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Evolutionary algorithms; Wigner distribution; Image fusion;
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BibRef
Alparone, L.[Luciano],
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BibRef
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IP(18), No. 7, July 2009, pp. 1409-1423.
IEEE DOI
0906
BibRef
Earlier: A2, A1, A4, A3:
An image quality assessment metric based contourlet,
ICIP08(1172-1175).
IEEE DOI
0810
BibRef
He, L.[Lihuo],
Tao, D.C.[Da-Cheng],
Li, X.L.[Xue-Long],
Gao, X.B.[Xin-Bo],
Sparse representation for blind image quality assessment,
CVPR12(1146-1153).
IEEE DOI
1208
BibRef
Ma, L.[Lin],
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Ngan, K.N.[King Ngi],
No-Reference Retargeted Image Quality Assessment Based on Pairwise
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MultMed(18), No. 11, November 2016, pp. 2228-2237.
IEEE DOI
1609
distortion
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Ma, L.[Lin],
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SP:IC(28), No. 8, 2013, pp. 884-902.
Elsevier DOI
1309
Image quality assessment (IQA)
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Ma, L.[Lin],
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Adaptive Block-size Transform based Just-Noticeable Difference model
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Elsevier DOI
1104
BibRef
Earlier: A1, A3, A4, A2:
Video Quality Assessment based on Adaptive Block-size Transform
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ICIP10(2501-2504).
IEEE DOI
1009
Just-Noticeable Difference (JND); Adaptive Block-size Transform (ABT);
Human Visual System (HVS); Spatial Content Similarity (SCS); Motion
Characteristic Similarity (MCS)
BibRef
Ma, L.[Lin],
Li, S.N.[Song-Nan],
Ngan, K.N.[King N.],
Motion trajectory based visual saliency for video quality assessment,
ICIP11(233-236).
IEEE DOI
1201
BibRef
Ma, L.[Lin],
Li, S.N.[Song-Nan],
Zhang, F.[Fan],
Ngan, K.N.[King Ngi],
Reduced-Reference Image Quality Assessment Using Reorganized DCT-Based
Image Representation,
MultMed(13), No. 4, 2011, pp. 824-829.
IEEE DOI
1108
See also Image Quality Assessment by Separately Evaluating Detail Losses and Additive Impairments.
BibRef
Wu, Q.B.[Qing-Bo],
Li, H.L.[Hong-Liang],
Meng, F.M.[Fan-Man],
Ngan, K.N.[King Ngi],
Zhu, S.Y.[Shu-Yuan],
No reference image quality assessment metric via multi-domain
structural information and piecewise regression,
JVCIR(32), No. 1, 2015, pp. 205-216.
Elsevier DOI
1511
No reference image quality assessment
BibRef
Ye, P.[Peng],
Doermann, D.[David],
No-Reference Image Quality Assessment Using Visual Codebooks,
IP(21), No. 7, July 2012, pp. 3129-3138.
IEEE DOI
1206
BibRef
Earlier:
No-reference image quality assessment based on visual codebook,
ICIP11(3089-3092).
IEEE DOI
1201
BibRef
Xu, J.T.[Jing-Tao],
Ye, P.[Peng],
Li, Q.H.[Qiao-Hong],
Du, H.Q.[Hai-Qing],
Liu, Y.[Yong],
Doermann, D.[David],
Blind Image Quality Assessment Based on High Order Statistics
Aggregation,
IP(25), No. 9, September 2016, pp. 4444-4457.
IEEE DOI
1609
feature extraction
BibRef
Earlier: A1, A3, A2, A4, A5, Only:
Local feature aggregation for blind image quality assessment,
VCIP15(1-4)
IEEE DOI
1605
BibRef
Earlier: A1, A3, A2, A4, A5, Only:
Statistical metric fusion for image quality assessment,
VCIP14(133-136)
IEEE DOI
1504
Correlation.
image fusion
BibRef
Li, Q.H.[Qiao-Hong],
Lin, W.S.[Wei-Si],
Xu, J.T.[Jing-Tao],
Fang, Y.M.[Yu-Ming],
Blind Image Quality Assessment Using Statistical Structural and
Luminance Features,
MultMed(18), No. 12, December 2016, pp. 2457-2469.
IEEE DOI
1612
Data mining
BibRef
Wu, J.J.[Jin-Jian],
Zeng, J.C.[Ji-Chen],
Liu, Y.X.[Yong-Xu],
Shi, G.M.[Guang-Ming],
Lin, W.S.[Wei-Si],
Hierarchical Feature Degradation Based Blind Image Quality Assessment,
PBVDL17(510-517)
IEEE DOI
1802
Degradation, Distortion, Feature extraction, Image quality,
Semantics, Visual perception, Visualization
BibRef
Xu, J.T.[Jing-Tao],
Ye, P.[Peng],
Li, Q.H.[Qiao-Hong],
Liu, Y.[Yong],
Doermann, D.[David],
No-Reference Document Image Quality Assessment Based on High Order
Image Statistics,
ICIP16(3289-3293)
IEEE DOI
1610
Databases
See also Visual Structural Degradation Based Reduced-Reference Image Quality Assessment.
BibRef
Ye, P.[Peng],
Kumar, J.[Jayant],
Doermann, D.[David],
Beyond Human Opinion Scores: Blind Image Quality Assessment Based on
Synthetic Scores,
CVPR14(4241-4248)
IEEE DOI
1409
image quality;unsupervised learning
BibRef
Ye, P.[Peng],
Doermann, D.[David],
Active Sampling for Subjective Image Quality Assessment,
CVPR14(4249-4256)
IEEE DOI
1409
active learning;quality of experience;subjective image quality
BibRef
Ye, P.[Peng],
Kumar, J.[Jayant],
Kang, L.[Le],
Doermann, D.[David],
Real-Time No-Reference Image Quality Assessment Based on Filter
Learning,
CVPR13(987-994)
IEEE DOI
1309
BibRef
Earlier:
Unsupervised feature learning framework for no-reference image quality
assessment,
CVPR12(1098-1105).
IEEE DOI
1208
image quality assessment
BibRef
Xu, J.T.[Jing-Tao],
Ye, P.[Peng],
Liu, Y.[Yong],
Doermann, D.[David],
No-reference video quality assessment via feature learning,
ICIP14(491-495)
IEEE DOI
1502
Feature extraction
BibRef
Peng, P.[Peng],
Li, Z.N.[Ze-Nian],
A Mixture of Experts Approach to Multi-strategy Image Quality
Assessment,
ICIAR12(I: 123-130).
Springer DOI
1206
BibRef
Kang, L.[Le],
Ye, P.[Peng],
Li, Y.[Yi],
Doermann, D.[David],
Convolutional Neural Networks for No-Reference Image Quality
Assessment,
CVPR14(1733-1740)
IEEE DOI
1409
Convolutional Neural Network;image quality assessment
BibRef
Kumar, J.[Jayant],
Ye, P.[Peng],
Doermann, D.[David],
A Dataset for Quality Assessment of Camera Captured Document Images,
CBDAR13(113-125).
Springer DOI
1404
BibRef
Kang, L.[Le],
Ye, P.[Peng],
Li, Y.[Yi],
Doermann, D.[David],
A deep learning approach to document image quality assessment,
ICIP14(2570-2574)
IEEE DOI
1502
Accuracy
BibRef
Ye, P.[Peng],
Doermann, D.,
Document Image Quality Assessment: A Brief Survey,
ICDAR13(723-727)
IEEE DOI
1312
document image processing
BibRef
Serir, A.[Amina],
Beghdadi, A.[Azeddine],
Kerouh, F.,
No-reference blur image quality measure based on multiplicative
multiresolution decomposition,
JVCIR(24), No. 7, 2013, pp. 911-925.
Elsevier DOI
1309
Blur
BibRef
De, K.[Kanjar],
Masilamani, V.,
A Spatial Domain Object Separability Based No-Reference Image Quality
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IJIG(13), No. 2, April 2013, pp. 1340005.
DOI Link
1308
BibRef
Fang, Y.M.[Yu-Ming],
Ma, K.[Kede],
Wang, Z.[Zhou],
Lin, W.S.[Wei-Si],
Fang, Z.J.[Zhi-Jun],
Zhai, G.T.[Guang-Tao],
No-Reference Quality Assessment of Contrast-Distorted Images Based on
Natural Scene Statistics,
SPLetters(22), No. 7, July 2015, pp. 838-842.
IEEE DOI
1412
distortion
BibRef
Virtanen, T.,
Nuutinen, M.,
Vaahteranoksa, M.,
Oittinen, P.,
Hakkinen, J.,
CID2013: A Database for Evaluating No-Reference Image Quality
Assessment Algorithms,
IP(24), No. 1, January 2015, pp. 390-402.
IEEE DOI
1502
Dataset, Image Quality. cameras
BibRef
Nuutinen, M.,
Virtanen, T.,
Vaahteranoksa, M.,
Vuori, T.,
Oittinen, P.,
Häkkinen, J.,
CVD2014: A Database for Evaluating No-Reference Video Quality
Assessment Algorithms,
IP(25), No. 7, July 2016, pp. 3073-3086.
IEEE DOI
1606
image sequences
BibRef
Guan, J.W.[Jing-Wei],
Zhang, W.[Wei],
Gu, J.[Jason],
Ren, H.L.[Hong-Liang],
No-reference blur assessment based on edge modeling,
JVCIR(29), No. 1, 2015, pp. 1-7.
Elsevier DOI
1504
Image quality assessment
BibRef
Zhang, M.[Min],
Muramatsu, C.,
Zhou, X.R.,
Hara, T.,
Fujita, H.,
Blind Image Quality Assessment Using the Joint Statistics of
Generalized Local Binary Pattern,
SPLetters(22), No. 2, February 2015, pp. 207-210.
IEEE DOI
1410
Gaussian processes
BibRef
Zhang, M.[Min],
Xie, J.[Jin],
Zhou, X.R.[Xiang-Rong],
Fujita, H.,
No reference image quality assessment based on local binary pattern
statistics,
VCIP13(1-6)
IEEE DOI
1402
feature extraction
BibRef
Søgaard, J.[Jacob],
Forchhammer, S.[Søren],
Korhonen, J.,
No-Reference Video Quality Assessment Using Codec Analysis,
CirSysVideo(25), No. 10, October 2015, pp. 1637-1650.
IEEE DOI
1511
discrete cosine transforms
BibRef
Huang, X.[Xin],
Søgaard, J.[Jacob],
Forchhammer, S.[Søren],
No-reference pixel based video quality assessment for HEVC decoded
video,
JVCIR(43), No. 1, 2017, pp. 173-184.
Elsevier DOI
1702
BibRef
Earlier:
No-reference video quality assessment by HEVC codec analysis,
VCIP15(1-4)
IEEE DOI
1605
HEVC analysis.
Elastic Net
BibRef
Wu, Q.,
Li, H.,
Meng, F.,
Ngan, K.N.,
Luo, B.,
Huang, C.,
Zeng, B.,
Blind Image Quality Assessment Based on Multichannel Feature Fusion
and Label Transfer,
CirSysVideo(26), No. 3, March 2016, pp. 425-440.
IEEE DOI
1603
Discrete cosine transforms
BibRef
Wu, Q.,
Li, H.,
Wang, Z.,
Meng, F.,
Luo, B.,
Li, W.,
Ngan, K.N.,
Blind Image Quality Assessment Based on Rank-Order Regularized
Regression,
MultMed(19), No. 11, November 2017, pp. 2490-2504.
