[2] [2] WANG Z, BOVIK A C.Modern Image Quality Assessment[M]. San Rafael: Morgan & Claypool Publishers, 2006.

[3] [3] 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.

[4] [4] XUE W F, ZHANG L, MOU X Q, et al. Gradient magnitude similarity deviation: a highly efficient perceptual image quality index [J]. IEEE Transactions on Image Processing, 2014, 23(2):684-695.

[5] [5] LI L D, XIA W H, FANG Y M, et al. Color image quality assessment based on sparse representation and reconstruction residual [J]. Journal of Visual Communication and Image Representation, 2016, 38(7):550-560.

[6] [6] PREISS J, FERNANDES F, URBAN P. Color-image quality assessment: from prediction to optimization [J].IEEE transactions on image processing, 2014, 23(3):1366-1378.

[7] [7] TEMEL D, ALREGIB G. Perceptual image quality assessment through spectral analysis of error representations[J]. Signal Processing: Image Communication, 2019, 70(2):37-46.

[8] [8] SUN W, LIAO Q M, XUE J H, et al. SPSIM: a superpixel-based similarity index for full-reference image quality assessment [J]. IEEE Transactions on Image Processing, 2018, 27(9):4232-4244.

[9] [9] TEMEL D, ALREGIB G. PerSIM: Multi-resolution image quality assessment in the perceptually uniform color domain [C]∥ 2015 IEEE International Conference on Image Processing, Quebec City, QC, Canada:ICIP, 2015:1682-1686.

[10] [10] ZHANG L, ZHANG L, MOU X Q, et al. FSIM: a feature similarity index for image quality assessment[J].IEEE Transactions on Image Processing, 2011, 20(8): 2378–2386.

[11] [11] KINSMAN T, FAIRCHILD M, PELZ J. Color is not a metric space implications for pattern recognition, machine learning, and computer vision [C]∥2012 Western New York Image Processing Workshop, Rochester, NY, USA:WNYIPW, 2012:37–40.

[12] [12] WEIJER J, SCHMID C, VERBEEK J, et al. Learning color names for real-world applications[J]. IEEE Transactions on Image Processing, 2009,18(7):1512-1523.

[14] [14] HOFMANN T. Probabilistic latent semantic indexing[C]∥The 22nd annual international ACM SIGIR conference on Research and development. Berkeley, California, USA: SIGIR, 1999: 50-57.

[15] [15] LEVINA E, BICKEL P. The earth movers distance is the mallows distance: some insights from statistics[C]∥ Proceedings Eighth IEEE International Conference on Computer Vision, Vancouver, BC, Canada:ICCV, 2001: 251-256.

[16] [16] JAHNE B, HAUSSECKER H, GEISSLER P. Handbook of computer vision and applications [M]. San Diego: Academic press, 1999.

[17] [17] GEUSEBROEK J M, VANDEN B R, SMEULDERS A W M, et al. Color and scale: the spatial structure of color images [C]∥European Conference on Computer Vision, Berlin, Germany:ECCV, 2000: 331-341.

[18] [18] VANDEN B L, CHRISTIAN J. Vision models and applications to image and video processing[M]. Berlin:Springer Science & Business Media, 2013.

[19] [19] WU J J, SHI G M, LIN W S. Survey of visual just noticeable difference estimation[J].Frontiers of Computer Science, 2019, 13(1): 4-15.

[20] [20] ZHANG L, GU Z Y, LI H Y. SDSP: a novel saliency detection method by combining simple priors[C]∥2013 IEEE international conference on image processing. Melbourne, VIC, Australia: ICIP, 2013: 171-175.

[21] [21] PONOMARENKO N, LUKIN V, ZELENSKY A, et al. TID2008-a database for evaluation of full-reference visual quality assessment metrics[J]. Advances of Modern Radioelectronics, 2009, 10(4): 30-45.

[22] [22] PONOMARENKO N, IEREMEIEV O, LUKIN V, et al. Color image database TID2013: Peculiarities and preliminary results[C]∥European workshop on visual information processing. Paris, France: EUVIP, 2013: 106-111.

[23] [23] LARSON E C, CHANDLER D M. Most apparent distortion: full-reference image quality assessment and the role of strategy[J]. Journal of electronic imaging, 2010, 19(1): 011006.

[24] [24] SHEIKH H R, SABIR M F, BOVIK A C. A statistical evaluation of recent full reference image quality assessment algorithms[J].IEEE Transactions on Image Processing, 2006, 15(11): 3440-3451.

[25] [25] LIN H H, HOSU V, SAUPE D.KADID-10k: A large-scale artificially distorted IQA database[C]∥2019 Eleventh International Conference on Quality of Multimedia Experience. Berlin, Germany: QoMEX, 2019: 1-3.

[26] [26] WANG Z, SIMONCELLI E P, BOVIK A C. Multiscale structural similarity for image quality assessment[C]. The Thrity-Seventh Asilomar Conference on Signals, Systems & Computers. Pacific Grove, CA, USA: ACSSC, 2003, 2:1398-1402.

[27] [27] YANG G Y, LI D S, LU F, et al. RVSIM: a feature similarity method for full-reference image quality assessment[J]. EURASIP Journal on Image and Video Processing, 2018(1): 1-15.