A New Contrast Measurement Index Based on Logarithmic Image Processing Model

  • Mridul Trivedi
  • Anupam Jaiswal
  • Vikrant Bhateja
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 199)


With the introduction of more complex enhancement algorithms, there is a need for an effective method of enhancement measurement that can assess image quality in accordance with Human Visual System (HVS) characteristics. This paper presents a new quality index for measurement of contrast in digital images based on Logarithmic Image Processing (LIP) model. The proposed quality index evaluates the degree of contrast manipulation (provided by an enhancement algorithm) by considering the difference in the average gray level values in its foreground to that of background. The calculated statistical parameters for foreground and background regions are mathematically combined using the LIP operators to ensure processing of images from HVS point of view. The quality index is computed for different contrast manipulating algorithms which are applied to test images taken from standard MATLAB library as well as LIVE Database. Simulation results illustrate the precision and efficiency of the proposed index in comparison to other contrast evaluation methods proposed in literature.


No-Reference LIP Model Contrast Measurement Index (CMI) Image Quality Assessment Hadamard Transform 


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  1. 1.
    Morrow, W.M., et al.: Region-Based Contrast Enhancement of Mammograms. IEEE Transaction on Medical Imaging 11(2), 121–134 (1992)Google Scholar
  2. 2.
    Sheikh, H.R., Saber, M.F., Bovik, A.C.: A Statistical Evaluation of Recent Full Reference Image Quality Assessment Algorithm. IEEE Transactions on Image Processing 15(11), 3441–3452 (2006)CrossRefGoogle Scholar
  3. 3.
    Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image Quality Assessment from Error Visibility to Structural Similarity. IEEE Transactions on Image Processing 13(4), 600–612 (2004)CrossRefGoogle Scholar
  4. 4.
    Wang, Z., Simoncelli, E.P.: Reduced-Reference Image Quality. In: Proceedings of International Symposium on Electronic Imaging, San Jose, CA, USA (2005)Google Scholar
  5. 5.
    Agaian, S.S., Panetta, K., Grigoryan, A.M.: Transform-based Image Enhancement Algorithms with Performance Measure. IEEE Transactions on Image Processing 10(3), 367–382 (2001)CrossRefMATHGoogle Scholar
  6. 6.
    Panetta, K., Wharton, E.J., Agaian, S.S.: Human Visual System based Image Enhancement and Logarithmic Contrast Measure. IEEE Transactions on Image Processing 38(1), 174–188 (2008)Google Scholar
  7. 7.
    Tripathi, A.K., Mukhopadhyay, S., Dhara, A.K.: Performance metrics for image contrast. In: IEEE conference on Image Information Processing, Shimla, India (2011)Google Scholar
  8. 8.
    Sheikh, H.R., Wang, Z., Cormack, L., Bovik, A.C.: LIVE Image Quality Assessment Database Release 2,
  9. 9.
    Chen, S.D., Ramli, A.R.: Contrast Enhancement using Recursive Mean-Separate Histogram Equalization for Scalable Brightness Preservation. IEEE Transaction on Consumer Electronics 49(4), 1301–1309 (2003)CrossRefGoogle Scholar
  10. 10.
    Zuiderveld, K.: Contrast Limited Adaptive Histogram Equalization. In: Graphic Gems IV, pp. 474–485. Academic Press Professional, San Diego (1994)CrossRefGoogle Scholar
  11. 11.
    Panetta, K., Zhou, Y., Agaian, S.S., Jia, H.: Nonlinear Unsharp Masking for Mammogram Enhancement. IEEE Transaction on Information Technology in Biomedicine 15(6), 234–255 (2011)CrossRefGoogle Scholar
  12. 12.
    Gonzalez, R.C., Woods, R.E.: Digital Image Processing. Addison-Wesley, Reading (2002)Google Scholar
  13. 13.
    Gao, X., Wang, Y., Li, X., Tao, D.: On combining Morphological Component analysis and Concentric Morphology Model for Mammographic Mass Detection. IEEE Transaction on Information Technology in Biomedicine 14(2), 266–273 (2010)CrossRefGoogle Scholar
  14. 14.
    Jourlin, M., Pinoli, J.C.: Logarithmic Image Processing, The Mathematical and Physical Framework for the Representation and Processing of Transmitted Images. Advances in Imaging and Electron Physics 115(2), 129–196 (2001)CrossRefGoogle Scholar
  15. 15.
    Panetta, K., Wharton, E.J., Agaian, S.S.: Human Visual System based Image Enhancement and Logarithmic Contrast Measure. IEEE Transaction on Image Processing 38(1), 174–188 (2008)Google Scholar
  16. 16.
    Beauchamp, K.G.: Applications of Walsh and Related Functions. Academic Press (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Mridul Trivedi
    • 1
  • Anupam Jaiswal
    • 1
  • Vikrant Bhateja
    • 1
  1. 1.Deptt. of Electronics and Communication EngineeringShri Ramswaroop Memorial Group of Professional CollegesLucknowIndia

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