IEEE DOI
1710
Distortion, Image quality, Learning systems, Measurement,
Optimization, Predictive models, Training,
rank-order, regularized regression
BibRef
Li, J.[Jie],
Zou, L.[Lian],
Yan, J.[Jia],
Deng, D.X.[De-Xiang],
Qu, T.[Tao],
Xie, G.H.[Gui-Hui],
No-Reference Image Quality Assessment Using Prewitt Magnitude Based on
Convolutional Neural Networks,
SIViP(10), No. 4, April 2016, pp. 609-616.
Springer DOI
1604
BibRef
Hadizadeh, H.[Hadi],
Bajic, I.V.[Ivan V.],
No-reference image quality assessment using statistical
wavelet-packet features,
PRL(80), No. 1, 2016, pp. 144-149.
Elsevier DOI
1609
Image quality assessment
BibRef
Hadizadeh, H.[Hadi],
Bajic, I.V.[Ivan V.],
Color Gaussian Jet Features For No-Reference Quality Assessment of
Multiply-Distorted Images,
SPLetters(23), No. 12, December 2016, pp. 1717-1721.
IEEE DOI
1612
feature extraction
BibRef
Hadizadeh, H.[Hadi],
Bajic, I.V.[Ivan V.],
Full-Reference Objective Quality Assessment of Tone-Mapped Images,
MultMed(20), No. 2, February 2018, pp. 392-404.
IEEE DOI
1801
Distortion, Dynamic range, Feature extraction,
Image color analysis, Indexes, Quality assessment, Visualization,
tone mapping
BibRef
Javaran, T.A.[Taiebeh Askari],
Hassanpour, H.[Hamid],
Abolghasemi, V.[Vahid],
A noise-immune no-reference metric for estimating blurriness value of
an image,
SP:IC(47), No. 1, 2016, pp. 218-228.
Elsevier DOI
1610
No-reference metric
BibRef
Javaran, T.A.[Taiebeh Askari],
Hassanpour, H.[Hamid],
Abolghasemi, V.[Vahid],
Non-blind image deconvolution using a regularization based on
re-blurring process,
CVIU(154), No. 1, 2017, pp. 16-34.
Elsevier DOI
1612
Non-blind image deconvolution
BibRef
Nafchi, H.Z.,
Shahkolaei, A.,
Hedjam, R.,
Cheriet, M.,
MUG: A Parameterless No-Reference JPEG Quality Evaluator Robust to
Block Size and Misalignment,
SPLetters(23), No. 11, November 2016, pp. 1577-1581.
IEEE DOI
1609
Discrete cosine transforms
BibRef
Goodall, T.R.[Todd R.],
Katsavounidis, I.[Ioannis],
Li, Z.[Zhi],
Aaron, A.[Anne],
Bovik, A.C.[Alan C.],
Blind Picture Upscaling Ratio Prediction,
SPLetters(23), No. 12, December 2016, pp. 1801-1805.
IEEE DOI
1612
image processing
BibRef
Yang, L.P.[Lu-Ping],
Du, H.Q.[Hai-Qing],
Xu, J.T.[Jing-Tao],
Liu, Y.[Yong],
Blind Image Quality Assessment on Authentically Distorted Images with
Perceptual Features,
ICIP16(2042-2046)
IEEE DOI
1610
Decision support systems
BibRef
Vega, M.T.[Maria Torres],
Mocanu, D.C.[Decebal Constantin],
Stavrou, S.[Stavros],
Liotta, A.[Antonio],
Predictive no-reference assessment of video quality,
SP:IC(52), No. 1, 2017, pp. 20-32.
Elsevier DOI
1701
Quality of experience
BibRef
Ma, C.[Chao],
Yang, C.Y.[Chih-Yuan],
Yang, X.K.[Xiao-Kang],
Yang, M.H.[Ming-Hsuan],
Learning a no-reference quality metric for single-image
super-resolution,
CVIU(158), No. 1, 2017, pp. 1-16.
Elsevier DOI
1704
Image quality assessment
BibRef
Zhang, Y.[Yi],
Phan, T.D.[Thien D.],
Chandler, D.M.[Damon M.],
Reduced-reference image quality assessment based on distortion
families of local perceived sharpness,
SP:IC(55), No. 1, 2017, pp. 130-145.
Elsevier DOI
1705
Reduced-reference, quality, assessment
BibRef
Zhang, Y.[Yi],
Chandler, D.M.[Damon M.],
Learning natural statistics of binocular contrast for no reference
quality assessment of stereoscopic images,
ICIP17(186-190)
IEEE DOI
1803
Distortion, Feature extraction, Image quality, Solid modeling,
Stereo image processing,
no-reference quality assessment
BibRef
Kundu, D.,
Ghadiyaram, D.,
Bovik, A.C.,
Evans, B.L.,
No-Reference Quality Assessment of Tone-Mapped HDR Pictures,
IP(26), No. 6, June 2017, pp. 2957-2971.
IEEE DOI
1705
Distortion, Feature extraction, Image color analysis,
Image quality, Predictive models, Standards, Visualization,
Image quality assessment, high dynamic range,
natural scene statistics, no-reference
BibRef
Kundu, D.,
Ghadiyaram, D.,
Bovik, A.C.,
Evans, B.L.,
Large-Scale Crowdsourced Study for Tone-Mapped HDR Pictures,
IP(26), No. 10, October 2017, pp. 4725-4740.
IEEE DOI
1708
crowdsourcing, visual databases,
ESPL-LIVE HDR image database, MEF databases, SDR,
bypass HDR creation,
high-dynamic range images, human opinion,
multiexposure fusion,
standard dynamic range images, Algorithm design and analysis,
Databases, Dynamic range, Image coding, Observers,
Standards, Image quality assessment,
high dynamic range, subjective study
BibRef
Li, S.[Shuang],
Yang, Z.W.[Ze-Wei],
Li, H.S.[Hong-Sheng],
Statistical Evaluation of No-Reference Image Quality Assessment
Metrics for Remote Sensing Images,
IJGI(6), No. 5, 2017, pp. xx-yy.
DOI Link
1706
BibRef
Qin, M.[Min],
Lv, X.X.[Xiao-Xin],
Chen, X.H.[Xiao-Hui],
Wang, W.D.[Wei-Dong],
access icon openaccess Hybrid NSS features for no-reference image
quality assessment,
IET-IPR(11), No. 6, June 2017, pp. 443-449.
DOI Link
1706
BibRef
Zhang, L.[Lin],
Gu, Z.Y.[Zhong-Yi],
Liu, X.X.[Xiao-Xu],
Li, H.Y.[Hong-Yu],
Lu, J.W.[Jian-Wei],
Training Quality-Aware Filters for No-Reference Image Quality
Assessment,
MultMedMag(21), No. 4, October 2014, pp. 67-75.
IEEE DOI
1502
filtering theory
BibRef
Zhang, L.[Lin],
Zhang, L.[Lei],
Mou, X.Q.[Xuan-Qin],
Zhang, D.[David],
A comprehensive evaluation of full reference image quality assessment
algorithms,
ICIP12(1477-1480).
IEEE DOI
1302
BibRef
Fang, R.,
Al-Bayaty, R.,
Wu, D.,
BNB Method for No-Reference Image Quality Assessment,
CirSysVideo(27), No. 7, July 2017, pp. 1381-1391.
IEEE DOI
1707
Feature extraction, Image quality, Measurement, Media,
Nonlinear distortion, Support vector machines, Artifact metric,
Laplace distribution, image quality assessment (IQA),
no-reference, (NR)
BibRef
Ma, K.,
Liu, W.,
Liu, T.,
Wang, Z.,
Tao, D.,
dipIQ: Blind Image Quality Assessment by Learning-to-Rank
Discriminable Image Pairs,
IP(26), No. 8, August 2017, pp. 3951-3964.
IEEE DOI
1707
differentiation, image processing,
learning (artificial intelligence), DIL inferred quality index,
DIP inferred quality index, ListNet algorithm, RankNet,
blind image quality assessment,
digital image quality prediction, dipIQ index,
gigantic image space,
group maximum differentiation competition method,
image processing, learning BIQA model,
learning-to-rank discriminable image pairs,
listwise L2R algorithm, opinion-unaware BIQA,
pairwise learning-to-rank algorithm,
quality-discriminable image lists, Electronics packaging,
Feature extraction, Image quality, Indexes, Predictive models,
Training, Blind image quality assessment (BIQA), RankNet, dipIQ,
gMAD, learning-to-rank (L2R), quality-discriminable, image, pair, (DIP)
BibRef
Li, J.[Jie],
Yan, J.[Jia],
Deng, D.X.[De-Xiang],
Shi, W.X.[Wen-Xuan],
Deng, S.F.[Song-Feng],
No-reference image quality assessment based on hybrid model,
SIViP(11), No. 6, September 2017, pp. 985-992.
Springer DOI
1708
BibRef
Li, L.,
Xia, W.,
Lin, W.S.[Wei-Si],
Fang, Y.M.[Yu-Ming],
Wang, S.,
No-Reference and Robust Image Sharpness Evaluation Based on
Multiscale Spatial and Spectral Features,
MultMed(19), No. 5, May 2017, pp. 1030-1040.
IEEE DOI
1704
Computational modeling
BibRef
Li, Q.H.[Qiao-Hong],
Lin, W.S.[Wei-Si],
Fang, Y.M.[Yu-Ming],
No-Reference Quality Assessment for Multiply-Distorted Images in
Gradient Domain,
SPLetters(23), No. 4, April 2016, pp. 541-545.
IEEE DOI
1604
Databases
BibRef
Xu, L.[Long],
Li, J.[Jia],
Lin, W.S.[Wei-Si],
Zhang, Y.B.[Yong-Bing],
Ma, L.[Lin],
Fang, Y.M.[Yu-Ming],
Yan, Y.H.[Yi-Hua],
Multi-Task Rank Learning for Image Quality Assessment,
CirSysVideo(27), No. 9, September 2017, pp. 1833-1843.
IEEE DOI
1709
Distortion, Image quality, Predictive models,
Solid modeling, Training, Transform coding,
Image quality assessment (IQA), machine learning (ML),
mean opinion score (MOS), pairwise comparison, rank, learning
BibRef
Wang, Z.H.[Zhi-Hua],
Ma, K.[Kede],
Active Fine-Tuning From gMAD Examples Improves Blind Image Quality
Assessment,
PAMI(44), No. 9, September 2022, pp. 4577-4590.
IEEE DOI
2208
Computational modeling, Databases, Adaptation models, Training,
Predictive models, Task analysis, Image quality,
active learning
BibRef
Zhang, W.X.[Wei-Xia],
Li, D.Q.[Ding-Quan],
Ma, C.[Chao],
Zhai, G.T.[Guang-Tao],
Yang, X.K.[Xiao-Kang],
Ma, K.[Kede],
Continual Learning for Blind Image Quality Assessment,
PAMI(45), No. 3, March 2023, pp. 2864-2878.
IEEE DOI
2302
Distortion, Training, Image quality, Biological system modeling,
Databases, Computational modeling, Robustness, subpopulation shift
BibRef
Gu, K.[Ke],
Zhai, G.T.[Guang-Tao],
Yang, X.K.[Xiao-Kang],
Zhang, W.J.[Wen-Jun],
Deep Learning Network for Blind Image Quality Assessment,
ICIP14(511-515)
IEEE DOI
1502
Biological neural networks
BibRef
Ma, K.[Kede],
Liu, X.L.[Xue-Lin],
Fang, Y.M.[Yu-Ming],
Simoncelli, E.P.[Eero P.],
Blind Image Quality Assessment by Learning from Multiple Annotators,
ICIP19(2344-2348)
IEEE DOI
1910
Blind image quality assessment, convolutional neural networks, gMAD competition
BibRef
Shao, F.[Feng],
Tian, W.J.[Wei-Jun],
Lin, W.S.[Wei-Si],
Jiang, G.Y.[Gang-Yi],
Dai, Q.H.[Qiong-Hai],
Learning Sparse Representation for No-Reference Quality Assessment of
Multiply Distorted Stereoscopic Images,
MultMed(19), No. 8, August 2017, pp. 1821-1836.
