W. Hou, X. Gao, D. Tao, X. Li, Blind image quality assessment via deep learning. IEEE Trans. Neural. Netw. Learn. Syst.26(6), 1275–1286 (2015).
Article
MathSciNet
Google Scholar
M. Oszust, Full-reference image quality assessment with linear combination of genetically selected quality measures. PloS ONE. 11(6), 0158333 (2016).
Article
Google Scholar
H. Khosravi, M. H. Hassanpour, Model-based full reference image blurriness assessment. Multimed. Tools Appl.76(2), 2733–2747 (2017).
Article
Google Scholar
Z. Chen, J. Lin, N. Liao, C. W. Chen, Full reference quality assessment for image retargeting based on natural scene statistics modeling and bi-directional saliency similarity. IEEE Trans. Image Process. (2017).
A. Saha, Q. J. Wu, Full-reference image quality assessment by combining global and local distortion measures. Signal Process.128:, 186–197 (2016).
Article
Google Scholar
Y. Ding, S. Wang, D. Zhang, Full-reference image quality assessment using statistical local correlation. Electron. Lett.50(2), 79–81 (2014).
Article
Google Scholar
S. Rezazadeh, S. Coulombe, A novel discrete wavelet transform framework for full reference image quality assessment. Signal. Image Video Process.7(3), 559–573 (2013).
Article
Google Scholar
A. Nafchi, H. Z. Shahkolaei, R. Hedjam, M. Cheriet, Mean deviation similarity index: efficient and reliable full-reference image quality evaluator. IEEE Access. 4:, 5579–5590 (2016).
Article
Google Scholar
J. Yang, Y. Lin, B. Ou, X. Zhao, Image decomposition-based structural similarity index for image quality assessment. EURASIP J. Image Video Process.2016(1), 31 (2016).
Article
Google Scholar
G. Yang, D. Li, F. Lu, Y. Liao, W. Yang, RVSIM: a feature similarity method for full-reference image quality assessment. EURASIP J. Image Video Process.2018(1), 6 (2018).
Article
Google Scholar
Y. Liu, G. Zhai, K. Gu, X. Liu, D. Zhao, W. Gao, Reduced-reference image quality assessment in free-energy principle and sparse representation. IEEE Trans. Multimedia. 20:, 379–391 (2017).
Article
Google Scholar
D. Liu, F. Li, H. Song, Regularity of spectral residual for reduced reference image quality assessment. IET Image Processing. 11:, 1135–1141 (2017).
Article
Google Scholar
S. Golestaneh, L. J. Karam, Reduced-reference quality assessment based on the entropy of DWT coefficients of locally weighted gradient magnitudes. IEEE Trans. Image Process.25(11), 5293–5303 (2016).
Article
MathSciNet
MATH
Google Scholar
J. Wu, W. Lin, Y. Fang, L. Li, G. Shi, I. Niwas, Visual structural degradation based reduced-reference image quality assessment. Signal Process. Image Commun.47:, 16–27 (2016).
Article
Google Scholar
J. Wu, W. Lin, G. Shi, L. Li, Y. Fang, Orientation selectivity based visual pattern for reduced-reference image quality assessment. Inf. Sci.351:, 18–29 (2016).
Article
Google Scholar
S. Bosse, Q. Chen, M. Siekmann, W. Samek, T. Wiegand, in Image Processing (ICIP), 2016 IEEE International Conference On. Shearlet-based reduced reference image quality assessment (IEEEPiscataway, 2016), pp. 2052–2056.
Chapter
Google Scholar
Y. Zhang, T. D. Phan, DM Chandler, Reduced-reference image quality assessment based on distortion families of local perceived sharpness. Signal Process. Image Commun.55:, 130–145 (2017).
Article
Google Scholar
Q. Wu, H. Li, F. Meng, B. Ngan, K. N. Luo, C. Huang, B. Zeng, Blind image quality assessment based on multichannel feature fusion and label transfer. IEEE Trans. Circ. Syst. Video Technol.26(3), 425–440 (2016).
