Chow, T.W.S.,
Tan, H.Z.,
Order-recursive blind identification of linear models using mixed
cumulants,
VISP(147), No. 2, April 2000, pp. 139.
0005
BibRef
Gabarda, S.[Salvador],
Cristóbal, G.[Gabriel],
Blind image quality assessment through anisotropy,
JOSA-A(24), No. 12, December 2007, pp. B42-B51.
DOI Link
0801
BibRef
Gabarda, S.[Salvador],
Cristobal, G.[Gabriel],
An evolutionary blind image deconvolution algorithm through the
pseudo-Wigner distribution,
JVCIR(17), No. 5, October 2006, pp. 1040-1052.
Elsevier DOI
0711
Evolutionary algorithms; Wigner distribution; Image fusion;
Image enhancement; Quality assessment
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
Li, A.[Aobo],
Wu, J.J.[Jin-Jian],
Liu, Y.X.[Yong-Xu],
Li, L.[Leida],
Dong, W.S.[Wei-Sheng],
Shi, G.M.[Guang-Ming],
Blind Image Quality Assessment Based on Perceptual Comparison,
MultMed(26), 2024, pp. 9671-9682.
IEEE DOI
2410
Feature extraction, Task analysis, Distortion, Image quality,
Training, Visualization, Robustness,
perceptual comparison
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
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
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,
learning BIQA model,
listwise L2R algorithm, opinion-unaware BIQA,
quality-discriminable image lists, Electronics packaging,
Training, Blind image quality assessment (BIQA), RankNet, dipIQ,
gMAD, learning-to-rank (L2R), quality-discriminable, image, pair, (DIP)
BibRef
Gao, F.[Fei],
Yu, J.[Jun],
Zhu, S.[Suguo],
Huang, Q.M.[Qing-Ming],
Tian, Q.[Qi],
Blind image quality prediction by exploiting multi-level deep
representations,
PR(81), 2018, pp. 432-442.
Elsevier DOI
1806
Image quality assessment, Deep learning,
Convolutional Neural Networks (CNN),
Support vector regression
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
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
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
Liu, Y.T.[Yu-Tao],
Gu, K.[Ke],
Wang, S.Q.[Shi-Qi],
Zhao, D.B.[De-Bin],
Gao, W.[Wen],
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
Liu, Y.[Yue],
Ni, Z.K.[Zhang-Kai],
Wang, S.Q.[Shi-Qi],
Wang, H.[Hanli],
Kwong, S.[Sam],
High Dynamic Range Image Quality Assessment Based on Frequency
Disparity,
CirSysVideo(33), No. 8, August 2023, pp. 4435-4440.
IEEE DOI
2308
Feature extraction, Gabor filters, Information filters,
Image quality, Image edge detection, Image coding, Data mining,
Butterworth feature
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
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
Oszust, M.,
Local Feature Descriptor and Derivative Filters for Blind Image
Quality Assessment,
SPLetters(26), No. 2, February 2019, pp. 322-326.
IEEE DOI
1902
feature extraction, image filtering, image representation,
learning (artificial intelligence), regression analysis,
support vector regression
BibRef
Wu, J.J.[Jin-Jian],
Zeng, J.C.[Ji-Chen],
Dong, W.S.[Wei-Sheng],
Shi, G.M.[Guang-Ming],
Lin, W.S.[Wei-Si],
Blind Image Quality Assessment with Hierarchy:
Degradation from Local Structure to Deep Semantics,
JVCIR(58), 2019, pp. 353-362.
Elsevier DOI
1901
Blind image quality assessment,
Hierarchical feature degradation, Local structure, Deep semantics
BibRef
Wu, J.J.[Jin-Jian],
Lin, W.S.[Wei-Si],
Shi, G.M.[Guang-Ming],
Liu, A.[Anmin],
Reduced-Reference Image Quality Assessment with Visual Information
Fidelity,
MultMed(15), No. 7, 2013, pp. 1700-1705.
