5.3.10 Image Quality Evaluation, Visual Quality, Quality Assessment, and Imaging Models

Fellgett, P.B., Linfoot, E.H.,
On the Assessment of Optical Images,
Royal(A-247), No. 931, 1955, pp. 369-407. BibRef 5500

Linfoot, E.H.,
Image Quality and Optical Resolution,
Optica Acta(4), No. 1, 1957, pp. 12-17. BibRef 5700

Linfoot, E.H.,
Convoluted Spot Diagrams and the Quality Evaluation of Photographic Images,
Optica Acta(9), No. 1, January 1962, pp. 81-110. BibRef 6201

Nill, N.B., Norman, B.D., Bouzas, B.H., Brian, H.,
Objective Image Quality Measure Derived from Digital Image Power Spectra,
OptEng(31), April 1992, pp. 813-825.
WWW Link. BibRef 9204

Barrett, H.H., Gouley, T., Girodias, K., Rolland, J., White, T., Yao, J.,
Linear Discriminants and Image Quality,
IVC(10), No. 6, July-August 1992, pp. 451-460.
Elsevier DOI BibRef 9207

Barrett, H.H., Abbey, C.K., Clarkson, E.W.,
Objective Assessment of Image Quality: III, ROC Metrics, Ideal Observers, And Likelihood-Generating Functions,
JOSA-A(15), No. 6, June 1998, pp. 1520-1535. 9806
BibRef

Huck, F.O., Fales, C.L., and Rahman, Z.,
An information theory of visual communication,
Royal(A-354), 1996, pp. 2193-2248. BibRef 9600
Earlier:
On the information-theoretic assessment of visual communication,
ICIP96(II: 437-440).
IEEE DOI 9610
BibRef

Leachtenauer, J.C., Malila, W., Irvine, J., Colburn, L., Salvaggio, N.,
General Image Quality Equation: GIQE,
AppOpt(36), No. 32, November 10 1997, pp. 8322-8328. 9711
BibRef

LaValle, S.M., Moorney, K.J., Hutchinson, S.A.,
Methods for Numerical Integration of High Dimensional Posterior Densities with Application to Statistical Image Models,
IP(6), No. 12, December 1997, pp. 1659-1672.
IEEE DOI 9712
BibRef

O'Sullivan, J.A., Blahut, R.E., Snyder, D.L.,
Information-Theoretic Image-Formation,
IT(44), No. 6, October 1998, pp. 2094-2123. 9810
BibRef

Stuller, J.A., Shah, R.,
An Image Model-Based on Occluding Object Images and Maximum-Entropy,
IP(7), No. 9, September 1998, pp. 1300-1310.
IEEE DOI 9809
BibRef

Allende, H.[Héctor], Galbiati, J.[Jorge], Vallejos, R.[Ronny],
Robust image modeling on image processing,
PRL(22), No. 11, September 2001, pp. 1219-1231.
Elsevier DOI 0108

See also non-parametric filter for digital image restoration, using cluster analysis, A. BibRef

Driggers, R.G., Cox, P.G., Leachtenauer, J.C., Vollmerhausen, R., Scribner, D.A.,
Targeting and Intelligence Electro-optical Recognition Modeling: A Juxtaposition of the Probabilities of Discrimination and the General Image Quality Equation,
OptEng(37), No. 3, March 1998, pp. 789-797. 9804
BibRef

Jiménez, A.R., Ceres, R., Pons, J.L.,
A new adaptive filter and quality evaluation index for image restoration,
PR(34), No. 2, February 2001, pp. 457-467.
Elsevier DOI 0011
BibRef

Segovia-Martinez, M., Black, A., Kondoz, A.M.,
Investigation of the effects of finite precision conversion on linear predictive coefficients,
VISP(147), No. 5, October 2000, pp. 415-422. 0101
BibRef

O'Sullivan, J.A., Jiang, M.[Ming], Ma, X.M.[Xiao-Ming], Wang, G.[Ge],
Axiomatic quantification of multidimensional image resolution,
SPLetters(9), No. 4, April 2002, pp. 120-122.
IEEE Top Reference. 0206
BibRef

Bex, P.J.[Peter J.], Makous, W.[Walter],
Spatial frequency, phase, and the contrast of natural images,
JOSA-A(19), No. 6, June 2002, pp. 1096-1106.
DOI Link 0206
BibRef

Petrov, Y.A.[Yury A.], Li, Z.P.[Zhao-Ping],
Local correlations, information redundancy, and sufficient pixel depth in natural images,
JOSA-A(20), No. 1, January 2003, pp. 56-66.
DOI Link 0304
BibRef

Shahram, M., Milanfar, P.,
Imaging Below the Diffraction Limit: A Statistical Analysis,
IP(13), No. 5, May 2004, pp. 677-689.
IEEE DOI 0404
BibRef

Milanfar, P., Shakouri, A.,
A statistical analysis of diffraction-limited imaging,
ICIP02(I: 864-867).
IEEE DOI 0210
BibRef

Núñez, J.[Jorge], Otazu, X.[Xavier], Merino, M.T.[María Teresa],
A multiresolution-based method for the determination of the relative resolution between images: First application to remote sensing and medical images,
IJIST(15), No. 5, 2005, pp. 225-235.
DOI Link 0512
BibRef

Shnayderman, A., Gusev, A., Eskicioglu, A.M.,
An SVD-Based Grayscale Image Quality Measure for Local and Global Assessment,
IP(15), No. 2, February 2006, pp. 422-429.
IEEE DOI 0602
BibRef

van der Weken, D.[Dietrich], Nachtegael, M.[Mike], Kerre, E.E.[Etienne E.],
Combining neighbourhood-based and histogram similarity measures for the design of image quality measures,
IVC(25), No. 2, February 2007, pp. 184-195.
Elsevier DOI 0701
Fuzzy similarity measures; Image quality evaluation
See also Using similarity measures and homogeneity for the comparison of images. BibRef

Vansteenkiste, E.[Ewout], van der Weken, D.[Dietrich], Philips, W.[Wilfried], Kerre, E.E.[Etienne E.],
Perceived Image Quality Measurement of State-of-the-Art Noise Reduction Schemes,
ACIVS06(114-126).
Springer DOI 0609
BibRef

Park, S.[Subok], Barrett, H.H.[Harrison H.], Clarkson, E.W.[Eric W.], Kupinski, M.A.[Matthew A.], Myers, K.J.[Kyle J.],
Channelized-ideal observer using Laguerre-Gauss channels in detection tasks involving non-Gaussian distributed lumpy backgrounds and a Gaussian signal,
JOSA-A(24), No. 12, December 2007, pp. B136-B150.
DOI Link 0801
Image Quality. Spatial discrimination. BibRef

Kupinski, M.K.[Meredith K.], Clarkson, E.W.[Eric W.],
Method for optimizing channelized quadratic observers for binary classification of large-dimensional image datasets,
JOSA-A(32), No. 4, April 2015, pp. 549-565.
DOI Link 1504
Pattern recognition; Image detection systems; Algorithms BibRef

Kupinski, M.K.[Meredith K.], Clarkson, E.W.[Eric W.],
Optimal channels for channelized quadratic estimators,
JOSA-A(33), No. 6, June 2016, pp. 1214-1225.
DOI Link 1606
Pattern recognition; Image detection systems; Algorithms BibRef

Kupinski, M.K.[Meredith K.], Bankhead, J.[Jaden], Stohn, A.[Adriana], Chipman, R.[Russell],
Binary classification of Mueller matrix images from an optimization of Poincare coordinates,
JOSA-A(34), No. 6, June 2017, pp. 983-990.
DOI Link 1706
Pattern recognition, Image detection systems, Algorithms BibRef

