International journal of advanced smart convergence

The Institute of Internet, Broadcasting and Communication (한국인터넷방송통신학회)
권호 표지
DOI QR코드

DOI QR Code

An Adaptive Image Quality Assessment Algorithm

  • Sankar, Ravi (iCONS Research Group, Dept. of Electrical Engineering, University of South Florida) ;
  • Ivkovic, Goran (iCONS Research Group, Dept. of Electrical Engineering, University of South Florida)
  • Received : 2012.05.10
  • Published : 2012.05.31
Download PDF
⟨ Previous Next ⟩

Abstract

An improved algorithm for image quality assessment is presented. First a simple model of human visual system, consisting of a nonlinear function and a 2-D filter, processes the input images. This filter has one user-defined parameter, whose value depends on the reference image. This way the algorithm can adapt to different scenarios. In the next step the average value of locally computed correlation coefficients between the two processed images is found. This criterion is closely related to the way in which human observer assesses image quality. Finally, image quality measure is computed as the average value of locally computed correlation coefficients, adjusted by the average correlation coefficient between the reference and error images. By this approach the proposed measure differentiates between the random and signal dependant distortions, which have different effects on human observer. Performance of the proposed quality measure is illustrated by examples involving images with different types of degradation.

Keywords

References

  1. VQEG (Video Quality Expert Group) documentation, ITU, http://www.vqeg.org [Online]
  2. "Methodology for the subjective assessment of the quality of the television picture," ITU-R BT 500-10 Recommendation.
  3. G. -M. Muntean, P. Perry, and L. Murphy, "Subjective assessment of the quality-oriented adaptive scheme," IEEE Trans. on Broadcasting, Vol. 51, No. 3, pp. 276-286, Sep. 2005. https://doi.org/10.1109/TBC.2005.846187
  4. N. Montard and P. Bretillon, "Objective quality monitoring issues in digital broadcasting networks," IEEE Trans. on Broadcasting, Vol. 51, No.3, pp. 269-275, Sep. 2005. https://doi.org/10.1109/TBC.2005.851700
  5. S. Daly, "The visible difference predictor: an algorithm for assessment of image fidelity," in Digital Images and Human Vision, A. B. Watson (ed.), MIT Press, Cambridge, MA, pp. 179-206, 1993.
  6. R. J. Safranek and J. D. Johnston, "A perceptually tuned sub-band image coder with image dependant quantization and post-quantization data compression," in Proc. Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP), Vol. 3, pp. 1945-1948, 1989.
  7. N. Damera-Venkata, T. D. Kite, W. Geisler, B. L. Evans and A. C. Bovik, "Image quality assessment based on a degradation model," IEEE Trans. on Image Processing, Vol. 9, No. 4, pp. 630-650, April 2000.
  8. T. N. Pappas and R. J. Safranek, "Perceptual criteria for image quality evaluation," in Handbook of image and video processing, Academic Press, May 2000.
  9. T. N. Pappas, T. A. Michel and R. O. Hinds, "Supra-threshold perceptual image coding," in Proc. Int. Conf. on Image Processing (ICIP), Vol. I, pp. 237-240, 1996.
  10. Z. Wang, A. C. Bovik and L. Lu, "Why is image quality assessment so difficult?," in Proc. Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP), Vol. 4, pp. 3313-3316, 2002.
  11. Z. Wang and A. C. Bovik, "Universal image quality index," IEEE Signal Processing Letters, Vol. 9, No. 3, pp. 81-84, March 2002. https://doi.org/10.1109/97.995823
  12. Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, "Image quality assessment: From error visibility to structural similarity" IEEE Trans. on Image Processing, Vol. 13, No. 4, pp. 600-612, April 2004. https://doi.org/10.1109/TIP.2003.819861
  13. H. R. Sheikh and A. C. Bovik, "Image information and visual quality," IEEE Trans. on Image Processing, Vol. 15, No. 2, pp. 430-444, Feb. 2006. https://doi.org/10.1109/TIP.2005.859378
  14. A. Shnayderman, A. Gusev, and A. M. Eskicioglu, "An SVD-based grayscale image quality measure for local and global assessment," IEEE Trans. on Image Processing, Vol. 15, No. 2, pp. 422-429, Feb. 2006. https://doi.org/10.1109/TIP.2005.860605
  15. J. L. Mannos and D. J. Sakrison, "The effects of a visual fidelity criterion on the encoding of images," IEEE Trans. on Information Theory, Vol. IT-20, No. 4, pp. 525-536, July 1974.
  16. T. N. Pappas and D. L. Neuhoff, "Least-squares model based halftoning," IEEE Transactions on Image Processing, Vol. 8, No. 8, pp. 1102-1116, August 1999. https://doi.org/10.1109/83.777090
  17. G. Ivkovic and R. Sankar, "An algorithm for image quality assessment," in Proc. Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP), Vol. 3, pp. 713-716, 2004.
  18. Z. Xie and T. G. Stockham, Jr., "Toward the unification of three visual laws and two visual models in brightness perception," IEEE Trans. on Systems, Man and Cybernetics, Vol. 19, No. 2, pp. 379 -387, March-April 1989. https://doi.org/10.1109/21.31040

Cited by

  1. Auto Detection System of Personal Information based on Images and Document Analysis vol.15, pp.5, 2015, https://doi.org/10.7236/JIIBC.2015.15.5.183