IEEE DOI
1708
Databases, Distortion, Image quality, Measurement,
Stereo image processing,
Visualization, Blind/no reference, binocular combination,
multiply distorted stereoscopic image (MDSI), sparse, representation
BibRef
Jiang, Q.P.[Qiu-Ping],
Shao, F.[Feng],
Gao, W.[Wei],
Chen, Z.[Zhuo],
Jiang, G.Y.[Gang-Yi],
Ho, Y.S.[Yo-Sung],
Unified No-Reference Quality Assessment of Singly and Multiply
Distorted Stereoscopic Images,
IP(28), No. 4, April 2019, pp. 1866-1881.
IEEE DOI
1901
feature extraction, image reconstruction,
learning (artificial intelligence), regression analysis,
local visual primitive
BibRef
Hu, B.[Bo],
Li, L.[Leida],
Wu, J.J.[Jin-Jian],
Wang, S.Q.[Shi-Qi],
Tang, L.[Lu],
Qian, J.S.[Jian-Sheng],
No-reference quality assessment of compressive sensing image recovery,
SP:IC(58), No. 1, 2017, pp. 165-174.
Elsevier DOI
1710
Image, quality, assessment
BibRef
Gu, K.,
Jakhetiya, V.,
Qiao, J.F.,
Li, X.,
Lin, W.,
Thalmann, D.,
Model-Based Referenceless Quality Metric of 3D Synthesized Images
Using Local Image Description,
IP(27), No. 1, January 2018, pp. 394-405.
IEEE DOI
1712
augmented reality, autoregressive processes, image texture,
rendering (computer graphics), video signal processing,
saliency
BibRef
Kerouh, F.[Fatma],
Ziou, D.[Djemel],
Serir, A.[Amina],
Histogram modelling-based no reference blur quality measure,
SP:IC(60), No. 1, 2018, pp. 22-28.
Elsevier DOI
1712
BibRef
And:
A multiresolution DCT-based blind blur quality measure,
IPTA17(1-6)
IEEE DOI
1804
blind source separation, discrete cosine transforms,
exponential distribution, image coding, image enhancement,
probability density function.
Blind image quality
BibRef
Siahaan, E.[Ernestasia],
Hanjalic, A.[Alan],
Redi, J.A.[Judith A.],
Semantic-aware blind image quality assessment,
SP:IC(60), No. 1, 2018, pp. 237-252.
Elsevier DOI
1712
Blind image quality assessment
BibRef
Ma, K.[Kede],
Liu, W.T.[Wen-Tao],
Zhang, K.[Kai],
Duanmu, Z.F.[Zheng-Fang],
Wang, Z.[Zhou],
Zuo, W.M.[Wang-Meng],
End-to-End Blind Image Quality Assessment Using Deep Neural Networks,
IP(27), No. 3, March 2018, pp. 1202-1213.
IEEE DOI
1801
distortion, image representation,
learning (artificial intelligence), neural nets, MEON index,
multi-task learning
BibRef
Zhang, W.X.[Wei-Xia],
Ma, K.[Kede],
Yan, J.[Jia],
Deng, D.X.[De-Xiang],
Wang, Z.[Zhou],
Blind Image Quality Assessment Using a Deep Bilinear Convolutional
Neural Network,
CirSysVideo(30), No. 1, January 2020, pp. 36-47.
IEEE DOI
2002
convolutional neural nets, feature extraction, gradient methods,
image classification, image representation,
perceptual image processing
BibRef
Wang, Z.L.[Zhong-Ling],
Athar, S.[Shahrukh],
Wang, Z.[Zhou],
Blind Quality Assessment of Multiply Distorted Images Using Deep Neural
Networks,
ICIAR19(I:89-101).
Springer DOI
1909
BibRef
Kottayil, N.K.,
Valenzise, G.,
Dufaux, F.,
Cheng, I.,
Blind Quality Estimation by Disentangling Perceptual and Noisy
Features in High Dynamic Range Images,
IP(27), No. 3, March 2018, pp. 1512-1525.
IEEE DOI
1801
BibRef
And: A1, A3, A4, A2:
ICIP18(281-285)
IEEE DOI
1809
Distortion, Dynamic range, Image quality, Predictive models,
Support vector machines, Visual systems, Visualization,
no reference quality assessment.
Distortion measurement, Mathematical model, Training
BibRef
Wu, Q.,
Li, H.,
Meng, F.,
Ngan, K.N.,
Generic Proposal Evaluator: A Lazy Learning Strategy Toward Blind
Proposal Quality Assessment,
ITS(19), No. 1, January 2018, pp. 306-319.
IEEE DOI
1801
Algorithm design and analysis, Measurement, Proposals,
Quality assessment, Training, Visualization, Object proposal,
lazy learning
BibRef
Liu, T.J.,
Liu, K.H.,
No-Reference Image Quality Assessment by Wide-Perceptual-Domain
Scorer Ensemble Method,
IP(27), No. 3, March 2018, pp. 1138-1151.
IEEE DOI
1801
feature extraction, image enhancement, image fusion,
learning (artificial intelligence),
wide-perceptual-domain scorer (WPDS)
BibRef
Bianco, S.[Simone],
Celona, L.[Luigi],
Napoletano, P.[Paolo],
Schettini, R.[Raimondo],
On the use of deep learning for blind image quality assessment,
SIViP(12), No. 2, February 2018, pp. 355-362.
Springer DOI
1802
BibRef
Tian, S.,
Zhang, L.,
Morin, L.,
Déforges, O.,
NIQSV: A No-Reference Synthesized View Quality Assessment Metric,
IP(27), No. 4, April 2018, pp. 1652-1664.
IEEE DOI
1802
data compression, image texture, rendering (computer graphics),
video coding, No,
view synthesis
BibRef
Tian, S.,
Zhang, L.,
Morin, L.,
Déforges, O.,
A Benchmark of DIBR Synthesized View Quality Assessment Metrics on a
New Database for Immersive Media Applications,
MultMed(21), No. 5, May 2019, pp. 1235-1247.
IEEE DOI
1905
rendering (computer graphics), video coding, visual databases,
DIBR-synthesized views, video coding,
quality assessment
BibRef
Gu, J.[Jie],
Meng, G.F.[Gao-Feng],
Redi, J.A.,
Xiang, S.M.[Shi-Ming],
Pan, C.H.[Chun-Hong],
Blind Image Quality Assessment via Vector Regression and Object
Oriented Pooling,
MultMed(20), No. 5, May 2018, pp. 1140-1153.
IEEE DOI
1805
Feature extraction, Image quality, Neural networks,
Object detection, Object oriented modeling, Proposals,
vector regression
BibRef
Gu, J.[Jie],
Meng, G.F.[Gao-Feng],
Xiang, S.M.[Shi-Ming],
Pan, C.H.[Chun-Hong],
Blind image quality assessment via learnable attention-based pooling,
PR(91), 2019, pp. 332-344.
Elsevier DOI
1904
Image quality assessment, Perceptual image quality,
Visual attention, Convolutional neural network, Learnable pooling
BibRef
Jiang, Q.,
Shao, F.,
Lin, W.,
Gu, K.,
Jiang, G.,
Sun, H.,
Optimizing Multistage Discriminative Dictionaries for Blind Image
Quality Assessment,
MultMed(20), No. 8, August 2018, pp. 2035-2048.
IEEE DOI
1808
feature extraction, image representation, natural scenes,
optimisation, regression analysis, singular value decomposition,
reconstruction residual
BibRef
Min, X.,
Gu, K.,
Zhai, G.,
Liu, J.,
Yang, X.,
Chen, C.W.,
Blind Quality Assessment Based on Pseudo-Reference Image,
MultMed(20), No. 8, August 2018, pp. 2049-2062.
IEEE DOI
1808
distortion, image processing, natural scenes, regression analysis,
perfect quality image, distorted image, conventional IQA metrics,
noisiness
BibRef
Al-Bandawi, H.[Hussein],
Deng, G.[Guang],
Blind image quality assessment based on Benford's law,
IET-IPR(12), No. 11, November 2018, pp. 1983-1993.
DOI Link
1810
BibRef
Zhou, Y.[Yu],
Li, L.[Leida],
Wu, J.J.[Jin-Jian],
Gu, K.[Ke],
Dong, W.S.[Wei-Sheng],
Shi, G.M.[Guang-Ming],
Blind Quality Index for Multiply Distorted Images Using Biorder
Structure Degradation and Nonlocal Statistics,
MultMed(20), No. 11, November 2018, pp. 3019-3032.
IEEE DOI
1810
Distortion, Degradation, Distortion measurement, Graphical models,
Distribution functions, Transform coding, Quality evaluation,
nonlocal statistics
BibRef
Cai, H.[Hao],
Li, L.[Leida],
Yi, Z.L.[Zi-Li],
Gong, M.L.[Ming-Lun],
Towards a blind image quality evaluator using multi-scale
second-order statistics,
SP:IC(71), 2019, pp. 88-99.
Elsevier DOI
1901
Image quality assessment, Bivariate statistics,
Derivative pattern, No-reference
BibRef
Joshi, P.[Piyush],
Prakash, S.[Surya],
Rawat, S.[Sonika],
Continuous wavelet transform-based no-reference quality assessment of
deblocked images,
VC(34), No. 12, December 2018, pp. 1739-1748.
WWW Link.
1811
BibRef
Oszust, M.[Mariusz],
No-reference image quality assessment with local features and
high-order derivatives,
JVCIR(56), 2018, pp. 15-26.
Elsevier DOI
1811
Image quality assessment, No-reference, Local features,
Support vector regression
BibRef
Miao, X.K.[Xi-Kui],
Lee, D.J.[Dah-Jye],
Cheng, X.Z.[Xiang-Zheng],
Yang, X.Y.[Xiao-Yu],
Reduced-Reference Image Quality Assessment Based on Improved Local
Binary Pattern,
ISVC18(382-394).
Springer DOI
1811
BibRef
Freitas, P.G.,
Akamine, W.Y.L.,
Farias, M.C.Q.,
No-Reference Image Quality Assessment Using Orthogonal Color Planes
Patterns,
MultMed(20), No. 12, December 2018, pp. 3353-3360.
IEEE DOI
1812
feature extraction, image colour analysis, image representation,
image texture, regression analysis, vectors, input vector,
orthogonal color plane binary patterns
BibRef
Mahmoudpour, S.[Saeed],
Schelkens, P.[Peter],
Reduced-reference quality assessment of multiply-distorted images
based on structural and uncertainty information degradation,
JVCIR(57), 2018, pp. 125-137.
Elsevier DOI
1812
Image quality, Multiply-distortion types, Shearlet transform,
Entropy analysis, Support vector regression, Privileged information
BibRef
Dendi, S.V.R.,
Dev, C.,
Kothari, N.,
Channappayya, S.S.,
Generating Image Distortion Maps Using Convolutional Autoencoders
With Application to No Reference Image Quality Assessment,
SPLetters(26), No. 1, January 2019, pp. 89-93.
IEEE DOI
1901
data compression, distortion, feedforward neural nets,
image coding, image restoration, convolutional autoencoder,
human visual system (HVS)
BibRef
Liu, Y.,
Gu, K.,
Wang, S.,
Zhao, D.,
Gao, W.,
Blind Quality Assessment of Camera Images Based on Low-Level and
High-Level Statistical Features,
MultMed(21), No. 1, January 2019, pp. 135-146.