Clarkson, E.W.[Eric W.], Shen, F.F.[Fang-Fang],
Fisher information and surrogate figures of merit for the task-based assessment of image quality,
JOSA-A(27), No. 10, October 2010, pp. 2313-2326.
DOI Link 1011
BibRef

Clarkson, E.W.[Eric W.], Cushing, J.B.[Johnathan B.],
Shannon information for joint estimation/detection tasks and complex imaging systems,
JOSA-A(33), No. 3, March 2016, pp. 286-292.
DOI Link 1603
Image analysis; Image quality assessment BibRef

Clarkson, E.W.[Eric W.],
Relation between Bayesian Fisher information and Shannon information for detecting a change in a parameter,
JOSA-A(36), No. 7, July 2019, pp. 1209-1214.
DOI Link 1912
Ideal observers, Medical imaging, Praseodymium, Remote sensing, Silicon BibRef

An, K.B.[Ke-Bin], Sun, J.[Jun], Du, W.[Weina],
Homogeneity Based Image Objective Quality Metric,
IEICE(E89-D), No. 10, October 2006, pp. 2682-2685.
DOI Link 0610
BibRef

Pang, J.X.[Jian-Xin], Zhang, R.[Rong], Lu, L.[Lu], Tang, J.H.[Jin-Hui], Liu, Z.K.[Zheng-Kai],
A projection-based image quality measure,
IJIST(18), No. 2-3, 2007, pp. 94-100.
DOI Link 0804
BibRef

Lin, Q.[Qian],
Method and system for assessing the photo quality of a captured image in a digital still camera,
US_Patent7,362,354, Apr 22, 2008
WWW Link. BibRef 0804

Kim, D.O., Park, R.H.,
New Image Quality Metric Using the Harris Response,
SPLetters(16), No. 7, July 2009, pp. 616-619.
IEEE DOI 0905
BibRef

Kim, D.O., Park, R.H.,
New image quality metric using random projection,
IET-IPR(6), No. 9, 2012, pp. 1246-1255.
DOI Link 1302
BibRef

Kim, D.O., Park, R.H., Lee, J.W.,
New image quality metric using derivative filters and compressive sensing,
ICIP10(3357-3360).
IEEE DOI 1009
BibRef

He, L.[Lihuo], Gao, X.B.[Xin-Bo], Lu, W.[Wen], Li, X.L.[Xue-Long], Tao, D.C.[Da-Cheng],
Image quality assessment based on S-CIELAB model,
SIViP(5), No. 3, September 2011, pp. 283-290.
WWW Link. 1109
BibRef

Wunderlich, A., Noo, F.,
Estimation of Channelized Hotelling Observer Performance With Known Class Means or Known Difference of Class Means,
MedImg(28), No. 8, August 2009, pp. 1198-1207.
IEEE DOI 0909
task-based image quality assessment BibRef

Cheng, F.C.[Fan-Chieh], Ruan, S.J.[Shanq-Jang],
Image Quality Analysis of a Novel Histogram Equalization Method for Image Contrast Enhancement,
IEICE(E93-D), No. 7, July 2010, pp. 1773-1779.
WWW Link. 1008
BibRef

Horiuchi, T.[Takahiko], Tominaga, S.[Shoji],
HDR Image Quality Enhancement Based on Spatially Variant Retinal Response,
JIVP(2010), No. 2010, pp. xx-yy.
DOI Link 1011
BibRef

Nachlieli, H., Shaked, D.,
Measuring the Quality of Quality Measures,
IP(20), No. 1, January 2011, pp. 76-87.
IEEE DOI 1101
BibRef

Wei, C.M.[Chuan-Ming], Kaplan, L.M., Burks, S.D., Blum, R.S.,
Diffuse Prior Monotonic Likelihood Ratio Test for Evaluation of Fused Image Quality Measures,
IP(20), No. 2, February 2011, pp. 327-344.
IEEE DOI 1102
BibRef

Zhang, M., Mou, X.Q.[Xuan-Qin], Zhang, L.,
Non-Shift Edge Based Ratio (NSER): An Image Quality Assessment Metric Based on Early Vision Features,
SPLetters(18), No. 5, May 2011, pp. 315-318.
IEEE DOI 1104
BibRef

Xue, W.F.[Wu-Feng], Mou, X.Q.[Xuan-Qin],
An image quality assessment metric based on Non-shift Edge,
ICIP11(3309-3312).
IEEE DOI 1201
BibRef

Zhang, L.[Lin], Zhang, L.[Lei], Mou, X.Q.[Xuan-Qin], Zhang, D.,
FSIM: A Feature Similarity Index for Image Quality Assessment,
IP(20), No. 8, August 2011, pp. 2378-2386.
IEEE DOI 1108
BibRef
Earlier: A1, A2, A3, Only:
RFSIM: A feature based image quality assessment metric using Riesz transforms,
ICIP10(321-324).
IEEE DOI 1009
BibRef

Deng, C.[Cheng], Li, J.[Jie], Zhang, Y.F.[Yi-Fan], Huang, D.Y.[Dong-Yu], An, L.L.[Ling-Ling],
An Image Quality Metric Based On Biologically Inspired Feature Model,
IJIG(11), No. 2, April 2011, pp. 265-279.
DOI Link 1107
BibRef

Chang, H.W.[Hua-Wen], Wang, M.H.[Ming-Hui],
Sparse correlation coefficient for objective image quality assessment,
SP:IC(26), No. 10, November 2011, pp. 577-588.
Elsevier DOI 1111
Image quality assessment; Sparse coding; Independent component analysis; Natural image statistics; Receptive field BibRef

Engelke, U., Kaprykowsky, H., Zepernick, H.J., Ndjiki-Nya, P.,
Visual Attention in Quality Assessment,
SPMag(28), No. 1, 2011, pp. 50-59.
IEEE DOI 1112
BibRef

Almansa, A.[Andrés], Morel, J.M.[Jean-Michel],
The Non-parametric Sub-pixel Local Point Spread Function Estimation Is a Well Posed Problem,
IJCV(96), No. 2, February 2012, pp. 175-194.
WWW Link. 1201
PSF of standard digital camera is an aliased version of the exact one. To analyze camera image quality. BibRef

Delbracio, M.[Mauricio], Almansa, A.[Andrés], Morel, J.M.[Jean-Michel], Musé, P.[Pablo],
Subpixel Point Spread Function Estimation from Two Photographs at Different Distances,
SIIMS(5), No. 4, 2012, pp. 1234-1260.
DOI Link 1211
Code:
See also Recovering the Subpixel PSF from Two Photographs at Different Distances. BibRef

Delbracio, M.[Mauricio], Almansa, A.[Andrés], Musé, P.[Pablo],
Recovering the Subpixel PSF from Two Photographs at Different Distances,
IPOL(2013), No. 2013, pp. 232-241.
DOI Link 1311
Code, Point Spread Function.
See also Subpixel Point Spread Function Estimation from Two Photographs at Different Distances. BibRef

Delbracio, M.[Mauricio], Musé, P.[Pablo], Almansa, A.[Andrés],
Non-parametric sub-pixel local point spread function estimation,
IPOL(2012), No. 2012, pp. xx-yy.
DOI Link 1204
Code, PSF Estimation. BibRef

Houdard, A.[Antoine], Almansa, A.[Andrés], Delon, J.[Julie],
Demystifying the Asymptotic Behavior of Global Denoising,
JMIV(59), No. 3, November 2017, pp. 456-480.
Springer DOI 1710
BibRef

