Cognitive Computation

, Volume 2, Issue 2, pp 120–131 | Cite as

A Video Quality Assessment Metric Based on Human Visual System

  • Wen Lu
  • Xuelong LiEmail author
  • Xinbo Gao
  • Wenjian Tang
  • Jing Li
  • Dacheng Tao


It is important for practical application to design an effective and efficient metric for video quality. The most reliable way is by subjective evaluation. Thus, to design an objective metric by simulating human visual system (HVS) is quite reasonable and available. In this paper, the video quality assessment metric based on visual perception is proposed. Three-dimensional wavelet is utilized to decompose video and then extract features to mimic the multichannel structure of HVS. Spatio-temporal contrast sensitivity function (S-T CSF) is employed to weight coefficient obtained by three-dimensional wavelet to simulate nonlinearity feature of the human eyes. Perceptual threshold is exploited to obtain visual sensitive coefficients after S-T CSF filtered. Visual sensitive coefficients are normalized representation and then visual sensitive errors are calculated between reference and distorted video. Finally, temporal perceptual mechanism is applied to count values of video quality for reducing computational cost. Experimental results prove the proposed method outperforms the most existing methods and is comparable to LHS and PVQM.


Video quality assessment Human visual system Three-dimensional wavelet Contrast sensitivity Temporal perceptual mechanism 



We want to thank the helpful comments and suggestions from the anonymous reviewers. This research was supported by the National Natural Science Foundation of China (60771068, 60702061, 60832005), the Ph.D. Programs Foundation of Ministry of Education of China (No. 20090203110002), the Natural Science Basic Research Plane in Shaanxi Province of China (2009JM8004), the Open Project Program of the National Laboratory of Pattern Recognition (NLPR) in China and the National Laboratory of Automatic Target Recognition, Shenzhen University, China.