IEEE DOI
1901
Feature extraction, Image quality, Distortion, Cameras, Degradation,
Computer science, Estimation, Image quality assessment (IQA),
natural image statistics
BibRef
Wang, T.[Tonghan],
Zhang, L.[Lu],
Jia, H.Z.[Hui-Zhen],
An effective general-purpose NR-IQA model using natural scene
statistics (NSS) of the luminance relative order,
SP:IC(71), 2019, pp. 100-109.
Elsevier DOI
1901
Image quality assessment, Relative order,
Natural scene statistics, No reference, Random forest
BibRef
Chen, P.F.[Peng-Fei],
Li, L.[Leida],
Zhang, X.F.[Xin-Feng],
Wang, S.S.[Shan-She],
Tan, A.[Allen],
Blind quality index for tone-mapped images based on luminance
partition,
PR(89), 2019, pp. 108-118.
Elsevier DOI
1902
Tone-mapping operators, Tone-mapped image, Human visual system,
Luminance partition, Multi-resolution representation, Random forest regression
BibRef
Deng, C.,
Wang, S.,
Li, Z.,
Huang, G.,
Lin, W.,
Content-Insensitive Blind Image Blurriness Assessment Using Weibull
Statistics and Sparse Extreme Learning Machine,
SMCS(49), No. 3, March 2019, pp. 516-527.
IEEE DOI
1902
Image edge detection, Feature extraction, Measurement,
Image quality, Discrete cosine transforms, Robustness,
Weibull statistics
BibRef
Hu, W.J.[Wen-Jin],
Ye, Y.Q.[Yu-Qi],
Meng, J.H.[Jia-Hao],
Zeng, F.L.[Fu-Liang],
No reference quality assessment for Thangka color image based on
superpixel,
JVCIR(59), 2019, pp. 407-414.
Elsevier DOI
1903
Image quality, No reference assessment, Superpixel,
Information entropy, Thangka image
BibRef
Fang, Y.M.[Yu-Ming],
Liu, J.Y.[Jia-Ying],
Zhang, Y.[Yabin],
Lin, W.S.[Wei-Si],
Guo, Z.M.[Zong-Ming],
Reduced-Reference Quality Assessment of Image Super-Resolution by
Energy Change and Texture Variation,
JVCIR(60), 2019, pp. 140-148.
Elsevier DOI
1903
Image quality assessment (IQA), Image super-resolution,
Reduced-reference (RR) quality assessment, Energy change, Texture variation
BibRef
Po, L.,
Liu, M.,
Yuen, W.Y.F.,
Li, Y.,
Xu, X.,
Zhou, C.,
Wong, P.H.W.,
Lau, K.W.,
Luk, H.,
A Novel Patch Variance Biased Convolutional Neural Network for
No-Reference Image Quality Assessment,
CirSysVideo(29), No. 4, April 2019, pp. 1223-1229.
IEEE DOI
1904
Training, Image quality, Estimation, Image color analysis,
Convolution, Convolutional neural networks, Deep learning,
no-reference image quality assessment
BibRef
Cai, H.[Hao],
Li, L.[Leida],
Yi, Z.L.[Zi-Li],
Gong, M.L.[Ming-Lun],
Blind quality assessment of gamut-mapped images via local and global
statistical analysis,
JVCIR(61), 2019, pp. 250-259.
Elsevier DOI
1906
Image quality assessment, Gamut mapping, Natural scene statistics
BibRef
Lv, Z.Y.[Zheng-Yi],
Wang, X.C.[Xiao-Chuan],
Wang, K.[Kai],
Liang, X.H.[Xiao-Hui],
A Deep Blind Image Quality Assessment with Visual Importance Based
Patch Score,
ACCV18(II:147-162).
Springer DOI
1906
BibRef
Alaql, O.[Omar],
Lu, C.C.[Cheng-Chang],
No-reference image quality metric based on multiple deep belief
networks,
IET-IPR(13), No. 8, 20 June 2019, pp. 1321-1327.
DOI Link
1906
BibRef
Rodrigues, F.,
Ascenso, J.,
Rodrigues, A.,
Queluz, M.P.,
Blind Quality Assessment of 3-D Synthesized Views Based on Hybrid
Feature Classes,
MultMed(21), No. 7, July 2019, pp. 1737-1749.
IEEE DOI
1906
Measurement, Quality assessment, Cameras, Distortion,
Rendering (computer graphics),
synthesized image dataset
BibRef
Zhou, Y.,
Li, L.,
Wang, S.,
Wu, J.,
Fang, Y.,
Gao, X.,
No-Reference Quality Assessment for View Synthesis Using DoG-Based
Edge Statistics and Texture Naturalness,
IP(28), No. 9, Sep. 2019, pp. 4566-4579.
IEEE DOI
1908
edge detection, feature extraction, Gaussian processes,
image representation, image texture, random forests,
gray level gradient co-occurrence matrix
BibRef
Zhu, W.,
Zhai, G.,
Min, X.,
Hu, M.,
Liu, J.,
Guo, G.,
Yang, X.,
Multi-Channel Decomposition in Tandem With Free-Energy Principle for
Reduced-Reference Image Quality Assessment,
MultMed(21), No. 9, September 2019, pp. 2334-2346.
IEEE DOI
1909
Visualization, Wavelet transforms, Image quality,
Feature extraction, Measurement, Brain modeling,
human visual system
BibRef
Liu, L.,
Wang, T.,
Huang, H.,
Pre-Attention and Spatial Dependency Driven No-Reference Image
Quality Assessment,
MultMed(21), No. 9, September 2019, pp. 2305-2318.
IEEE DOI
1909
Image color analysis, Measurement, Visualization, Distortion,
Feature extraction, Image quality, Visual perception,
chromatic data
BibRef
Yue, G.,
Hou, C.,
Gu, K.,
Zhou, T.,
Liu, H.,
No-Reference Quality Evaluator of Transparently Encrypted Images,
MultMed(21), No. 9, September 2019, pp. 2184-2194.
IEEE DOI
1909
Measurement, Feature extraction, Visualization, Encryption,
Distortion, Quality evaluation, visual security, encrypted image,
no-reference
BibRef
Sandic-Stankovic, D.D.,
Kukolj, D.D.,
Le Callet, P.[Patrick],
Fast Blind Quality Assessment of DIBR-Synthesized Video Based on
High-High Wavelet Subband,
IP(28), No. 11, November 2019, pp. 5524-5536.
IEEE DOI
1909
Measurement, Image edge detection, Quality assessment,
Nonlinear distortion,
synthesized view quality prediction
BibRef
Chen, W.,
Gu, K.,
Lin, W.,
Xia, Z.,
Le Callet, P.,
Cheng, E.,
Reference-Free Quality Assessment of Sonar Images via Contour
Degradation Measurement,
IP(28), No. 11, November 2019, pp. 5336-5351.
IEEE DOI
1909
Sonar measurements, Degradation, Image quality,
Acoustic distortion, Image quality assessment (IQA),
bagging
BibRef
Yang, J.C.[Jia-Chen],
Huang, Z.H.[Zhi-Hui],
Sim, K.[Kyohoon],
Lu, W.[Wen],
Liu, K.[Kai],
Liu, H.[Hehan],
No-reference image quality assessment focusing on human facial region,
SP:IC(78), 2019, pp. 51-61.
Elsevier DOI
1909
Image quality assessment, No-reference, Facial region, Face detection
BibRef
Yang, J.C.[Jia-Chen],
Zhao, Y.[Yang],
Liu, J.C.[Jia-Cheng],
Jiang, B.[Bin],
Meng, Q.G.[Qing-Gang],
Lu, W.[Wen],
Gao, X.B.[Xin-Bo],
No Reference Quality Assessment for Screen Content Images Using
Stacked Autoencoders in Pictorial and Textual Regions,
Cyber(52), No. 5, May 2022, pp. 2798-2810.
IEEE DOI
2206
Measurement, Feature extraction, Visualization, Databases,
Distortion, Image quality, unsupervised approach
BibRef
Miao, X.[Xikui],
Chu, H.R.[Hai-Rong],
Liu, H.[Hui],
Yang, Y.[Yao],
Li, X.L.[Xiao-Long],
Quality assessment of images with multiple distortions based on phase
congruency and gradient magnitude,
SP:IC(79), 2019, pp. 54-62.
Elsevier DOI
1911
No-reference, Image quality assessment, Phase congruency,
Local binary pattern, Image gradient
BibRef
Wang, G.,
Wang, Z.,
Gu, K.,
Li, L.,
Xia, Z.,
Wu, L.,
Blind Quality Metric of DIBR-Synthesized Images in the Discrete
Wavelet Transform Domain,
IP(29), No. 1, 2020, pp. 1802-1814.
IEEE DOI
1912
Distortion, Distortion measurement, Image edge detection,
Quality assessment, Complexity theory, Feature extraction,
image complexity
BibRef
Zhou, Z.H.[Zi-Heng],
Lu, W.[Wen],
Yang, J.C.[Jia-Chen],
He, W.Q.[Wei-Quan],
No-reference image quality assessment based on neighborhood
co-occurrence matrix,
SP:IC(81), 2020, pp. 115680.
Elsevier DOI
1912
No-reference image quality assessment,
Neighborhood co-occurrence matrix, Natural scene statistics
BibRef
Yang, X.C.[Xi-Chen],
Wang, T.S.[Tian-Shu],
Ji, G.L.[Gen-Lin],
No-reference image quality assessment via structural information
fluctuation,
IET-IPR(14), No. 2, February 2020, pp. 384-396.
DOI Link
2001
BibRef
Zhang, Y.,
Mou, X.,
Chandler, D.M.,
Learning No-Reference Quality Assessment of Multiply and Singly
Distorted Images With Big Data,
IP(29), 2020, pp. 2676-2691.
IEEE DOI
2001
Distortion, Feature extraction, Image coding, Transform coding,
Image quality, Prediction algorithms, Databases,
contrast change
BibRef
Zhou, W.,
Shi, L.,
Chen, Z.,
Zhang, J.,
Tensor Oriented No-Reference Light Field Image Quality Assessment,
IP(29), 2020, pp. 4070-4084.
IEEE DOI
2002
Light field, image quality assessment, objective model,
tensor theory, angular consistency
BibRef
Shi, L.,
Zhou, W.,
Chen, Z.,
Zhang, J.,
No-Reference Light Field Image Quality Assessment Based on
Spatial-Angular Measurement,
CirSysVideo(30), No. 11, November 2020, pp. 4114-4128.
IEEE DOI
2011
Feature extraction, Image quality,
Visualization,
angular consistency
BibRef
Shi, L.,
Zhao, S.,
Chen, Z.,
Belif: Blind Quality Evaluator Of Light Field Image With Tensor
Structure Variation Index,
ICIP19(3781-3785)
IEEE DOI
1910
Light field, Image quality assessment, Objective model, Tensor,
Angular consistency
BibRef
Hosu, V.,
Lin, H.,
Sziranyi, T.,
Saupe, D.,
KonIQ-10k: An Ecologically Valid Database for Deep Learning of Blind
Image Quality Assessment,
IP(29), 2020, pp. 4041-4056.
IEEE DOI
2002
Image database, diversity sampling, crowdsourcing,
blind image quality assessment,
deep learning
BibRef
Khosravi, M.H.,
Hassanpour, H.,
Blind Quality Metric for Contrast-Distorted Images Based on
Eigendecomposition of Color Histograms,
CirSysVideo(30), No. 1, January 2020, pp. 48-58.
