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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
Article

Abstract

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.

Keywords

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

Notes

Acknowledgements

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.

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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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