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Co-histogram and Image Degradation Evaluation

  • Pengwei Hao
  • Chao Zhang
  • Anrong Dang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3211)

Abstract

The tool for image degradation evaluation addressed in this paper is called co-histogram, which is a statistic graph generated by counting the corresponding pixel pairs of two images. The graph is a two-dimensional joint probability distribution of the two images. A co-histogram shows how the pixels are distributed among combinations of two image pixel values. By means of co-histogram, we can have a visual understanding of PSNR, and the symmetry of a co-histogram is also significant for objective evaluation of image degradation. Our experiments with image degradation models of image compression, convolution blurring and geometric distortion perform the importance of the co-histogram.

Keywords

Mean Square Error Compression Ratio Image Compression Mean Opinion Score Geometric Distortion 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Baird, H.: The State of the Art of Document Image Degradation Modeling. In: Proc. of 4 th IAPR International Workshop on Document Analysis Systems, Rio de Janeiro, Brazil, pp. 1–16 (2000)Google Scholar
  2. 2.
    Lee, H.-C.: Review of image-blur models in a photographic system using principles of optics. Optical Engineering 29(5), 405–421 (1990)CrossRefGoogle Scholar
  3. 3.
    Pappas, T.N., Safranek, R.J.: Perceptual criteria for image quality evaluation. In: Bovik, A. (ed.) Handbook of Image and Video Processing, Academic Press, London (2000)Google Scholar
  4. 4.
    Petitcolas, F.A.P., Anderson, R.J., Kuhn, M.G.: Attacks on copyright marking systems. In: Proc. of Second International Workshop on Information Hiding, Oregon, USA, pp. 219–239 (1998)Google Scholar
  5. 5.
    Taubman, D.S., Marcellin, M.W.: JPEG 2000: standard for interactive imaging. Proceedings of the IEEE 90(8), 1336–1357 (2002)CrossRefGoogle Scholar
  6. 6.
    Wallace, G.: The JPEG Still Picture Compression Standard. Communications of the ACM 34(4), 30–44 (1991)CrossRefGoogle Scholar
  7. 7.
    Wang, Z., Bovik, A.C., Lu, L.: Why is image quality assessment so difficult? In: IEEE International Conference on Acoustics, Speech, Signal Processing, Orlando, FL, vol. 4, pp. 3313–3316 (2002)Google Scholar
  8. 8.
    Hao, P., Shi, Q.-Y., Chen, Y.: Co-Histogram and Its Application in Remote Sensing Image Compression Evaluation. In: Proceedings of International Conference on Image Processing (ICIP), Barcelona, Spain, vol. 3, pp. 177–180 (2003)Google Scholar
  9. 9.
    Hao, P., Chen, Y.: Co-Histogram and Its Application in Video Analysis. In: Proceedings of IEEE International Conference on Multimedia and Expo (ICME), Taiwan (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Pengwei Hao
    • 1
    • 2
  • Chao Zhang
    • 1
  • Anrong Dang
    • 3
  1. 1.Center for Information SciencePeking UniversityBeijingChina
  2. 2.Department of Computer Science, Queen MaryUniversity of LondonUK
  3. 3.Center for Science of Human SettlementsTsinghua UniversityBeijingChina

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