IEEE DOI
2002
distortion, eigenvalues and eigenfunctions, feature extraction,
image colour analysis, image enhancement,
no-reference/blind
BibRef
Li, Y.H.[Yun-Hong],
Zhang, H.H.[Huan-Huan],
Chen, J.N.[Jin-Ni],
Song, P.[Peng],
Ren, J.[Jie],
Zhang, Q.M.[Qiu-Ming],
Jia, K.L.[Kai-Li],
Non-Reference Image Quality Assessment Based on Deep Clustering,
SP:IC(83), 2020, pp. 115781.
Elsevier DOI
2003
Deep clustering, Quality evaluation, Feature extraction, Contracted autoencoder
BibRef
Hou, R.[Rui],
Zhao, Y.H.[Yun-Hao],
Hu, Y.[Yang],
Liu, H.[Huan],
No-reference video quality evaluation by a deep transfer CNN
architecture,
SP:IC(83), 2020, pp. 115782.
Elsevier DOI
2003
Video quality, Feature extraction, VGG-net, VQA, Average pooling,
Human perception
BibRef
Huang, Y.[Yipo],
Li, L.[Leida],
Zhou, Y.[Yu],
Hu, B.[Bo],
No-reference quality assessment for live broadcasting videos in
temporal and spatial domains,
IET-IPR(14), No. 4, 27 March 2020, pp. 774-781.
DOI Link
2003
BibRef
Chen, P.F.[Peng-Fei],
Li, L.[Leida],
Wu, J.J.[Jin-Jian],
Dong, W.S.[Wei-Sheng],
Shi, G.M.[Guang-Ming],
Unsupervised Curriculum Domain Adaptation for No-Reference Video
Quality Assessment,
ICCV21(5158-5167)
IEEE DOI
2203
Adaptation models, Correlation, Measurement uncertainty,
Predictive models, Distortion, Data models, Quality assessment,
Transfer/Low-shot/Semi/Unsupervised Learning
BibRef
Deng, J.C.[Jun-Chen],
Wang, C.[Ci],
Liu, S.Q.[Shi-Qi],
No Reference Image Quality Assessment by Information Decomposition,
MMMod20(I:826-838).
Springer DOI
2003
BibRef
Liu, H.[Hao],
Li, C.[Ce],
Zhang, D.[Dong],
Zhou, Y.N.[Yan-Nan],
Du, S.Y.[Shao-Yi],
Enhanced image no-reference quality assessment based on colour space
distribution,
IET-IPR(14), No. 5, 17 April 2020, pp. 807-817.
DOI Link
2004
BibRef
Liu, Y.,
Gu, K.,
Zhang, Y.,
Li, X.,
Zhai, G.,
Zhao, D.,
Gao, W.,
Unsupervised Blind Image Quality Evaluation via Statistical
Measurements of Structure, Naturalness, and Perception,
CirSysVideo(30), No. 4, April 2020, pp. 929-943.
IEEE DOI
2004
Feature extraction, Image quality, Distortion, Brain modeling,
Predictive models, Degradation, Distortion measurement,
free-energy principle
BibRef
Reddy Dendi, S.V.,
Channappayya, S.S.,
No-Reference Video Quality Assessment Using Natural Spatiotemporal
Scene Statistics,
IP(29), 2020, pp. 5612-5624.
IEEE DOI
2005
Spatiotemporal phenomena, Quality assessment, Optical distortion,
Feature extraction, Video recording, Distortion, Streaming media,
SVR and 3D-MSCN
See also Full-Reference 3-D Video Quality Assessment Using Scene Component Statistical Dependencies.
BibRef
Lyu, W.J.[Wen-Jing],
Lu, W.[Wei],
Ma, M.[Ming],
No-reference quality metric for contrast-distorted image based on
gradient domain and HSV space,
JVCIR(69), 2020, pp. 102797.
Elsevier DOI
2006
Digital image forensics,
No-reference image quality assessment, Contrast distortion,
HSV color space
BibRef
Chen, D.,
Wang, Y.,
Gao, W.,
No-Reference Image Quality Assessment: An Attention Driven Approach,
IP(29), 2020, pp. 6496-6506.
IEEE DOI
2007
BibRef
Earlier:
Add A3:
Ren, H.,
WACV19(376-385)
IEEE DOI
1904
Task analysis, Distortion, Image restoration,
Computational modeling, Feature extraction, Image quality,
attention model.
image restoration, learning (artificial intelligence),
recurrent neural nets, human beings, distorted image,
BibRef
Bao, L.[Long],
Panetta, K.A.[Karen A.],
Agaian, S.[Sos],
Neural network-based image quality comparator without collecting the
human score for training,
IET-IPR(14), No. 9, 20 July 2020, pp. 1787-1793.
DOI Link
2007
BibRef
Wu, J.,
Ma, J.,
Liang, F.,
Dong, W.,
Shi, G.,
Lin, W.,
End-to-End Blind Image Quality Prediction With Cascaded Deep Neural
Network,
IP(29), 2020, pp. 7414-7426.
IEEE DOI
2007
Blind image quality assessment (BIQA),
hierarchical degradation concatenation, end-to-end,
deep convolutional neural network
BibRef
Yan, J.B.[Jie-Bin],
Fang, Y.M.[Yu-Ming],
Du, R.G.[Ren-Gang],
Zeng, Y.[Yan],
Zuo, Y.F.[Yi-Fan],
No Reference Quality Assessment for 3D Synthesized Views by Local
Structure Variation and Global Naturalness Change,
IP(29), 2020, pp. 7443-7453.
IEEE DOI
2007
Depth image based rendering (DIBR), View synthesis,
no reference (NR), image quality assessment
BibRef
Sui, X.J.[Xiang-Jie],
Ding, M.N.[Meng-Na],
Yan, J.B.[Jie-Bin],
Fang, Y.M.[Yu-Ming],
Zuo, Y.F.[Yi-Fan],
Tan, Z.W.[Zuo-Wen],
Objective quality assessment of synthesized images by local variation
measurement,
SP:IC(92), 2021, pp. 116096.
Elsevier DOI
2101
Depth-image-based rending (DIBR), Neutrosophic domain, Image quality assessment
BibRef
Mahmoudpour, S.,
Schelkens, P.,
A Multi-Attribute Blind Quality Evaluator for Tone-Mapped Images,
MultMed(22), No. 8, August 2020, pp. 1939-1954.
IEEE DOI
2007
Feature extraction, Visualization, Image color analysis,
Dynamic range, Brightness, Imaging, High dynamic range imaging,
Color harmony
BibRef
Krasula, L.,
Fliegel, K.,
Le Callet, P.,
FFTMI: Features Fusion for Natural Tone-Mapped Images Quality
Evaluation,
MultMed(22), No. 8, August 2020, pp. 2038-2047.
IEEE DOI
2007
Feature extraction, Indexes, Image quality, Dynamic range,
Quality assessment, Reliability, High dynamic range imaging,
feature selection
BibRef
Chen, Y.,
Zhao, Y.,
Li, S.,
Zuo, W.,
Jia, W.,
Liu, X.,
Blind Quality Assessment for Cartoon Images,
CirSysVideo(30), No. 9, September 2020, pp. 3282-3288.
IEEE DOI
2009
Image edge detection, Image coding, Histograms, Distortion,
Image quality, Complexity theory, Measurement, Cartoon image,
blind image quality assessment (BIQA)
BibRef
Chetouani, A.[Aladine],
Li, L.[Leida],
On the use of a scanpath predictor and convolutional neural network
for blind image quality assessment,
SP:IC(89), 2020, pp. 115963.
Elsevier DOI
2010
Image quality, CNN model, Saliency, Scanpath prediction
BibRef
Wang, X.J.[Xue-Jin],
Jiang, Q.P.[Qiu-Ping],
Shao, F.[Feng],
Gu, K.[Ke],
Zhai, G.T.[Guang-Tao],
Yang, X.K.[Xiao-Kang],
Exploiting Local Degradation Characteristics and Global Statistical
Properties for Blind Quality Assessment of Tone-Mapped HDR Images,
MultMed(23), 2021, pp. 692-705.
IEEE DOI
2102
Feature extraction, Distortion, Degradation, Dynamic range,
Image quality, Standards, Quality assessment, tone-mapped image,
no reference
BibRef
Tian, C.Z.[Chong-Zhen],
Chai, X.L.[Xiong-Li],
Shao, F.[Feng],
Stitched image quality assessment based on local measurement errors
and global statistical properties,
JVCIR(81), 2021, pp. 103324.
Elsevier DOI
2112
Image stitching, Stitched image quality assessment,
Structural distortion, Geometric error, Quality aggregation
BibRef
Xia, W.,
Yang, Y.,
Xue, J.H.,
Xiao, J.,
Domain Fingerprints for No-Reference Image Quality Assessment,
CirSysVideo(31), No. 4, April 2021, pp. 1332-1341.
IEEE DOI
2104
Distortion, Image quality, Image restoration, Degradation,
Feature extraction, Task analysis, Visualization,
generative adversarial network
BibRef
Xu, J.H.[Jia-Hua],
Zhou, W.[Wei],
Chen, Z.B.[Zhi-Bo],
Blind Omnidirectional Image Quality Assessment With Viewport Oriented
Graph Convolutional Networks,
CirSysVideo(31), No. 5, 2021, pp. 1724-1737.
IEEE DOI
2105
BibRef
Khalid, H.[Hassan],
Ali, D.M.[Dr. Muhammad],
Ahmed, N.[Nisar],
Gaussian Process-based Feature-Enriched Blind Image Quality
Assessment,
JVCIR(77), 2021, pp. 103092.
Elsevier DOI
2106
Image quality assessment (IQA), No-reference (NR),
Natural scene statistics, Feature selection,
Blind image quality assessment (BIQA)
BibRef
Deng, J.F.[Jing-Fang],
Zhang, X.G.[Xiao-Gang],
Chen, H.[Hua],
Wu, L.Y.[Le-Yuan],
BGT: A blind image quality evaluator via gradient and texture
statistical features,
SP:IC(96), 2021, pp. 116315.
Elsevier DOI
2106
Blind image quality assessment (BIQA),
Human visual system (HVS), Natural scene statistics (NSS),
Joint statistics
BibRef
Chang, H.W.[Hua-Wen],
Bi, X.D.[Xiao-Dong],
Kai, C.[Chen],
Blind Image Quality Assessment by Visual Neuron Matrix,
SPLetters(28), 2021, pp. 1803-1807.
IEEE DOI
2109
Visualization, Feature extraction, Neurons, Training, Image quality,
Brain modeling, Image color analysis, Human visual system,
image quality assessment
BibRef
Li, J.H.[Jun-Hui],
Qiao, S.[Shuang],
Zhao, C.[Chenyi],
Zhang, T.[Tian],
No-reference image quality assessment based on multiscale feature
representation,
IET-IPR(15), No. 13, 2021, pp. 3318-3331.
DOI Link
2110
BibRef
Han, H.[Han],
Zhuo, L.[Li],
Li, J.F.[Jia-Feng],
Zhang, J.[Jing],
Wang, M.[Meng],
Blind Image Quality Assessment with Channel Attention Based Deep
Residual Network and Extended LargeVis Dimensionality Reduction,
JVCIR(80), 2021, pp. 103296.
Elsevier DOI
2110
Blind image quality assessment, ResNet-50,
Channel attention mechanism, LargeVis dimensionality reduction
BibRef
Yang, X.H.[Xiao-Han],
Li, F.[Fan],
Liu, H.T.[Han-Tao],
TTL-IQA: Transitive Transfer Learning Based No-Reference Image
Quality Assessment,
MultMed(23), 2021, pp. 4326-4340.
