EQ: An Eigen Image Quality Assessment based on the Complement Feature

Full Text (PDF, 579KB), PP.13-18

Views: 0 Downloads: 0


Salah Ameer 1,*

1. Adjunct Professor in Ontario Colleges, Canada

* Corresponding author.

DOI: https://doi.org/10.5815/ijigsp.2020.06.02

Received: 10 Jun. 2020 / Revised: 20 Aug. 2020 / Accepted: 28 Oct. 2020 / Published: 8 Dec. 2020

Index Terms

Image quality assessment, Eigen Value, Complement Feature.


An Eigen formulation is proposed for image quality assessment IQA. Each block is represented by an array composed of feature vectors (intensity/color at this stage). After attaching the complement feature(s), the auto-correlation matrix is computed for each block. The proposed full reference FR-IQA is simply the deviation of the Eigen values of the degraded image from that of the original image. Interestingly, the second largest Eigen value was sufficient to perform this comparison. Results and comparisons with SSIM and GMSD schemes on different types of degradation are demonstrated to show the effectiveness of the proposed schemes. Using TID2013 database, the proposed scheme outperforms SSIM. In addition, the proposed schemes is closer to the MOS score compared to GMSD; however, the correlation with MOS is inferior as illustrated in the tables. These results are concluded from the average behaviour on all the images using all degradations (with 5 levels) on the database.

Cite This Paper

Salah Ameer, " EQ: An Eigen Image Quality Assessment based on the Complement Feature", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.12, No.6, pp. 13-18, 2020. DOI: 10.5815/ijigsp.2020.06.02


[1] M. Pedersen and Y. Hardeberg, “Survey of full-reference image quality metrics,” The Norwegian Color Research Laboratory, Gjøik University College, ISSN: 1890-520X, 2009.

[2] B. Hu, L. Li, J. Wub, and J. Qian, “Subjective and objective quality assessment for image restoration: A critical survey,” Signal Processing: Image Communication, 85, pp 1-19, 2020, https://doi.org/10.1016/j.image.2020.115839.

[3] G. Zhai, and X. Min, “Perceptual image quality assessment: a survey,” Sci. China Inf. Sci. 63, 211301, 2020, https://doi.org/10.1007/s11432-019-2757-1.

[4] Z. Wang, A. Bovik, H. Sheikh and E. Simoncelli, “Image quality assessment: From error visibility to structural similarity,” IEEE Transactions on Image Processing, 13: pp 600-612, 2004, DOI: 10.1109/TIP.2003.819861.

[5] L. Zhang, L. Zhang, X. Mou and D. Zhang, “FSIM: a feature similarity index for image quality assessment,” IEEE Transactions on Image Processing, 20(8), pp. 2378-2386, 2011, doi: 10.1109/TIP.2011.2109730.

[6] A. Beghdadi, and B. Popescu, “A new image distortion measure based on wavelet decomposition,” Seventh International Symposium on Signal Processing and Its Applications, pp. 485-488, 2003, doi: 10.1109/ISSPA.2003.1224745.

[7] H. Sheikh, A. Bovik, and G. deVeciana, “An information fidelity criterion for image quality assessment using natural scene statistics,” IEEE Trans. Image Processing, 14(12), pp. 2117-2128, 2005, doi: 10.1109/TIP.2005.859389.

[8] W. Xue, L. Zhang, X. Mou and A. Bovik, “Gradient magnitude similarity deviation: a highly efficient perceptual image quality index,” IEEE Trans. Image Processing, 23(2), pp. 684-695, 2013, doi: 10.1109/TIP.2013.2293423.

[9] K. Sharifi, and A. Leon-Garcia, “Estimation of shape parameter for generalized Gaussian distributions in subband decompositions of video,” IEEE Transactions on Circuits and Systems for Video Technology, 5(1), pp. 52-56, 1995, doi: 10.1109/76.350779.

[10] A. Moorthy, and A. Bovik, “Blind image quality assessment: from natural scene statistics to perceptual quality,” IEEE Transactions on Image Processing, 20(12), pp. 3350-3364, 2011, doi: 10.1109/TIP.2011.2147325.

[11] M. Saad, A. Bovik, and C. Charrier, “Blind image quality assessment: a natural scene statistics approach in the DCT domain,” IEEE Transactions on Image Processing, 21(8), pp. 3339-3352, 2012, doi: 10.1109/TIP.2012.2191563.

[12] A. Mittal, A. Moorthy, and A. Bovik, “No-reference image quality assessment in the spatial domain,” IEEE Transactions on Image Processing, 21(12), pp. 4695-4708, 2012, doi: 10.1109/TIP.2012.2214050.

[13] Y. Zhang, and D. Chandler, “No-reference image quality assessment based on log-derivative statistics of natural scenes,” J. of Electronic Imaging, 22(4), pp 043025 1-22, 2013, https://doi.org/10.1117/1.JEI.22.4.043025.

[14] L. Zhang, L. Zhang, and A. Bovik, “A feature-enriched completely blind image quality evaluator,” IEEE Transactions on Image Processing, 24(8), pp. 2579-2591, 2015, doi: 10.1109/TIP.2015.2426416.

[15] P. Ye, J. Kumar, and D. Doermann, “Beyond human opinion scores: blind image quality assessment based on synthetic scores,” IEEE Conference on Computer Vision and Pattern Recognition, pp. 4241-4248, 2014, doi: 10.1109/CVPR.2014.540.

[16] J. Redi, P. Gastaldo, I. Heynderickx and R. Zunino, “Color distribution information for the reduced-reference assessment of perceived image quality,” IEEE Transactions on Circuits and Systems for Video Technology, 20(12), pp. 1757-1769, 2010, doi: 10.1109/TCSVT.2010.2087456.

[17] K. Gu, G. Zhai, X. Yang and W. Zhang, “A new reduced-reference image quality assessment using structural degradation model,” IEEE International Symposium on Circuits and Systems (ISCAS), Beijing, pp. 1095-1098, 2013, doi: 10.1109/ISCAS.2013.6572041.

[18] S. Ameer, “Image thresholding using the complement feature,” American Journal of Engineering and Applied Sciences, pp 311–317, 2020.

[19] I. Jolliffe and J. Cadima, “Principal component analysis: a review and recent developments,” Phil. Trans. R. Soc. A. 374: 20150202, http://doi.org/10.1098/rsta.2015.0202. 2016.

[20] A. Shnayderman, A. Gusev, and A. Eskicioglu, “An SVD-based grayscale image quality measure for local and global assessment,” IEEE Transactions on Image Processing, 15(2), pp. 422-429, 2006, doi: 10.1109/TIP.2005.860605.

[21] Z. Wang, and Q. Li, “Information content weighting for perceptual image quality assessment,” IEEE Transactions on Image Processing, 20(5), pp. 1185-1198, 2011, doi: 10.1109/TIP.2010.2092435.

[22] N. Ponomarenko et al, “Color image database TID2013: peculiarities and preliminary results,” Proceedings of 4th Europian Workshop on Visual Information Processing EUVIP2013, pp. 106-111, 2013.