General Framework of Image Quality Assessment

  • Yong DingEmail author


The study upon objective image quality assessment (IQA) has entered the era of solid theoretical fundaments and rigorous experimental flows. Although there are extensive IQA methods been proposed, they generally follow similar frameworks in design. The main differences between frameworks are according to how much the implement of a specific method is dependent upon reference images, in which way three classes, full-reference (FR), reduced-reference (RR), and no-reference (NR), are defined. For methods of all the three categories, evitable processing contains quality-aware feature extraction, feature quantification, quality index mapping, and statistical performance evaluation, and the related fields include image processing, statistics, machine learning at the very least. In this chapter, we attempt to introduce the general frameworks that modern IQA methods adopt, explain the specific flow of the methods step-by-step, during which major knowledge about the design and evaluation of the methods would be concerned.


Full-reference Reduced-reference No-reference Feature extraction Statistical evaluation Machine learning 


  1. Bjerhammar, A. (1951). Application of calculus of matrices to method of least squares: With special references to geodetic calculations. Transactions of the Royal Institute of Technology, Stockholm, Sweden: 49.Google Scholar
  2. Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123–140.MathSciNetzbMATHGoogle Scholar
  3. Casella, G., & Berger, R. L. (2001). Statistical inference. Duxbury, MA: Duxbury Press.zbMATHGoogle Scholar
  4. Chang, H., Zhang, Q., Wu, Q., & Gan, Y. (2015). Perceptual image quality assessment by independent feature detector. Neurocomputing, 151, 1142–1152.CrossRefGoogle Scholar
  5. Cormack, L. K. (2005). Computational models of early human vision. In A. C. Bovik (Ed.), Handbook of image and video processing (2nd ed.). Amsterdam, Netherlands: Elsevier Academic Press.Google Scholar
  6. Cortes, C., & Vapnik, V. N. (1995). Support vector networks. Machine Learning, 20(3), 273–297.zbMATHGoogle Scholar
  7. Daly, S. (1992). The visible difference predictor: An algorithm for the assessment of image fidelity. Proceedings of SPIE, 1616, 2–15.CrossRefGoogle Scholar
  8. Ding, Y., Wang, S., & Zhang, D. (2014). Full-reference image quality assessment using statistical local correlation. Electronics Letters, 50(2), 79–80.CrossRefGoogle Scholar
  9. Geisler, W. S., & Banks, M. S. (1995). In M. Bass (Ed.), Handbook of optics. Manhattan, NY: McGraw-Hill.Google Scholar
  10. Haykin, S. O. (1998). Neural networks and learning machines. New York: Pearson Education Inc.Google Scholar
  11. Hinton, G., Osindero, S., & The, Y.-W. (2006). A fast learning algorithm for deep belief nets. Neural Computation, 18(7), 1527–1554.MathSciNetCrossRefzbMATHGoogle Scholar
  12. Ho, T. K., Hull, J. J., & Srihari, S. N. (1994). Decision combination in multiple classifier systems. IEEE Transactions on Pattern Analysis and Machine Intelligence, 16(1), 66–75.CrossRefGoogle Scholar
  13. Kittler, J., Hatef, M., Duin, R., & Matas, J. (1998). On combination classifiers. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(3), 226–239.CrossRefGoogle Scholar
  14. Larson, R. J., & Marx, M. L. (2005). An introduction to mathematical statistics and its applications. Upper Saddle River, NJ: Prentice Hall Inc.Google Scholar
  15. LeCun, Y. & Bengio, Y. (1995). Convolutional networks for images, speech, and time-series. The handbook of brain theory and neural networks. Cambridge, MA: MIT Press.Google Scholar
  16. LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278–2324.CrossRefGoogle Scholar
  17. Li, Q., & Wang, Z. (2009). Reduced-reference image quality assessment using divisive normalization-based image representation. IEEE Journal of Selected Topics in Signal Processing, 3(2), 202–211.CrossRefGoogle Scholar
  18. Liu, A., Lin, W., & Narwaria, M. (2012). Image quality assessment based on gradient similarity. IEEE Transactions on Image Processing, 21(4), 1500–1512.MathSciNetCrossRefzbMATHGoogle Scholar
  19. Lubin, J. (1993). The use of psychophysical data and models in the analysis of display system performance. In A. B. Watson (Ed.), Digital images and human vision. Cambridge, MA: The MIT Press.Google Scholar
  20. Marziliano, P., Dufaux, F., Winkler, S., & Chen, T. (2002). Perceptual blur and ringing metrics: Application to JPEG2000. Signal Processing: Image Communication, 19(2), 163–172.Google Scholar
  21. Mood, A. M., Graybill, F. A., & Boes, D. C. (1974). Introduction to the theory of statistics. New York: McGraw-Hill Book Company.zbMATHGoogle Scholar
  22. Moore, E. H. (1920). On the reciprocal of the general algebraic matrix. Bulletin of the American Mathematical Society, 26(9), 394–395.CrossRefGoogle Scholar