IEEE DOI
2112
Task analysis, Distortion, Image quality, Databases,
Image recognition, Feature extraction, Deep learning,
generative adversarial network
BibRef
Li, N.[Ning],
Teurnier, B.L.[Benjamin Le],
Boffety, M.[Matthieu],
Goudail, F.[François],
Zhao, Y.Q.[Yong-Qiang],
Pan, Q.[Quan],
No-Reference Physics-Based Quality Assessment of Polarization Images
and Its Application to Demosaicking,
IP(30), 2021, pp. 8983-8998.
IEEE DOI
2112
Redundancy, Imaging, Noise measurement, Quality assessment,
Measurement uncertainty, Polarization, Measurement errors,
image demosaicking
BibRef
Li, F.[Fan],
Zhang, Y.[Yangfan],
Cosman, P.C.[Pamela C.],
MMMNet: An End-to-End Multi-Task Deep Convolution Neural Network With
Multi-Scale and Multi-Hierarchy Fusion for Blind Image Quality
Assessment,
CirSysVideo(31), No. 12, December 2021, pp. 4798-4811.
IEEE DOI
2112
Task analysis, Feature extraction, Distortion, Image quality,
Databases, Visualization, Semantics, Image quality assessment,
convolutional neural network
BibRef
Gao, R.[Rui],
Huang, Z.Q.[Zi-Qing],
Liu, S.G.[Shi-Guang],
QL-IQA: Learning distance distribution from quality levels for blind
image quality assessment,
SP:IC(101), 2022, pp. 116576.
Elsevier DOI
2201
No-reference image quality assessment, Pseudo Siamese network,
Clustering, Convolutional neural network
BibRef
You, J.[Junyong],
Korhonen, J.[Jari],
Attention Integrated Hierarchical Networks for No-Reference Image
Quality Assessment,
JVCIR(82), 2022, pp. 103399.
Elsevier DOI
2201
Attention, Hierarchical networks,
Image quality assessment (IQA), Perceptual mechanisms, Quality perception
BibRef
Ma, X.S.[Xiao-Shuang],
Hu, H.M.[Hong-Ming],
Wu, P.H.[Peng-Hai],
A No-Reference Edge-Preservation Assessment Index for SAR Image
Filters under a Bayesian Framework Based on the Ratio Gradient,
RS(14), No. 4, 2022, pp. xx-yy.
DOI Link
2202
BibRef
Zhu, H.C.[Han-Cheng],
Li, L.[Leida],
Wu, J.J.[Jin-Jian],
Dong, W.S.[Wei-Sheng],
Shi, G.M.[Guang-Ming],
Generalizable No-Reference Image Quality Assessment via Deep
Meta-Learning,
CirSysVideo(32), No. 3, March 2022, pp. 1048-1060.
IEEE DOI
2203
BibRef
Earlier:
MetaIQA: Deep Meta-Learning for No-Reference Image Quality Assessment,
CVPR20(14131-14140)
IEEE DOI
2008
Distortion, Measurement, Task analysis, Image quality,
Adaptation models, Data models, Training,
convolutional neural networks.
Measurement, Databases
BibRef
Viqar, M.[Maryam],
Moinuddin, A.A.[Athar A.],
Khan, E.[Ekram],
Ghanbari, M.,
Frequency-domain blind quality assessment of blurred and
blocking-artefact images using Gaussian Process Regression model,
SP:IC(103), 2022, pp. 116651.
Elsevier DOI
2203
Image quality assessment, Multiple distortions,
Blocking artefacts, Blurriness, Discrete Fourier Transform
BibRef
Zhou, W.[Wei],
Xu, J.H.[Jia-Hua],
Jiang, Q.P.[Qiu-Ping],
Chen, Z.B.[Zhi-Bo],
No-Reference Quality Assessment for 360-Degree Images by Analysis of
Multifrequency Information and Local-Global Naturalness,
CirSysVideo(32), No. 4, April 2022, pp. 1778-1791.
IEEE DOI
2204
Visualization, Image quality, Quality assessment,
Feature extraction, Image coding, Distortion measurement,
human visual systems
BibRef
He, Q.L.[Qing-Lin],
Yang, C.[Chao],
Yang, F.[Fanxi],
An, P.[Ping],
Unsupervised blind image quality assessment based on joint structure
and natural scene statistics features,
JVCIR(87), 2022, pp. 103579.
Elsevier DOI
2208
Blind image quality assessment, Structure information,
Natural scene statistics, Karhunen-Loéve transform
BibRef
Zhang, J.Q.[Jia-Qi],
Fang, Z.G.[Zhi-Gao],
Yu, L.[Lu],
A no-reference perceptual image quality assessment database for
learned image codecs,
JVCIR(88), 2022, pp. 103617.
Elsevier DOI
2210
Image quality assessment, Learning-based image compression,
Generated image compression
BibRef
Chen, B.L.[Bao-Liang],
Zhu, L.Y.[Ling-Yu],
Kong, C.Q.[Chen-Qi],
Zhu, H.[Hanwei],
Wang, S.Q.[Shi-Qi],
Li, Z.[Zhu],
No-Reference Image Quality Assessment by Hallucinating Pristine
Features,
IP(31), 2022, pp. 6139-6151.
IEEE DOI
2210
Feature extraction, Distortion, Training, Task analysis,
Distortion measurement, Predictive models, Image quality,
pseudo-reference feature
BibRef
Song, T.S.[Tian-Shu],
Li, L.[Leida],
Chen, P.F.[Peng-Fei],
Liu, H.T.[Han-Tao],
Qian, J.S.[Jian-Sheng],
Blind Image Quality Assessment for Authentic Distortions by
Intermediary Enhancement and Iterative Training,
CirSysVideo(32), No. 11, November 2022, pp. 7592-7604.
IEEE DOI
2211
Measurement, Training, Distortion, Feature extraction, Image quality,
Adaptation models, Neural networks, Image quality assessment,
generalization
BibRef
Li, L.[Leida],
Song, T.[Tianshu],
Wu, J.J.[Jin-Jian],
Dong, W.S.[Wei-Sheng],
Qian, J.S.[Jian-Sheng],
Shi, G.M.[Guang-Ming],
Blind Image Quality Index for Authentic Distortions With Local and
Global Deep Feature Aggregation,
CirSysVideo(32), No. 12, December 2022, pp. 8512-8523.
IEEE DOI
2212
Measurement, Feature extraction, Distortion, Image quality,
Transformers, Computer architecture, Deep learning, generalization
BibRef
Lamichhane, K.[Kamal],
Carli, M.[Marco],
Battisti, F.[Federica],
A CNN-based no reference image quality metric exploiting content
saliency,
SP:IC(111), 2023, pp. 116899.
Elsevier DOI
2301
Artificial intelligence, Convolutional neural network,
Deep learning, Image quality assessment, Saliency
BibRef
Wang, Z.H.[Zhi-Hua],
Tang, Z.R.[Zhi-Ri],
Zhang, J.G.[Jian-Guo],
Fang, Y.M.[Yu-Ming],
Toward a blind image quality evaluator in the wild by learning beyond
human opinion scores,
PR(137), 2023, pp. 109296.
Elsevier DOI
2302
Blind image quality assessment, Opinion-free,
Pseudo binary label, Unsupervised domain adaptation, gMAD competition
BibRef
Lian, Q.[Qiye],
Xie, X.H.[Xiao-Hua],
Zheng, H.C.[Hui-Cheng],
Zhang, Y.D.[Yong-Dong],
Variance of Local Contribution:
An Unsupervised Image Quality Assessment for Face Recognition,
ICPR22(4665-4670)
IEEE DOI
2212
Image quality, Face recognition, Task analysis
BibRef
Babnik, Ž.[Žiga],
Peer, P.[Peter],
Štruc, V.[Vitomir],
FaceQAN: Face Image Quality Assessment Through Adversarial Noise
Exploration,
ICPR22(748-754)
IEEE DOI
2212
Image quality, Deep learning, Analytical models, Image recognition,
Face recognition, Computational modeling, Source coding
BibRef
Legrand, A.[Antoine],
Macq, B.[Benoît],
de Vleeschouwer, C.[Christophe],
Forward Error Correction Applied to JPEG-XS Codestreams,
ICIP22(3723-3727)
IEEE DOI
2211
Image quality, Reed-Solomon codes, Image coding, Redundancy,
Rate-distortion, Forward error correction, Propagation losses,
Unequal Error Protection
BibRef
van Damme, S.[Sam],
Vega, M.T.[Maria Torres],
van der Hooft, J.[Jeroen],
de Turck, F.[Filip],
Clustering-Based Psychometric No-Reference Quality Model for Point
Cloud Video,
ICIP22(1866-1870)
IEEE DOI
2211
Point cloud compression, Measurement, Adaptation models,
Machine learning, Streaming media, Predictive models, quality modelling
BibRef
Yang, S.[Sidi],
Wu, T.[Tianhe],
Shi, S.W.[Shu-Wei],
Lao, S.S.[Shan-Shan],
Gong, Y.[Yuan],
Cao, M.D.[Ming-Deng],
Wang, J.H.[Jia-Hao],
Yang, Y.J.[Yu-Jiu],
MANIQA: Multi-dimension Attention Network for No-Reference Image
Quality Assessment,
NTIRE22(1190-1199)
IEEE DOI
2210
Image quality, Databases, Distortion, Feature extraction,
Transformers, Quality assessment, Pattern recognition
BibRef
Yang, Q.[Qi],
Liu, Y.P.[Yi-Peng],
Chen, S.[Siheng],
Xu, Y.L.[Yi-Ling],
Sun, J.[Jun],
No-Reference Point Cloud Quality Assessment via Domain Adaptation,
CVPR22(21147-21156)
IEEE DOI
2210
Point cloud compression, Measurement, Solid modeling, Databases,
Transfer learning, Media, Datasets and evaluation,
Self- semi- meta- unsupervised learning
BibRef
Conde, M.V.[Marcos V.],
Burchi, M.[Maxime],
Timofte, R.[Radu],
Conformer and Blind Noisy Students for Improved Image Quality
Assessment,
NTIRE22(939-949)
IEEE DOI
2210
Image quality, Training, Predictive models, Transformers, Distortion,
Prediction algorithms, Data models
BibRef
Wang, J.[Jing],
Fan, H.[Haotian],
Hou, X.X.[Xiao-Xia],
Xu, Y.T.[Yi-Tian],
Li, T.[Tao],
Lu, X.[Xuechao],
Fu, L.[Lean],
MSTRIQ: No Reference Image Quality Assessment Based on Swin
Transformer with Multi-Stage Fusion,
NTIRE22(1268-1277)
IEEE DOI
2210
Image quality, Training, Predictive models, Transformers,
Prediction algorithms, Distortion
BibRef
Fu, B.[Biying],
Chen, C.[Cong],
Henniger, O.[Olaf],
Damer, N.[Naser],
A Deep Insight into Measuring Face Image Utility with General and
Face-specific Image Quality Metrics,
WACV22(1121-1130)
IEEE DOI
2202
Measurement, Image quality, Training, Visualization, Correlation,
Face recognition, Stability analysis, Biometrics Biometrics -> Face Processing
BibRef
Golestaneh, S.A.[S. Alireza],
Dadsetan, S.[Saba],
Kitani, K.M.[Kris M.],
No-Reference Image Quality Assessment via Transformers, Relative
Ranking, and Self-Consistency,
WACV22(3989-3999)
IEEE DOI
2202
Image quality, Uncertainty, Correlation, Feature extraction,
Transformers, Robustness, Quality assessment,
Evaluation and Comparison of Vision Algorithms
BibRef
Sendjasni, A.[Abderrezzaq],
Larabi, M.C.[Mohamed-Chaker],
Cheikh, F.A.[Faouzi Alaya],
Perceptually-Weighted Cnn for 360-Degree Image Quality Assessment
Using Visual Scan-Path and Jnd,
ICIP21(1439-1443)
IEEE DOI
2201
Image quality, Visualization, Adaptation models, Databases,
Predictive models, Visual systems, Observers, 360-degree images,
blind image quality assessment
BibRef
Zhu, M.M.[Meng-Meng],
Hou, G.Q.[Guan-Qun],
Chen, X.J.[Xin-Jia],
Xie, J.X.[Jia-Xing],
Lu, H.X.[Hai-Xian],
Che, J.[Jun],
Saliency-Guided Transformer Network combined with Local Embedding for
No-Reference Image Quality Assessment,
AIM21(1953-1962)
IEEE DOI
2112
Image quality, Adaptation models, Visualization, Image resolution,
Machine vision, Predictive models, Transformers
BibRef
Wang, Z.H.[Zhi-Hua],
Wang, H.T.[Hao-Tao],
Chen, T.L.[Tian-Long],
Wang, Z.Y.[Zhang-Yang],
Ma, K.[Kede],
Troubleshooting Blind Image Quality Models in the Wild,
CVPR21(16251-16260)
IEEE DOI
2111
Image quality, Measurement, Computational modeling, Mathematical models,
Pattern recognition, Computational efficiency
BibRef
Ou, F.Z.[Fu-Zhao],
Chen, X.Y.[Xing-Yu],
Zhang, R.X.[Rui-Xin],
Huang, Y.[Yuge],
Li, S.X.[Shao-Xin],
Li, J.L.[Ji-Lin],
Li, Y.[Yong],
Cao, L.J.[Liu-Juan],
Wang, Y.G.[Yuan-Gen],
SDD-FIQA: Unsupervised Face Image Quality Assessment with Similarity
Distribution Distance,
CVPR21(7666-7675)
IEEE DOI
2111
Image quality, Training, Measurement,
Image recognition, Uncertainty, Face recognition
BibRef
Khrulkov, V.[Valentin],
Babenko, A.[Artem],
Neural Side-By-Side:
Predicting Human Preferences for No-Reference Super-Resolution Evaluation,
CVPR21(4986-4995)
IEEE DOI
2111
Industries, Image quality, Computational modeling,
Superresolution, Tools, Predictive models
BibRef
Tworski, M.[Marcelin],
Lathuilière, S.[Stéphane],
Belkarfa, S.[Salim],
Fiandrotti, A.[Attilio],
Cagnazzo, M.[Marco],
DR2S: Deep Regression with Region Selection for Camera Quality
Evaluation,
ICPR21(6173-6180)
IEEE DOI
2105
Training, Lighting, Estimation, Cameras, Time measurement, Pattern recognition
BibRef
Liu, Z.Y.J.[Zong-Yi Joe],
Ferry, B.[Bruce],
Lacasse, S.[Simon],
A Scalable Deep Neural Network to Detect Low Quality Images Without a
Reference,
ICPR21(324-330)
IEEE DOI
2105
Measurement, Neural networks, Superresolution, Transform coding,
Streaming media, Motion pictures, User experience
BibRef
Lu, T.[Tan],
Dooms, A.[Ann],
A Novel Contractive GAN Model for a Unified Approach Towards Blind
Quality Assessment of Images from Heterogeneous Sources,
ISVC20(I:27-38).
