Video Quality Prediction Using a 3D Dual-Tree Complex Wavelet Structural Similarity Index

  • K. Yonis
  • R. M. Dansereau
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6134)

Abstract

In this paper, we test the performance of the complex wavelet structural similarity index (ℂW-SSIM) using the 2D dual-tree complex wavelet transform (DT-ℂWT). Also, we propose using a 3D DT-ℂWT with the ℂW-SSIM algorithm, to predict the quality of digital video signals. The 2D algorithm was tested against the LIVE image database and has shown higher correlation with the subjective results than peak signal-to-noise ratio (PSNR), structural similarity (SSIM), and the initial steerable pyramid implementation of ℂW-SSIM. The proposed 3D DT-ℂWT implementation of the ℂW-SSIM is tested against a set of subjectively scored video sequences from the video quality experts group’s (VQEG) multimedia (MM) project and gave promising results. Both implementations were validated to be good quality assessment tools to be embedded with DT-ℂWT based image and video denoising algorithms as well as DT-ℂWT image and video coding algorithms.

Keywords

Objective video quality assessment dual-tree wavelet structural similarity 

References

  1. 1.
    Wang, Z., Simoncelli, E.: Translation insensitive image similarity in complex wavelet domain. In: Proc. of IEEE Intern. Conf. on Acoustics, Speech and Signal Processing, March 2005, pp. 573–576 (2005)Google Scholar
  2. 2.
    Brooks, A.C., Zhao, X., Pappas, T.N.: Structural similarity quality metrics in a coding context: Exploring the space of realistic distortions. IEEE Trans. on Image Processing 17(8), 1261–1273 (2008)CrossRefMathSciNetGoogle Scholar
  3. 3.
    Voran, S.: The development of objective video quality measures that emulate human perception. In: Proc. of GLOBECOM, pp. 1776–1781 (1991)Google Scholar
  4. 4.
    van den Branden Lambrecht, C., Verscheure, O.: Perceptual quality measure using a spatio-temporal model of the human visual system. In: Proc. SPIE, San Jose, CA, January 1996, vol. 2668, pp. 450–461 (1996)Google Scholar
  5. 5.
    Winkler, S.: A perceptual distortion metric for digital color video. In: Proc. SPIE, pp. 1–4 (1999)Google Scholar
  6. 6.
    Wolf, S., Pinson, M.H.: Video quality measurement techniques. National Telecommunications and Information Administration, Report 02-392 (June 2002)Google Scholar
  7. 7.
    Yao, S., Lin, W., Ong, E., Lu, Z.: A wavelet-based visible distortion measure for video quality evaluation. In: Proc. of IEEE Intern. Conf. on Image Processing, October 2006, pp. 2937–2940 (2006)Google Scholar
  8. 8.
    Wang, Z., Bovik, A., Sheikh, H., Simoncelli, E.: Image quality assessment: From error visibility to structural similarity. IEEE Trans. on Image Processing 13(4), 600–612 (2004)CrossRefGoogle Scholar
  9. 9.
    Wang, Z., Lu, L., Bovik, A.: Video quality assessment based on structural distortion measurement. Signal Processing: Image Communication 19(2), 121–132 (2004)CrossRefGoogle Scholar
  10. 10.
    Selesnick, I.W., Baraniuk, R.G., Kingsbury, N.G.: The dual-tree complex wavelet transform. IEEE Signal Processing Magazine 22(6), 123–151 (2005)CrossRefGoogle Scholar
  11. 11.
    Selesnick, I.W., Li, K.Y.: Video denoising using 2d and 3d dual-tree complex wavelet transforms. In: Wavelet Appl. Signal Image Proc. X, Proc. SPIE 5207, pp. 607–618 (2003)Google Scholar
  12. 12.
    Wang, B., Wang, Y., Selesnick, I., Vetro, A.: Video coding using 3d dual-tree wavelet transform. EURASIP J. on Image and Video Processing 2007(1), 15 (2007)Google Scholar
  13. 13.
    Sheikh, H.R., Wang, Z., Cormack, L., Bovik, A.C.: Live image quality assessment database release 2, http://live.ece.utexas.edu/research/quality
  14. 14.
    VQEG, FRTV phase II report, final report from the video quality experts group on the validation of objective models of video quality assessment, VQEG, Tech. Rep. (August 2003)Google Scholar
  15. 15.
    VQEG, “Final report from the video quality experts group on the validation of objective models of multimedia quality assessment, phase I,” VQEG, Tech. Rep. (September 2008)Google Scholar
  16. 16.
    Shi, F., Selesnick, I.W.: Video denoising using oriented complex wavelet transforms. In: Proc. of IEEE Intern. Conf. on Acoustics, Speech, and Signal Processing, May 2004, vol. 2, pp. 949–952 (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • K. Yonis
    • 1
  • R. M. Dansereau
    • 1
  1. 1.Department of Systems and Computer EngineeringCarleton UniversityOttawaCanada

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