Article
Google Scholar
Q. Li, W. Lin, J. Xu, Y. Fang, Blind image quality assessment using statistical structural and luminance features. IEEE Trans. Multimedia. 18(12), 2457–2469 (2016).
Article
Google Scholar
W. Lu, T. Xu, Y. Ren, L. He, Statistical modeling in the shearlet domain for blind image quality assessment. Multimedia Tools Appl.75(22), 14417–14431 (2016).
Article
Google Scholar
Y. Zhang, J. Wu, X. Xie, L. Li, G. Shi, Blind image quality assessment with improved natural scene statistics model. Digit. Signal Process.57:, 56–65 (2016).
Article
MathSciNet
Google Scholar
M. Nizami, I. F. Majid, H. Afzal, K. Khurshid, Impact of feature selection algorithms on blind image quality assessment. Arab. J. Sci. Eng.43:, 1–14 (2017).
Google Scholar
S. Du, Y. Yan, Y. Ma, Blind image quality assessment with the histogram sequences of high-order local derivative patterns. Digit. Signal Process.55:, 1–12 (2016).
Article
Google Scholar
Y. Zhang, A. K. Moorthy, D. M. Chandler, A. C. Bovik, C-diivine: No-reference image quality assessment based on local magnitude and phase statistics of natural scenes. Signal Process. Image Commun.29(7), 725–747 (2014).
Article
Google Scholar
G. Yang, Y. Liao, Q. Zhang, D. Li, W. Yang, No-reference quality assessment of noise-distorted images based on frequency mapping. IEEE Access. 5:, 23146–23156 (2017).
Article
Google Scholar
M. Nizami, I. F. Majid, K. Khurshid, in Applied Sciences and Technology (IBCAST), 2017 14th International Bhurban Conference On. Efficient feature selection for blind image quality assessment based on natural scene statistics (IEEEPiscataway, 2017), pp. 318–322.
Chapter
Google Scholar
L. Li, Y. Yan, Z. Lu, J. Wu, K. Gu, S. Wang, No-reference quality assessment of deblurred images based on natural scene statistics. IEEE Access. 5:, 2163–2171 (2017).
Article
Google Scholar
K. Panetta, A. Samani, S. Agaian, A robust no-reference, no-parameter, transform domain image quality metric for evaluating the quality of color images (IEEE, Piscataway, 2018).
Book
Google Scholar
H. R. Sheikh, A. C. Bovik, L. Cormack, No-reference quality assessment using natural scene statistics: Jpeg2000. IEEE Trans. Image Process.14(11), 1918–1927 (2005).
Article
Google Scholar
W. Xue, X. Mou, L. Zhang, X. Bovik, A. C. Feng, Blind image quality assessment using joint statistics of gradient magnitude and Laplacian features. IEEE Trans. Image Process.23(11), 4850–4862 (2014).
Article
MathSciNet
MATH
Google Scholar
L. Liu, H. Dong, H. Huang, A. C. Bovik, No-reference image quality assessment in curvelet domain. Signal Process. Image Commun.29(4), 494–505 (2014).
Article
Google Scholar
D. Ghadiyaram, A. C. Bovik, Perceptual quality prediction on authentically distorted images using a bag of features approach. J. Vis.17(1), 32–32 (2017).
Article
Google Scholar
E. Siahaan, A. Hanjalic, J. A. Redi, Semantic-aware blind image quality assessment. Signal Process. Image Commun.60:, 237–252 (2018).
Article
Google Scholar
B. Appina, S. Khan, S. S. Channappayya, No-reference stereoscopic image quality assessment using natural scene statistics. Signal Process. Image Commun.43:, 1–14 (2016).
Article
Google Scholar
W. Hachicha, M. Kaaniche, A. Beghdadi, F. A. Cheikh, No-reference stereo image quality assessment based on joint wavelet decomposition and statistical models. Signal Process. Image Commun.54:, 107–117 (2017).
