Advertisement

Multimedia Tools and Applications

, Volume 76, Issue 8, pp 11127–11142 | Cite as

A new research on contrast sensitivity function in 3D space

  • Jiachen Yang
  • Yun Liu
  • Wei Wei
  • Qinggang Meng
  • Zhiqun Gao
  • Yancong Lin
Article
  • 135 Downloads

Abstract

In this paper, it tries to extend the characteristics of human eyes’ contrast sensitivity Function(CSF) into (3D) space, but the experimental results show that the traditional characteristics of CSF have limitations in 3D space for lack of depth information. In order to investigate the characteristics of CSF in 3D space, traditional CSF tests are further developed to measure the corresponding properties of CSF with different inclined planes, and describe the 𝜃C S F characteristics of human eyes based on the inclined angles 𝜃. According to the tests, the mathematical expression of 𝜃C S F is built up. In addition, the concept of spatial frequency in the direction of depth (f D ) is proposed, and f D C S F characteristic surface is also achieved. The proposed 3D CSF has significant effects on the research of human visual characteristics and 3D image processing.

Keywords

Human visual system Contrast sensitivity function Spatial frequency Three-dimensional space 

Notes

Acknowledgments

This research is supported by the National Natural Science Foundation of China (No. 61471260 and No.61271324), and Program for New Century Excellent Talents in University (NCET-12-0400).