  23. Moorthy, A. K., & Bovik, A. C. (2010). A two-step framework for constructing blind image quality indices. IEEE Signal Processing Letters, 17(5), 513–516.CrossRefGoogle Scholar
  24. Moorthy, A. K., & Bovik, A. C. (2011). Blind image quality assessment: From natural scene statistics to perceptual quality. IEEE Transactions on Image Processing, 20(12), 3350–3364.MathSciNetCrossRefzbMATHGoogle Scholar
  25. Penrose, R. (1955). A generalized inverse for matrices. Proceedings of the Cambridge Philosophical Society, 51, 406–413.CrossRefzbMATHGoogle Scholar
  26. Platt, J. (1998). Sequential minimal optimization: A fast algorithm for training support vector machines. (Technical Report MSR-TR-98-14). Microsoft Research.Google Scholar
  27. Portilla, J., Strela, V., Wainwright, M. J., & Simoncelli, E. P. (2003). Image denoising using scale mixtures of Gaussians in the wavelet domain. IEEE Transactions on Image Processing, 12(11), 1338–1351.MathSciNetCrossRefzbMATHGoogle Scholar
  28. Rao, K. R., & Yip, P. (1990). Discrete cosine transform: Algorithms, advantage, applications. New York: Academic Press.CrossRefzbMATHGoogle Scholar
  29. Saad, M., Bovik, A. C., & Charrier, C. (2012). Blind image quality assessment: A natural scene statistics approach to perceptual quality. IEEE Transactions on Image Processing, 21(8), 3339–3352.MathSciNetCrossRefzbMATHGoogle Scholar
  30. Safranek, R. J., & Johnston, J. D. (1989). A perceptual tuned sub-band image coder with image dependent quantization and post-quantization data compression. In Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (pp. 1945–1948).Google Scholar
  31. Sheikh, H. R., & Bovik, A. C. (2006). Image information and visual quality. IEEE Transactions on Image Processing, 15(2), 430–444.CrossRefGoogle Scholar
  32. Sheikh, H. R., Bovik, A. C., & Cormack, L. (2005). No-reference quality assessment using natural scene statistics: JPEG2000. IEEE Transactions on Image Processing, 14(11), 1918–1927.CrossRefGoogle Scholar
  33. Sheikh, H. R., Sabir, M. F., & Bovik, A. C. (2006). A statistical evaluation of recent full reference image quality algorithms. IEEE Transactions on Image Processing, 15(11), 3441–3452.CrossRefGoogle Scholar
  34. Sheikh, H. R., Wang, Z., Cormack, L. & Bovik, A. C. (2002). Blind quality assessment for JPEG2000 compressed images. In Proceedings of IEEE Asilomar Conference on Signals, Systems, and Computers (pp. 1403–1407).Google Scholar
  35. Simoncelli, E. P., & Olshausen, B. A. (2001). Natural image statistics and neural representation. Annual Review of Neuroscience, 24, 1193–1216.CrossRefGoogle Scholar
  36. Teo, P. C., & Heeger, D. J. (1994). Perceptual image distortion. Proceedings of SPIE, 2179, 127–141.CrossRefGoogle Scholar
  37. Wandell, B. A. (1995). Foundations of vision. Cary, NC: Sinauer Associates Inc.Google Scholar
  38. Wang, Z., & Bovik, A. C. (2006). Modern image quality assessment. San Rafael, CA: Morgan & Claypool.Google Scholar
  39. 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.CrossRefGoogle Scholar
  40. Wang, Z., Bovik, A. C., & Evans, B. L. (2000). Blind measurement of blocking artifacts in images. Proceedings of IEEE International Conference on Image Processing, 3, 981–984.Google Scholar
  41. Wang, Z., Sheikh, H. R., & Bovik, A. C. (2002). No-reference perceptual quality assessment of JPEG compressed images. In Proceedings of IEEE International Conference on Image Processing (pp. 477–480).Google Scholar
  42. Wang, Z., Wu, G., Sheikh, H. R., Simoncelli, E. P., Yang, E. H., & Bovik, A. C. (2006). Quality-aware images. IEEE Transactions on Image Processing, 15(6), 1680–1689.CrossRefGoogle Scholar
  43. Wu, H. R., & Yuen, M. (1997). A generalized block-edge impairment metric for video coding. IEEE Signal Processing Letters, 4(11), 317–320.CrossRefGoogle Scholar
  44. Xu, L., Krzyzak, A., & Suen, C. Y. (1992). Methods of combining multiple classifiers and their applications to handwriting recognition. IEEE Transactions on Systems, Man, and Cybernetics, 22(3), 418–435.CrossRefGoogle Scholar
  45. Yu, Z., Wu, H. R., Winkler, S., & Chen, T. (2002). Vision-model-based impairment metric to evaluate blocking artifact in digital video. Proceedings of IEEE, 90, 154–169.CrossRefGoogle Scholar
  46. Zhang, L., Shen, Y., & Li, H. (2014). VSI: A visual saliency-induced index for perceptual image quality assessment. IEEE Transactions on Image Processing, 23(10), 4270–4281.MathSciNetCrossRefzbMATHGoogle Scholar
  47. Zhang, L., Zhang, L., Mou, X., & Zhang, D. (2011). FSIM: A feature similarity index for image quality assessment. IEEE Transactions on Image Processing, 20(8), 2378–2386.MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© Zhejiang University Press, Hangzhou and Springer-Verlag GmbH Germany 2018

Authors and Affiliations

  1. 1.Zhejiang UniversityHangzhouChina

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