Houdard, A.[Antoine], Bouveyron, C., Delon, J.[Julie],
High-Dimensional Mixture Models for Unsupervised Image Denoising (HDMI),
SIIMS(11), No. 4, 2018, pp. 2815-2846.
DOI Link 1901
BibRef

Thung, K.H.[Kim-Han], Paramesran, R.[Raveendran], Lim, C.L.[Chern-Loon],
Content-based image quality metric using similarity measure of moment vectors,
PR(45), No. 6, June 2012, pp. 2193-2204.
Elsevier DOI 1202
Image quality assessment; Tchebichef moments; Similarity measure; Block classification BibRef

Zhu, J., Wang, N.,
Image Quality Assessment by Visual Gradient Similarity,
IP(21), No. 3, March 2012, pp. 919-933.
IEEE DOI 1203
BibRef

Grotta, S.W., Grotta, D.,
Not all pixels are created equal,
Spectrum(49), No. 5, May 2012, pp. 22-24.
IEEE DOI 1202
Tools & Toys paper. For consumer cameras, pixel count is quantity, not quality. BibRef

Jiménez-Sánchez, A.R.[Angélica R.], Santillán, I.[Israel], Resendiz, J.R.[Juvenal Rodriguez], Gonzalez-Gutierrez, C.A.[Carlos A.], Mendiola-Santibañez, J.D.[Jorge D.],
Morphological contrast index based on the Weber's law,
IJIST(22), No. 2, June 2012, pp. 137-144.
DOI Link 1202
quantify the contrast in a processed image. BibRef

Saleem, A.[Amina], Beghdadi, A.[Azeddine], Boashash, B.[Boualem],
Image fusion-based contrast enhancement,
JIVP(2012), No. 1 2012, pp. xx-yy.
DOI Link 1205
BibRef
Earlier:
Image quality metrics based multifocus image fusion,
EUVIP11(77-82).
IEEE DOI 1110
BibRef

Narwaria, M., Lin, W., McLoughlin, I.V., Emmanuel, S., Chia, L.T.,
Fourier Transform-Based Scalable Image Quality Measure,
IP(21), No. 8, August 2012, pp. 3364-3377.
IEEE DOI 1208
BibRef

Solh, M., Al Regib, G.I.,
MIQM: A Multicamera Image Quality Measure,
IP(21), No. 9, September 2012, pp. 3902-3914.
IEEE DOI 1208
BibRef

Jahn, H.[Herbert], Reulke, R.[Ralf],
A Sensor-Based Approach to Image Quality,
PFG(2012), No. 1, 2012, pp. 19-27.
WWW Link. 1211
BibRef

Zhang, X.D.[Xuan-De], Feng, X.C.[Xiang-Chu], Wang, W.W.[Wei-Wei], Xue, W.F.[Wu-Feng],
Edge Strength Similarity for Image Quality Assessment,
SPLetters(20), No. 4, April 2013, pp. 319-322.
IEEE DOI 1303
BibRef

Liu, T.J., Lin, W., Kuo, C.C.J.,
Image Quality Assessment Using Multi-Method Fusion,
IP(22), No. 5, May 2013, pp. 1793-1807.
IEEE DOI 1303
BibRef

Horé, A.[Alain], Ziou, D.[Djemel],
Is there a relationship between peak-signal-to-noise ratio and structural similarity index measure?,
IET-IPR(7), No. 1, 2013, pp. 12-24.
DOI Link 1303
2 image quality metrics. BibRef

Haddad, Z.[Zehira], Beghdadi, A.[Azeddine], Serir, A.[Amina], Mokraoui, A.[Anissa],
Wave atoms based compression method for fingerprint images,
PR(46), No. 9, September 2013, pp. 2450-2464.
Elsevier DOI 1305
BibRef
Earlier:
Image quality assessment based on wave atoms transform,
ICIP10(305-308).
IEEE DOI 1009
Biometrics; Fingerprint compression; Image Quality Metric (IQM); Wavelets; Ridgelets; Curvelets; Wave atoms; Wavelet Scalar Quantization (WSQ); Human Visual System (HVS)
See also Iris features extraction using wave atoms. BibRef

Soundararajan, R.[Rajiv], Bovik, A.C.[Alan C.],
Survey of information theory in visual quality assessment,
SIViP(7), No. 3, May 2013, pp. 391-401.
WWW Link. 1305
Survey, Visual Quality. BibRef

Bruni, V.[Vittoria], Rossi, E.[Elisa], Vitulano, D.[Domenico],
Jensen-Shannon divergence for visual quality assessment,
SIViP(7), No. 3, May 2013, pp. 411-421.
WWW Link. 1305
BibRef

Bruni, V.[Vittoria], Vitulano, D.[Domenico],
Jensen Shannon Divergence as Reduced Reference Measure for Image Denoising,
ACIVS16(311-323).
Springer DOI 1611

See also Wavelets and partial differential equations for image denoising. BibRef

Javaherian, A.[Ashkan], Movafeghi, A.[Amir], Faghihi, R.[Reza], Yahaghi, E.[Effat],
An exhaustive criterion for estimating quality of images in electrical impedance tomography with application to clinical imaging,
JVCIR(24), No. 7, 2013, pp. 773-785.
Elsevier DOI 1309
Electrical impedance tomography BibRef

Zhang, H.[Hu], Huang, Y.[Yan], Chen, X.[Xi], Deng, D.X.[De-Xiang],
MLSIM: A Multi-Level Similarity index for image quality assessment,
SP:IC(28), No. 10, 2013, pp. 1464-1477.
Elsevier DOI 1312
Image quality assessment (IQA) BibRef

Chandler, D.M.[Damon M.], Phan, T.[Thien], Alam, M.M.[M. Mushfiqul],
Seven challenges for image quality research,
SPIE(Newsroom), January 15, 2014
DOI Link 1402
Examining the limitations of current research into image quality assessment opens doors for further studies. BibRef

Zhou, F.[Fei], Sun, W.[Wen], Liao, Q.M.[Qing-Min],
Image Quality Assessment Based on Multi-Order Visual Comparison,
IEICE(E97-D), No. 5, May 2014, pp. 1379-1381.
WWW Link. 1405
BibRef

Li, L.[Lin], Luo, H.[Heng], Zhu, H.H.[Hai-Hong],
Estimation of the Image Interpretability of ZY-3 Sensor Corrected Panchromatic Nadir Data,
RS(6), No. 5, 2014, pp. 4409-4429.
DOI Link 1407
Quality measure to take advantage of satellite data. BibRef

Dumic, E., Grgic, S., Grgic, M.,
IQM2: new image quality measure based on steerable pyramid wavelet transform and structural similarity index,
SIViP(8), No. 6, September 2014, pp. 1159-1168.
WWW Link. 1408
BibRef

Bjelopera, A., Dumic, E., Grgic, S.,
Classification of image degradation using Riesz transform,
WSSIP16(1-4)
IEEE DOI 1608
image classification BibRef

Zhang, H.[Hang], Ding, Y.[Yong], Wu, P.W.[Peng Wei], Bai, X.T.[Xue Tong], Huang, K.[Kai],
Image Quality Assessment by Quantifying Discrepancies of Multifractal Spectrums,
IEICE(E97-D), No. 9, September 2014, pp. 2453-2460.
WWW Link. 1410
BibRef

Nehab, D.[Diego], Hoppe, H.[Hugues],
A Fresh Look at Generalized Sampling,
FTCGV(8), Issue 1, 2012, pp. 1-84.
DOI Link 1410
Generalized sampling Published March 2014. BibRef

