Citation: | Chenyang SHI and Yandan LIN, “No Reference Image Sharpness Assessment Based on Global Color Difference Variation,” Chinese Journal of Electronics, vol. 33, no. 1, pp. 293–302, 2024 doi: 10.23919/cje.2022.00.058 |
[1] |
W. Kim, A. D. 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. Y. Shi and Y. D. Lin, “Image quality assessment based on three features fusion in three fusion steps,” Symmetry, 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. Z. 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, 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. D. Li, W. H. Xia, W. S. 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, article 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. Y. Shi and Y. D. Lin, “Objective image quality assessment based on image color appearance and gradient features,” Acta Physica Sinica, vol. 69, no. 22, article no. 228701, 2020. (in Chinese) 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. Y. Hu, J. Yan, W. X. 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. F. Fan, J. Y. 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, 2014. doi: 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, Cairo, Egypt, pp. 4369–4372, 2009.
|
[21] |
L. D. 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. Y. 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. Y. Qian, H. J. 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] |
S. D. Yu, S. B. Wu, L. Wang, 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. 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: https://qualinet.github.io/databases/image/categorical_image_quality_csiq_database/#:~:text=The%20Image%20Coding%20and%20Analysis%20Lab%20at%20the,at%20four%20to%20five%20different%20levels%20of%20distortion, 2009.
|
[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, et al., “TID2008-A database for evaluation of Full-Reference visual quality assessment metrics,” Advances of Modern Radioelectronics, vol. 10, no. 4, pp. 30–45, 2009.
|
[46] |
N. Ponomarenko, L. N. 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 ELMAR-2011, Zadar, Croatia, pp. 105–110, 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. 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 & 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] |
A. 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. B. Zhan and R. Zhang, “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, 2018. 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 “Completely Blind” 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. L. Wang, J. Fu, W. S. 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, 2017. 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,” Chinese Journal of Electronics, doi: 10.23919/cje.2022.00.058.
|