The week's pick
Random Articles
Reseach Article
A New No-reference Method for Color Image Quality Assessment
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 40 - Number 17 |
| Year of Publication: 2012 |
| Authors: Sonia Ouni, Ezzeddine Zagrouba, Majed Chambah |
10.5120/5073-7470
|
Sonia Ouni, Ezzeddine Zagrouba, Majed Chambah . A New No-reference Method for Color Image Quality Assessment. International Journal of Computer Applications. 40, 17 ( February 2012), 24-31. DOI=10.5120/5073-7470
Abstract
Image quality assessment (IQA) is a complex problem due to subjective nature of human visual perception. Human have always seen the world in color. The widely objective metrics used are mean squared error (MSE), peak signal to noise ratio (PSNR), and human visual system based on structural similarity and edge based similarity. The problem of these objective metrics that they evaluate the quality of grayscale images only and don’t make use of image color information. Also, we must have the presence of original image. Unfortunately, the field of no-reference (NR) color IQA has been largely unexplored although the color is a powerful descriptor that often simplifies the object identification and extraction from a scene so color information also could influence human beings’ judgments. So, in this paper a new no reference methods for color IQA are proposed. These methods are based on different statistical analyses and easy to calculate and applicable to various image processing. This proposed metrics are mathematically defined and overcame the limitations of existing metrics to assess the quality of the color in the image. The experiment results on various image distortion show that our proposed no reference metrics have a comparable performance to the other traditional error summation metrics and to the leading metrics available in literature.
References
- N. Thakur and S. Devi. "A new Method for Color Image Quality Assessment ". International Journal of Computer Applications 15(2):10–17, February 2011.
- S. Ouni, M. Herbin, E. Zagrouba. "Are existing procedures enough? Image and video quality assessment: review of subjective and objective metrics ". Image Quality and System Performance, SPIE/IS&T Electronic Imaging, Proc. SPIE 6808, 68080Q, California, USA, 28-30 January 2008.
- Rec. ITU-R BT.500-11. Methodology for the subjective assessment of the quality of television pictures. ITU-R, 1974-2002.
- Y. Tian, M. Zhu, L. Wang, Analysis and Design of No-Reference Image Quality Assessment, pp.349-352, 2008 International Conference on Multi Media and Information Technology, 2008.
- S. Ouni, E. Zagrouba, M. Chambah, M. Herbin. No-Reference Image Semantic Quality Approach Using Neural Network. IEEE International Symposium on Signal Processing and Information Technology (ISSPIT) pp.95-102, December 14-17, Bilbao – Spain, 2011.
- S. Ouni, M. Chambah, M. Herbin, E. Zagrouba. SCID: Full Reference Spatial Color Image Quality Metric, SPIE/IS&T Electronic Imaging, Proc. SPIE 7242, 72420U, California, USA, 28-30 January 2009.
- Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli. "Image quality assessment: From error measurement to structural similarity", IEEE Transaction on Image Processing, vol. 13, pp. 600-612, 2004.
- Y. Shi, Y. Ding Z, R.hang, Jun Li. "Structure and Hue Similarity for Color Image Quality Assessment". In Proceedings of International Conference on Electronic Computer Technology, pp. 329 – 33, 2009.
- E. Dumic, S. Grgic and M. Grgic, "New image-quality measure based on wavelets". J. Electron. Imaging 19, 011018, 2010.
- Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, "Image quality assessment: From error visibility to structural similarity", IEEE Transactions on Image Processing, vol. 13, no. 4, pp. 600-612, April 2004.
- A. B. Watson, A. J. Ahumada.A standard model for foveal detection of spatial contrast. Journal of Vision, vol. 5, no. 9, pp. 717-740. 2005. http://vision.arc.nasa.gov/dctune/
- Z. Wang and A. C. Bovik. "A universal image quality index", IEEE Signal Processing Letters, vol. 9, pp. 81-84, 2002.
- Wen Chen, Yun Q. Shi, Guorong Xuan, "Identifying Computer Graphics Using HSV Color Model and Statistical Moments of Characteristic Functions", 2007 IEEE International Conference on Multimedia and Expo (ICME 2007), Beijing, China, July2-5, 2007.
- S. Rao Jammalamadaka, A. Sengupta, Topics in Circular Statistics, World Scientific Publishing Company. 2001.
- A. Bouzerdoum, A. Havstad and A. Beghdadi. Image quality assessment using a neural network approach, Proc. Fourth IEEE Intern. Symposium on Signal Processing and Information Technology (ISSPIT-2004), pp. 330–333, Rome, Italy, 18-21 Dec. 2004.
- Ponomarenko N., Carli M., Lukin V., Egiazarian K., Astola J., Battisti F. Color Image Database for Evaluation of Image Quality Metrics, Proc. of Intern. Workshop on Multimedia Signal Processing, Australia, pp. 403-408, Oct. 2008. http://www.ponomarenko.info/tid2008.htm
- H. R. Sheikh, Z. Wang, L. Cormack, and A. C. Bovik, LIVE Image Quality Assessment Database, Rel. 2, 2005. http://live.ece.utexas.edu/research/quality
- Z. Wang and A. C. Bovik. "Mean squared error: love it or leave it? - A new look at signal fidelity measures". IEEE Signal Processing Magazine, vol. 26, no. 1, pp. 98-117, Jan. 2009.
- Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli. "Image quality assessment: From error measurement to structural similarity", IEEE Transaction on Image Processing, vol. 13, pp. 600-612. 2004.
Index Terms
Keywords