Binary Image Quality Assessment—A Hybrid Approach Based on Binarization Evaluation Methods

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 464)

Abstract

In the paper the idea of multiple metrics fusion for binary image quality assessment is presented together with experimental results obtained using the images from Bilevel Image Similarity Ground Truth Archive. As the performance evaluation of any full-reference image quality assessment metric requires both the knowledge of reference images with perfect quality and the results of subjective evaluation of distorted images, several such datasets have been developed during recent years. Nevertheless, the specificity of binary images requires the use of some other metrics which should also be verified in view of their correlation with subjective perception. Such task can be done using a dedicated database of binary images followed by the combination of multiple metrics leading to even higher correlation with subjective scores presented in this paper.

References

  1. 1.
    Chen, G.H., Yang, C.L., Xie, S.L.: Gradient-based structural similarity for image quality assessment. In: Proceedings of 13th IEEE International Conference on Image Processing (ICIP), pp. 2929–2932. Atlanta, Georgia (2006)Google Scholar
  2. 2.
    Forczmański, P., Markiewicz, A.: Stamps detection and classification using simple features ensemble. Math. Probl. Eng. Article ID 367879, 15 (2015)Google Scholar
  3. 3.
    Lech, P., Okarma, K.: Prediction of the optical character recognition accuracy based on the combined assessment of image binarization results. Elektronika Ir Elektrotechnika 21(6), 62–65 (2015)CrossRefGoogle Scholar
  4. 4.
    Li, C., Bovik, A.C.: Three-component weighted structural similarity index. In: Proceedings of SPIE—Image Quality and System Performance VI. vol. 7242, p. 72420Q. San Jose, California (2009)Google Scholar
  5. 5.
    Liu, T.J., Lin, W., Kuo, C.C.J.: Image quality assessment using multi-method fusion. IEEE Trans. Image Process. 22(5), 1793–1807 (2013)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Lu, H., Kot, A., Shi, Y.: Distance-reciprocal distortion measure for binary document images. IEEE Signal Process. Lett. 11(2), 228–231 (2004)CrossRefGoogle Scholar
  7. 7.
    Okarma, K.: Combined full-reference image quality metric linearly correlated with subjective assessment. In: Rutkowski, L., Scherer, R., Tadeusiewicz, R., Zadeh, L., Zurada, J. (eds.) ICAISC 2010, LNCS, vol. 6113, pp. 539–546. Springer, Heidelberg (2010)Google Scholar
  8. 8.
    Okarma, K.: Combined image similarity index. Opt. Rev. 19(5), 249–254 (2012)CrossRefGoogle Scholar
  9. 9.
    Oszust, M.: Decision fusion for image quality assessment using an optimization approach. IEEE Signal Process. Lett. 23(1), 65–69 (2016)CrossRefGoogle Scholar
  10. 10.
    Ponomarenko, N., Ieremeiev, O., Lukin, V., Egiazarian, K., Jin, L., Astola, J., Vozel, B., Chehdi, K., Carli, M., Battisti, F., Kuo, C.C.: Color image database TID2013: peculiarities and preliminary results. In: Proceedings of 4th European Workshop on Visual Information Processing (EUVIP), pp. 106–111. Paris, France (2013)Google Scholar
  11. 11.
    Sampat, M., Wang, Z., Gupta, S., Bovik, A., Markey, M.: Complex wavelet structural similarity: a new image similarity index. IEEE Trans. Image Process. 18(11), 2385–2401 (2009)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Sokolova, M., Lapalme, G.: A systematic analysis of performance measures for classification tasks. Inf. Process. Manage. 45(4), 427–437 (2009)CrossRefGoogle Scholar
  13. 13.
    Wang, Z., Bovik, A.C., Sheikh, H., Simoncelli, E.: Image quality assessment: from error measurement to Structural Similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)CrossRefGoogle Scholar
  14. 14.
    Wang, Z., Simoncelli, E., Bovik, A.C.: Multi-scale structural similarity for image quality assessment. In: Proceedings of 37th IEEE Asilomar Conference on Signals, Systems and Computers. Pacific Grove, California (2003)Google Scholar
  15. 15.
    Young, D., Ferryman, J.: Pets metrics: on-line performance evaluation service. In: Proceedings of 2nd Joint IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance, pp. 317–324 (2005)Google Scholar
  16. 16.
    Zhai, Y., Neuhoff, D., Pappas, T.: Subjective similarity evaluation for scenic bilevel images. In: Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 156–160. Florence, Italy (2014)Google Scholar
  17. 17.
    Zhang, F., Cao, K., Zhang, J.L.: A simple quality evaluation method of binary images based on border distance. Optik Int. J. Light Electron Opt. 122(14), 1236–1239 (2011)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Department of Signal Processing and Multimedia Engineering, Szczecin Faculty of Electrical EngineeringWest Pomeranian University of TechnologySzczecinPoland

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