Abstract

Perceptual quality measurement of three-dimensional (3D) visual signals has become a fundamental challenge in 3D imaging fields. This paper proposes a novel no-reference (NR) 3D visual quality measurement (VQM) metric that uses simulations of the primary visual cortex (V1) of binocular vision. As the major technical contribution of this study, perceptual properties of simple and complex cells are considered for NR 3D-VQM. More specifically, the metric simulates the receptive fields of simple cells (one class of V1 neurons) using Gaussian derivative functions, and the receptive fields of complex cells (the other class of V1 neurons) using disparity energy responses and binocular rivalry responses. Subsequently, various quality-aware features are extracted from the primary visual cortex; these will change in the presence of distortions. Finally, those features are mapped to the subjective quality score of the distorted 3D visual signal by using support vector regression (SVR). Experiments on two publicly available 3D databases confirm the effectiveness of our proposed metric, compared to the relevant full-reference (FR) and NR metrics.

© 2015 Optical Society of America

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References

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2015 (8)

K. Lee and S. Lee, “3D perception based quality pooling: stereopsis, binocular rivalry and binocular suppression,” IEEE J. Sel. Top. Signal Process. 9(3), 533–545 (2015).
[Crossref]

J. W. Brascamp, P. C. Klink, and W. J. M. Levelt, “The ‘laws’ of binocular rivalry: 50 years of Levelt’s propositions,” Vision Res. 109(Pt A), 20–37 (2015).
[Crossref] [PubMed]

M. Zhang, C. Muramatsu, X. Zhou, T. Hara, and H. Fujita, “Blind image quality assessment using the joint statistics of generalized local binary pattern,” IEEE Signal Process. Lett. 22(2), 207–210 (2015).
[Crossref]

Z. Liu, X. Li, F. Li, and G. Zhang, “Flexible dynamic measurement method of three-dimensional surface profilometry based on multiple vision sensors,” Opt. Express 23(1), 384–400 (2015).
[Crossref] [PubMed]

J. Wang, Y. Song, Z. H. Li, A. Kempf, and A. Z. He, “Multi-directional 3D flame chemiluminescence tomography based on lens imaging,” Opt. Lett. 40(7), 1231–1234 (2015).
[Crossref] [PubMed]

S. Wei, S. Wang, C. Zhou, K. Liu, and X. Fan, “Binocular vision measurement using Dammann grating,” Appl. Opt. 54(11), 3246–3251 (2015).
[Crossref] [PubMed]

D. Zhao, B. Su, G. Chen, and H. Liao, “360 degree viewable floating autostereoscopic display using integral photography and multiple semitransparent mirrors,” Opt. Express 23(8), 9812–9823 (2015).
[Crossref] [PubMed]

Y. Gong, D. Meng, and E. J. Seibel, “Bound constrained bundle adjustment for reliable 3D reconstruction,” Opt. Express 23(8), 10771–10785 (2015).
[Crossref] [PubMed]

2014 (8)

K. C. Huang, Y. H. Chou, L. C. Lin, H. Y. Lin, F. H. Chen, C. C. Liao, Y. H. Chen, K. Lee, and W. H. Hsu, “Investigation of designated eye position and viewing zone for a two-view autostereoscopic display,” Opt. Express 22(4), 4751–4767 (2014).
[Crossref] [PubMed]

I. Mehra and N. K. Nishchal, “Image fusion using wavelet transform and its application to asymmetric cryptosystem and hiding,” Opt. Express 22(5), 5474–5482 (2014).
[Crossref] [PubMed]

Y. Cui, F. Zhou, Y. Wang, L. Liu, and H. Gao, “Precise calibration of binocular vision system used for vision measurement,” Opt. Express 22(8), 9134–9149 (2014).
[Crossref] [PubMed]

J. Kim, P. V. Johnson, and M. S. Banks, “Stereoscopic 3D display with color interlacing improves perceived depth,” Opt. Express 22(26), 31924–31934 (2014).
[Crossref] [PubMed]

Y. H. Lin and J. L. Wu, “Quality assessment of stereoscopic 3D image compression by binocular integration behaviors,” IEEE Trans. Image Process. 23(4), 1527–1542 (2014).
[Crossref] [PubMed]

W. Zhou, G. Jiang, M. Yu, Z. Wang, Z. Peng, and F. Shao, “Reduced reference stereoscopic image quality assessment using digital watermarking,” Comput. Electr. Eng. 40(8), 104–116 (2014).
[Crossref]

