[4] [4] LI Y M, PO L M, XU X Y, et al.No-reference image quality assessment using shearlet transform and stacked autoencoders［C］//IEEE International Symposium on Circuits and Systems(ISCAS).Lisbon:IEEE, 2015:1594-1597.

[5] [5] WANG Z, BOVIK A C, SHEIKH H R, et al.Image quality assessment:from error visibility to structural similarity［J］.IEEE Transactions on Image Processing, 2004, 13(4):600-612.

[6] [6] ZHANG L, LI H Y.SR-SIM:a fast and high performance IQA index based on spectral residual［C］//The 19th IEEE International Conference on Image Processing.Orlando:IEEE, 2012:1473-1476.

[7] [7] MOORTHY A K, BOVIK A C.A two-step framework for constructing blind image quality indices［J］.IEEE Signal Processing Letters, 2010, 17(5):513-516.

[8] [8] MOORTHY A K, BOVIK A C.Blind image quality assessment:from natural scene statistics to perceptual quality［J］.IEEE Transactions on Image Processing, 2011, 20(12):3350-3364.

[9] [9] MITTAL A, SOUNDARARAJAN R, BOVIK A C.Making a‘completely blind’ image quality analyzer［J］.IEEE Signal Processing Letters, 2013, 20(3):209-212.

[10] [10] GU S Y, BAO J M, CHEN D, et al.GIQA:generated image quality assessment［C］//Computer Vision-ECCV 2020.Cham:Springer, 2020:369-385.

[11] [11] HOSU V, LIN H H, SZIRANYI T, et al.KonIQ-10k:an ecologically valid database for deep learning of blind image quality assessment［J］.IEEE Transactions on Image Processing, 2020, 29(3):4041-4056.

[12] [12] LIU X L, JOOST V, BAGDANOV A D.Exploiting unlabeled data in CNNs by self-supervised learning to rank［J］.IEEE Transactions on Pattern Analysis & Machine Intelligence, 2019, 41(8):1862-1878.

[13] [13] TALEBI H, MILANFAR P.NIMA:neural image assessment［J］.IEEE Transactions on Image Processing, 2018, 27(8):3998-4011.

[14] [14] ZHANG W X, MA K D, ZHAI G T, et al.Learning to blindly assess image quality in the laboratory and wild［C］//IEEE International Conference on Image Processing(ICIP). Abu Dhabi:IEEE, 2020:111-115.