Citation: | SHI Chenyang and LIN Yandan, “No Reference Image Sharpness Assessment Based on Global Color Difference Variation,” Chinese Journal of Electronics, in press, doi: 10.23919/cje.2022.00.058, 2024. |
[1] |
W. Kim, A. Nguyen, S. Lee, et al., “Dynamic receptive field generation for full-reference image quality assessment,” IEEE Transactions on Image Processing, vol.29, pp.4219–4231, 2020. doi: 10.1109/TIP.2020.2968283
|
[2] |
C. Shi and Y. Lin, “Image quality assessment based on three features fusion in three fusion steps,” Symmetry-Basel, vol.14, no.4, article no.773, 2022. doi: 10.3390/sym14040773
|
[3] |
C. Y. Shi and Y. D. Lin, “Full reference image quality assessment based on visual salience with color appearance and gradient similarity,” IEEE Access, vol.8, pp.97310–97320, 2020. doi: 10.1109/access.2020.2995420
|
[4] |
Z. G. Cui, Z. L. Gan, G. J. Tang, et al., “Image signature based mean square error for image quality assessment,” Chinese Journal of Electronics, vol.24, no.4, pp.755–760, 2015. doi: 10.1049/cje.2015.10.015
|
[5] |
A. Hu, R. Zhang, D. Yin, et al., “Perceptual quality assessment of sar image compression based on image content partition and neural network,” Chinese Journal of Electronics, vol.22, no.3, pp.543–548, 2013.
|
[6] |
D. Temel and G. Alregib, "Image quality assessment and color difference,” in Proceeding of 2014 IEEE Global Conference on Signal and Information Processing (GlobalSIP), Atlanta, GA, USA, pp.970–974, 2014.
|
[7] |
K. Bahrami and A. C. Kot, “A fast approach for no-reference image sharpness assessment based on maximum local variation,” IEEE Signal Processing Letters, vol.21, no.6, pp.751–755, 2014. doi: 10.1109/Lsp.2014.2314487
|
[8] |
L. Li, W. Xia, W. Lin, et al., “No-Reference and robust image sharpness evaluation based on multiscale spatial and spectral features,” IEEE Transactions on Multimedia, vol.19, no.5, pp.1030–1040, 2017. doi: 10.1109/tmm.2016.2640762
|
[9] |
M. J. Chen and A. C. Bovik, “No-Reference image blur assessment using multiscale gradient,” Eurasip Journal on Image and Video Processing, vol.2011, no.3, 2011. doi: 10.1186/1687-5281-2011-3
|
[10] |
L. Zhang, Y. Shen, and H. Y. Li, “Vsi: A visual Saliency-Induced index for perceptual image quality assessment,” IEEE Transactions on Image Processing, vol.23, no.10, pp.4270–4281, 2014. doi: 10.1109/Tip.2014.2346028
|
[11] |
S. Athar and Z. Wang, “A comprehensive performance evaluation of image quality assessment algorithms,” IEEE Access, vol.7, pp.140030–140070, 2019. doi: 10.1109/Access.2019.2943319
|
[12] |
C. Shi and Y. Lin, “Objective image quality assessment based on image color appearance and gradient features,” Acta Physica Sinica, vol.69, no.22, 2020. doi: 10.7498/aps.69.20200753
|
[13] |
B. Z. Zhou, F. Shao, X. C. Meng, et al., “No-Reference quality assessment for pansharpened images via Opinion-Unaware learning,” IEEE Access, vol.7, pp.40388–40401, 2019. doi: 10.1109/Access.2019.2905615
|
[14] |
S. Hu, J. Yan, W. Zhang, et al., “No-reference quality assessment for contrast-altered images using an end-to-end deep framework,” Journal of Electronic Imaging, vol.28, no.1, article no.013041, 2019. doi: 10.1117/1.JEI.28.1.013041
|
[15] |
K. Fan, J. Liang, F. Li, et al., “CNN based No-Reference HDR image quality assessment,” Chinese Journal of Electronics, vol.30, no.2, pp.282–288, 2021. doi: 10.1049/cje.2021.01.008
|
[16] |
W. S. Lin and C. C. J. Kuo, “Perceptual visual quality metrics: A survey,” Journal of Visual Communication and Image Representation, vol.22, no.4, pp.297–312, 2011. doi: 10.1016/j.jvcir.2011.01.005
|
[17] |
R. Reisenhofer, S. Bosse, G. Kutyniok, et al., “A Haar wavelet-based perceptual similarity index for image quality assessment,” Signal Processing-Image Communication, vol.61, pp.33–43, 2018. doi: 10.1016/j.image.2017.11.001
|
[18] |
K. Bahrami and A. C. Kot, “Efficient image sharpness assessment based on content aware total variation,” IEEE Transactions on Multimedia, vol.18, no.8, pp.1568–1578, 2016. doi: 10.1109/Tmm.2016.2573139
|
[19] |
Q. B. Sang, H. X. Qi, X. J. Wu, et al., “No-reference image blur index based on singular value curve,” Journal of Visual Communication and Image Representation, vol.25, no.7, pp.1625–1630, doi: 2014.10.1016/j.jvcir.2014.08.002
|
[20] |
L. H. Liang, J. H. Chen, S. W. Ma, et al., “A no-reference perceptual blur metric using histogram of gradient profile sharpness,” in Proceeding of 2009 16th IEEE International Conference on Image Processing, Cario, Egypt, pp. 4369-4372, 2009.