W. Xue, X. Mou, L. Zhang, A. C. Bovik, and X. Feng, “Blind image quality assessment using joint statistics of gradient magnitude and Laplacian features,” IEEE Trans. Image Process. 23(11), 4850–4862 (2014).
[Crossref] [PubMed]

S. Ryu and K. Sohn, “No-reference quality assessment for stereoscopic images based on binocular quality perception,” IEEE Trans. Circ. Syst. Video Tech. 24(4), 591–602 (2014).
[Crossref]

2013 (3)

N. Krüger, P. Janssen, S. Kalkan, M. Lappe, A. Leonardis, J. Piater, A. J. Rodríguez-Sánchez, and L. Wiskott, “Deep hierarchies in the primate visual cortex: what can we learn for computer vision?” IEEE Trans. Pattern Anal. Mach. Intell. 35(8), 1847–1871 (2013).
[Crossref] [PubMed]

M. J. Chen, L. K. Cormack, and A. C. Bovik, “No-reference quality assessment of natural stereopairs,” IEEE Trans. Image Process. 22(9), 3379–3391 (2013).
[Crossref] [PubMed]

M. J. Chen, C. C. Su, D. K. Kwon, L. K. Cormack, and A. C. Bovik, “Full-reference quality assessment of stereopairs accounting for rivalry,” Signal Process. Image Commun. 28(9), 1143–1155 (2013).
[Crossref]

2012 (3)

A. Mittal, A. K. Moorthy, and A. C. Bovik, “No-reference image quality assessment in the spatial domain,” IEEE Trans. Image Process. 21(12), 4695–4708 (2012).
[Crossref] [PubMed]

M. A. Saad, A. C. Bovik, and C. Charrier, “Blind image quality assessment: a natural scene statistics approach in the DCT domain,” IEEE Trans. Image Process. 21(8), 3339–3352 (2012).
[Crossref] [PubMed]

R. Sabesan, L. Zheleznyak, and G. Yoon, “Binocular visual performance and summation after correcting higher order aberrations,” Biomed. Opt. Express 3(12), 3176–3189 (2012).
[Crossref] [PubMed]

2011 (2)

A. K. Moorthy and A. C. Bovik, “Blind image quality assessment: from natural scene statistics to perceptual quality,” IEEE Trans. Image Process. 20(12), 3350–3364 (2011).
[Crossref] [PubMed]

L. Zhang, L. Zhang, X. Mou, and D. Zhang, “FSIM: a feature similarity index for image quality assessment,” IEEE Trans. Image Process. 20(8), 2378–2386 (2011).
[Crossref] [PubMed]

2010 (2)

R. Akhter, Z. M. Parvez Sazzad, Y. Horita, and J. Baltes, “No-reference stereoscopic image quality assessment,” Proc. SPIE 7525, 75240T (2010).
[Crossref]

Q. Peng and B. E. Shi, “The changing disparity energy model,” Vision Res. 50(2), 181–192 (2010).
[Crossref] [PubMed]

2009 (1)

Q. Li and Z. Wang, “Reduced-reference image quality assessment using divisive normalization-based image representation,” IEEE J. Sel. Top. Signal Process. 3(2), 202–211 (2009).
[Crossref]

2004 (2)

A. J. Smola and B. Schölkopf, “A tutorial on support vector regression,” Stat. Comput. 14(3), 199–222 (2004).
[Crossref]

Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: from error visibility to structural similarity,” IEEE Trans. Image Process. 13(4), 600–612 (2004).
[Crossref] [PubMed]

2002 (1)

T. Ojala, M. Pietikainen, and T. Maenpaa, “Multiresolution gray-scale and rotation invariant texture classification with local binary patterns,” IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002).
[Crossref]

1997 (1)

A. J. Bell and T. J. Sejnowski, “The “independent components” of natural scenes are edge filters,” Vision Res. 37(23), 3327–3338 (1997).
[Crossref] [PubMed]

1987 (1)

1986 (1)

J. Canny, “A computational approach to edge detection,” IEEE Trans. Pattern Anal. Mach. Intell. PAMI-8(6), 679–698 (1986).
[Crossref] [PubMed]

1985 (1)

R. Blake and K. Boothroyd, “The precedence of binocular fusion over binocular rivalry,” Percept. Psychophys. 37(2), 114–124 (1985).
[Crossref] [PubMed]

1969 (1)

D. Marr, “A theory of cerebellar cortex,” J. Physiol. 202(2), 437–470 (1969).
[Crossref] [PubMed]

Akhter, R.

R. Akhter, Z. M. Parvez Sazzad, Y. Horita, and J. Baltes, “No-reference stereoscopic image quality assessment,” Proc. SPIE 7525, 75240T (2010).
[Crossref]

Baltes, J.

