Real-time no-reference image quality assessment based on filter learning

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

Volumn , Issue , 2013, Pages 987-994

  Ye, Peng ^a   Kumar, Jayant ^a   Kang, Le ^a   Doermann, David ^a  

^a UNIVERSITY OF MARYLAND   (United States)

Author keywords

image quality assessment

Indexed keywords

CROSS-DOMAIN MODELS; HUMAN PERCEPTION; IMAGE QUALITY ASSESSMENT; LOCAL FEATURE EXTRACTION; NATURAL SCENE IMAGES; NO-REFERENCE IMAGE QUALITY ASSESSMENTS; REAL-TIME APPLICATION; SUPERVISED METHODS;

FEATURE EXTRACTION; MATHEMATICAL MODELS; REGRESSION ANALYSIS;

IMAGE QUALITY;

EID: 84887324508     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2013.132     Document Type: Conference Paper

References (22)

1. ISRI-OCR evaluation tool UNLV/ISRI. Available Online: http://code. google. com/p/isri-ocr-evaluation-tools/, 2010.
2. A. Antonacopoulos, C. Clausner, C. Papadopoulos, and S. Pletschacher. Historical document layout analysis competition. In ICDAR, pages 1516-1520, Beijing, China, Sept. 2011.
3. T. Brandao and M. P. Queluz. No-reference image quality assessment based on DCT domain statistics. Signal Processing, 88:822-833, Apr. 2008.
4. C.-C. Chang and C.-J. Lin. LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology, 2:27:1-27:27, 2011.
5. I. Guyon, R. M. Haralick, J. J. Hull, and I. T. Phillips. Data sets for OCR and document image understanding research. In In Proceedings of the SPIE-Document Recognition IV, pages 779-799. World Scientific, 1997.
6. A. K. Jain and K. Karu. Learning texture discrimination masks. IEEE Trans. Pattern Anal. Mach. Intell., 18(2):195-205, Feb. 1996.
7. Z. Jiang, Z. Lin, and L. S. Davis. Learning a discriminative dictionary for sparse coding via label consistent k-svd. In IEEE Conf. on Computer Vision and Pattern Recognition, pages 1697-1704. IEEE, 2011.
8. J. Kumar, P. Ye, and D. Doermann. DIQA: Document image quality assesment datasets. Available Online: http://lampsrv02. umiacs. umd. edu/projdb/project. php?id=73.
9. D. Lewis. Building a test collection for complex document information processing. In 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 665-666, 2006.
10. J. Mairal, F. Bach, J. Ponce, G. Sapiro, and A. Zisserman. Supervised dictionary learning. In NIPS, pages 1033-1040, 2008.
11. P. Marziliano, F. Dufaux, S. Winkler, and T. Ebrahimi. Perceptual blur and ringing metrics: application to JPEG2000. Signal Processing: Image Communication, 19(2):163-172, 2004.
12. A. Mittal, A. Moorthy, and A. Bovik. No-reference image quality assessment in the spatial domain. IEEE Transactions on Image Processing, PP(99):1, 2012.
13. A. K. Moorthy and A. C. Bovik. Blind image quality assessment: From natural scene statistics to perceptual quality. IEEE Transactions on Image Processing, 20(12):3350-3364, Dec. 2011.
14. N. Ponomarenko, V. Lukin, A. Zelensky, K. Egiazarian, M. Carli, and F. Battisti. Tid2008-a database for evaluation of full-reference visual quality assessment metrics. Advances of Modern Radio Electronics, 10:30-45, 2009.
15. L. Prechelt. Early stopping-but when? In Neural Networks: Tricks of the Trade, volume 1524 of LNCS, Chapter 2, pages 55-69. Springer-Verlag, 1997.
16. M. Saad, A. Bovik, and C. Charrier. Blind image quality assessment: A natural scene statistics approach in the DCT domain. IEEE Transactions on Image Processing, 21(8):3339-3352, Aug. 2012.
17. H. R. Sheikh, M. F. Sabir, and A. C. Bovik. A statistical evaluation of recent full reference image quality assessment algorithms. IEEE Transactions on Image Processing, 15(11):3440-3451, 2006.
18. H. R. Sheikh, Z. Wang, L. Cormack, and A. C. Bovik. LIVE image quality assessment database release 2. Online, http://live. ece. utexas. edu/research/quality.
19. H. Tang, N. Joshi, and A. Kapoor. Learning a blind measure of perceptual image quality. In IEEE Conference on Computer Vision and Pattern Recognition, pages 305-312, 2011.
20. Z. Wang, A. C. Bovik, and L. Lu. Why is image quality assessment so difficult? In IEEE International Conference on Acoustics, Speech, and Signal Processing, volume 4, pages IV-3313-IV-3316, May 2002.
21. J. Yang, K. Yu, and T. Huang. Supervised translationinvariant sparse coding. In IEEE Conf. on Computer Vision and Pattern Recognition, pages 3517-3524, 2010.
22. P. Ye, J. Kumar, L. Kang, and D. Doermann. Unsupervised Feature Learning Framework for No-reference Image Quality Assessment. In IEEE Conf. on Computer Vision and Pattern Recognition, pages 1098-1105, 2012.