portilla and E. P. Simoncelli. A parametric texture model based on joint statistics of complex wavelet coefficients. International Journal of Computer Vision, 40:49-71, 2000.
19. S. Roth and M. J. Black. Fields of experts: A framework for learning image priors. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 860-867, 2005.
20. M. A. Saad, A. C. Bovik, and C. Charrier. A dct statistics-based blind image quality index. IEEE Signal Processing Letters, 17(6):583-586, 2010.
21. B. Schölkopf, A. J. Smola, R. C. Williamson, and P. L. Bartlett. New support vector algorithms. Neural Computation, 12(5):1207-1245, 2000.
22. H. R. Sheikh, A. C. Bovik, and G. D. Veciana. An information fidelity criterion for image quality assessment using natural scene statistics. IEEE Transaction on Image Processing, 14:2117-2128, 2005.
23. H. R. Sheikh, M. F. Sabir, and A. C. Bovik. A statistical evaluation of recent full reference quality assessment algorithms. IEEE Transactions on Image Processing, 15(11):3440-3451, 2006.
24. H. R. Sheikh, Z. Wang, L. Cormack, and A. C. Bovik. LIVE image quality assessment database release 2. http://live.ece.utexas.edu/research/quality.
25. P. C. Teo and D. J. Heeger. Perceptual image distortion. IEEE International Conference Image Processing, 2:982-986, 1994.
26. L. J. P. van der Maaten and G. E. Hinton. Visualizing high-dimensional data using t-sne. Journal of Machine Learning Research, 9:2579-2605, 2009.
27. H. R. S. Z. Wang, A. C. Bovik and E. P. Simoncelli. Image quality assessment: From error visibility to structural similarity. IEEE Transactions on Image Processing, 3(4):600-612, 2004.
28. D. Zoran and Y. Weiss. Scale invariance and noise in natural images. In IEEE International Conference on Computer Vision (ICCV), pages 2209-2216, 2009.