R. Akhter, Z. M. Parvez Sazzad, Y. Horita, and J. Baltes, “No-reference stereoscopic image quality assessment,” Proc. SPIE 7525, 75240T (2010).
[Crossref]

Banks, M. S.

Bell, A. J.

A. J. Bell and T. J. Sejnowski, “The “independent components” of natural scenes are edge filters,” Vision Res. 37(23), 3327–3338 (1997).
[Crossref] [PubMed]

Blake, R.

R. Blake and K. Boothroyd, “The precedence of binocular fusion over binocular rivalry,” Percept. Psychophys. 37(2), 114–124 (1985).
[Crossref] [PubMed]

Boothroyd, K.

R. Blake and K. Boothroyd, “The precedence of binocular fusion over binocular rivalry,” Percept. Psychophys. 37(2), 114–124 (1985).
[Crossref] [PubMed]

Bovik, A. C.

W. Xue, X. Mou, L. Zhang, A. C. Bovik, and X. Feng, “Blind image quality assessment using joint statistics of gradient magnitude and Laplacian features,” IEEE Trans. Image Process. 23(11), 4850–4862 (2014).
[Crossref] [PubMed]

M. J. Chen, C. C. Su, D. K. Kwon, L. K. Cormack, and A. C. Bovik, “Full-reference quality assessment of stereopairs accounting for rivalry,” Signal Process. Image Commun. 28(9), 1143–1155 (2013).
[Crossref]

M. J. Chen, L. K. Cormack, and A. C. Bovik, “No-reference quality assessment of natural stereopairs,” IEEE Trans. Image Process. 22(9), 3379–3391 (2013).
[Crossref] [PubMed]

A. Mittal, A. K. Moorthy, and A. C. Bovik, “No-reference image quality assessment in the spatial domain,” IEEE Trans. Image Process. 21(12), 4695–4708 (2012).
[Crossref] [PubMed]

M. A. Saad, A. C. Bovik, and C. Charrier, “Blind image quality assessment: a natural scene statistics approach in the DCT domain,” IEEE Trans. Image Process. 21(8), 3339–3352 (2012).
[Crossref] [PubMed]

A. K. Moorthy and A. C. Bovik, “Blind image quality assessment: from natural scene statistics to perceptual quality,” IEEE Trans. Image Process. 20(12), 3350–3364 (2011).
[Crossref] [PubMed]

Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: from error visibility to structural similarity,” IEEE Trans. Image Process. 13(4), 600–612 (2004).
[Crossref] [PubMed]

Brascamp, J. W.

J. W. Brascamp, P. C. Klink, and W. J. M. Levelt, “The ‘laws’ of binocular rivalry: 50 years of Levelt’s propositions,” Vision Res. 109(Pt A), 20–37 (2015).
[Crossref] [PubMed]

Canny, J.

J. Canny, “A computational approach to edge detection,” IEEE Trans. Pattern Anal. Mach. Intell. PAMI-8(6), 679–698 (1986).
[Crossref] [PubMed]

Charrier, C.

M. A. Saad, A. C. Bovik, and C. Charrier, “Blind image quality assessment: a natural scene statistics approach in the DCT domain,” IEEE Trans. Image Process. 21(8), 3339–3352 (2012).
[Crossref] [PubMed]

Chen, F. H.

Chen, G.

Chen, M. J.

M. J. Chen, C. C. Su, D. K. Kwon, L. K. Cormack, and A. C. Bovik, “Full-reference quality assessment of stereopairs accounting for rivalry,” Signal Process. Image Commun. 28(9), 1143–1155 (2013).
[Crossref]

M. J. Chen, L. K. Cormack, and A. C. Bovik, “No-reference quality assessment of natural stereopairs,” IEEE Trans. Image Process. 22(9), 3379–3391 (2013).
[Crossref] [PubMed]

Chen, Y. H.

Chou, Y. H.

Cormack, L. K.

M. J. Chen, L. K. Cormack, and A. C. Bovik, “No-reference quality assessment of natural stereopairs,” IEEE Trans. Image Process. 22(9), 3379–3391 (2013).
[Crossref] [PubMed]

M. J. Chen, C. C. Su, D. K. Kwon, L. K. Cormack, and A. C. Bovik, “Full-reference quality assessment of stereopairs accounting for rivalry,” Signal Process. Image Commun. 28(9), 1143–1155 (2013).
[Crossref]

Cui, Y.

Fan, X.

Feng, X.