Battisti, F.[Federica], Bosc, E.[Emilie], Carli, M.[Marco], Le Callet, P.[Patrick], Perugia, S.[Simone],
Objective image quality assessment of 3D synthesized views,
SP:IC(30), No. 1, 2015, pp. 78-88.
Elsevier DOI 1412
Objective image quality BibRef

Cheon, M.[Manri], Vigier, T.[Toinon], Krasula, L.[Lukáš], Lee, J.[Junghyuk], Le Callet, P.[Patrick], Lee, J.S.[Jong-Seok],
Ambiguity of objective image quality metrics: A new methodology for performance evaluation,
SP:IC(93), 2021, pp. 116150.
Elsevier DOI 2103
Quality of experience, Objective quality assessment, Ambiguity interval, Viewing distance BibRef

Devaraju, A.[Anusuriya], Jirka, S.[Simon], Kunkel, R.[Ralf], Sorg, J.[Juergen],
Q-SOS: A Sensor Observation Service for Accessing Quality Descriptions of Environmental Data,
IJGI(4), No. 3, 2015, pp. 1346.
DOI Link 1508
BibRef

Zhao, P.P.[Pei-Pei], Li, L.[Leida], Cai, H.[Hao],
Saliency Guided Gradient Similarity for Fast Perceptual Blur Assessment,
IEICE(E98-D), No. 8, August 2015, pp. 1613-1616.
WWW Link. 1509
BibRef

Jadhav, M.[Manisha], Dandawate, Y.H.[Yogesh H.], Pisharoty, N.[Narayan],
Colour image quality assessment using Laplacian pyramid decomposition,
IJCVR(5), No. 4, 2015, pp. 407-421.
DOI Link 1511
BibRef

Oszust, M.,
Decision Fusion for Image Quality Assessment using an Optimization Approach,
SPLetters(23), No. 1, January 2016, pp. 65-69.
IEEE DOI 1601
genetic algorithms BibRef

Oszust, M.,
No-Reference Image Quality Assessment Using Image Statistics and Robust Feature Descriptors,
SPLetters(24), No. 11, November 2017, pp. 1656-1660.
IEEE DOI 1710
human visual system, robust feature descriptors, Distortion measurement, Gray-scale, Histograms, Image quality, BibRef

Hanhart, P.[Philippe], Bernardo, M.[Marco], Pereira, M.[Manuela], Pinheiro, A.G.[Antonio G.], Ebrahimi, T.[Touradj],
Benchmarking of objective quality metrics for HDR image quality assessment,
JIVP(2015), No. 1, 2015, pp. 39.
DOI Link 1601
BibRef

Chung, S.[Soyoung], Chung, M.G.[Min Gyo],
An Image Quality Assessment Using Mean-Centered Weber Ratio and Saliency Map,
IEICE(E99-D), No. 1, January 2016, pp. 138-140.
WWW Link. 1601
BibRef

Wu, X.Y.[Xian-Yan], Han, Q.[Qi], Niu, X.[Xiamu],
Quality Assessment for Reassembled Image Files,
SPLetters(23), No. 2, February 2016, pp. 232-236.
IEEE DOI 1602
image processing BibRef

Simon, L.[Loïc], Morel, J.M.[Jean-Michel],
Influence of Unknown Exterior Samples on Interpolated Values for Band-Limited Images,
SIIMS(9), No. 1, 2016, pp. 152-184.
DOI Link 1604
As size and resolution increases, where are the sources for errors in data. BibRef

Clarkson, E.[Eric], Barrett, H.H.[Harrison H.],
Characteristic functionals in imaging and image-quality assessment: tutorial,
JOSA-A(33), No. 8, August 2016, pp. 1464-1475.
DOI Link 1608
Imaging systems; Image analysis; Image quality assessment BibRef

Kuo, T.Y.[Tien-Ying], Su, P.C.[Po-Chyi], Tsai, C.M.[Cheng-Mou],
Improved visual information fidelity based on sensitivity characteristics of digital images,
JVCIR(40, Part A), No. 1, 2016, pp. 76-84.
Elsevier DOI 1609
Image quality assessment BibRef

Golestaneh, S.A.[S. Alireza], Karam, L.J.[Lina J.],
Reduced-Reference Quality Assessment Based on the Entropy of DWT Coefficients of Locally Weighted Gradient Magnitudes,
IP(25), No. 11, November 2016, pp. 5293-5303.
IEEE DOI 1610
BibRef
Earlier:
Reduced-reference quality assessment based on the entropy of DNT coefficients of locally weighted gradients,
ICIP15(4117-4120)
IEEE DOI 1512
Computational modeling. Gaussian filtering BibRef

Golestaneh, S.A.[S. Alireza], Karam, L.J.[Lina J.],
Spatially-Varying Blur Detection Based on Multiscale Fused and Sorted Transform Coefficients of Gradient Magnitudes,
CVPR17(596-605)
IEEE DOI 1711
Cameras, Discrete cosine transforms, Estimation, Image edge detection, Kernel, Robustness BibRef

Temel, D., Prabhushankar, M., Al Regib, G.,
UNIQUE: Unsupervised Image Quality Estimation,
SPLetters(23), No. 10, October 2016, pp. 1414-1418.
IEEE DOI 1610
decoding BibRef

Panetta, K.A., Gao, C., Agaian, S., Nercessian, S.,
A New Reference-Based Edge Map Quality Measure,
SMCS(46), No. 11, November 2016, pp. 1505-1517.
IEEE DOI 1609
edge detection BibRef

Lee, K.[Kwanghyun], Lee, S.H.[Sang-Hoon],
A New Framework for Measuring 2D and 3D Visual Information in Terms of Entropy,
CirSysVideo(26), No. 11, November 2016, pp. 2015-2027.
IEEE DOI 1609
Complexity theory BibRef

Manap, R.A., Shao, L., Frangi, A.F.,
Nonparametric Quality Assessment of Natural Images,
MultMedMag(23), No. 4, October 2016, pp. 22-30.
IEEE DOI 1612
feature extraction BibRef

Ma, K.[Kede], Duanmu, Z.F.[Zheng-Fang], Wu, Q.B.[Qing-Bo], Wang, Z.[Zhou], Yong, H.W.[Hong-Wei], Li, H.L.[Hong-Liang], Zhang, L.[Lei],
Waterloo Exploration Database: New Challenges for Image Quality Assessment Models,
IP(26), No. 2, February 2017, pp. 1004-1016.
IEEE DOI 1702
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Group MAD Competition? A New Methodology to Compare Objective Image Quality Models,
CVPR16(1664-1673)
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JVCIR(43), No. 1, 2017, pp. 108-118.
Elsevier DOI 1702
Image quality assessment BibRef

Chen, C.F.[Chao-Feng], Mo, J.[Jiadi], Hou, J.W.[Jing-Wen], Wu, H.[Haoning], Liao, L.[Liang], Sun, W.X.[Wen-Xiu], Yan, Q.[Qiong], Lin, W.S.[Wei-Si],
TOPIQ: A Top-Down Approach from Semantics to Distortions for Image Quality Assessment,
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IEEE DOI Code:
WWW Link. 2404
Semantics, Feature extraction, Distortion, Image quality, Quality assessment, Transformers, Residual neural networks, cross-scale attention BibRef

Ding, L., Huang, H., Zang, Y.,
Image Quality Assessment Using Directional Anisotropy Structure Measurement,
IP(26), No. 4, April 2017, pp. 1799-1809.
IEEE DOI 1704
Anisotropic magnetoresistance BibRef

