Skip to main content
Springer Nature Link
Log in

Video image assessment with a distortion-weighing spatiotemporal visual attention model

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

For the purpose of extracting attention regions from distorted videos, a distortion-weighing spatiotemporal visual attention model is proposed. On the impact of spatial and temporal saliency maps, visual attention regions are acquired directed in a bottom-up manner. Meanwhile, the blocking artifact saliency map is detected according to intensity gradient features. An attention selection is applied to identify one of visual attention regions with more relatively serious blocking artifact as the Focus of Attention (FOA) directed in a top-down manner. Experimental results show that the proposed model can not only accurately analyze the spatiotemporal saliency based on the intensity, the texture, and the motion features, but also able to estimate the blocking artifact of distortions in comparing with Walther’s and You’s models.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

Abbreviations

HVS:

Human Visual System

FOA:

Focus of Attention

LGN:

Lateral Geniculate Nucleus

hMT+:

human Middle Temporal+

IPS:

Intra Parietal Sulcus

FEF:

Frontal Eye Field

VQEG:

Video Quality Experts Group

HRC:

Hypothetical Reference Circuits

References

  1. Aziz MZ, Mertsching B (2008) Fast and robust generation of feature maps for region-based visual attention. IEEE Trans Image Process 17(5):633–644

    Article  MathSciNet  Google Scholar 

  2. Chen WH, Wang CW, Wu JL (2007) Video adaptation for small display based on content recomposition. IEEE Trans Circuits Syst Video Technol 17(1):43–58

    Article  Google Scholar 

  3. Kalanit GS, Rafael M (2004) The human visual cortex. Annu Rev Neurosci 27:649–677

    Article  Google Scholar 

  4. Koch C, Ullman S (1985) Shifts in selection in visual attention: toward the underlying neural circuitry. Hum Neurobiol 4(4):219–227

    Google Scholar 

  5. Ma YF, Zhang HJ (2002) A model of motion attention for video skimming. Proc Int Conf Image Processing 1:22–25

    Google Scholar 

  6. Niebur E, Koch C (1998) Computational architectures for attention. In: Parasuraman R (ed) The attentive brain. MIT, Cambridge, pp 163–186

    Google Scholar 

  7. Rapantzikos K, Tsapatsoulis N, Avrithis Y, Kollias S (2007) Bottom-up spatiotemporal visual attention model for video analysis. IET Image Processing 1(2):237–248

    Article  Google Scholar 

  8. Serences JT, Yantis S (2006) Selective visual attention and perceptual coherence. TRENDS Cognit Sci 10(1):38–45

    Article  Google Scholar 

  9. Shih HC, Hwang JN, Huang CL (2009) Content-based attention ranking using visual and contextual attention model for baseball videos. IEEE Trans Multimedia 11(2):244–255

    Article  Google Scholar 

  10. Stefan W, Praveen M (2008) The evolution of video quality measurement: from PSNR to hybrid metrics. IEEE Trans Broadcast 54(3):660–668

    Article  Google Scholar 

  11. Tang CW (2007) Spatiotemporal visual considerations for video coding. IEEE Trans Multimedia 9(2):231–238

    Article  Google Scholar 

  12. Tang CW, Chen CH, Yu YH, Tsai CJ (2006) Visual sensitivity guided bit allocation for video coding. IEEE Trans Multimedia 8(1):11–18

    Article  Google Scholar 

  13. Treisman AM, Gelade G (1980) A feature-integration theory of attention. Cogn Psychol 12(1):97–136

    Article  Google Scholar 

  14. VQEG (2000) Final Report from the Video Quality Expert Group on the Validation of Objective Models of Video Quality assessment. VQEG. http://www.vqeg.org

  15. Walther D, Koch C (2006) Modeling attention to salient proto-objects. Neural Netw 19:1395–1407

    Article  MATH  Google Scholar 

  16. You JY, Liu GZ, Li HL (2007) A novel attention model and its application in video analysis. Appl Math Comput 185:963–975

    Article  MATH  Google Scholar 

  17. Yuen M, Wu HR (1998) A survey of hybrid MC/DPCM/DCT video coding distortions. Signal Processing 70:247–278

    Article  MATH  Google Scholar 

  18. Zheng YY (2008) Research on H.264 Region-of-Interest Coding Based on Visual Perception. PhD Thesis, Zhejiang University. China (in Chinese)

Download references

Acknowledgements

The authors would like to thank the editor and anonymous reviewers for their careful reviews and valuable comments.

Author information

Authors and Affiliations

  1. Institute of Advanced Digital Technology and Instrumentation, Zhejiang University, Hangzhou, 310027, People’s Republic of China

    Hua Zhang, Xiang Tian & Yaowu Chen

Authors
  1. Hua Zhang
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Xiang Tian
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Yaowu Chen
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Xiang Tian.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Zhang, H., Tian, X. & Chen, Y. Video image assessment with a distortion-weighing spatiotemporal visual attention model. Multimed Tools Appl 52, 221–233 (2011). https://doi.org/10.1007/s11042-010-0470-x

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-010-0470-x

Keywords