W. Xue, X. Mou, L. Zhang, A. C. Bovik, and X. Feng, “Blind image quality assessment using joint statistics of gradient magnitude and Laplacian features,” IEEE Trans. Image Process. 23(11), 4850–4862 (2014).
[Crossref] [PubMed]

Field, D. J.

Fujita, H.

M. Zhang, C. Muramatsu, X. Zhou, T. Hara, and H. Fujita, “Blind image quality assessment using the joint statistics of generalized local binary pattern,” IEEE Signal Process. Lett. 22(2), 207–210 (2015).
[Crossref]

Gao, H.

Gong, Y.

Hara, T.

M. Zhang, C. Muramatsu, X. Zhou, T. Hara, and H. Fujita, “Blind image quality assessment using the joint statistics of generalized local binary pattern,” IEEE Signal Process. Lett. 22(2), 207–210 (2015).
[Crossref]

He, A. Z.

Horita, Y.

R. Akhter, Z. M. Parvez Sazzad, Y. Horita, and J. Baltes, “No-reference stereoscopic image quality assessment,” Proc. SPIE 7525, 75240T (2010).
[Crossref]

Hsu, W. H.

Huang, K. C.

Janssen, P.

N. Krüger, P. Janssen, S. Kalkan, M. Lappe, A. Leonardis, J. Piater, A. J. Rodríguez-Sánchez, and L. Wiskott, “Deep hierarchies in the primate visual cortex: what can we learn for computer vision?” IEEE Trans. Pattern Anal. Mach. Intell. 35(8), 1847–1871 (2013).
[Crossref] [PubMed]

Jiang, G.

W. Zhou, G. Jiang, M. Yu, Z. Wang, Z. Peng, and F. Shao, “Reduced reference stereoscopic image quality assessment using digital watermarking,” Comput. Electr. Eng. 40(8), 104–116 (2014).
[Crossref]

Johnson, P. V.

Kalkan, S.

N. Krüger, P. Janssen, S. Kalkan, M. Lappe, A. Leonardis, J. Piater, A. J. Rodríguez-Sánchez, and L. Wiskott, “Deep hierarchies in the primate visual cortex: what can we learn for computer vision?” IEEE Trans. Pattern Anal. Mach. Intell. 35(8), 1847–1871 (2013).
[Crossref] [PubMed]

Kempf, A.

Kim, J.

Klink, P. C.

J. W. Brascamp, P. C. Klink, and W. J. M. Levelt, “The ‘laws’ of binocular rivalry: 50 years of Levelt’s propositions,” Vision Res. 109(Pt A), 20–37 (2015).
[Crossref] [PubMed]

Krüger, N.

N. Krüger, P. Janssen, S. Kalkan, M. Lappe, A. Leonardis, J. Piater, A. J. Rodríguez-Sánchez, and L. Wiskott, “Deep hierarchies in the primate visual cortex: what can we learn for computer vision?” IEEE Trans. Pattern Anal. Mach. Intell. 35(8), 1847–1871 (2013).
[Crossref] [PubMed]

Kwon, D. K.

M. J. Chen, C. C. Su, D. K. Kwon, L. K. Cormack, and A. C. Bovik, “Full-reference quality assessment of stereopairs accounting for rivalry,” Signal Process. Image Commun. 28(9), 1143–1155 (2013).
[Crossref]

Lappe, M.

N. Krüger, P. Janssen, S. Kalkan, M. Lappe, A. Leonardis, J. Piater, A. J. Rodríguez-Sánchez, and L. Wiskott, “Deep hierarchies in the primate visual cortex: what can we learn for computer vision?” IEEE Trans. Pattern Anal. Mach. Intell. 35(8), 1847–1871 (2013).
[Crossref] [PubMed]

Lee, K.

Lee, S.

K. Lee and S. Lee, “3D perception based quality pooling: stereopsis, binocular rivalry and binocular suppression,” IEEE J. Sel. Top. Signal Process. 9(3), 533–545 (2015).
[Crossref]

Leonardis, A.

N. Krüger, P. Janssen, S. Kalkan, M. Lappe, A. Leonardis, J. Piater, A. J. Rodríguez-Sánchez, and L. Wiskott, “Deep hierarchies in the primate visual cortex: what can we learn for computer vision?” IEEE Trans. Pattern Anal. Mach. Intell. 35(8), 1847–1871 (2013).
[Crossref] [PubMed]

Levelt, W. J. M.

J. W. Brascamp, P. C. Klink, and W. J. M. Levelt, “The ‘laws’ of binocular rivalry: 50 years of Levelt’s propositions,” Vision Res. 109(Pt A), 20–37 (2015).
[Crossref] [PubMed]

Li, F.