Springer DOI
2103
BibRef
Yermakov, A.[Andrii],
Franc, V.[Vojtech],
CNN Based Predictor of Face Image Quality,
MOI2QDN20(679-693).
Springer DOI
2103
BibRef
Zhang, H.,
Li, D.,
Wu, L.,
Xia, Z.,
No-Reference Objective Quality Assessment Method of Display Products,
VCIP20(322-325)
IEEE DOI
2102
Image color analysis, Observers, Feature extraction, Brightness,
Databases, Visualization, Complexity theory, display product,
objective quality assessment
BibRef
You, J.[Junyong],
Korhonen, J.[Jari],
Transformer for Image Quality Assessment,
ICIP21(1389-1393)
IEEE DOI
2201
Image quality, Adaptation models, Image resolution, Databases,
Computational modeling, Attention,
Transformer
BibRef
Su, Y.,
Korhonen, J.,
Blind Natural Image Quality Prediction Using Convolutional Neural
Networks And Weighted Spatial Pooling,
ICIP20(191-195)
IEEE DOI
2011
Image quality, Training, Image resolution, Databases,
Feature extraction, Convolution, Graphics processing units,
Visual perception
BibRef
Zhang, W.,
Zhai, K.,
Zhai, G.,
Yang, X.,
Learning To Blindly Assess Image Quality In The Laboratory And Wild,
ICIP20(111-115)
IEEE DOI
2011
Databases, Training, Distortion, Computational modeling, Entropy,
Image quality, Testing, Blind image quality assessment,
fidelity loss.
BibRef
Terhörst, P.,
Kolf, J.N.,
Damer, N.,
Kirchbuchner, F.,
Kuijper, A.,
SER-FIQ: Unsupervised Estimation of Face Image Quality Based on
Stochastic Embedding Robustness,
CVPR20(5650-5659)
IEEE DOI
2008
Face, Face recognition, Robustness, Image quality,
Stochastic processes, Quality assessment, Measurement
BibRef
Su, S.,
Yan, Q.,
Zhu, Y.,
Zhang, C.,
Ge, X.,
Sun, J.,
Zhang, Y.,
Blindly Assess Image Quality in the Wild Guided by a Self-Adaptive
Hyper Network,
CVPR20(3664-3673)
IEEE DOI
2008
Distortion, Feature extraction, Image quality, Semantics, Databases,
Task analysis, Predictive models
BibRef
Yang, D.,
Peltoketo, V.,
Kämäräinen, J.,
CNN-Based Cross-Dataset No-Reference Image Quality Assessment,
CLI19(3913-3921)
IEEE DOI
2004
convolutional neural nets, feature extraction,
image classification, cross-dataset deep NR-IQA, aesthetics
BibRef
Zhussip, M.[Magauiya],
Soltanayev, S.[Shakarim],
Chun, S.Y.[Se Young],
Training Deep Learning Based Image Denoisers From Undersampled
Measurements Without Ground Truth and Without Image Prior,
CVPR19(10247-10256).
IEEE DOI
2002
BibRef
Yang, X.,
Li, F.,
Liu, H.,
A Comparative Study of DNN-Based Models for Blind Image Quality
Prediction,
ICIP19(1019-1023)
IEEE DOI
1910
deep learning, blind image quality assessment (BIQA), deep neural networks (DNN)
BibRef
Ou, F.,
Wang, Y.,
Zhu, G.,
A Novel Blind Image Quality Assessment Method Based on Refined
Natural Scene Statistics,
ICIP19(1004-1008)
IEEE DOI
1910
Image quality assessment, natural scene statistics, image distortion
BibRef
Deng, B.[Bin],
Zhang, X.F.[Xin-Feng],
Wang, S.S.[Shan-She],
Pan, X.F.[Xiao-Fei],
Ma, S.W.[Si-Wei],
Xiong, R.Q.[Rui-Qin],
Referenceless Quality Assessment for Contrast Distorted Image Using
Hybrid Features,
ICIP19(2354-2358)
IEEE DOI
1910
Image quality assessment, contrast distortion, unpredictability,
information entropy, colorfulness
BibRef
Yan, B.,
Bare, B.,
Tan, W.,
Naturalness-Aware Deep No-Reference Image Quality Assessment,
MultMed(21), No. 10, October 2019, pp. 2603-2615.
IEEE DOI
1910
distortion, feature extraction, image representation,
learning (artificial intelligence), natural scenes, neural nets,
naturalness-aware deep image quality assessment
BibRef
Zhang, K.,
Zhu, D.,
Jing, J.,
Gao, X.,
Learning a Cascade Regression for No-Reference Super-Resolution Image
Quality Assessment,
ICIP19(450-453)
IEEE DOI
1910
AdaBoost Decision Tree Regression,
image quality assessment (IQA), no-reference (NR), super-resolution (SR)
BibRef
Zhao, M.,
Shen, L.,
Jiang, M.,
Zheng, L.,
An, P.,
A Novel No-Reference Quality Assessment Model of Tone-Mapped HDR
Image,
ICIP19(3202-3206)
IEEE DOI
1910
image quality assessment, high dynamic range, tone mapping, no reference
BibRef
Zhang, Y.B.[Ya-Bin],
Wang, H.Q.[Hai-Qiang],
Tan, F.F.[Feng-Feng],
Chen, W.J.[Wen-Jun],
Wu, Z.R.[Zu-Rong],
No-Reference Image Sharpness Assessment Based on Rank Learning,
ICIP19(2359-2363)
IEEE DOI
1910
Image sharpness, Rank learning, Image quality assessment
BibRef
Su, L.[Li],
Cosman, P.[Pamela],
Peng, Q.[Qihang],
No-Reference Video Quality Assessment Based on Ensemble of Knowledge
and Data-Driven Models,
MMMod19(II:231-242).
Springer DOI
1901
BibRef
Lin, K.,
Wang, G.,
Hallucinated-IQA:
No-Reference Image Quality Assessment via Adversarial Learning,
CVPR18(732-741)
IEEE DOI
1812
Task analysis, Distortion, Image quality, Feature extraction,
Semantics, Predictive models
BibRef
Hosseini, M.S.,
Plataniotis, K.N.,
Image Sharpness Metric Based on Maxpol Convolution Kernels,
ICIP18(296-300)
IEEE DOI
1809
Sensitivity, Visualization, Kernel, Cutoff frequency,
Image edge detection, Databases, Correlation, Visual sensitivity,
No-reference image sharpness assessment
BibRef
Kim, J.,
Ahn, S.,
Oh, H.,
Lee, S.,
CNN-Based Blind Quality Prediction On Stereoscopic Images Via Patch
To Image Feature Pooling,
ICIP19(1745-1749)
IEEE DOI
1910
Stereoscopic 3D, no-reference quality assessment,
convolutional neural network, feature pooling.
