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Evaluation of Image Quality Assessment Metrics: Color Quantization Noise

Mohammed Hassan, Chakravarthy Bhagvati Published in Image Processing

International Journal of Applied Information Systems
Year of Publication: 2015
© 2015 by IJAIS Journal
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  1. Mohammed Hassan and Chakravarthy Bhagvati. Article: Evaluation of Image Quality Assessment Metrics: Color Quantization Noise. International Journal of Applied Information Systems 9(1):1-8, June 2015. BibTeX

    	author = "Mohammed Hassan and Chakravarthy Bhagvati",
    	title = "Article: Evaluation of Image Quality Assessment Metrics: Color Quantization Noise",
    	journal = "International Journal of Applied Information Systems",
    	year = 2015,
    	volume = 9,
    	number = 1,
    	pages = "1-8",
    	month = "June",
    	note = "Published by Foundation of Computer Science, New York, USA"


Although color quantization noise is frequently met in practice, it has not been given too much attention in color image visual quality assessment. In this paper, a new image database for the evaluation of image quality metrics over color quantization noise is described. It contains 25 reference images and 875 test images produced by five popular quantization algorithms. Each of the quantized images was evaluated by 22 human subjects and more than 19200 individual human quality judgments were carried out to obtain the final mean opinion scores. A comparative analysis of several well-known image quality metrics is presented and their correlation with the human opinion scores is evaluated. This image database has been made freely available for downloading for research in image quality assessment and other applications [10].