IEEE DOI
1312
image processing
See also Just Noticeable Difference Estimation for Images With Free-Energy Principle.
BibRef
Li, H.[Hui],
Liao, L.[Liang],
Chen, C.F.[Chao-Feng],
Fan, X.P.[Xiao-Peng],
Zuo, W.M.[Wang-Meng],
Lin, W.S.[Wei-Si],
Continual Learning of No-Reference Image Quality Assessment With
Channel Modulation Kernel,
CirSysVideo(34), No. 12, December 2024, pp. 13029-13043.
IEEE DOI
2501
Continuing education, Task analysis, Distortion, Image quality,
Kernel, Face recognition, Cameras,
domain-transfer continual learning
BibRef
Wu, J.J.[Jin-Jian],
Zhang, M.[Man],
Shi, G.M.[Guang-Ming],
Xie, X.M.[Xue-Mei],
Lin, W.S.[Wei-Si],
No-Reference Image Quality Assessment with Orientation Selectivity
Mechanism,
ICIP17(3150-3154)
IEEE DOI
1803
Correlation, Databases, Degradation, Distortion, Histograms,
Image quality, Visualization, Image Quality Assessment (IQA),
Visual Pattern Degradation
BibRef
Ji, W.P.[Wei-Ping],
Wu, J.J.[Jin-Jian],
Shi, G.M.[Guang-Ming],
Wan, W.F.[Wen-Fei],
Xie, X.M.[Xue-Mei],
Blind Image Quality Assessment with Semantic Information,
JVCIR(58), 2019, pp. 195-204.
Elsevier DOI
1901
No-reference image quality assessment, Human perception,
Semantic network, Structural semantics, Spatial semantics
BibRef
Wu, J.J.[Jin-Jian],
Lin, W.S.[Wei-Si],
Shi, G.M.[Guang-Ming],
Image Quality Assessment with Degradation on Spatial Structure,
SPLetters(21), No. 4, April 2014, pp. 437-440.
IEEE DOI
1403
Degradation
BibRef
Li, Q.H.[Qiao-Hong],
Lin, W.S.[Wei-Si],
Xu, J.T.[Jing-Tao],
Fang, Y.M.[Yu-Ming],
Thalmann, D.[Daniel],
No-reference Image Quality Assessment Based on Structural and Luminance
Information,
MMMod16(I: 301-312).
Springer DOI
1601
BibRef
Wu, J.J.[Jin-Jian],
Liu, Y.X.[Yong-Xu],
Shi, G.M.[Guang-Ming],
Lin, W.S.[Wei-Si],
Saliency Change Based Reduced Reference Image Quality Assessment,
VCIP17(1-4)
IEEE DOI
1804
feature extraction, image texture, object detection, LSWH,
RR IQA model, global saliency, image processing systems,
Visual Saliency
BibRef
Yang, W.[Wen],
Wu, J.J.[Jin-Jian],
Tian, S.W.[Shi-Wei],
Li, L.[Leida],
Dong, W.S.[Wei-Sheng],
Shi, G.M.[Guang-Ming],
Fine-Grained Image Quality Caption With Hierarchical Semantics
Degradation,
IP(31), 2022, pp. 3578-3590.
IEEE DOI
2206
Semantics, Degradation, Image quality, Feature extraction,
Distortion, Databases, Bidirectional control,
deep neural network
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
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
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
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
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
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
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
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
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
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
Sun, S.[Simeng],
Yu, T.[Tao],
Xu, J.H.[Jia-Hua],
Zhou, W.[Wei],
Chen, Z.B.[Zhi-Bo],
GraphIQA: Learning Distortion Graph Representations for Blind Image
Quality Assessment,
MultMed(25), 2023, pp. 2912-2925.