BibRef
Kim, J.,
Nguyen, A.,
Ahn, S.,
Luo, C.,
Lee, S.,
Multiple Level Feature-Based Universal Blind Image Quality Assessment
Model,
ICIP18(291-295)
IEEE DOI
1809
Distortion, Databases, Feature extraction, Transform coding,
Image quality, Correlation, Task analysis,
no-reference image quality assessment
BibRef
Liu, Y.,
Song, L.,
Xie, R.,
Zhang, W.,
A generic method to improve no-reference image blur metric accuracy
in video contents,
VCIP17(1-4)
IEEE DOI
1804
image restoration, neural nets, video signal processing,
blur assessment techniques, content clustering,
no reference (NR)
BibRef
Wang, S.,
Wang, S.,
Gu, K.,
Guo, X.,
Ma, S.,
Gao, W.,
Internal generative mechanism inspired reduced reference image
quality assessment with entropy of primitive,
VCIP17(1-4)
IEEE DOI
1804
entropy, image representation, visual perception, HVS,
RR-IQA framework, best sparse description, entropy-of-primitive,
sparse representation
BibRef
Nath, P.S.[P. Shabari],
Gandhi, H.K.[Harsh K.],
Chouhan, R.[Rajlaxmi],
Quantifying image naturalness using differential curvelet features,
IVCNZ21(1-6)
IEEE DOI
2201
Training, Measurement, Image quality, Image recognition,
Social networking (online), Image synthesis, Estimation
BibRef
Kumar, V.,
Chouhan, R.,
No-reference image quality assessment using Gabor-based smoothness
and latent noise estimation,
IPTA17(1-6)
IEEE DOI
1804
AWGN, Gabor filters, image processing, natural scenes,
singular value decomposition, smoothing methods, Gabor response,
Visual systems
BibRef
Gu, K.,
Qiao, J.F.,
Le Callet, P.,
Xia, Z.,
Lin, W.,
Using multiscale analysis for blind quality assessment of
DIBR-synthesized images,
ICIP17(745-749)
IEEE DOI
1803
Distortion, Distortion measurement, Geometry, Predictive models,
Quality assessment, Reliability, Image quality assessment (IQA),
virtual reality (VR)
BibRef
Chetouani, A.,
Convolutional Neural Network and Saliency Selection for Blind Image
Quality Assessment,
ICIP18(2835-2839)
IEEE DOI
1809
Degradation, Measurement, Image quality, Databases,
Computational modeling, Convolutional neural networks,
Saliency
BibRef
Chetouani, A.,
Blind Utility and Quality Assessment Using a Convolutional Neural
Network and a Patch Selection,
ICIP19(459-463)
IEEE DOI
1910
Image utility, Image quality, Convolutional Neural Network, Patch selection
BibRef
Abouelaziz, I.,
Chetouani, A.,
Hassouni, M.E.,
Latecki, L.J.,
Cherifi, H.,
Convolutional Neural Network for Blind Mesh Visual Quality Assessment
Using 3D Visual Saliency,
ICIP18(3533-3537)
IEEE DOI
1809
BibRef
Earlier: A1, A3, A5, Only:
A convolutional neural network framework for blind mesh visual
quality assessment,
ICIP17(755-759)
IEEE DOI
1803
Visualization, Correlation, Databases,
Distortion, Quality assessment, Convolutional neural networks,
mesh visual saliency.
Convolution, Feature extraction, mean curvature
BibRef
Liu, X.,
Pedersen, M.,
Charrier, C.,
Bours, P.,
Can no-reference image quality metrics assess visible wavelength iris
sample quality?,
ICIP17(3530-3534)
IEEE DOI
1803
Cameras, Image quality, Iris, Iris recognition, Measurement,
Quality assessment, Quality assessment, image quality metric,
visible wavelength
BibRef
Rouis, K.,
Larabi, M.C.,
Belhadj Tahar, J.,
Blind image quality assessment in the complex frequency domain,
ICIP17(770-774)
IEEE DOI
1803
Distortion, Feature extraction, Frequency-domain analysis,
Image quality, Transforms, Visualization, No-reference IQA,
relative phase and magnitude
BibRef
Liu, X.,
van de Weijer, J.[Joost],
Bagdanov, A.D.,
RankIQA: Learning from Rankings for No-Reference Image Quality
Assessment,
ICCV17(1040-1049)
IEEE DOI
1802
backpropagation, feature extraction, image classification,
image colour analysis, image representation, image texture,
Tuning
BibRef
Charrier, C.[Christophe],
Saadane, A.[Abdelhakim],
Fernandez-Maloigne, C.[Christine],
No-Reference Learning-Based and Human Visual-Based Image Quality
Assessment Metric,
CIAP17(II:245-257).
Springer DOI
1711
BibRef
Huang, R.X.[Ri-Xing],
No reference image quality assessments based on edge-blur measure and
its applications in printed sheet blurs classification,
ICIVC17(793-797)
IEEE DOI
1708
Image edge detection, blur classification,
edge-blur measure (EBM),
no reference image quality assessments, printed, sheet, image
BibRef
Becker, S.[Sören],
Wiegand, T.[Thomas],
Bosse, S.[Sebastian],
Curiously Effective Features for Image Quality Prediction,
ICIP21(1399-1403)
IEEE DOI
2201
Image quality, Visualization, Analytical models, Correlation,
Computational modeling, Neural networks, Linear regression,
feature extraction
BibRef
Bosse, S.,
Maniry, D.,
Wiegand, T.,
Samek, W.,
A deep neural network for image quality assessment,
ICIP16(3773-3777)
IEEE DOI
1610
Correlation
BibRef
Outtas, M.,
Zhang, L.,
Deforges, O.,
Hammidouche, W.,
Serir, A.,
Cavaro-Menard, C.,
A study on the usability of opinion-unaware no-reference natural
image quality metrics in the context of medical images,
ISIVC16(308-313)
IEEE DOI
1704
Biomedical imaging
BibRef
Yan, J.[Jia],
Zhang, W.X.[Wei-Xia],
Feng, T.P.[Tian-Peng],
Blind Image Quality Assessment Based on Natural Redundancy Statistics,
ACCV16(IV: 3-18).
Springer DOI
1704
BibRef
Wu, J.,
Xia, Z.,
Ren, Y.,
Li, H.,
No-reference quality assessment for contrast-distorted image,
IPTA16(1-5)
IEEE DOI
1703
feature extraction
BibRef
Wu, Q.,
Li, H.,
Meng, F.,
Ngan, K.N.,
Q-DNN: A quality-aware deep neural network for blind assessment of
enhanced images,
VCIP16(1-4)
IEEE DOI
1701
Convolution
BibRef
Headlee, J.M.,
Balster, E.J.[Eric J.],
Turri, W.F.[William F.],
A no-reference image enhancement quality metric and fusion technique,
ICVNZ15(1-6)
IEEE DOI
1701
image enhancement
BibRef
Li, Y.J.,
Di, X.G.,
A no-reference infrared image sharpness assessment based on singular
value decomposition,
VCIP16(1-4)
IEEE DOI
1701
Databases
BibRef
Pan, C.,
Xu, Y.,
Yan, Y.,
Gu, K.,
Yang, X.,
Exploiting neural models for no-reference image quality assessment,
VCIP16(1-4)
IEEE DOI
1701
Databases
BibRef
Qian, X.C.[Xin-Chun],
Zhou, W.G.[Wen-Gang],
Li, H.Q.[Hou-Qiang],
No-Reference Image Quality Assessment Based on Internal Generative
Mechanism,
MMMod17(I: 264-276).
Springer DOI
1701
BibRef
Scott, E.T.[Edward T.],
Hemami, S.S.[Sheila. S.],
Image utility estimation using difference-of-Gaussian scale space,
ICIP16(101-105)
IEEE DOI
1610
Databases
BibRef
Sankisa, A.,
Pandremmenou, K.,
Kondi, L.P.,
Katsaggelos, A.K.,
A novel cumulative distortion metric and a no-reference sparse
prediction model for packet prioritization in encoded video
transmission,
ICIP16(2097-2101)
IEEE DOI
1610
Distortion
BibRef
Zhang, Y.,
Cui, W.H.,
Yang, F.,
Wu, Z.C.,
No-reference Image Quality Assessment For Zy3 Imagery In Urban Areas
Using Statistical Model,
ISPRS16(B3: 949-954).
DOI Link
1610
BibRef
Kim, W.,
Kim, H.,
Oh, H.,
Kim, J.,
Lee, S.,
No-reference perceptual sharpness assessment for
ultra-high-definition images,
ICIP16(86-90)
IEEE DOI
1610
Adaptation models
BibRef
Gaata, M.,
Puech, W.,
Sadkhn, S.,
Hasson, S.,
No-reference quality metric for watermarked images based on combining
of objective metrics using neural network,
IPTA12(229-234)
IEEE DOI
1503
filtering theory
BibRef
Soares, J.R.S.[Joao R.S.],
da Silva Cruz, L.A.[Luis A.],
Assuncao, P.[Pedro],
Marinheiro, R.[Rui],
No-reference lightweight estimation of 3D video objective quality,
ICIP14(763-767)
IEEE DOI
1502
Accuracy
BibRef
Zhao, H.J.[Heng-Jun],
No-inference image sharpness assessment based on wavelet transform
and image saliency map,
ICWAPR16(43-48)
IEEE DOI
1611
Image edge detection
BibRef
Zhao, H.J.[Heng-Jun],
Fang, B.[Bin],
Tang, Y.Y.[Yuan Yan],
A no-reference image sharpness estimation based on expectation of
wavelet transform coefficients,
ICIP13(374-378)
IEEE DOI
1402
Discrete wavelet transforms
BibRef
Wu, Q.B.[Qing-Bo],
Wang, Z.[Zhou],
Li, H.L.[Hong-Liang],
A highly efficient method for blind image quality assessment,
ICIP15(339-343)
IEEE DOI
1512
Image quality assessment
BibRef
Jenadeleh, M.[Mohsen],
Moghaddam, M.E.[Mohsen Ebrahimi],
Blind Image Quality Assessment Through Wakeby Statistics Model,
ICIAR15(14-21).
Springer DOI
1507
BibRef
Song, L.[Li],
Chen, C.[Chen],
Xu, Y.[Yi],
Xue, G.J.[Gen-Jian],
Zhou, Y.[Yi],
Blind image quality assessment based on a new feature of nature scene
statistics,
VCIP14(37-40)
IEEE DOI
1504
Gaussian distribution
BibRef
Tang, H.X.[Hui-Xuan],
Joshi, N.[Neel],
Kapoor, A.[Ashish],
Blind Image Quality Assessment Using Semi-supervised Rectifier
Networks,
CVPR14(2877-2884)
IEEE DOI
1409
BibRef
Xue, W.F.[Wu-Feng],
Zhang, L.[Lei],
Mou, X.Q.[Xuan-Qin],
Learning without Human Scores for Blind Image Quality Assessment,
CVPR13(995-1002)
IEEE DOI
1309
bind image quality assessment; clustering; qualiyt aware
BibRef
Ramírez-Rozo, T.J.[Thomas J.],
Non-referenced Quality Assessment of Image Processing Methods in
Infrared Non-destructive Testing,
CIAP13(II:121-130).
Springer DOI
1309
BibRef
De, K.[Kanjar],
Masilamani, V,
A new no-reference image quality measure to determine the quality of a
given image using object separability,
IMVIP12(92-95).
IEEE DOI
1302
BibRef
Chu, Y.[Ying],
Mou, X.Q.[Xuan-Qin],
Hong, W.[Wei],
Ji, Z.[Zhen],
A novel no-reference image quality assessment metric based on
statistical independence,
VCIP12(1-6).
IEEE DOI
1302
BibRef
Ojansivu, V.[Ville],
Lepistö, L.[Leena],
Ilmoniemi, M.[Martti],
Heikkilä, J.[Janne],
Degradation Based Blind Image Quality Evaluation,
SCIA11(306-316).
Springer DOI
1105
BibRef
Zhang, Y.[Yan],
An, P.[Ping],
Zhang, Q.[Qiuwen],
Shen, L.Q.[Li-Quan],
Zhang, Z.Y.[Zhao-Yang],
A No-Reference Image Quality Evaluation Based on Power Spectrum,
3DTV11(1-4).
IEEE DOI
1105
BibRef
Serir, A.[Amina],
No-reference blurred image quality assessment,
EUVIP11(168-173).
IEEE DOI
1110
BibRef
Luo, H.T.[Hui-Tao],
A training-based no-reference image quality assessment algorithm,
ICIP04(V: 2973-2976).
IEEE DOI
0505
BibRef
Luxen, M.[Marc],
Forstner, W.[Wolfgang],
Characterizing Image Quality: Blind Estimation of the Point Spread
Function from a Single Image,
PCV02(A: 205).
HTML Version.
0305
BibRef
Li, X.[Xin],
Blind image quality assessment,
ICIP02(I: 449-452).
IEEE DOI
0210
BibRef
Chapter on Image Processing, Restoration, Enhancement, Filters, Image and Video Coding continues in
Color Image Quality, Hyperspectral Image Quality .