Li, Q.

Q. Li and Z. Wang, “Reduced-reference image quality assessment using divisive normalization-based image representation,” IEEE J. Sel. Top. Signal Process. 3(2), 202–211 (2009).
[Crossref]

Li, X.

Li, Z. H.

Liao, C. C.

Liao, H.

Lin, H. Y.

Lin, L. C.

Lin, Y. H.

Y. H. Lin and J. L. Wu, “Quality assessment of stereoscopic 3D image compression by binocular integration behaviors,” IEEE Trans. Image Process. 23(4), 1527–1542 (2014).
[Crossref] [PubMed]

Liu, K.

Liu, L.

Liu, Z.

Maenpaa, T.

T. Ojala, M. Pietikainen, and T. Maenpaa, “Multiresolution gray-scale and rotation invariant texture classification with local binary patterns,” IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002).
[Crossref]

Marr, D.

D. Marr, “A theory of cerebellar cortex,” J. Physiol. 202(2), 437–470 (1969).
[Crossref] [PubMed]

Mehra, I.

Meng, D.

Mittal, A.

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Moorthy, A. K.

A. Mittal, A. K. Moorthy, and A. C. Bovik, “No-reference image quality assessment in the spatial domain,” IEEE Trans. Image Process. 21(12), 4695–4708 (2012).
[Crossref] [PubMed]

A. K. Moorthy and A. C. Bovik, “Blind image quality assessment: from natural scene statistics to perceptual quality,” IEEE Trans. Image Process. 20(12), 3350–3364 (2011).
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Mou, X.

W. Xue, X. Mou, L. Zhang, A. C. Bovik, and X. Feng, “Blind image quality assessment using joint statistics of gradient magnitude and Laplacian features,” IEEE Trans. Image Process. 23(11), 4850–4862 (2014).
[Crossref] [PubMed]

L. Zhang, L. Zhang, X. Mou, and D. Zhang, “FSIM: a feature similarity index for image quality assessment,” IEEE Trans. Image Process. 20(8), 2378–2386 (2011).
[Crossref] [PubMed]

Muramatsu, C.

M. Zhang, C. Muramatsu, X. Zhou, T. Hara, and H. Fujita, “Blind image quality assessment using the joint statistics of generalized local binary pattern,” IEEE Signal Process. Lett. 22(2), 207–210 (2015).
[Crossref]

Nishchal, N. K.

Ojala, T.

T. Ojala, M. Pietikainen, and T. Maenpaa, “Multiresolution gray-scale and rotation invariant texture classification with local binary patterns,” IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002).
[Crossref]

Parvez Sazzad, Z. M.

R. Akhter, Z. M. Parvez Sazzad, Y. Horita, and J. Baltes, “No-reference stereoscopic image quality assessment,” Proc. SPIE 7525, 75240T (2010).
[Crossref]

Peng, Q.

Q. Peng and B. E. Shi, “The changing disparity energy model,” Vision Res. 50(2), 181–192 (2010).
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Peng, Z.

W. Zhou, G. Jiang, M. Yu, Z. Wang, Z. Peng, and F. Shao, “Reduced reference stereoscopic image quality assessment using digital watermarking,” Comput. Electr. Eng. 40(8), 104–116 (2014).
[Crossref]

Piater, J.

N. Krüger, P. Janssen, S. Kalkan, M. Lappe, A. Leonardis, J. Piater, A. J. Rodríguez-Sánchez, and L. Wiskott, “Deep hierarchies in the primate visual cortex: what can we learn for computer vision?” IEEE Trans. Pattern Anal. Mach. Intell. 35(8), 1847–1871 (2013).
[Crossref] [PubMed]

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T. Ojala, M. Pietikainen, and T. Maenpaa, “Multiresolution gray-scale and rotation invariant texture classification with local binary patterns,” IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002).
[Crossref]

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N. Krüger, P. Janssen, S. Kalkan, M. Lappe, A. Leonardis, J. Piater, A. J. Rodríguez-Sánchez, and L. Wiskott, “Deep hierarchies in the primate visual cortex: what can we learn for computer vision?” IEEE Trans. Pattern Anal. Mach. Intell. 35(8), 1847–1871 (2013).
[Crossref] [PubMed]

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S. Ryu and K. Sohn, “No-reference quality assessment for stereoscopic images based on binocular quality perception,” IEEE Trans. Circ. Syst. Video Tech. 24(4), 591–602 (2014).
[Crossref]

Saad, M. A.

