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Reseach Article

Structural Similarity Measure for Color Images

by Mohammed Hassan, Chakravarthy Bhagvati
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 43 - Number 14
Year of Publication: 2012
Authors: Mohammed Hassan, Chakravarthy Bhagvati
10.5120/6169-8590

Mohammed Hassan, Chakravarthy Bhagvati . Structural Similarity Measure for Color Images. International Journal of Computer Applications. 43, 14 ( April 2012), 7-12. DOI=10.5120/6169-8590

@article{ 10.5120/6169-8590,
author = { Mohammed Hassan, Chakravarthy Bhagvati },
title = { Structural Similarity Measure for Color Images },
journal = { International Journal of Computer Applications },
issue_date = { April 2012 },
volume = { 43 },
number = { 14 },
month = { April },
year = { 2012 },
issn = { 0975-8887 },
pages = { 7-12 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume43/number14/6169-8590/ },
doi = { 10.5120/6169-8590 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:33:22.969091+05:30
%A Mohammed Hassan
%A Chakravarthy Bhagvati
%T Structural Similarity Measure for Color Images
%J International Journal of Computer Applications
%@ 0975-8887
%V 43
%N 14
%P 7-12
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Color images reveal more meaningful information to the human observers rather than grayscale ones. Regardless of the advantages of the existing well-known objective image quality measures, one of the common and major limitations of these measures is that they evaluate the quality of grayscale images only and don't make use of color information. In this paper we propose an improved method for image quality assessment that adds a color comparison to the criteria of the well-known Multiscale Structural Similarity index (MSSIM). We evaluated the new color image quality measure through human subjective experiments. Our human subjective evaluation data contains 25 reference images and 875 test images produced by five popular color quantization algorithms. Each of the quantized images was evaluated by twenty two subjects and more than 19200 individual human quality judgments were carried out to obtain the final mean opinion scores. We also tested the proposed method on TID2008 image database to further verify our results. These results indicate that adding color comparison improves MSSIM for many distortions in TID2008 and for assessing quantized images in our database.

References
  1. Ouni, S. , Chambah, M. , Herbin, M. , and Zagrouba, E. , 2008. Are Existing Procedures Enough? Image and Video Quality Assessment: Review of Subjective and Objective Metrics. Electronic Imaging, Image Quality and System Performance, SPIE, San Jose, CA, USA. Ding, W. and Marchionini, G. 1997 A Study on Video Browsing Strategies. Technical Report. University of Maryland at College Park.
  2. Wang, Z. , Bovik, A. C. , and Lu, L. , 2002. Why is image quality assessment so difficult?. IEEE International Conference on Acoustics, Speech, & Signal Processing, 4, 3313-3316.
  3. Mitsa, T. , and Varkur, K. . , 1993. Evaluation of contrast sensitivity functions for the formulation of quality measures incorporated in halftoning algorithms. IEEE International Conference on Acoustic, Speech, and Signal processing, 5, 301- 304.
  4. Miyahara, M. , Kotani, K. , Algazi, V. R. , 1998. Objective Picture Quality Scale (PQS) for image coding. IEEE Transactions on Communications, 46(9), 1215-1226.
  5. Wang, Z. , Bovik, A. C. , 2002. A universal image quality index. IEEE Signal Processing Letters, 9(3),81–84.
  6. Wang, Z. , Bovik, A. C. , Sheikh, H. R. , and Simoncelli, E. P. , 2004. Image quality assessment: From error measurement to structural similarity. IEEE Transaction on Image Processing, 13 (4), 600-612.
  7. Wang, Z. , Simoncelli, E. P. , and Bovik, A. C. , 2003. Multiscale structural similarity for image quality assessment. 37th IEEE Asilomar Conference on Signals, Systems, and Computers, 2, 1398- 1402.
  8. Sheikh, H. R. , Bovik, A. C. , and 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.
  9. Sheikh, H. R. , and Bovik, A. C. , 2006. Image Information and Visual Quality. IEEE Transactions on Image Processing, 15(2), 430-444.
  10. Shnayderman, A. , Gusev, A. , and Eskicioglu, A. M. , 2006. An SVD-Based Gray-Scale Image Quality Measure for Local and Global Assessment. IEEE Transaction on Image Processing, 15(2), 422-429.
  11. Chandler, D. M. , Hemami, S. S. , 2007. VSNR: A Wavelet base Visual Signal-to-Noise Ratio for Natural Images. IEEE Transaction on Image Processing, 16(9), 2284–2298.
  12. Scheunders, P. , 1997. A genetic C-means clustering algorithm applied to color image quantization. Pattern Recognition, 30(6), 859-866.
  13. Mahy, M. , Van Eycken, L. , and Oosterlinck, A. , 1994. Evaluation of uniform color spaces developed after the adoption of CIELAB and CIELUV. Color research and application, 19(2), 105–121.
  14. Zhang, X. , Wandell, B. A. , 1996. A spatial extension of CIELAB for digital color image reproduction. Proceedings of the SID Symposium Technical Digest, 27, 731-734.
  15. Color Quantization Database. Available: http://dcis. uohyd. ernet. in/~hassan/Color_Quantization_Database. rar.
  16. Lloyd, S. P. , 1982. Least squares quantization in PCM. IEEE Transactions on Information Theory, 28 (2): 129–137.
  17. Heckbert, P. , 1982. Color image quantization for frame buffer display. ACM Trans. Computer Graphics (SIGGRAPH), 16 (3), 297–307.
  18. Wu, X. , 1991. Efficient statistical computations for optimal color quantization. Graphics Gems, 11, J. Arvo, Ed. New York: Academic, 126-133.
  19. Gervautz, M. , and Purgathofer, W. , 1988. A simple Method for Color Quantization: Octree Quantization. New Trends in Computer Graphics, Springer Verlag, Berlin. 219-231.
  20. Dekker, A. H. , 1994. Kohonen neural networks for optimal colour quantization. Network Computation in Neural Systems, 5(3), 351-367.
  21. ITU-R, 2002. Methodology for the Subjective Assessment of the Quality for Television Pictures, Recommendation ITU-R BT. 500-11. Geneva.
  22. Rosner, B. , 1983. Percentage points for a generalized ESD many-Outlier Procedure. Technometrics, 25(2), 165–172.
  23. Van Dijk, A. M, Martens, J-B, Watson, A. ?. , 1995. Quality assessment of coded images using numerical category scaling. Proc. SPIE, 2451, 90–101.
  24. Sheikh, H. R. , Sabir, M. F. , and Bovik, A. C. , 2006. A Statistical Evaluation of Recent Full Reference Image Quality Assessment Algorithms. IEEE Transactions on Image Processing, 15(11), 3441-3452.
  25. Ponomarenko, N. , Lukin, V. , Zelensky, A. , Egiazarian, K. , Carli, M. , and Battisti, F. , 2009. TID2008 - A Database for Evaluation of Full-Reference Visual Quality Assessment Metrics. Advances of Modern Radio electronics, 10, 30-45.
Index Terms

Computer Science
Information Sciences

Keywords

Image Quality Assessment Structural Similarity Index Color Quantization