IEEE DOI
2307
Distortion, Task analysis, Image quality, Training,
Representation learning, Codes, Predictive models, pre-training
BibRef
Wang, H.S.[Hua-Sheng],
Liu, J.[Jiang],
Tan, H.C.[Hong-Chen],
Lou, J.X.[Jian-Xun],
Liu, X.C.[Xiao-Chang],
Zhou, W.[Wei],
Liu, H.T.[Han-Tao],
Blind Image Quality Assessment via Adaptive Graph Attention,
CirSysVideo(34), No. 10, October 2024, pp. 10299-10309.
IEEE DOI Code:
WWW Link.
2411
Transformers, Image quality, Feature extraction, Adaptation models,
Predictive models, Deep learning, Task analysis, deep learning
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
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
Li, F.[Fan],
Zhang, Y.F.[Yang-Fan],
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
Celona, L.[Luigi],
Schettini, R.[Raimondo],
Blind quality assessment of authentically distorted images,
JOSA-A(39), No. 6, June 2022, pp. B1-B10.
DOI Link
2503
Image metrics, Image quality, Imaging techniques,
Neural networks, Physiology, Spatial resolution
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
Huang, Y.[Yipo],
Li, L.[Leida],
Yang, Y.Z.[Yu-Zhe],
Li, Y.Q.[Ya-Qian],
Guo, Y.D.[Yan-Dong],
Explainable and Generalizable Blind Image Quality Assessment via
Semantic Attribute Reasoning,
MultMed(25), 2023, pp. 7672-7685.
IEEE DOI
2312
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
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
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
BibRef
And:
Corrigendum:
PR(139), 2023, pp. 109465.
Elsevier DOI
2304
Blind image quality assessment, Opinion-free,
Pseudo binary label, Unsupervised domain adaptation, gMAD competition
BibRef
Liu, Y.[Yun],
Yin, X.H.[Xiao-Hua],
Yue, G.H.[Guang-Hui],
Zheng, Z.[Zhi],
Jiang, J.H.[Jin-He],
He, Q.G.[Quan-Gui],
Li, X.Z.[Xin-Zhuang],
Blind Omnidirectional Image Quality Assessment with Representative
Features and Viewport Oriented Statistical Features,
JVCIR(91), 2023, pp. 103770.
Elsevier DOI
2303
Omnidirectional images, Quality assessment,
Cross-channel color feature, Natural scene statistics
BibRef
Liu, M.[Manni],
Huang, J.B.[Jia-Bin],
Zeng, D.[Delu],
Ding, X.H.[Xing-Hao],
Paisley, J.[John],
A Multiscale Approach to Deep Blind Image Quality Assessment,
IP(32), 2023, pp. 1656-1667.
IEEE DOI
2303
Image quality, Feature extraction, Distortion, Sensitivity,
Visualization, Task analysis, Predictive models, CNN
BibRef
Liu, H.[Hao],
Li, C.[Ce],
Jin, S.G.[Shan-Gang],
Gao, W.Z.[Wei-Zhe],
Liu, F.H.[Feng-Hua],
Du, S.[Shaoyi],
Ying, S.H.[Shi-Hui],
PGF-BIQA: Blind image quality assessment via probability
multi-grained cascade forest,
CVIU(232), 2023, pp. 103695.
Elsevier DOI
2305
Blind image quality assessment, Probability gcForest, Equal image classification
BibRef
Lee, S.H.[Se-Ho],
Kim, S.W.[Seung-Wook],
Dual-branch vision transformer for blind image quality assessment,
JVCIR(94), 2023, pp. 103850.
Elsevier DOI
2306
Blind image quality assessment, No-reference image quality assessment,
Vision transformer, Perceptual image processing
BibRef
Zhu, Y.C.[Yu-Cheng],
Li, Y.H.[Yun-Hao],
Sun, W.[Wei],
Min, X.K.[Xiong-Kuo],
Zhai, G.T.[Guang-Tao],
Yang, X.K.[Xiao-Kang],
Blind Image Quality Assessment via Cross-View Consistency,
MultMed(25), 2023, pp. 7607-7620.