Ding, Y.[Yong], Zhao, Y.[Yang], Zhao, X.Y.[Xin-Yu],
Image quality assessment based on multi-feature extraction and synthesis with support vector regression,
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Elsevier DOI 1704
Image quality assessment BibRef

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EO-1 Data Quality and Sensor Stability with Changing Orbital Precession at the End of a 16 Year Mission,
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IEICE(E100-D), No. 6, June 2017, pp. 1303-1315.
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A Structural Variation Classification Model for Image Quality Assessment,
MultMed(19), No. 8, August 2017, pp. 1837-1847.
IEEE DOI 1708
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ICIP16(2072-2076)
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Acoustic distortion, Additives, Deformable models, Distortion measurement, Image quality, Mathematical model, Fuzzy logic, image quality assessment (IQA), structural variation classification BibRef

Zhou, Y.[Yu], Li, L.[Leida], Gu, K.[Ke], Lu, Z.L.[Zhao-Lin], Chen, B.[Beijing], Tang, L.[Lu],
DIBR-Synthesized Image Quality Assessment via Statistics of Edge Intensity and Orientation,
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No-Reference Image Quality Assessment for Image Auto-Denoising,
IJCV(126), No. 5, May 2018, pp. 537-549.
Springer DOI 1804
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Kong, X.F.[Xiang-Fei], Li, K.[Kuan], Yang, Q.X.[Qing-Xiong], Liu, W.Y.[Wen-Yin], Yang, M.H.[Ming-Hsuan],
A New Image Quality Metric for Image Auto-denoising,
ICCV13(2888-2895)
IEEE DOI 1403
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Yao, J.[Juncai], Liu, G.Z.[Gui-Zhong],
Improved SSIM IQA of contrast distortion based on the contrast sensitivity characteristics of HVS,
IET-IPR(12), No. 6, June 2018, pp. 872-879.
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Liu, Y.H.[Yu-Hong], Yan, H.M.[Hong-Mei], Gao, S.B.[Shao-Bing], Yang, K.F.[Kai-Fu],
Criteria to evaluate the fidelity of image enhancement by MSRCR,
IET-IPR(12), No. 6, June 2018, pp. 880-887.
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Image quality assessment in first-person videos,
JVCIR(54), 2018, pp. 123-132.
Elsevier DOI 1806
BibRef
Earlier:
Mutual reference frame-quality assessment for first-person videos,
ICIP17(290-294)
IEEE DOI 1803
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Earlier:
Characterizing distortions in first-person videos,
ICIP16(2440-2444)
IEEE DOI 1610
First-person videos, Local Visual Information (LVI), Mutual reference, Image quality assessment, Pseudo-reference. Cameras, Estimation, Partitioning algorithms, Videos, Visualization, LVI. near-set BibRef

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A strategy to jointly test image quality estimators subjectively,
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IEEE DOI 1302
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Systematic stress testing of image quality estimators,
ICIP11(3101-3104).
IEEE DOI 1201
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Zhu, R., Zhou, F., Yang, W., Xue, J.H.,
On Hypothesis Testing for Comparing Image Quality Assessment Metrics,
SPMag(35), No. 4, July 2018, pp. 133-136.
IEEE DOI 1807
[Tips amp; Tricks] Correlation, Gaussian distribution, Gaussian noise, Image quality, Measurement, Testing BibRef

Kruggel, F.,
A Simple Measure for Acuity in Medical Images,
IP(27), No. 11, November 2018, pp. 5225-5233.
IEEE DOI 1809
image enhancement, image reconstruction, image resolution, medical image processing, image quality, medical imaging, medical imaging BibRef

Torkamani-Azar, F.[Farah], Parkkinen, J.[Jussi],
Image quality assessment using block-based weighted SVD,
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Asymmetrically Compressed Stereoscopic 3D Videos: Quality Assessment and Rate-Distortion Performance Evaluation,
IP(26), No. 3, March 2017, pp. 1330-1343.
IEEE DOI 1703
BibRef
Earlier:
Quality prediction of asymmetrically compressed stereoscopic videos,
ICIP15(3427-3431)
IEEE DOI 1512
data compression. 3D video BibRef

Pan, X.F.[Xiao-Fei], Zhang, J.Q.[Jia-Qi], Wang, S.S.[Shan-She], Wang, S.Q.[Shi-Qi], Zhou, Y.[Yun], Ding, W.H.[Wen-Hua], Yang, Y.H.[Ya-Hui],
HDR video quality assessment: Perceptual evaluation of compressed HDR video,
JVCIR(57), 2018, pp. 76-83.
Elsevier DOI 1812
High dynamic range (HDR), Subjective quality assessment, Video compression BibRef

Kim, D., Lee, S., Kim, C.,
Contextual Information Based Quality Assessment for Contrast-Changed Images,
SPLetters(26), No. 1, January 2019, pp. 109-113.
IEEE DOI 1901
entropy, feature extraction, image colour analysis, image enhancement, image processing, image representation, 2-D histogram and spatial entropy BibRef

Zhang, C., Cheng, W.[Wu], Hirakawa, K.[Keigo],
Corrupted Reference Image Quality Assessment of Denoised Images,
IP(28), No. 4, April 2019, pp. 1732-1747.
IEEE DOI 1901
BibRef
Earlier: A2, A3, Only:
Corrupted reference image quality assessment,
ICIP12(1485-1488).
IEEE DOI 1302
image denoising, image restoration, corrupted reference image quality assessment, denoised images, image quality assessment BibRef

Yue, G.H.[Guang-Hui], Hou, C.P.[Chun-Ping], Gu, K.[Ke], Zhou, T.W.[Tian-Wei], Zhai, G.T.[Guang-Tao],
Combining Local and Global Measures for DIBR-Synthesized Image Quality Evaluation,
IP(28), No. 4, April 2019, pp. 2075-2088.
IEEE DOI 1901
computational geometry, image restoration, rendering (computer graphics), stereo image processing, sharpness BibRef

Gao, Y.X.[Yi-Xuan], Min, X.K.[Xiong-Kuo], Zhu, W.H.[Wen-Han], Zhang, X.P.[Xiao-Ping], Zhai, G.T.[Guang-Tao],
Image Quality Score Distribution Prediction via Alpha Stable Model,
CirSysVideo(33), No. 6, June 2023, pp. 2656-2671.
IEEE DOI 2306
BibRef
Earlier:
Modeling Image Quality Score Distribution Using Alpha Stable Model,
ICIP21(1574-1578)
IEEE DOI 2201
Image quality, Feature extraction, Databases, Histograms, Predictive models, Distortion, Data mining, support vector regressors. Support vector machines, Analytical models, Gaussian distribution BibRef

Gao, Y.X.[Yi-Xuan], Min, X.K.[Xiong-Kuo], Zhu, Y.C.[Yu-Cheng], Zhang, X.P.[Xiao-Ping], Zhai, G.T.[Guang-Tao],
Blind Image Quality Assessment: A Fuzzy Neural Network for Opinion Score Distribution Prediction,
CirSysVideo(34), No. 3, March 2024, pp. 1641-1655.
IEEE DOI 2403
Feature extraction, Image quality, Uncertainty, Databases, Fuzzy neural networks, Fuzzy sets, Standards, cumulative density function BibRef

Gomez, L.[Luis], Ospina, R.[Raydonal], Frery, A.C.[Alejandro C.],
Statistical Properties of an Unassisted Image Quality Index for SAR Imagery,
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Quality assessment tool for performance measurement of image contrast enhancement methods,
IET-IPR(13), No. 5, 18 April 2019, pp. 833-842.
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Reconstruction-Aware Imaging System Ranking by Use of a Sparsity-Driven Numerical Observer Enabled by Variational Bayesian Inference,
MedImg(38), No. 5, May 2019, pp. 1251-1262.
IEEE DOI 1905
Optimization of imaging system performance should be guided by task-based measures of image quality. Task analysis, Reconstruction algorithms, Computational modeling, Image reconstruction, Observers, Noise measurement, sparse image reconstruction BibRef