|
[21] |
L. Li, Y. Zhou, K. Gu, et al., “Blind realistic blur assessment based on discrepancy learning,” IEEE Transactions on Circuits and Systems for Video Technology, vol.30, no.11, pp.3859–3869, 2020. doi: 10.1109/tcsvt.2019.2947450
|
[22] |
J. Chen, S. Li, and L. Lin, “A no-reference blurred colourful image quality assessment method based on dual maximum local information,” IET Signal Processing, vol.15, no.9, pp.597–611, 2021. doi: 10.1049/sil2.12064
|
[23] |
R. Ferzli and L. J. Karam, “A no-reference objective image sharpness metric based on the notion of just noticeable blur (JNB),” IEEE Transactions on Image Processing, vol.18, no.4, pp.717–728, 2009. doi: 10.1109/Tip.2008.2011760
|
[24] |
N. D. Narvekar and L. J. Karam, “A no-reference image blur metric based on the cumulative probability of blur detection (CPBD),” IEEE Transactions on Image Processing, vol.20, no.9, pp.2678–2683, 2011. doi: 10.1109/Tip.2011.2131660
|
[25] |
C. T. Vu, T. D. Phan, and D. M. Chandler, “S3: A spectral and spatial measure of local perceived sharpness in natural images,” IEEE Transactions on Image Processing, vol.21, no.3, pp.934–945, 2012. doi: 10.1109/TIP.2011.2169974
|
[26] |
P. V. Vu and D. M. Chandler, “A fast Wavelet-Based algorithm for global and local image sharpness estimation,” IEEE Signal Processing Letters, vol.19, no.7, pp.423–426, 2012. doi: 10.1109/LSP.2012.2199980
|
[27] |
R. Hassen, Z. Wang, and M. M. A. Salama, “Image sharpness assessment based on local phase coherence,” IEEE Transactions on Image Processing, vol.22, no.7, pp.2798–2810, 2013. doi: 10.1109/Tip.2013.2251643
|
[28] |
L. D. Li, D. Wu, J. J. Wu, et al., “Image sharpness assessment by sparse representation,” IEEE Transactions on Multimedia, vol.18, no.6, pp.1085–1097, 2016. doi: 10.1109/Tmm.2016.2545398
|
[29] |
G. Gvozden, S. Grgic, and M. Grgic, “Blind image sharpness assessment based on local contrast map statistics,” Journal of Visual Communication and Image Representation, vol.50, pp.145–158, 2018. doi: 10.1016/j.jvcir.2017.11.017
|
[30] |
J. Qian, H. Zhao, J. Fu, et al., “No-reference image sharpness assessment via difference quotients,” Journal of Electronic Imaging, vol.28, no.1, article no.013032, 2019. doi: 10.1117/1.JEI.28.1.013032
|
[31] |
K. Gu, G. T. Zhai, W. S. Lin, et al., “No-reference image sharpness assessment in autoregressive parameter space,” IEEE Transactions on Image Processing, vol.24, no.10, pp.3218–3231, 2015. doi: 10.1109/Tip.2015.2439035
|
[32] |
L. D. Li, W. S. Lin, X. S. Wang, et al., “No-Reference image blur assessment based on discrete orthogonal moments,” IEEE Transactions on Cybernetics, vol.46, no.1, pp.39–50, 2016. doi: 10.1109/Tcyb.2015.2392129
|
[33] |
M. R. Luo, G. Cui, and B. Rigg, “The development of the CIE 2000 Colour-Difference formula: CIEDE2000,” Color Research & Application, vol.26, no.5, pp.340–350, 2001. doi: 10.1002/col.1049
|
[34] |
D. Temel and G. AlRegib, “CSV: Image quality assessment based on color, structure, and visual system,” Signal Processing-Image Communication, vol.48, pp.92–103, 2016. doi: 10.1016/j.image.2016.08.008