IEEE DOI
2311
BibRef
Liu, J.Z.[Jian-Zhao],
Zhou, W.[Wei],
Li, X.[Xin],
Xu, J.H.[Jia-Hua],
Chen, Z.B.[Zhi-Bo],
LIQA: Lifelong Blind Image Quality Assessment,
MultMed(25), 2023, pp. 5358-5373.
IEEE DOI
2311
BibRef
Fu, J.[Jun],
Zhou, W.[Wei],
Jiang, Q.P.[Qiu-Ping],
Liu, H.T.[Han-Tao],
Zhai, G.T.[Guang-Tao],
Vision-Language Consistency Guided Multi-Modal Prompt Learning for
Blind AI Generated Image Quality Assessment,
SPLetters(31), 2024, pp. 1820-1824.
IEEE DOI
2408
Task analysis, Measurement, Visualization, Transformers,
Image quality, Correlation, Multi-modal prompt learning,
AGIQA
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
Zhang, W.X.[Wei-Xia],
Zhai, G.T.[Guang-Tao],
Wei, Y.[Ying],
Yang, X.K.[Xiao-Kang],
Ma, K.[Kede],
Blind Image Quality Assessment via Vision-Language Correspondence: A
Multitask Learning Perspective,
CVPR23(14071-14081)
IEEE DOI
2309
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
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
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
Shi, J.S.[Jin-Song],
Gao, P.[Pan],
Smolic, A.[Aljosa],
Blind Image Quality Assessment via Transformer Predicted Error Map
and Perceptual Quality Token,
MultMed(26), 2024, pp. 4641-4651.
IEEE DOI
2403
Distortion, Transformers, Predictive models, Image quality,
Feature extraction, Visualization, Task analysis, NR-IQA,
pceptual quality token
BibRef
Zhang, W.X.[Wei-Xia],
Ma, K.[Kede],
Zhai, G.T.[Guang-Tao],
Yang, X.K.[Xiao-Kang],
Task-Specific Normalization for Continual Learning of Blind Image
Quality Models,
IP(33), 2024, pp. 1898-1910.
IEEE DOI
2403
Task analysis, Computational modeling, Training, Head, Convolution,
Adaptation models, Learning systems,
task-specific normalization
BibRef
Zheng, L.M.[Li-Min],
Luo, Y.[Yu],
Zhou, Z.H.[Zi-Han],
Ling, J.[Jie],
Yue, G.H.[Guang-Hui],
CDINet: Content Distortion Interaction Network for Blind Image
Quality Assessment,
MultMed(26), 2024, pp. 7089-7100.
IEEE DOI
2405
Distortion, Image quality, Image restoration, Feature extraction,
Decoding, Dams, Visualization, Blind image quality assessment,
deep neural networks
BibRef
Chen, H.W.[Hang-Wei],
Wang, X.J.[Xue-Jin],
Shao, F.[Feng],
Blind cartoon image quality assessment based on local structure and
chromatic statistics,
JVCIR(101), 2024, pp. 104152.
Elsevier DOI
2406
No-reference image quality assessment, Cartoon image,
Decorative line detection, Multiscale chromatic statistics
BibRef
Adhikari, A.[Astha],
Lee, S.W.[Sang-Woong],
AM-BQA: Enhancing blind image quality assessment using attention
retractable features and multi-dimensional learning,
IVC(147), 2024, pp. 105076.
Elsevier DOI Code:
WWW Link.
2406
Blind image quality assessment, No-reference image quality assessment,
Multi-scale feature, Progressive multi-task learning
BibRef
Zhou, Z.[Zihan],
Li, J.[Jing],
Zhong, D.X.[De-Xiang],
Xu, Y.[Yong],
Le Callet, P.[Patrick],
Deep Blind Image Quality Assessment Using Dynamic Neural Model With
Dual-Order Statistics,
CirSysVideo(34), No. 7, July 2024, pp. 6279-6290.