Weiss, P.[Pierre], Escande, P.[Paul], Bathie, G.[Gabriel], Dong, Y.Q.[Yi-Qiu],
Contrast Invariant SNR and Isotonic Regressions,
IJCV(127), No. 8, August 2019, pp. 1144-1161.
Springer DOI 1907
Image quality measure independent of contrast changes. BibRef

Huang, J.C.[Jui-Chan], Huang, H.C.[Hao-Chen], Chu, S.H.[Su-Hui],
Research on image quality in decision management system and information system framework,
JVCIR(63), 2019, pp. 102588.
Elsevier DOI 1909
Image quality, Quality model, Decision management, Information system, Decision support system BibRef

Cao, J.C.[Jing-Chao], Wang, S.Q.[Shi-Qi], Wang, R.[Ran], Zhang, X.F.[Xin-Feng], Kwong, S.[Sam],
Content-oriented image quality assessment with multi-label SVM classifier,
SP:IC(78), 2019, pp. 388-397.
Elsevier DOI 1909
Image quality assessment, Image content classification, Subjective quality, Objective quality BibRef

Maksimovic-Moicevic, S.[Sanja], Lukac, Ž.[Željko], Temerinac, M.[Miodrag],
Objective estimation of subjective image quality assessment using multi-parameter prediction,
IET-IPR(13), No. 13, November 2019, pp. 2428-2435.
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Pérez-Ortiz, M., Mikhailiuk, A., Zerman, E., Hulusic, V., Valenzise, G., Mantiuk, R.K.[Rafal K.],
From Pairwise Comparisons and Rating to a Unified Quality Scale,
IP(29), 2020, pp. 1139-1151.
IEEE DOI 1911
Observers, Standards, Protocols, Quality assessment, Training, Image quality, Probabilistic logic, Psychometric scaling, dataset fusion BibRef

He, S.Y.[Si-Yuan], Liu, Z.Z.[Ze-Zheng],
Image quality assessment based on adaptive multiple Skyline query,
SP:IC(80), 2020, pp. 115676.
Elsevier DOI 1912
Image quality, Skyline, Feature fusion, Gabor wavelet BibRef

Tran, H.T.T.[Huyen T. T.], Hoang, T.H.[Trang H.], Minh, P.N.[Phu N.], Ngoc, N.P.[Nam Pham], Thang, T.C.[Truong Cong],
A Weighted Viewport Quality Metric for Omnidirectional Images,
IEICE(E103-D), No. 1, January 2020, pp. 67-70.
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Yu, M.M.[Miao-Miao], Zheng, Y.L.[Yuan-Lin], Liao, K.Y.[Kai-Yang], Tang, Z.S.[Zhi-Sen],
Image quality assessment via spatial-transformed domains multi-feature fusion,
IET-IPR(14), No. 4, 27 March 2020, pp. 648-657.
DOI Link 2003
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Diamant, R.[Roee], Shachar, I.[Ilan], Makovsky, Y.[Yizhaq], Ferreira, B.M.[Bruno Miguel], Cruz, N.A.[Nuno Alexandre],
Cross-Sensor Quality Assurance for Marine Observatories,
RS(12), No. 21, 2020, pp. xx-yy.
DOI Link 2011
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Li, Y.J.[Ya-Jing], Huang, B.X.[Bao-Xiang], Yang, H.[Huan], Hou, G.J.[Guo-Jia], Zhang, P.F.[Peng-Fei], Duan, J.M.[Jin-Ming],
Efficient image structural similarity quality assessment method using image regularised feature,
IET-IPR(14), No. 16, 19 December 2020, pp. 4401-4411.
DOI Link 2103
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Yi, W.[Wei], Wang, Y.H.[Yu-Hao], Zeng, Y.[Yong], Wang, Y.Q.[Ya-Qin], Xu, J.F.[Jian-Fei],
Comprehensive Evaluation of the GF-4 Satellite Image Quality from 2015 to 2020,
IJGI(10), No. 6, 2021, pp. xx-yy.
DOI Link 2106
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Elmezayen, M.R.[Mohamed R.], Ay, S.U.[Suat U.],
A new blind image conversion complexity metric for intelligent CMOS image sensors,
IET-IPR(15), No. 3, 2021, pp. 683-695.
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Sensor quality. BibRef

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Application of the XBoost Regressor for an A Priori Prediction of UAV Image Quality,
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Chai, X.L.[Xiong-Li], Shao, F.[Feng],
M2OVQA: Multi-space signal characterization and multi-channel information aggregation for quality assessment of compressed omnidirectional videos,
JVCIR(82), 2022, pp. 103419.
Elsevier DOI 2201
Omnidirectional video quality assessment, Image quality assessment, Compression distortion, Information aggregation BibRef

Zhang, K.[Keke], Fang, Y.[Ying], Chen, W.L.[Wei-Ling], Xu, Y.W.[Yi-Wen], Zhao, T.S.[Tie-Song],
A Display-Independent Quality Assessment for HDR Images,
SPLetters(29), 2022, pp. 464-468.
IEEE DOI 2202
Image quality, Image coding, Task analysis, Visualization, Signal processing algorithms, Optimized production technology, gradient magnitude similarity (GMS) BibRef

Sang, Q.B.[Qing-Bing], Cao, Y.J.[Yu-Jie], Liu, L.X.[Li-Xiong], Hu, C.[Cong], Wu, X.J.[Xiao-Jun],
MP2020: Visual quality assessment database for macro photography images,
IET-IPR(16), No. 4, 2022, pp. 985-991.
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Zhou, Y.[Yu], Sun, Y.J.[Yan-Jing], Li, L.D.[Lei-Da], Gu, K.[Ke], Fang, Y.M.[Yu-Ming],
Omnidirectional Image Quality Assessment by Distortion Discrimination Assisted Multi-Stream Network,
CirSysVideo(32), No. 4, April 2022, pp. 1767-1777.
IEEE DOI 2204
Task analysis, Measurement, Distortion, Quality assessment, Sun, Image coding, Visualization, Image quality assessment, distortion discrimination BibRef

Zhang, X.F.[Xin-Feng], Lin, W.S.[Wei-Si], Huang, Q.M.[Qing-Ming],
Fine-Grained Image Quality Assessment: A Revisit and Further Thinking,
CirSysVideo(32), No. 5, May 2022, pp. 2746-2759.
IEEE DOI 2205
Distortion, Image coding, Databases, Image quality, Visualization, Transform coding, Image processing, Image quality assessment, human visual system BibRef

Saunier, S.[Sebastien], Karakas, G.[Gizem], Yalcin, I.[Ilyas], Done, F.[Fay], Mannan, R.[Rubinder], Albinet, C.[Clement], Goryl, P.[Philippe], Kocaman, S.[Sultan],
SkySat Data Quality Assessment within the EDAP Framework,
RS(14), No. 7, 2022, pp. xx-yy.
DOI Link 2205
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Hofbauer, H.[Heinz], Autrusseau, F.[Florent], Uhl, A.[Andreas],
Low Quality and Recognition of Image Content,
MultMed(24), 2022, pp. 3595-3610.
IEEE DOI 2207
Encryption, Databases, Observers, Image recognition, Cryptography, Visualization, Distortion, Selective encryption, image recognition, visual quality indices BibRef