M. A. Saad, A. C. Bovik, and C. Charrier, “Blind image quality assessment: a natural scene statistics approach in the DCT domain,” IEEE Trans. Image Process. 21(8), 3339–3352 (2012).
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A. J. Smola and B. Schölkopf, “A tutorial on support vector regression,” Stat. Comput. 14(3), 199–222 (2004).
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W. Zhou, G. Jiang, M. Yu, Z. Wang, Z. Peng, and F. Shao, “Reduced reference stereoscopic image quality assessment using digital watermarking,” Comput. Electr. Eng. 40(8), 104–116 (2014).
[Crossref]

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Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: from error visibility to structural similarity,” IEEE Trans. Image Process. 13(4), 600–612 (2004).
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Q. Peng and B. E. Shi, “The changing disparity energy model,” Vision Res. 50(2), 181–192 (2010).
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Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: from error visibility to structural similarity,” IEEE Trans. Image Process. 13(4), 600–612 (2004).
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A. J. Smola and B. Schölkopf, “A tutorial on support vector regression,” Stat. Comput. 14(3), 199–222 (2004).
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S. Ryu and K. Sohn, “No-reference quality assessment for stereoscopic images based on binocular quality perception,” IEEE Trans. Circ. Syst. Video Tech. 24(4), 591–602 (2014).
[Crossref]

Song, Y.

Su, B.

Su, C. C.

M. J. Chen, C. C. Su, D. K. Kwon, L. K. Cormack, and A. C. Bovik, “Full-reference quality assessment of stereopairs accounting for rivalry,” Signal Process. Image Commun. 28(9), 1143–1155 (2013).
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Wang, S.

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W. Zhou, G. Jiang, M. Yu, Z. Wang, Z. Peng, and F. Shao, “Reduced reference stereoscopic image quality assessment using digital watermarking,” Comput. Electr. Eng. 40(8), 104–116 (2014).
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Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: from error visibility to structural similarity,” IEEE Trans. Image Process. 13(4), 600–612 (2004).
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Wei, S.

Wiskott, L.

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Y. H. Lin and J. L. Wu, “Quality assessment of stereoscopic 3D image compression by binocular integration behaviors,” IEEE Trans. Image Process. 23(4), 1527–1542 (2014).
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W. Xue, X. Mou, L. Zhang, A. C. Bovik, and X. Feng, “Blind image quality assessment using joint statistics of gradient magnitude and Laplacian features,” IEEE Trans. Image Process. 23(11), 4850–4862 (2014).
[Crossref] [PubMed]

Yoon, G.

Yu, M.

W. Zhou, G. Jiang, M. Yu, Z. Wang, Z. Peng, and F. Shao, “Reduced reference stereoscopic image quality assessment using digital watermarking,” Comput. Electr. Eng. 40(8), 104–116 (2014).
[Crossref]

Zhang, D.

L. Zhang, L. Zhang, X. Mou, and D. Zhang, “FSIM: a feature similarity index for image quality assessment,” IEEE Trans. Image Process. 20(8), 2378–2386 (2011).
[Crossref] [PubMed]

Zhang, G.

Zhang, L.

W. Xue, X. Mou, L. Zhang, A. C. Bovik, and X. Feng, “Blind image quality assessment using joint statistics of gradient magnitude and Laplacian features,” IEEE Trans. Image Process. 23(11), 4850–4862 (2014).
[Crossref] [PubMed]

L. Zhang, L. Zhang, X. Mou, and D. Zhang, “FSIM: a feature similarity index for image quality assessment,” IEEE Trans. Image Process. 20(8), 2378–2386 (2011).
[Crossref] [PubMed]

L. Zhang, L. Zhang, X. Mou, and D. Zhang, “FSIM: a feature similarity index for image quality assessment,” IEEE Trans. Image Process. 20(8), 2378–2386 (2011).
[Crossref] [PubMed]

Zhang, M.

M. Zhang, C. Muramatsu, X. Zhou, T. Hara, and H. Fujita, “Blind image quality assessment using the joint statistics of generalized local binary pattern,” IEEE Signal Process. Lett. 22(2), 207–210 (2015).
[Crossref]

Zhao, D.

Zheleznyak, L.

Zhou, C.

Zhou, F.

Zhou, W.

W. Zhou, G. Jiang, M. Yu, Z. Wang, Z. Peng, and F. Shao, “Reduced reference stereoscopic image quality assessment using digital watermarking,” Comput. Electr. Eng. 40(8), 104–116 (2014).
[Crossref]

Zhou, X.

