Image Quality Assessment using ANFIS Approach

El-Sayed M. El-Alfy, Mohammed R. Riaz

2014

Abstract

Due to the increasing use of digital images in electronic systems, it becomes important to evaluate the degradation in image quality during acquisition, processing, storage and transmission. In this paper, we investigate the ability of the adaptive neuro-fuzzy inference system (ANFIS) for quality assessment of digital images with respect to original (reference) images. Several metrics for objective quality assessment are calculated and used as inputs to an adaptive fuzzy inference system which in turn estimates a differential mean opinion score (DMOS) for different types of distortions. The predicted values are compared with the actual DMOS values using correlation and error measures. With 7-input ANFIS network, the results show that predicted DMOS values are highly correlated to the actual values using a publicly available and subjectively rated image database. For example, for distorted images due to JPEG 2000 compression, the attained results for correlation coefficient, Spearman’s ranked correlation, and RMSE are 0.9944, 0.9902, and 3.32, respectively. These results show that combining the advantages of neural networks with fuzzy systems can be a promising approach for predicting the subjective quality of digital images.

References

  1. Balamurugan, P., Rajesh, R., 2007. Greenery image and non-greenery image classification using adaptive neuro-fuzzy inference system. In International Conference on Computational Intelligence and Multimedia Applications.
  2. Bouzerdoum, A., Havstad, A., Beghdadi. A., 2004. Image quality assessment using a neural network approach. In Fourth IEEE International Symposium on Signal Processing and Information Technology.
  3. Chetouani, A., Beghdadi, A., Deriche, M., 2010. Image distortion analysis and classification scheme using a neural approach. In 2nd European Workshop on Visual Information Processing (EUVIP).
  4. Damera-Venkata, N., Kite, T., Geisler, W., Evans, B., Bovik, A., 2000. Image quality assessment based on a degradation model. IEEE Transactions on Image Processing, 9(4): 636-650.
  5. De, I., Sil, J., 2009. No-reference quality prediction of distorted/decompressed images using ANFIS. In International Conference on Computer Technology and Development.
  6. He, L., Gao, F., Hou W., Hao, L., 2013. Objective image quality assessment: a survey. International Journal of Computer Mathematics.
  7. Kaya, S., Milanova, M., Talburt, J., Tsou, B., Altynova, M. 2011. Subjective image quality prediction based on neural network. In Proceedings of the 16th International Conference on Information Quality.
  8. Kudelka Jr., M., 2012. Image quality assessment. WDS'12 Proceedings of Contributed Papers, Part I, 94-99.
  9. Kung, C.-H., Yang, W.-S., Huang, C.-Y., Kung, C.-M., 2010. Investigation of the image quality assessment using neural networks and structure similarity. In Proceedings of the 3rd International Symposium Computer Science and Computational Technology.
  10. Jang, J. S. R., 1993. ANFIS: Adaptive-network-based fuzzy inference system. IEEE Transactions on Systems, Man, Cybernetics, 23(5/6):665-685.
  11. Khuntia, S. R., Panda, S. 2011. ANFIS approach for SSSC controller design for the improvement of transient stability performance. Mathematical and Computer Modelling, 57(1-2): 289-300.
  12. Larson, E. C., Chandler, D. M. 2010. Most apparent distortion: full-reference image quality assessment and the role of strategy. Journal of Electronic Imaging, 19 (1): 011006: 1-011006:21.
  13. Li, C., Bovik, A. C., Wu, X., 2011. Blind image quality assessment using a general regression neural network. IEEE Trans. Neural Network, 22(5): 793-799.
  14. Lin, W., and Kuo, C-C., 2011. Perceptual visual quality metrics: A survey. Journal of Visual Communication and Image Representation, 22(4): 297-312.
  15. Meena, R. S., Revathi, P., Begum, H. M. R., Singh, A. B., 2012. Performance analysis of neural network and ANFIS in brain MR image classification. In: S. Patnaik & Y.-M. Yang (Eds.): Soft Computing Techniques in Vision Sci., SCI 395, pp. 101-113.
  16. Meharrar, A., Tioursi, M., Hatti, M., Stambouli, A., 2011. A variable speed wind generator maximum power tracking based on adaptive neuro-fuzzy inference system. Expert Systems with Applications, 38(6): 7659-7664.
  17. Rehman, A., Wang, Z., 2012. Reduced-reference image quality assessment by structural similarity estimation. IEEE Transactions on Image Processing, 21(8): 3378- 3389.
  18. Sheikh, H.R., Bovik, A., De Veciana, G., 2005. An information fidelity criterion for image quality assessment using natural scene statistics. IEEE Transactions on Image Processing, 14(12): 2117- 2128.
  19. Sheikh, H. R., Bovik, A., 2006. Image information and visual quality. IEEE Transactions on Image Processing, 15(2): 430-444.
  20. Wang, Z., Bovik, A. C., Sheikh, H. R., Simoncelli, E. P., 2004. Image quality assessment: From error visibility to structural similarity. IEEE Transactions on Image Processing, 13(4): 600-612.
  21. Wang, Z., Bovik, A. C., 2009. Mean squared error: Love it or leave it? A new look at signal fidelity measures. IEEE Signal Processing Magazine, 26(1):98-117.
  22. Wee, C.-Y., Paramesran, R., Mukundan, R., Jiang, X., 2010. Image quality assessment by discrete orthogonal moments. Pattern Recognition 43(12): 4055-4068.
  23. Yi, Y., Yu, X., Wang, L., Yang, Z., 2008. Image quality assessment based on structural distortion and image definition. International Conference on Computer Science and Software Engineering.
  24. Zhang, F., Ma, L., Li, S., Ngan, K. N., 2011. Practical image quality metric applied to image coding. IEEE Transactions on Multimedia, 13(4): 615-624.
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Paper Citation


in Harvard Style

M. El-Alfy E. and R. Riaz M. (2014). Image Quality Assessment using ANFIS Approach . In Proceedings of the 6th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART, ISBN 978-989-758-015-4, pages 169-177. DOI: 10.5220/0004823901690177


in Bibtex Style

@conference{icaart14,
author={El-Sayed M. El-Alfy and Mohammed R. Riaz},
title={Image Quality Assessment using ANFIS Approach},
booktitle={Proceedings of the 6th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,},
year={2014},
pages={169-177},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004823901690177},
isbn={978-989-758-015-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 6th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,
TI - Image Quality Assessment using ANFIS Approach
SN - 978-989-758-015-4
AU - M. El-Alfy E.
AU - R. Riaz M.
PY - 2014
SP - 169
EP - 177
DO - 10.5220/0004823901690177