  1. I. Avcibas, B. Sankur, and K. Sayood. Statistical evaluation of image quality measures. Journal of Electronic imaging, 11(2):206–223, 2002.
  2. J. Braquelaire and L. Brun. Comparison and optimization of methods of color image quantization. IEEE Transactions on Image Processing, 6(7):1048–1052, 1997.
  3. P. Le Callet and F. Autrusseau. Subjective quality assessment IRCCyN/IVC database. http://www. irccyn. ec-nantes. fr/ivcdb/, 2005.
  4. D. Chandler and S. Hemami. VSNR: A wavelet base visual signal-to-noise ratio for natural images. IEEE Transactions on Image Processing, 16(9):2284–2298, 2007.
  5. D. Chandler and S. Hemami. VSNR online supplement. http://foulard. ece. cornell. edu/dmc27/ vsnr/vsnr. html, 2007.
  6. A. Dekker. Kohonen neural networks for optimal colour quantization. Network Computation in Neural Systems, 5(3):351–367, 1994.
  7. A. M. Eskicioglu and P. S. Fisher. Image quality measures and their performance. IEEE Transactions on Communications, 43(12):2959–2965, 1995.
  8. M. Gervautz and W. Purgathofer. A simple method for color quantization: Octree quantization. In Graphics Gems, pages 287–293. Academic Press Professional, Inc. , San Diego, CA, USA, 1990.
  9. H. S. Han, D. O. Kim, and R. H. Park. Structural information-based image quality assessment using LU factorization. IEEE Transactions on Consumer Electronics, 55(1):165–171, 2009.
  10. M. Hassan and C. Bhagvati. Color quantization database. http://dcis. uohyd. ernet. in/~hassan/Color_ Quantization_Database. rar, 2012.
  11. P. Heckbert. Color image quantization for frame buffer display. In Proceedings of SIGGRAPH, volume 16, pages 297–307, 1982.
  12. ITU-R. Methodology for the subjective assessment of the quality for television pictures, 2002. Recommendation ITU-R BT. 500-11. Geneva.
  13. D. O. Kim and R. H. Park. Image quality assessment using the amplitude/phase quantization code. IEEE Transactions on Consumer Electronics, 56(4):2756–2762, 2010.
  14. D. O. Kim and R. H. Park. Image quality measure using the phase quantization code. IEEE Transactions on Consumer Electronics, 56(2):937–945, 2010.
  15. E. Larson and D. Chandler. Most apparent distortion: fullreference image quality assessment and the role of strategy. Journal of Electronic Imaging, 19(1):011006–011006, 2010.
  16. Y. Linde, A. Buzo, and R. M. Gray. An algorithm for vector quantizer desing. IEEE Transactions on Communications, 28(1):84–95, 1980.
  17. A. Mayache, T. Eude, and H. Cherifi. A comparison of image quality models and metrics based on human visual sensitivity. In Proceedings of International Conference on Image Processing, pages 409–413, 1998.
  18. C. McCamy. On the number of discernible colors. Color Research and Application, 23(5):337–337, 1998.
  19. T. Mitsa and K. Varkur. Evaluation of contrast sensitivity functions for the formulation of quality measures incorporated in halftoning algorithms. In Proceedings of IEEE International Conference on Acoustic, Speech, and Signal processing, pages 301–304, 1993.
  20. M. Miyahara, K. Kotani, and V. Algazi. Objective picture quality scale (PQS) for image coding. IEEE Transactions on Communications, 46(9):1215–1226, 1998.
  21. A. Mojsilovic, J. Kovacevic, J. Hu, R. Safranek, and S. Ganapathy. Matching and retrieval based on the vocabulary and grammar of color patterns. IEEE Transactions on Image Processing, 9(1):38–54, 2000.
  22. A. Ninassi, P. Le Callet, and F. Autrusseau. Pseudo no reference image quality metric using perceptual data hiding. In Proceedings of SPIE, volume 6057, pages 146–157, 2006.
  23. 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 Radioelectronics, 10:30–45, 2009.
  24. B. Rosner. Percentage points for a generalized ESD manyoutlier procedure. Technometrics, 25(2):165–172, 1983.
  25. X. Rui, C. Chang, and T. Srikanthan. On the initialization and training methods for kohonen self-organizing feature maps in color image quantization. In Proceedings of the 1st IEEE international workshop on electronic design, test and applications, pages 321–325, 2002.
  26. Z. Parvez Sazzad, Y. Kawayoke, and Y. Horita. MICT image quality evaluation database. http://mict. eng. u-toyama. ac. jp/mictdb. html, 2008.
  27. P. Scheunders. A genetic c-means clustering algorithm applied to color image quantization. Pattern Recognition, 30(6):859–866, 1997.
  28. H. Sheikh and A. Bovik. Image information and visual quality. IEEE Transactions on Image Processing, 15(2):430–444, 2006.
  29. H. Sheikh, A. Bovik, and G. de Veciana. An information fidelity criterion for image quality assessment using natural scene statistics. IEEE Transactions on Image Processing, 14(12):2117–2128, 2005.
  30. H. Sheikh, M. Sabir, and A. Bovik. A statistical evaluation of recent full reference image quality assessment algorithms. IEEE Transactions on Image Processing, 15(11):3441–3452, 2006.
  31. H. Sheikh, Z. Wang, L. Cormack, and A. Bovik. LIVE image quality assessment database release 2. http://live. ece. utexas. edu/research/quality/ subjective. htm, 2006.
  32. A. Shnayderman, A. Gusev, and A. M. Eskicioglu. An SVD-based gray-scale image quality measure for local and global assessment. IEEE Transactions on Image Processing, 15(2):422–429, 2006.
  33. M. Swain and D. Ballard. Color indexing. International journal of computer vision, 7(1):11–32, 1991.
  34. E. van den Broek, T. Kok, T. Schouten, and L. Vuurpijl. Human-centered content-based image retrieval. In Proceedings of SPIE, volume 6806, page 54, 2008.
  35. A. van Dijk, J. Martens, and A. Watson. Quality assessment of coded images using numerical category scaling. In Proceedings of SPIE, volume 2451, pages 90–101, 1995.
  36. Z. Wang and A. Bovik. A universal image quality index. IEEE Signal Processing Letters, 9:81–84, 2002.
  37. Z. Wang, A. Bovik, H. Sheikh, and E. Simoncelli. Image quality assessment: from error visibility to structural similarity. IEEE Transactions on Image Processing, 13(4):600– 612, 2004.
  38. Z. Wang and Q. Li. Information content weighting for perceptual image quality assessment. IEEE Transactions on Image Processing, 20(5):1185–1198, 2011.
  39. Z. Wang, E. Simoncelli, and A. Bovik. Multi-scale structural similarity for image quality assessment. In Proceedings of the 37th IEEE Asilomar Conference on Signals, Systems and Computers, volume 2, pages 1398–1402, 2003.
  40. X. Wu. Efficient statistical computations for optimal color quantization. In Graphics Gems II, pages 126–133. New York: Academic, James Arvo edition, 1991.
  41. C. Yang andW. Tsai. Color image compression using quantization, thresholding, and edge detection techniques all based on the moment-preserving principle. Pattern Recognition Letters, 19:205–215, 1998.
  42. A. Zaric, N. Tatalovic, N. Brajkovic, H. Hlevnjak, M. Loncaric, E. Dumic, and S. Grgic. VCL@FER image quality assessment database. AUTOMATIKA, 53(4):344–354, 2012.
  43. L. Zhang, L. Zhang, X. Mou, and D. Zhang. FSIM: A feature similarity index for image quality assessment. IEEE Transactions on Image Processing, 20(8):2378– 2386, 2011.
  44. X. Zhang and B. Wandell. A spatial extension of CIELAB for digital color image reproduction. In Proceedings of SID International Symposium Digest of Technical Papers, volume 27, pages 731–734, 1996.


Image quality metrics,Color quantization, Image database