M. Zhang, C. Muramatsu, X. Zhou, T. Hara, and H. Fujita, “Blind image quality assessment using the joint statistics of generalized local binary pattern,” IEEE Signal Process. Lett. 22(2), 207–210 (2015).
[Crossref]

Appl. Opt. (1)

Biomed. Opt. Express (1)

Comput. Electr. Eng. (1)

W. Zhou, G. Jiang, M. Yu, Z. Wang, Z. Peng, and F. Shao, “Reduced reference stereoscopic image quality assessment using digital watermarking,” Comput. Electr. Eng. 40(8), 104–116 (2014).
[Crossref]

IEEE J. Sel. Top. Signal Process. (2)

K. Lee and S. Lee, “3D perception based quality pooling: stereopsis, binocular rivalry and binocular suppression,” IEEE J. Sel. Top. Signal Process. 9(3), 533–545 (2015).
[Crossref]

Q. Li and Z. Wang, “Reduced-reference image quality assessment using divisive normalization-based image representation,” IEEE J. Sel. Top. Signal Process. 3(2), 202–211 (2009).
[Crossref]

IEEE Signal Process. Lett. (1)

M. Zhang, C. Muramatsu, X. Zhou, T. Hara, and H. Fujita, “Blind image quality assessment using the joint statistics of generalized local binary pattern,” IEEE Signal Process. Lett. 22(2), 207–210 (2015).
[Crossref]

IEEE Trans. Circ. Syst. Video Tech. (1)

S. Ryu and K. Sohn, “No-reference quality assessment for stereoscopic images based on binocular quality perception,” IEEE Trans. Circ. Syst. Video Tech. 24(4), 591–602 (2014).
[Crossref]

IEEE Trans. Image Process. (8)

Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: from error visibility to structural similarity,” IEEE Trans. Image Process. 13(4), 600–612 (2004).
[Crossref] [PubMed]

L. Zhang, L. Zhang, X. Mou, and D. Zhang, “FSIM: a feature similarity index for image quality assessment,” IEEE Trans. Image Process. 20(8), 2378–2386 (2011).
[Crossref] [PubMed]

Y. H. Lin and J. L. Wu, “Quality assessment of stereoscopic 3D image compression by binocular integration behaviors,” IEEE Trans. Image Process. 23(4), 1527–1542 (2014).
[Crossref] [PubMed]

A. K. Moorthy and A. C. Bovik, “Blind image quality assessment: from natural scene statistics to perceptual quality,” IEEE Trans. Image Process. 20(12), 3350–3364 (2011).
[Crossref] [PubMed]

A. Mittal, A. K. Moorthy, and A. C. Bovik, “No-reference image quality assessment in the spatial domain,” IEEE Trans. Image Process. 21(12), 4695–4708 (2012).
[Crossref] [PubMed]

M. A. Saad, A. C. Bovik, and C. Charrier, “Blind image quality assessment: a natural scene statistics approach in the DCT domain,” IEEE Trans. Image Process. 21(8), 3339–3352 (2012).
[Crossref] [PubMed]

W. Xue, X. Mou, L. Zhang, A. C. Bovik, and X. Feng, “Blind image quality assessment using joint statistics of gradient magnitude and Laplacian features,” IEEE Trans. Image Process. 23(11), 4850–4862 (2014).
[Crossref] [PubMed]

M. J. Chen, L. K. Cormack, and A. C. Bovik, “No-reference quality assessment of natural stereopairs,” IEEE Trans. Image Process. 22(9), 3379–3391 (2013).
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IEEE Trans. Pattern Anal. Mach. Intell. (3)

T. Ojala, M. Pietikainen, and T. Maenpaa, “Multiresolution gray-scale and rotation invariant texture classification with local binary patterns,” IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002).
[Crossref]

N. Krüger, P. Janssen, S. Kalkan, M. Lappe, A. Leonardis, J. Piater, A. J. Rodríguez-Sánchez, and L. Wiskott, “Deep hierarchies in the primate visual cortex: what can we learn for computer vision?” IEEE Trans. Pattern Anal. Mach. Intell. 35(8), 1847–1871 (2013).
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Proc. SPIE (1)

R. Akhter, Z. M. Parvez Sazzad, Y. Horita, and J. Baltes, “No-reference stereoscopic image quality assessment,” Proc. SPIE 7525, 75240T (2010).
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Signal Process. Image Commun. (1)

M. J. Chen, C. C. Su, D. K. Kwon, L. K. Cormack, and A. C. Bovik, “Full-reference quality assessment of stereopairs accounting for rivalry,” Signal Process. Image Commun. 28(9), 1143–1155 (2013).
[Crossref]