References

  1. 1.
    Arundale K (1978) An investigation into the variation of human contrast sensitivity with age and ocular pathology. Br J Ophthalmol 62:213–215CrossRefGoogle Scholar
  2. 2.
    Bodis-Wollner I, Diamond SP (1976) The measurement of spatial contrast sensitivity in cases of blurred vision associated with cerebral lesions. J Neurol 99:695–710Google Scholar
  3. 3.
    Bradley AP (1999) A wavelet visible difference predictor. IEEE Trans Image Process 8(5):717–730CrossRefGoogle Scholar
  4. 4.
    Brand\(\tilde {\alpha }\) T, Queluz MP (2010) No-reference quality assessment of h. 264/AVC encoded video. IEEE Trans Circuits Syst Video Technol 20(11):1437–1447Google Scholar
  5. 5.
    Chen Y, Blum RS (2009) A new automated quality assessment algorithm for image fusion. Image Vis Comput 27(2)Google Scholar
  6. 6.
    Chen H, Guillemot C (2010) Perceptually-friendly H.264/AVC video coding based on foveated just-noticeable-distortion model. IEEE Trans Circuits Syst Video Technol 20(6)Google Scholar
  7. 7.
    Chen H, Varshney PK (2007) A human perception inspired quality metric for image fusion based on regional information. Information Fusion 8(2):193–207CrossRefGoogle Scholar
  8. 8.
    Chen WD, Weisi L, Bu-Sung L, Chiew TL (2012) Robust image coding based upon compressive sensing. IEEE Trans Multimedia 14(2):278–290CrossRefGoogle Scholar
  9. 9.
    Damera-Venkata N, Kite TD, Geisler WS et al (2000) Image quality assessment based on a degradation model. IEEE Trans Image Process 9(4):636–650CrossRefGoogle Scholar
  10. 10.
    Gaddipatti A, Machiraju R, Yagel R (1997) Steering image generation with wavelet based perceptual metric. Comput Graphics Forum 16(3):C241–C251CrossRefGoogle Scholar
  11. 11.
    Gao X, Lu W, Tao D, Li X (2009) Image quality assessment based on multiscale geometric analysis. IEEE Trans Image Process 18(7):1409–1423MathSciNetCrossRefGoogle Scholar
  12. 12.
    Imamoglu N, Lin W, Fang Y (2013) A saliency detection model using low-level features based on wavelet transform. IEEE Trans Multimedia 15(1):96–105CrossRefGoogle Scholar
  13. 13.
    James L, Mannos DJ (1974) Sakrison the effects of a visual fidelity criterion on the encoding of images. IEEE Trans Inf Theory IT-20(4):525–536MATHGoogle Scholar
  14. 14.
    Jayant N, Johnston J, Safranek R (1993) Signal compression based on models of human perception. Proc IEEE 81(10):1385–1422CrossRefGoogle Scholar
  15. 15.
    Jung S-W, Ko S-J (2012) Depth sensation enhancement using the just noticeable depth difference. IEEE Trans Image Process 21(8):3624–3637MathSciNetCrossRefGoogle Scholar
  16. 16.
    Lang M, Hornung A, Wang O et al (2010) Nonlinear disparity mapping for stereoscopic 3D. ACM Trans Graph 29(4):75CrossRefGoogle Scholar
  17. 17.
    Li S, Zhang F, Ma L, Ngi Ngan K (2011) Image quality assessment by separately evaluating detail losses and additive impairments. IEEE Trans Multimedia 13(5):935–949CrossRefGoogle Scholar
  18. 18.
    Li P, Wang M, Cheng J, Xu C, Lu H (2013) Spectral hashing with semantically consistent graph for image indexing. IEEE Trans Multimedia 15(1):141–152CrossRefGoogle Scholar
  19. 19.
    Liu F, Niu Y, Jin H (2013) Casual stereoscopic photo authoring. IEEE Trans Multimedia 15(1): 129–140CrossRefGoogle Scholar
  20. 20.
    Mller K, Merkle P (2011) Thomas wiegand, 3-D video representation using depth maps. Proc IEEE 99(4):643–656CrossRefGoogle Scholar
  21. 21.
    Mocan MC, Najera-Covarrubias M, Wright KW (2005) Comparison of visual acuity levels in pediatric patients with Amblyopia using Wright Figures((c)), Allen Optotypes, and Snellen Letters. J AAPOS 9:48–52CrossRefGoogle Scholar
  22. 22.
    Nadenau MJ, Reichel J, Kunt M (2003) Wavelet-based color image compression: exploiting the contrast sensitivity function. IEEE Trans Image Process 12(1)Google Scholar
  23. 23.
    Ng K-T, Zhu Z-Y, Wang C, Chan S-C, Shum H-Y (2012) A multi-camera approach to image-based rendering and 3-D/multiview display of ancient chinese artifacts. IEEE Trans Multimedia 14(6): 1631–1641CrossRefGoogle Scholar
  24. 24.
    Schade (1956) Optical and photoelectric analog of the eye. J Opt Soc Am 46 (9):721–738CrossRefGoogle Scholar
  25. 25.
    Shao F, Jiang G, Yu M, Chen K, Ho Y-S (2012) Asymmetric coding of Multi-View video plus depth based 3-D video for view rendering. IEEE Trans Multimedia 14(1):157–167CrossRefGoogle Scholar
  26. 26.
    Smolic A, Kauff P, Knorr S, Hornung A et al (2011) Three-dimentional video postproduction and processing. Proc IEEE 99(4):607–625CrossRefGoogle Scholar
  27. 27.
    Tao D, Li X, Lu W, Gao X (2009) Reduced-reference IQA in contourlet domain. IEEE Trans Syst Man Cybern B Cybern 39(6)Google Scholar
  28. 28.
    Wei ZY, Ngan KN (2009) Spatio-temporal just noticeable distortion profile for grey scale image/video in DCT domain. IEEE Trans Circuits Syst Video Technol 19 (3):337–346CrossRefGoogle Scholar
  29. 29.
    Wei W, Yong Q (2011) Information potential fields navigation in wireless Ad-Hoc sensor networks[J]. Sensors 11(5):4794–4807CrossRefGoogle Scholar
  30. 30.
    Wei W, Yang X L, Shen PY et al (2012) Holes detection in anisotropic sensornets: topological methods[J]. Int J Distrib Sens Netw 2012Google Scholar
  31. 31.
    Wei W, Yang XL, Zhou B et al (2012) Combined energy minimization for image reconstruction from few views[J]. Math Probl Eng 2012Google Scholar
  32. 32.
    Wei W, Qin X, Wang L et al (2014) GI/Geom/1 queue based on communication model for mesh networks[J]. Int J Commun Syst 27(11):3013–3029Google Scholar
  33. 33.
    Wu G-L, Wu T-H, Chien S-Y (2011) Algorithm and Architecture Design of Perception Engine for Video Coding Applications. IEEE Trans Multimedia 13(6)Google Scholar
  34. 34.
    Xing L, You J, Ebrahimi T, Perkis A (2012) Assessment of stereoscopic crosstalk perception. IEEE Trans Multimedia 14(2):326–337Google Scholar
  35. 35.
    Yan B, Zhou J (2012) Efficient frame concealment for depth image-based 3-D video transmission. IEEE Trans Multimedia 14(3):936–941Google Scholar
  36. 36.
    Zeng W, Daly S, Lei S (2002) An overview of the visual optimization tools in JPEG 2000. Signal Process Image Commun 17(1):85–104CrossRefGoogle Scholar
  37. 37.
    Zhang F, Ma L, Li S, Ngi Ngan K (2011) Practical image quality metric applied to image coding. IEEE Trans Multimedia 13(4)Google Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Jiachen Yang
    • 1
  • Yun Liu
    • 2
  • Wei Wei
    • 3
  • Qinggang Meng
    • 4
  • Zhiqun Gao
    • 1
  • Yancong Lin
    • 1
  1. 1.School of Electronic Information EngineeringTianjin UniversityTianjinPeople’s Republic of China
  2. 2.School of OptometryUniversity of CaliforniaBerkeleyUSA
  3. 3.School of Computer Science and EngineeringXi’an University of TechnologyXi’anChina
  4. 4.Department of Computer Science, School of ScienceLoughborough UniversityLoughboroughUK

Personalised recommendations