Yang, J.C.[Jia-Chen], Yang, Y.[Yue], Wen, J.[Jiabao], Li, Y.[Yang], Ercisli, S.[Sezai],
Remote Sensing Image Information Quality Evaluation via Node Entropy for Efficient Classification,
RS(14), No. 17, 2022, pp. xx-yy.
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Athar, S.[Shahrukh], Wang, Z.[Zhou],
Degraded Reference Image Quality Assessment,
IP(32), 2023, pp. 822-837.
IEEE DOI 2301
Distortion, Image coding, Computer architecture, Pipelines, Feature extraction, Codes, Quality assessment, image quality databases BibRef

Rehman, A.[Abdul], Wang, Z.[Zhou],
Reduced-reference SSIM estimation,
ICIP10(289-292).
IEEE DOI 1009
structural similarity for Image quality. BibRef

Wang, Z.Y.[Zhe-Yin], Shen, L.Q.[Li-Quan], Wang, Z.Y.[Zheng-Yong], Lin, Y.F.[Yu-Fei], Jin, Y.L.[Yan-Liang],
Generation-Based Joint Luminance-Chrominance Learning for Underwater Image Quality Assessment,
CirSysVideo(33), No. 3, March 2023, pp. 1123-1139.
IEEE DOI 2303
Distortion, Image color analysis, Image quality, Feature extraction, Optical distortion, Image enhancement, Color, sharpness distortion BibRef

Zhu, R.[Rui], Zhou, F.[Fei], Yang, W.M.[Wen-Ming], Xue, J.H.[Jing-Hao],
Statistical hypothesis testing as a novel perspective of pooling for image quality assessment,
SP:IC(114), 2023, pp. 116942.
Elsevier DOI 2305
Image quality assessment, Pooling strategy, Hypothesis testing BibRef

Yu, S.D.[Shao-De], Wang, J.Y.[Jia-Yi], Gu, J.C.[Jia-Cheng], Jin, M.X.[Ming-Xue], Ma, Y.L.[Yun-Ling], Yang, L.J.[Li-Juan], Li, J.G.[Jian-Guang],
A hybrid indicator for realistic blurred image quality assessment,
JVCIR(94), 2023, pp. 103848.
Elsevier DOI 2306
Image quality assessment, Realistic blur, Feature selection, Machine learning BibRef

Ding, Y.Z.[Yao-Zong], Gu, X.F.[Xing-Fa], Liu, Y.[Yan], Zhang, H.[Hu], Cheng, T.H.[Tian-Hai], Li, J.[Juan], Wei, X.Q.[Xiang-Qin], Gao, M.[Min], Liang, M.[Man], Zhang, Q.[Qian],
GF-1 WFV Surface Reflectance Quality Evaluation in Countries along 'the Belt and Road',
RS(15), No. 22, 2023, pp. 5382.
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Zhou, W.M.[Wei-Min], Villa, U.[Umberto], Anastasio, M.A.[Mark A.],
Ideal Observer Computation by Use of Markov-Chain Monte Carlo With Generative Adversarial Networks,
MedImg(42), No. 12, December 2023, pp. 3715-3724.
IEEE DOI 2312
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Li, X.[Xin], Lu, Y.T.[Yi-Ting], Chen, Z.B.[Zhi-Bo],
FreqAlign: Excavating Perception-Oriented Transferability for Blind Image Quality Assessment From a Frequency Perspective,
MultMed(26), 2024, pp. 4652-4666.
IEEE DOI 2403
Distortion, Frequency-domain analysis, Feature extraction, Image quality, Task analysis, Adaptation models, frequency alignment BibRef

Liu, J.Z.[Jian-Zhao], Li, X.[Xin], Peng, Y.[Yanding], Yu, T.[Tao], Chen, Z.B.[Zhi-Bo],
SwinIQA: Learned Swin Distance for Compressed Image Quality Assessment,
CLIC22(1794-1798)
IEEE DOI 2210
Image quality, Learning systems, Image coding, Image representation, Feature extraction, Transformers, Distortion BibRef

Zhou, Z.H.[Ze-Hong], Zhou, F.[Fei], Qiu, G.P.[Guo-Ping],
Blind Image Quality Assessment Based on Separate Representations and Adaptive Interaction of Content and Distortion,
CirSysVideo(34), No. 4, April 2024, pp. 2484-2497.
IEEE DOI 2404
Distortion, Feature extraction, Task analysis, Visualization, Image quality, Adaptation models, Predictive models, feature reweighting BibRef

Ding, Q.[Qing], Shen, L.Q.[Li-Quan], Yu, L.[Liangwei], Yang, H.[Hao], Xu, M.[Mai],
Blind Quality Enhancement for Compressed Video,
MultMed(26), 2024, pp. 5782-5794.
IEEE DOI 2404
Feature extraction, Image coding, Estimation, Video coding, Image enhancement, Noise level, Image denoising, video coding BibRef

Tofighi, N.J.[Nafiseh Jabbari], Elfkir, M.H.[Mohamed Hedi], Imamoglu, N.[Nevrez], Ozcinar, C.[Cagri], Erdem, A.[Aykut], Erdem, E.[Erkut],
Omnidirectional image quality assessment with local-global vision transformers,
IVC(148), 2024, pp. 105151.
Elsevier DOI 2407
360-degree images, Image quality assessment, Vision transformers BibRef

Fan, X.D.[Xiao-Dong], Peng, C.[Chang], Jiang, X.L.[Xiao-Li], Han, Y.[Ying], Hou, L.M.[Li-Min],
Stacked deformable convolution network with weighted non-local attention and branch residual connection for image quality assessment,
JVCIR(103), 2024, pp. 104214.
Elsevier DOI Code:
WWW Link. 2409
Image quality assessment, Deep learning, Deformable convolution, Self-attention BibRef

Akundy, V.A.[Vyas Anirudh], Wang, Z.[Zhou],
4k or Not? - Automatic Image Resolution Assessment,
ICIAR20(I:61-65).
Springer DOI 2007
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Fang, Z.[Zilin], Ignatov, A.[Andrey], Zamfir, E.[Eduard], Timofte, R.[Radu],
SQAD: Automatic Smartphone Camera Quality Assessment and Benchmarking,
ICCV23(20475-20485)
IEEE DOI Code:
WWW Link. 2401
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Lin, Y.B.[Yong-Bing], Wan, L.[Lei], Ma, S.[Sha], Zhang, P.[Peike],
Feature Structure Similarity Index for Hybrid Human and Machine Vision,
ICIP23(1480-1484)
IEEE DOI 2312
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Chetouani, A.[Aladine],
Comparative Study of Saliency- and Scanpath-Based Approaches for Patch Selection in Image Quality Assessment,
ICIP23(2670-2674)
IEEE DOI 2312
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Zhao, G.[Ganning], Magoulianitis, V.[Vasileios], You, S.[Suya], Kuo, C.C.J.[C.C. Jay],
LGSQE: Lightweight Generated Sample Quality Evaluation,
ICIP23(1915-1919)
IEEE DOI 2312
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You, J.Y.[Jun-Yong], Lin, Y.[Yuan], Korhonen, J.[Jari],
Half of an Image is Enough for Quality Assessment,
ICIP23(61-65)
IEEE DOI 2312
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Chahine, N.[Nicolas], Calarasanu, A.S.[Ana-Stefania], Garcia-Civiero, D.[Davide], Cayla, T.[Théo], Ferradans, S.[Sira], Ponce, J.[Jean],
An Image Quality Assessment Dataset for Portraits,
CVPR23(9968-9978)
IEEE DOI 2309
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Achddou, R.[Raphaël], Gousseau, Y.[Yann], Ladjal, S.[Saïd],
Hybrid Training of Denoising Networks to Improve the Texture Acutance of Digital Cameras,
SSVM23(314-325).
Springer DOI 2307
Testing of camera frequency accuracy. BibRef