Stat. Comput. (1)

A. J. Smola and B. Schölkopf, “A tutorial on support vector regression,” Stat. Comput. 14(3), 199–222 (2004).
[Crossref]

Vision Res. (3)

Q. Peng and B. E. Shi, “The changing disparity energy model,” Vision Res. 50(2), 181–192 (2010).
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A. J. Bell and T. J. Sejnowski, “The “independent components” of natural scenes are edge filters,” Vision Res. 37(23), 3327–3338 (1997).
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J. W. Brascamp, P. C. Klink, and W. J. M. Levelt, “The ‘laws’ of binocular rivalry: 50 years of Levelt’s propositions,” Vision Res. 109(Pt A), 20–37 (2015).
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Figures (6)

Fig. 1
Fig. 1 Proposed NR 3D-VQM framework for 3D visual signals
Fig. 2
Fig. 2 Simplified hierarchical structure of the primate’s visual cortex
Fig. 3
Fig. 3 Encoding procedure of MLBP operator (P = 8, R = 1, and β = 12)
Fig. 4
Fig. 4 Joint normalized histograms of natural 3D visual signals with different content.
Fig. 5
Fig. 5 Joint normalized histograms of distorted 3D visual signals for two distortion types with different visual content.
Fig. 6
Fig. 6 Joint normalized histograms of distorted 3D visual signals at different distortion levels.

Tables (5)

Tables Icon

Table 1 Cross-validation experiment results.

Tables Icon

Table 2 Overall performance of seven metrics on two databases

Tables Icon

Table 3 Performance of seven metrics for each distortion type

Tables Icon

Table 4 Database independent testing

Tables Icon

Table 5 Performance of each the quality-aware features in the proposed metric

Equations (15)

Equations on this page are rendered with MathJax. Learn more.

G l = [ S l h x ] 2 + [ S l h y ] 2
L l = S l h D O G
H G l ( g i ) = 1 M × N m = 1 , n = 1 M , N f ( G l ( m , n ) , g i l ) ,   i = 1 , 2 , K
H L l ( l i ) = 1 M × N m = 1 , n = 1 M , N f ( L l ( m , n ) , l i l )
E ˜ ( m , n ) = C ˜ l ( m , n ) + C ˜ r ( m , n ) e j Δ φ ( m , n ) 2
B ˜ ( m , n ) = W ^ l ( m , n ) M ˜ l ( m , n ) + W ^ r ( m + d , n ) M ˜ r ( m + d , n )
M L B P P , R P , B ( B ˜ c ) = p = 0 P 1 s ( B ˜ p B ˜ c ) 2 p , s ( B ˜ p B ˜ c ) = { 1 ,     if  B ˜ p B ˜ c β 0 ,     otherwise       
M L B P P , R , r i u 2 P , B = { p = 0 P 1 s ( B ˜ p B ˜ c )     i f U ( M L B P P , R P 2 ) P + 1                                                  
U ( M L B P P , R P , B ) = | s ( B ˜ P 1 B ˜ c ) s ( B ˜ 0 B ˜ c ) |                             + p = 1 P 1 | s ( B ˜ p B ˜ c ) s ( B ˜ p 1 B ˜ c ) |
M L B P P , R N , B ( B ˜ c ) = p = 0 P 1 s ( B ˜ p B ˜ c ) 2 p , s ( B ˜ p B ˜ c ) = { 1 ,     if  B ˜ p B ˜ c β 0 ,     otherwise       
τ p 1 , p 2 = P ( H M L B P P , R , r i u 2 P , B = p 1 , H M L B P P , R , r i u 2 N , B = p 2 ) , p 1 & p 2 [ 0 , P + 1 ]
{ H M L B P P , R , r i u 2 P , B ( M L B P P , R , r i u 2 P , B = p 1 ) = p 2 = 0 P + 1 τ p 1 , p 2 H M L B P P , R , r i u 2 P , B ( M L B P P , R , r i u 2 P , B = p 2 ) = p 1 = 0 P + 1 τ p 1 , p 2
F = [ H G l , H L l , H G r , H L r , H M L B P P , R , r i u 2 P , E , H M L B P P , R , r i u 2 N , E , H M L B P P , R , r i u 2 P , B , H M L B P P , R , r i u 2 N , B ]
f ( x ) = i = 1 k t i Φ ( x i ) , Φ ( x ) + b           = i = 1 k t i Φ ( x i ) , Φ ( x ) + b
f ( x ) = a 1 ( 1 2 1 1 + e a 2 ( x a 3 ) ) + a 4 x + a 5

Metrics