Blind Image Quality Assessment Based on Natural Statistics of Double-Opponency

Edwin Sybingco; Elmer P. Dadios

doi:10.20965/jaciii.2018.p0725

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JACIII Vol.22 No.5 pp. 725-730

doi: 10.20965/jaciii.2018.p0725

(2018)

Paper:

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Blind Image Quality Assessment Based on Natural Statistics of Double-Opponency

Edwin Sybingco and Elmer P. Dadios

De La Salle University
2401 Taft Avenue, Manila 0922, Philippines

Received:

March 26, 2018

Accepted:

June 18, 2018

Published:

September 20, 2018

Keywords:

double-opponency, generalized Gaussian distribution, asymmetric generalized Gaussian distribution, feedforward neural network

Abstract

One of the challenges in image quality assessment (IQA) is to determine the quality score without the presence of the reference image. In this paper, the authors proposed a no-reference image quality assessment method based on the natural statistics of double-opponent (DO) cells. It utilizes the statistical modeling of the three opponency channels using the generalized Gaussian distribution (GGD) and asymmetric generalized Gaussian distribution (AGGD). The parameters of GGD and AGGD are then applied to feedforward neural network to predict the image quality. Result shows that for any opposing channels, its natural statistics parameters when applied to feedforward neural network can achieve satisfactory prediction of image quality.

Cite this article as:

E. Sybingco and E. Dadios, “Blind Image Quality Assessment Based on Natural Statistics of Double-Opponency,” J. Adv. Comput. Intell. Intell. Inform., Vol.22 No.5, pp. 725-730, 2018.

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References

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This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.

[1] [1] 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, Vol.13, No.4, pp. 600-612, 2004.

[2] [2] Z. Wang, E. P. Simoncelli, and A. C. Bovik, “Multi-scale structural similarity for image quality assessment,” https://ece.uwaterloo.ca/z70wang/publications/msssim.pdf [accessed June 17, 2018].

[3] [3] H. R. Sheikh and A. C. Bovik, “Image information and visual quality,” IEEE Trans. on Image Processing, Vol.15, No.2, pp. 430-444, 2006.

[4] [4] D. M. Chandler and S. S. Hemami, “VSNR: A Wavelet-Based Visual Signal-to-Noise Ratio for Natural Images,” IEEE Trans. on Image Processing, Vol.16, No.9, pp. 2284-2298, 2007.

[5] [5] Y. Ding, S. Wang, and D. Zhang, “Full-reference image quality assessment using statistical local correlation,” Electronics Letters, Vol.50, No.2, pp. 79-81, 2014.

[6] [6] L. Liang, S. Wang, J. Chen, S. Ma, D. Zhao, and W. Gao, “No-reference perceptual image quality metric using gradient profiles for JPEG2000,” Signal Processing – Image Communication, Vol.25, No.7, pp. 502-516, 2010.

[7] [7] H. R. Sheikh, A. C. Bovik, and L. Cormack, “No-reference quality assessment using natural scene statistics: JPEG2000,” IEEE Trans. on Image Processing, Vol.14, No.11, pp. 1918-1927, 2005.

[8] [8] M. A. Saad, A. C. Bovik, and C. Charrier, “Natural DCT statistics approach to no-reference image quality assessment,” http://live.ece.utexas.edu/publications/2010/mas_icip_sep10.pdf [accessed June 17, 2018].

[9] [9] H. R. Sheikh, A. C. Bovik, and G. d. Veciana, “An information fidelity criterion for image quality assessment using natural scene statistics,” IEEE Trans. on Image Processing, Vol.14, No.12, pp. 2117-2128, 2005.

[10] [10] A. Mittal, A. K. Moorthy, and A. C. Bovik, “No-Reference Image Quality Assessment in the Spatial Domain,” IEEE Trans. on Image Processing, Vol.21, No.12, pp. 4695-4708, 2012.

[11] [11] L. Liu, Y. Hua, Q. Zhao, and A. C. Bovik, “Blind image quality assessment by relative gradient statistics and adaboosting neural network,” Signal Processing – Image Communication, Vol.40, No.1, pp. 1-15, 2016.

[12] [12] S. Gao, K.-F. Yang, C.-Y. Li, and Y. Li, “Color Constancy Using Double-Opponency,” IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol.37, No.10, pp. 1973-1985, 2015.

[13] [13] E. Reinhard, M. Adhikhmin, B. Gooch, and P. Shirley, “Color transfer between images,” IEEE Computer Graphics and Applications, Vol.21, No.5, pp. 34-41, 2001.

[14] [14] V. Gupte, “Color Constancy, by Marc Ebner (Wiley; 2007) pp. 394 ISBN 978-0-470-05829-9 (HB),” Coloration Technology, Vol.125, Issue 6, pp. 366-367, 2009.

[15] [15] D. L. Ruderman, “The Statistics of natural images,” Network: Computation in Neural System, Vol.5, pp. 517-548, 1994.

[16] [16] K. Sharifi and A. Leon-Garcia, “Estimation of shape parameter for generalized Gaussian distributions in subband decompositions of video,” IEEE Trans. on Circuits and Systems for Video Technology, Vol.5, No.1, pp. 52-56, 1995.

[17] [17] R. Shapley and M. J. Hawken, “Color in the Cortex: single- and double-opponent cells,” Vision Research, Vol.51, No.7, pp. 701-717, 2011.

[18] [18] K. Sharifi and A. Leon-Garcia, “Estimation of shape parameter for generalized Gaussian distributions in subband decompositions of video,” IEEE Trans. on Circuits and Systems for Video Technology, Vol.5, No.1, pp. 52-56, 1995.

[19] [19] P. C. Teo and D. J. Heeger, “Perceptual image distortion,” http://dblp.uni-trier.de/db/conf/icip/icip1994-2.html [accessed June 17, 2018].

[20] [20] H. R. Sheikh, Z. Wang, L. Cormack, and A. C. Bovik, “LIVE Image Quality Assessment Database Release 2,” http://live.ece.utexas.edu/research/quality [accessed June 17, 2018]

[21] [21] H. R. Sheikh, M. F. Sabir, and A. C. Bovik, “A statistical evaluation of recent full reference image quality assessment algorithms,” EEE Trans. on Image Processing, Vol.15, No.11, pp. 3440-3451, 2006.