|
[35] |
Y. Z. Niu, H. F. Zhang, W. Z. Guo, et al., “Image quality assessment for color correction based on color contrast similarity and color value difference,” IEEE Transactions on Circuits and Systems for Video Technology, vol.28, no.4, pp.849–862, 2018. doi: 10.1109/Tcsvt.2016.2634590
|
[36] |
I. Lissner, J. Preiss, P. Urban, et al., “Image-difference prediction: From grayscale to color,” IEEE Transactions on Image Processing, vol.22, no.2, pp.435–446, 2013. doi: 10.1109/Tip.2012.2216279
|
[37] |
J. Preiss, F. Fernandes, and P. Urban, “Color-image quality assessment: From prediction to optimization,” IEEE Transactions on Image Processing, vol.23, no.3, pp.1366–1378, 2014. doi: 10.1109/Tip.2014.2302684
|
[38] |
Yu Shaode, Wu Shibin, Wang Lei, et al., “A shallow convolutional neural network for blind image sharpness assessment,” PloS one, vol.12, no.5, article no.e0176632, 2017. doi: 10.1371/journal.pone.0176632
|
[39] |
D. Q. Li, T. T. Jiang, W. S. Lin, et al., “Which has better visual quality: The clear blue sky or a blurry animal?,” IEEE Transactions on Multimedia, vol.21, no.5, pp.1221–1234, 2019. doi: 10.1109/tmm.2018.2875354
|
[40] |
M. S. Hosseini, Y. Zhang, and K. N. Plataniotis, “Encoding visual sensitivity by maxpol convolution filters for image sharpness assessment,” IEEE Transactions on Image Processing, vol.28, no.9, pp.4510–4525, 2019. doi: 10.1109/TIP.2019.2906582
|
[41] |
M. A. Baig, A. A. Moinuddin, E. Khan, et al., “DFT-based no-reference quality assessment of blurred images,” Multimedia Tools and Applications, vol.81, no.6, pp.7895–7916, 2022. doi: 10.1007/s11042-022-11992-3
|
[42] |
B. Hill, T. Roger, and F. W. Vorhagen, “Comparative analysis of the quantization of color spaces on the basis of the CIELAB Color-Difference formula,” ACM Transactions on Graphics, vol.16, no.2, pp.109–154, 1997. doi: 10.1145/248210.248212
|
[43] |
E. C. Larson and D. M. Chandler, "Categorical image quality (CSIQ) database,” Available at: http://vision.eng.shizuoka.ac.jp/mod/page/view.php?id=23, 2009-01-19.
|
[44] |
H. R. Sheikh, M. F. Sabir, and A. C. Bovik, “A statistical evaluation of recent full reference image quality assessment algorithms,” IEEE Transactions on Image Processing, vol.15, no.11, pp.3440–3451, 2006. doi: 10.1109/Tip.2006.881959
|
[45] |
N. Ponomarenko, V. Lukin, and A. Zelensky, “TID2008-A database for evaluation of Full-Reference visual quality assessment metrics,” Advances of Modern Radioelectronics, vol.10, no.4, pp.30–45, 2004.
|
[46] |
N. Ponomarenko, L. Jin, O. Ieremeiev, et al., “Image database TID2013: Peculiarities, results and perspectives,” Signal Processing-Image Communication, vol.30, pp.57–77, 2015. doi: 10.1016/j.image.2014.10.009
|
[47] |
A. Zaric, N. Tatalovic, N. Brajkovic, et al., “VCL@FER image quality assessment database,” in Proceeding of 53rd International Symposium Elmar-2011, ELMAR, Zadar, Croatia, pp.344—354, 2011.
