Considering image structural properties while estimating compressed jpeg image quality

Authors

DOI:

https://doi.org/10.15587/1729-4061.2015.55978

Keywords:

JPEG image, quality, estimation, compression, DCT coefficients, probability distribution

Abstract

Application of lossy compression methods involves the occurrence of distortions, so the problem of evaluating the level of these distortions is urgent. When using the DCT transformation, the main objective of quality assessment is selecting a statistical distribution model of the DCT coefficients of the image and the methods for estimating the parameters of the model.

A universal method of estimating the level of distortions that arise due to JPEG compression of images of any structural content is developed in the paper. To determine the image quality, PSNR metric is used. A key feature of the proposed method lies in involving various statistical models for computing the quantization noise variance of the DCT coefficients. In particular, for models of the DCT coefficients in the form of the double gamma distribution and the generalized Cauchy distribution, calculation-handy expressions for the quantization noise variance of the DCT coefficients of JPEG images are obtained. Using the double gamma distribution to solve the above problem is first proposed.

The well-known Laplacian model provides a relatively accurate estimation of PSNR only for those images that mostly consist of regions rich in small parts. While for the images, which contain significant regions of monotonicity, the model in the form of the double gamma distribution provides a much better result.

The accuracy of the obtained theoretical expressions is confirmed by the results of experiments with grayscale JPEG images.

Author Biographies

Михайло Володимирович Родигін, National university of radio electronics Lenina 14, Kharkiv, Ukraine, 61116

Student

The department of telecommunication systems

Олексій Валерійович Федоров, National university of radio electronics Lenina 14, Kharkiv, Ukraine, 61116

Senior Lecturer

The department of communication networks

References

  1. Laghari, K. U. R., Connelly, K. (2012). Toward total quality of experience: A QoE model in a communication ecosystem. Communications Magazine, IEEE, 50 (4), 58–65. doi: 10.1109/mcom.2012.6178834
  2. Lim, J. S. (1990). Two-Dimensional signal and image processing. New Jersey: Prentice-Hall, 694.
  3. ISO/IEC 10918-1: 1993(E). CCIT. Terminal equipment and protocols for telematic services. Recommendation. T. 81. Available at: http://www.w3.org/Graphics/JPEG/itu-t81.pdf.
  4. Pratt, W. K. (2001). Digital Image Processing. 3rd edition. New York: John Wiley & Sons, Inc., 739. doi: 10.1002/0471221325
  5. Simoncelli, E. (2005). Statistical modeling of photographic images. Handbook of image and video processing. New York: Academic Press, 431–441. doi: 10.1016/b978-012119792-6/50089-9
  6. Lam, E. Y., Goodman, J. W. (2000). A mathematical analysis of the DCT coefficient distributions for images.IEEE Transactions on Image Processing, 9 (10), 1661–1666. doi: 10.1109/83.869177
  7. Narayanan, G., Shi, Y.-Q. (2010). A statistical model for quantized AC block DCT coefficients in JPEG compression and its application to detecting potential compression history in bitmap images. International Workshop on Digital Watermarking. Lecture Notes in Computer Science. Berlin: Springer, 6526, 75–89. doi: 10.1007/978-3-642-18405-5_7
  8. Thai, T. H., Cogranne, R., Retraint, F. (2014). Statistical model of quantized DCT coefficients: Application in the steganalysis of jsteg algorithm. IEEE Transactions on Image Processing, 23 (5), 1980–1993. doi: 10.1109/tip.2014.2310126
  9. Sallee, P. (2004). Model-based steganography. Digital Watermarking. Lecture Notes in Computer Science. Berlin: Springer, 2939, 154–167. doi: 10.1007/978-3-540-24624-4_12
  10. Fridrich, J. (2009). Steganography in digital media. New York: Cambridge University Press, 438. doi: 10.1017/cbo9781139192903
  11. Carrillo, R. E., Aysal, T. C., Barner, K. E. (2010). A generalized cauchy distribution framework for problems requiring robust behavior. EURASIP Journal on Advances in Signal Processing, 2010 (11), 11:1–11:19. doi: 10.1155/2010/312989
  12. ITU-R Rec. (2000). BT 500-10 : Methodology for the subjective assessment of the quality of television pictures.
  13. Pappas, T., Safranek, R. J., Chen, J. (2005). Perceptual criteria for image quality evaluation. Handbook of image and video processing. New York: Academic Press, 939–959.doi: 10.1016/b978-012119792-6/50118-2
  14. Wang, Z., Bovik, A. C., Simoncelli, E. P. (2005). Structural approaches to image quality assessment. Handbook of image and video processing. New York: Academic Press, 961–974. doi: 10.1016/b978-012119792-6/50119-4
  15. Sheikh, H., Bovik, A. C. (2005). Information theoretic approaches to image quality assessment. Handbook of image and video processing. New York: Academic Press, 975–989. doi: 10.1016/b978-012119792-6/50120-0
  16. Wong, P. W. (2005). Image quantization, halftoning, and printing. Handbook of image and video processing. New York: Academic Press, 925–937. doi: 10.1016/b978-012119792-6/50117-0
  17. Turaga, D. S., Chen, Y., Caviedes, J. E. (2004). No reference PSNR estimation for compressed pictures. Signal Processing: Image Communication, 19 (2), 173–184. doi: 10.1016/j.image.2003.09.001
  18. Brandão, T., Queluz, M. P. (2007). Estimation of DCT coefficient statistics from their quantized values: application to image quality evaluation. ConfTele 2007: 6th International Conference on Telecommunications. Peniche, Portugal, 461–464.
  19. Press, W. H., Teukolsky, S. A., Vetterling, W. T., Flannery, B. P. (2007). Numerical recipes: the art of scientific computing. 3rd edition.‑ New York: Cambridge University Press, 994, 1235.
  20. Lange, K. (2013). Optimization. Springer Texts in Statistics. 2nd edition. New York: Springer, 95, 546. doi: 10.1007/978-1-4614-5838-8
  21. Abramowitz, M., Stegun, I. A. (1964). Handbook of Mathematical Functions with Formulas, Graphs, and Mathematical Tables. 9th Dover printing, 10th GPO printing edition. New York: Dover, 1049.
  22. USC-SIPI image database. Available at: http://sipi.usc.edu/database/
  23. Independent JPEG group: libJPEG library. Available at: http://www.ijg.org/
  24. Arnold, T., Emerson, J. W. (2011). Nonparametric goodness-of-fit tests for discrete null distributions. The R Journal, 3 (2), 34–39.

Published

2015-12-22

How to Cite

Родигін, М. В., & Федоров, О. В. (2015). Considering image structural properties while estimating compressed jpeg image quality. Eastern-European Journal of Enterprise Technologies, 6(4(78), 54–64. https://doi.org/10.15587/1729-4061.2015.55978

Issue

Section

Mathematics and Cybernetics - applied aspects