Chen, G.Y.[Guang Yi], Krzyzak, A.[Adam], Valev, V.[Ventzeslav],
A New Preprocessing Method for Measuring Image Visual Quality Robust to Rotation and Spatial Shifts,
SSSPR22(94-102).
Springer DOI 2301
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Li, X.T.[Xiao-Tong], Armour, W.[Wesley],
Intensity-Sensitive Similarity Indexes for Image Quality Assessment,
ICPR22(1975-1981)
IEEE DOI 2212
Image quality, Sensitivity, Visual systems, Indexes, Remote sensing BibRef

Li, Q.[Qiang], Yao, Z.L.[Zhao-Liang], Wang, J.J.[Jing-Jing], Tian, Y.[Ye], Yang, P.[Pengju], Xie, D.[Di], Pu, S.L.[Shi-Liang],
Semi-Supervised Ranking for Object Image Blur Assessment,
ICIP22(21-25)
IEEE DOI 2211
Face recognition, Supervised learning, Self-supervised learning, Semi-supervised learning, Reliability, Object recognition BibRef

Venkataramanan, A.K.[Abhinau K.], Stejerean, C.[Cosmin], Bovik, A.C.[Alan C.],
Funque: Fusion of Unified Quality Evaluators,
ICIP22(2147-2151)
IEEE DOI 2211
Industries, Visualization, Computational modeling, Transforms, Visual systems, Prediction algorithms, Quality assessment, Visual Multimethod Assessment Fusion BibRef

Zhang, Z.Y.[Zheng-Yu], Tian, S.S.[Shi-Shun], Zou, W.B.[Wen-Bin], Morin, L.[Luce], Zhang, L.[Lu],
Deeblif: Deep Blind Light Field Image Quality Assessment by Extracting Angular and Spatial Information,
ICIP22(2266-2270)
IEEE DOI 2211
Measurement, Image quality, Codes, Training data, Predictive models, Media, Feature extraction, Immersive media, light field, CNN BibRef

Alamgeer, S.[Sana], Farias, M.C.Q.[Mylène C. Q.],
Light Field Image Quality Assessment with Dense Atrous Convolutions,
ICIP22(2441-2445)
IEEE DOI 2211
Image quality, Deep learning, Image coding, Convolution, Neural networks, Streaming media, Feature extraction, Light Field Images BibRef

You, J.Y.[Jun-Yong], Yan, J.[Jie],
Explore Spatial and Channel Attention in Image Quality Assessment,
ICIP22(26-30)
IEEE DOI 2211
Image quality, Visualization, Sensitivity, Codes, Spatial databases, Quality assessment, Contrast sensitivity, visual mechanism BibRef

Tliba, M.[Marouane], Sekhri, A.[Aymen], Kerkouri, M.A.[Mohamed Amine], Chetouani, A.[Aladine],
Deep-Based Quality Assessment of Medical Images Through Domain Adaptation,
ICIP22(3692-3696)
IEEE DOI 2211
Measurement, Image quality, Adaptation models, Ultrasonic imaging, Predictive models, Data models, Quality assessment, Medical images, self-attention BibRef

Sendjasni, A.[Abderrezzaq], Traparic, D.[David], Larabi, M.C.[Mohamed-Chaker],
Investigating Normalization Methods for CNN-Based Image Quality Assessment,
ICIP22(4113-4117)
IEEE DOI 2211
Training, Degradation, Image quality, Databases, Image color analysis, Robustness, Distance measurement, model performance BibRef

Jiang, W.[Wei], Li, L.[Litian], Ma, Y.[Yi], Zhai, Y.Q.[Yong-Qi], Yang, Z.[Zheng], Wang, R.G.[Rong-Gang],
Image Quality Assessment with Transformers and Multi-Metric Fusion Modules,
CLIC22(1804-1808)
IEEE DOI 2210
Measurement, Image quality, Nonlinear distortion, Superresolution, Transformers, Feature extraction, Quality assessment BibRef

Yu, L.W.[Liang-Wei], Wang, Z.[Zhao], Ye, Y.[Yan], Zhu, L.Y.[Ling-Yu], Wang, S.Q.[Shi-Qi],
A Soft-ranked Index Fusion Framework with Saliency Weighting for Image Quality Assessment,
CLIC22(1809-1813)
IEEE DOI 2210
Image quality, Visualization, Image coding, Memory, Robustness, Quality assessment BibRef

He, G.[Gang], Wang, Y.[Yong], Xu, L.[Li], Zhang, W.L.[Wen-Li], Sun, M.[Ming], Wen, X.[Xing],
Focused Feature Differentiation Network for Image Quality Assessment,
CLIC22(1799-1803)
IEEE DOI 2210
Image quality, Deep learning, Fuses, Convolution, Machine vision, Conferences, Neural networks BibRef

Estrada, D.N.D.[David Norman Díaz], Pedersen, M.[Marius],
Impact of Pooling Methods on Image Quality Metrics,
IPTA22(1-6)
IEEE DOI 2206
Measurement, Image quality, Databases, Image processing, image quality, metrics, pooling BibRef

Anikeeva, I., Chibunichev, A.,
Requirements for Aerial Images Quality, Obtained for Mapping Purposes,
ISPRS21(B2-2021: 777-784).
DOI Link 2201
BibRef

Yalcin, I., Kocaman, S., Saunier, S., Albinet, C.,
Radiometric Quality Assessment for Maxar HD Imagery,
ISPRS21(B3-2021: 797-804).
DOI Link 2201
BibRef

Bang, D.[Duhyeon], Shim, H.J.[Hyun-Jung],
MGGAN: Solving Mode Collapse Using Manifold-Guided Training,
MELEX21(2347-2356)
IEEE DOI 2112
Training, Manifolds, Image quality, Visualization, Transforms, Tools, Network architecture BibRef

Hammou, D.[Dounia], Fezza, S.A.[Sid Ahmed], Hamidouche, W.[Wassim],
EGB: Image Quality Assessment based on Ensemble of Gradient Boosting,
NTIRE21(541-549)
IEEE DOI 2109
Image quality, Measurement, Computational modeling, Tools, Boosting, Prediction algorithms, Feature extraction BibRef

Guo, Q.Y.[Qian-Yu], Wen, J.[Jing],
Multi-level Fusion Based Deep Convolutional Network for Image Quality Assessment,
MOI2QDN20(670-678).
Springer DOI 2103
BibRef

Xu, S.[Sascha], Bauer, J.[Jan], Axmann, B.[Benjamin], Maass, W.[Wolfgang],
Cd2: Combined Distances of Contrast Distributions for Image Quality Analysis,
ISVC20(II:444-457).
Springer DOI 2103
BibRef

Hao, S., Li, S.,
A Weighted Mean Absolute Error Metric for Image Quality Assessment,
VCIP20(330-333)
IEEE DOI 2102
Distortion, Image quality, Databases, Mathematical model, Image edge detection, Quality assessment, PSNR, Error map, MAE, FR-IQA, distortion significance coefficient BibRef

Gu, S.Y.[Shu-Yang], Bao, J.M.[Jian-Min], Chen, D.[Dong], Wen, F.[Fang],
GIQA: Generated Image Quality Assessment,
ECCV20(XI:369-385).
Springer DOI 2011
BibRef