|
[48] |
T. Virtanen, M. Nuutinen, M. Vaahteranoksa, et al., “CID2013: A database for evaluating no-reference image quality assessment algorithms,” IEEE Transactions on Image Processing, vol.24, no.1, pp.390–402, 2015. doi: 10.1109/Tip.2014.2378061
|
[49] |
A. Ciancio, A. L. N. T. da Costa, E. A. B. da Silva, et al., “No-reference blur assessment of digital pictures based on multifeature classifiers,” IEEE Transactions on Image Processing, vol.20, no.1, pp.64–75, 2011. doi: 10.1109/Tip.2010.2053549
|
[50] |
A. R. Robertson, “Historical development of CIE recommended color difference-equations,” Color Research and Application, vol.15, no.3, pp.167–170, 1990. doi: 10.1002/col.5080150308
|
[51] |
J. M. Geusebroek, R. van den Boomgaard, A. W. M. Smeulders, et al., “Color invariance,” IEEE Transactions on Pattern Analysis And Machine Intelligence, vol.23, no.12, pp.1338–1350, 2001. doi: 10.1109/34.977559
|
[52] |
C. C. Yang and S. H. Kwok, “Efficient gamut clipping for color image processing using LHS and YIQ,” Optical Engineering, vol.42, no.3, pp.701–711, 2003. doi: 10.1117/1.1544479
|
[53] |
Y. Zhan and Z. Rong, “No-reference image sharpness assessment based on maximum gradient and variability of gradients,” IEEE Transactions on Multimedia, vol.20, no.7, pp.1796–1808, 2018. doi: 10.1109/TMM.2017.2780770
|
[54] |
Z. F. Shi, K. X. Chen, K. Pang, et al., “A perceptual image quality index based on global and Double-Random window similarity,” Digital Signal Processing, vol.60, pp.277–286, 2017. doi: 10.1016/j.dsp.2016.09.013
|
[55] |
S. Q. Wang, K. Gu, K. Zeng, et al., “Objective quality assessment and perceptual compression of screen content images,” IEEE Computer Graphics and Applications, vol.38, no.1, pp.47–58, 2016. doi: 10.1109/MCG.2016.46
|
[56] |
A. M. Liu, W. S. Lin, and M. Narwaria, “Image quality assessment based on gradient similarity,” IEEE Transactions on Image Processing, vol.21, no.4, pp.1500–1512, 2012. doi: 10.1109/Tip.2011.2175935
|
[57] |
A. Mittal, A. K. Moorthy, and A. C. Bovik, “No-Reference image quality assessment in the spatial domain,” IEEE Transactions on Image Processing, vol.21, no.12, pp.4695–4708, 2012. doi: 10.1109/Tip.2012.2214050
|
[58] |
A. Mittal, R. Soundararajan, and A. C. Bovik, “Making a nCompletely Blindo image quality analyzer,” IEEE Signal Processing Letters, vol.20, no.3, pp.209–212, 2013. doi: 10.1109/Lsp.2012.2227726
|
[59] |
L. Zhang, L. Zhang, and A. C. Bovik, “A Feature-Enriched completely blind image quality evaluator,” IEEE Transactions on Image Processing, vol.24, no.8, pp.2579–2591, 2015. doi: 10.1109/tip.2015.2426416
|
[60] |
Z. Wang and Q. Li, “Information content weighting for perceptual image quality assessment,” IEEE Transactions on Image Processing, vol.20, no.5, pp.1185–1198, 2011. doi: 10.1109/Tip.2010.2092435
|
[61] |
W. F. Xue, L. Zhang, X. Q. Mou, et al., “Gradient magnitude similarity deviation: A highly efficient perceptual image quality index,” IEEE Transactions on Image Processing, vol.23, no.2, pp.684–695, 2014. doi: 10.1109/Tip.2013.2293423
|
[62] |
H. Wang, J. Fu, W. Lin, et al., “Image quality assessment based on local linear information and Distortion-Specific compensation,” IEEE Transactions on Image Processing, vol.26, no.2, pp.915–926, 2016. doi: 10.1109/TIP.2016.2639451
|
[63] |
C. Y. Shi and Y. D. Lin, "No reference image sharpness assessment based on global color difference variation,” Available at: https://github.com/AlAlien/CDV, 2022-03-29.
|