Article
Google Scholar
T. Zhu, L. Karam, A no-reference objective image quality metric based on perceptually weighted local noise. EURASIP J. Image Video Process.2014(1), 5 (2014).
Article
Google Scholar
M. Shahid, A. Rossholm, B. Lövström, H-J Zepernick, No-reference image and video quality assessment: a classification and review of recent approaches. EURASIP J. Image Video Process.2014(1), 40 (2014).
Article
Google Scholar
A. K. Moorthy, A. C. Bovik, Blind image quality assessment: from natural scene statistics to perceptual quality. IEEE Trans. Image Process.20(12), 3350–3364 (2011).
Article
MathSciNet
MATH
Google Scholar
M. A. Saad, A. C. Bovik, C. Charrier, Blind image quality assessment: a natural scene statistics approach in the DCT domain. IEEE Trans. Image Process.21(8), 3339–3352 (2012).
Article
MathSciNet
MATH
Google Scholar
M. A. Saad, A. C. Bovik, C. Charrier, A DCT statistics-based blind image quality index. IEEE Signal Process. Lett.17(6), 583–586 (2010).
Article
Google Scholar
A. Mittal, A. K. Moorthy, A. C. Bovik, No-reference image quality assessment in the spatial domain. IEEE Trans. Image Process.21(12), 4695–4708 (2012).
Article
MathSciNet
MATH
Google Scholar
A. Mittal, R. Soundararajan, A. C. Bovik, Making a “completely blind” image quality analyzer. IEEE Signal Process. Lett.20(3), 209–212 (2013).
Article
Google Scholar
C. Zhang, J. Pan, S. Chen, T. Wang, D. Sun, No reference image quality assessment using sparse feature representation in two dimensions spatial correlation. Neurocomputing. 173:, 462–470 (2016).
Article
Google Scholar
Y. Li, X. Po, L. -M. Xu, L. Feng, No-reference image quality assessment using statistical characterization in the shearlet domain. Signal Process Image Commun.29(7), 748–759 (2014).
Article
Google Scholar
L. Liu, B. Liu, H. Huang, A. C. Bovik, No-reference image quality assessment based on spatial and spectral entropies. Signal Process. Image Commun.29(8), 856–863 (2014).
Article
Google Scholar
A. K. Moorthy, A. C. Bovik, A two-step framework for constructing blind image quality indices. IEEE Signal Process. Lett.17(5), 513–516 (2010).
Article
Google Scholar
L. He, D. Tao, X. Li, X. Gao, in Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference On. Sparse representation for blind image quality assessment (IEEEPiscataway, 2012), pp. 1146–1153.
Google Scholar
Y. Lu, F. Xie, T. Liu, Z. Jiang, D. Tao, No reference quality assessment for multiply-distorted images based on an improved bag-of-words model. IEEE Signal Process. Lett.22(10), 1811–1815 (2015).
Article
Google Scholar
H. R. Sheikh, M. F. Sabir, A. C. Bovik, A statistical evaluation of recent full reference image quality assessment algorithms. IEEE Trans. Image Process.15(11), 3440–3451 (2006).
Article
Google Scholar
E. C. Larson, D. M. Chandler, Most apparent distortion: full-reference image quality assessment and the role of strategy. J. Electron. Imaging. 19(1), 011006–011006 (2010).
Article
Google Scholar
N. Ponomarenko, L. Jin, O. Ieremeiev, V. Lukin, K. Egiazarian, J. Astola, B. Vozel, K. Chehdi, M. Carli, F. Battisti, et al, Image database tid2013: Peculiarities, results and perspectives. Signal Process. Image Commun.30:, 57–77 (2015).
Article
Google Scholar
D. Ghadiyaram, A. C. Bovik, Massive online crowdsourced study of subjective and objective picture quality. IEEE Trans. Image Process.25(1), 372–387 (2016).
Article
MathSciNet
MATH
Google Scholar