  1. 1.
    Wang Z, Sheikh RH, Bovik CA. Objective video quality assessment. In: Furht B, Marques O, editors. The handbook of video databases: design and applications. Florida: CRC Press; 2003. p. 1041–78.Google Scholar
  2. 2.
    ITU-R BT.500-11. Methodology for the subjective assessment of the quality of television pictures. 2002. Google Scholar
  3. 3.
    Wang Z, Bovik CA. Modern image quality assessment. New York: Morgan and Claypool Publishing Company; 2006.Google Scholar
  4. 4.
    Yuan Y, Evans A, Monro D. Low complexity separable matching pursuits. IEEE Int Conf Acoust Speech Signal Process. 2004;3(17–21):725–8.Google Scholar
  5. 5.
    Yuan Y, Monro D. Improved matching pursuits image coding. IEEE Int Conf Acoust Speech Signal Process. 2005;2:201–4.Google Scholar
  6. 6.
    Monro D, Yuan Y. Bases for low complexity matching pursuits image coding. IEEE Int Conf Image Process. 2005;2(11–14):249–52.Google Scholar
  7. 7.
    Yuan Y, Monro D. 3D wavelet video coding with replicated matching pursuits. IEEE Int Conf Image Process. 2005;1(11–14):69–72.Google Scholar
  8. 8.
    Li X, Tao D, Gao X, Lu W. A natural image quality evaluation metric. Signal Process. 2009;89(4):548–55.CrossRefGoogle Scholar
  9. 9.
    ATIS Technical Report T1.TR.PP.74, Objective video quality measurement using a peak-signal-to-noise-ratio (PSNR) full reference technique. 2001.Google Scholar
  10. 10.
    Sarnoff Corporation, J. Lubin. Sarnoff JND Vision Model. Contribution to IEEE G-2.1.6 compression and processing subcommittee. 1997.Google Scholar
  11. 11.
    Winkler S. A perceptual distortion metric for digital color video. In Proc SPIE, 3644: 175–184, San Jose, CA, Jan. 23–29, 1999.Google Scholar
  12. 12.
    Watson AB, Hu J, McGowan JF. DVQ: a digital video quality metric based on human vision. J Electron Imag. 2001;10(1):20–9.CrossRefGoogle Scholar
  13. 13.
    Hekstra AP, Beerends JG, Ledermann D, de Caluwe FE, Kohler S, Koenen RH, Rihs S, Ehrsam M, Schlauss D. PVQM—a perceptual video quality measure. Signal Process Image Commun. 2002;17(10):781–98.CrossRefGoogle Scholar
  14. 14.
    VQEG. Final report from the video quality experts group on the validation of objective models of video quality assessment, phase I VQEG. 2000. 2000Available:
  15. 15.
    Wang Z, Lu L, Bovik AC. Video quality assessment based on structural distortion measurement. Signal Process Image Commun. 2004;19(2):121–32.CrossRefGoogle Scholar
  16. 16.
    Mei T, Hua X-S, Zhu C-Z, Zhou H-Q, Li S. Home video visual quality assessment with spatio-temporal factors. IEEE Trans Circuits Syst Video Technol. 2007;17(6):699–706.CrossRefGoogle Scholar
  17. 17.
    Lu Z, Lin W, Yang Xiaokang, Ong EP, Yao S. Modeling visual attention’s modulatory aftereffects on visual sensitivity and quality evaluation. IEEE Trans Image Process. 2009;14(11):1928–42.Google Scholar
  18. 18.
    Wang Z, Li Q. Video quality assessment using a statistical model of human visual speed perception. J Opt Soc Am A. 2007;24(12):B61–9.CrossRefGoogle Scholar
  19. 19.
    Wandell BA. Foundations of vision. Sinauer Associates. 1995.Google Scholar
  20. 20.
    Moyano E, Quiles FJ, Garrido A, Orozco-Barbosa L, Duato J. Efficient 3-D wavelet transform decomposition for video compression. International Workshop on Digital and Computational Video, 118–125, Feb. 2001.Google Scholar
  21. 21.
    Wang C, Ma K-L. A statistical approach to volume data quality assessment. IEEE Trans Vis Comput Graphics. 2008;14(3):590–602.CrossRefGoogle Scholar
  22. 22.
    Mallat SG. A theory for multiresolution decomposition: the wavelet representation. IEEE Trans Pattern Anal Mach Intell. 1989;11(7):674–93.CrossRefGoogle Scholar
  23. 23.
    Kelly DH. Motion and vision II. Stabilized spatio-temporal surface. J Opt Soc Am. 1979;69(10):1340–9.CrossRefPubMedGoogle Scholar
  24. 24.
    Jia Y, Lin W, Kassim AA. Estimating just-noticeable distortion for video. IEEE Trans Circuits Syst Video Technol. 2006;16(7):820–9.CrossRefGoogle Scholar
  25. 25.
    Narita N. Subjective-evaluation method for quality of coded images. IEEE Trans Broadcasting. 1994;40(1):7–13.CrossRefGoogle Scholar
  26. 26.
    Tan KT, Ghanbari M, Pearson DE. An objective measurement tool for MPEG video quality. Signal Process. 1998;70(3):279–94.CrossRefGoogle Scholar
  27. 27.
    Ninassi A, Meur OL, Callet PL, Barba D. Considering temporal variations of spatial visual distortions in video quality assessment. IEEE J Sel Topics Signal Process. 2009;3(2):253–65.CrossRefGoogle Scholar
  28. 28.
    Winkler S. A perceputal distortion metric for digital color video IV. Proc SPIE Human Vis Electron Imag. 1999;3644:175–84.CrossRefGoogle Scholar
  29. 29.
    Van den Branden Lambrecht CJ. A working spatio-temporal model of the human visual system for image restoration and quality assessment applications. Proc IEEE Int Conf Acoust Speech Signal Process. 1996;4:2291–4.CrossRefGoogle Scholar
  30. 30.
    Masry M, Hemami SS, Yegnaswamy S. A scalable waveletbased video distortion metric and applications. IEEE Trans Circuits Syst Video Technol. 2006;16(2):260–73.CrossRefGoogle Scholar
  31. 31.
    Chou C-H, Chen C-W. A perceptually optimized 3-D subband codec for video communication over wireless channels. IEEE Trans Circuits Syst Video Technol. 1996;6(2):143–56.CrossRefGoogle Scholar
  32. 32.
    Ong E, Lin W, Lu Z, Yao S, Etoh M. Visual distortion assessment with emphasis on spatially transitional regions. IEEE Trans Circuits Syst Video Technol. 2004;14(4):559–66.CrossRefGoogle Scholar
  33. 33.
    Gunawan IP, Ghanbari M. Reduced-reference video quality assessment using discriminative local harmonic strength with motion consideration. IEEE Trans Circuits Syst Video Technol. 2008;18(1):71–83.CrossRefGoogle Scholar
  34. 34.
    Pan Q, Zhang L, Dai G, Zhang H. Two de-noising methods by wavelet transform. IEEE Trans Signal Process. 1999;47(12):3401–6.CrossRefGoogle Scholar
  35. 35.
    Zhang L, Paul B, Wu X. Multiscale LMMSE-based image denoising with optimal wavelet selection. IEEE Trans Circuits Syst Video Technol. 2005;15(4):469–81.CrossRefGoogle Scholar
  36. 36.
    Gao X, Lu W, Tao D, Li X. Image quality assessment based on multiscale geometric analysis. IEEE Trans Image Process. 2009;18(7):1409–23.CrossRefPubMedGoogle Scholar
  37. 37.
    Lu W, Zeng K, Tao D, Yuan Y, Gao X. No-reference image quality assessment in contourlet domain. Neurocomputing. 2010;73(1):784–94.CrossRefGoogle Scholar
  38. 38.
    Li X, Lin S, Yan S, Xu D. Discriminant locally linear embedding with high-order tensor data. IEEE Trans Syst Man Cybern Part B. 2008;38(2):342–52.CrossRefGoogle Scholar
  39. 39.
    Tao D, Li X, Wu X, Maybank SJ. General tensor discriminant analysis and Gabor features for gait recognition. IEEE Trans Pattern Anal Mach Intell. 2007;29(10):1700–15.CrossRefPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • Wen Lu
    • 1
  • Xuelong Li
    • 2
    Email author
  • Xinbo Gao
    • 1
  • Wenjian Tang
    • 1
  • Jing Li
    • 1
  • Dacheng Tao
    • 3
  1. 1.School of Electronic EngineeringXidian UniversityXi’anPeople’s Republic of China
  2. 2.Center for OPTical IMagery Analysis and Learning (OPTIMAL), State Key Laboratory of Transient Optics and PhotonicsXi’an Institute of Optics and Precision Mechanics, Chinese Academy of SciencesXi’anPeople’s Republic of China
  3. 3.School of Computer EngineeringNanyang Technological UniversitySingaporeSingapore

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