Content Makes the Difference in Compression Standard Quality Assessment

  • Guido Manfredi
  • Djemel Ziou
  • Marie-Flavie Auclair-Fortier
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6915)


In traditional compression standard quality assessment, compressor parameters and performance measures are the main experimental variables. In this paper, we show that the image content is an equally crucial variable which still remains unused. We compare JPEG, JPEG2000 and a proprietary JPEG2000 on four visually different datasets. We base our comparison on PSNR, SSIM, time and bits rate measures. This approach reveals that the JPEG2000 vs. JPEG comparison strongly depends on compressed images visual content.


Image Content Quality Metrics Image Quality Assessment Main Experimental Variable Compressor Parameter 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Ahumada, A.J.: Computational image quality metrics: a review. In: SID International Symposium, Digest of Technical Papers, vol. 24, pp. 305–308 (1993)Google Scholar
  2. 2.
    Adams, M.: Jasper jpeg2000 codec (2007),
  3. 3.
    Avcibas, I., Sankur, B., Sayood, K.: Statistical evaluation of image quality measures. Journal of Electronic Imaging 11, 206–223 (2002)CrossRefGoogle Scholar
  4. 4.
    Ebrahimi, F., Chamik, M., Winkler, S.: JPEG vs. JPEG2000: an objective comparison of image encoding quality. In: Proceedings of SPIE Applications of Digital Image Processing, vol. 5558, pp. 300–308 (2004)Google Scholar
  5. 5.
    Eskicioglu, A.M.: Quality measurement for monochrome compressed images in the past 25 years. In: Proceedings. of IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2000, vol. 6, p. 1907 (2000)Google Scholar
  6. 6.
    IJG: Jpeglib jpeg codec, (2011)
  7. 7.
    Santa-Cruz, D., Grosbois, R., Ebrahimi, T.: JPEG 2000 performance evaluation and assessment. Signal Processing: Image Communication 17(1), 113–130 (2002)Google Scholar
  8. 8.
    Serra-Sngrista, J., Auli, F., Garcia, F., Gonzalez, J., Guiturt, P.: Evaluation of still image coders for remote sensing applications. In: IEEE Sensor Array and Multichannel Signal Processing Workshop (2004)Google Scholar
  9. 9.
    Simone, F.D., Ticca, D., Dufaux, F., Ansorge, M., Ebrahimi, T.: A comparative study of color image compression standards using perceptually driven quality metrics (2008),
  10. 10.
    Wang, Z., Bovik, A., Lu, L.: Why is image quality assessment so difficult? In: Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2002, vol. 4, pp. IV–3313–IV–3316 (2002)Google Scholar
  11. 11.
    Wang, Z., Bovik, A., Sheikh, H., Simoncelli, E.: Image quality assessment: from error visibility to structural similarity. IEEE Transactions on Image Processing 13(4), 600–612 (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Guido Manfredi
    • 1
  • Djemel Ziou
    • 1
  • Marie-Flavie Auclair-Fortier
    • 1
  1. 1.Centre MOIVREUniversite de SherbrookeSherbrookeCanada

Personalised recommendations