IEEE DOI
2407
Distortion, Feature extraction, Filter banks, Visualization,
Image quality, Biological system modeling, Semantics,
dynamic regression
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
Zhou, T.W.[Tian-Wei],
Tan, S.B.[Song-Bai],
Zhao, B.Q.[Bao-Quan],
Yue, G.H.[Guang-Hui],
Multitask Deep Neural Network With Knowledge-Guided Attention for
Blind Image Quality Assessment,
CirSysVideo(34), No. 8, August 2024, pp. 7577-7588.
IEEE DOI
2408
Distortion, Task analysis, Feature extraction, Image quality,
Databases, Transformers, transformer
BibRef
Karimi, M.[Maryam],
Nejati, M.[Mansour],
Learning sparse feature representation for blind quality assessment
of night-time images,
SP:IC(128), 2024, pp. 117167.
Elsevier DOI
2409
Image quality assessment, Night-time image quality assessment,
Unsupervised feature learning, Sparse representation, Ensemble regression
BibRef
Ni, Z.K.[Zhang-Kai],
Liu, Y.[Yue],
Ding, K.[Keyan],
Yang, W.H.[Wen-Han],
Wang, H.L.[Han-Li],
Wang, S.Q.[Shi-Qi],
Opinion-Unaware Blind Image Quality Assessment Using Multi-Scale Deep
Feature Statistics,
MultMed(26), 2024, pp. 10211-10224.
IEEE DOI
2410
Feature extraction, Training, Analytical models, Image quality,
Data models, Predictive models, Computational modeling,
multi-scale deep features
BibRef
Tang, L.[Long],
Han, Y.M.[Yong-Ming],
Yuan, L.[Liang],
Zhai, G.T.[Guang-Tao],
FsPN: Blind Image Quality Assessment Based on Feature-Selected
Pyramid Network,
SPLetters(32), 2025, pp. 1-5.
IEEE DOI
2501
Feature extraction, Image quality, Convolution, Distortion,
Residual neural networks, Computational modeling, Interpolation,
spatial selection module
BibRef
Yan, J.B.[Jie-Bin],
Tan, Z.W.[Zi-Wen],
Fang, Y.M.[Yu-Ming],
Rao, J.[Jiale],
Zuo, Y.F.[Yi-Fan],
Max360IQ: Blind Omnidirectional Image Quality Assessment with
Multi-Axis Attention,
PR(162), 2025, pp. 111429.
Elsevier DOI Code:
WWW Link.
2503
Omnidirectional images, Perceptual quality assessment, Multi-axis attention
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
Song, T.S.[Tian-Shu],
Li, L.[Leida],
Cheng, D.Q.[De-Qiang],
Chen, P.F.[Peng-Fei],
Wu, J.J.[Jin-Jian],
Active Learning-Based Sample Selection for Label-Efficient Blind
Image Quality Assessment,
CirSysVideo(34), No. 7, July 2024, pp. 5884-5896.
IEEE DOI Code:
WWW Link.
2407
Distortion, Training, Predictive models, Image quality, Uncertainty, Tuning,
Task analysis, Image quality assessment, active learning, data-efficient
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
Ma, J.[Jupo],
Wu, J.J.[Jin-Jian],
Li, L.D.[Lei-Da],
Dong, W.S.[Wei-Sheng],
Xie, X.M.[Xue-Mei],
Shi, G.M.[Guang-Ming],
Lin, W.S.[Wei-Si],
Blind Image Quality Assessment With Active Inference,
IP(30), 2021, pp. 3650-3663.
IEEE DOI
2103
Image quality, Generative adversarial networks,
Feature extraction, Distortion, Semantics,
convolutional neural network
BibRef
Song, T.S.[Tian-Shu],
Li, L.D.[Lei-Da],
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.S.[Tian-Shu],
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
Song, T.S.[Tian-Shu],
Li, L.D.[Lei-Da],
Wu, J.J.[Jin-Jian],
Yang, Y.Z.[Yu-Zhe],
Li, Y.Q.[Ya-Qian],
Guo, Y.D.[Yan-Dong],
Shi, G.M.[Guang-Ming],
Knowledge-Guided Blind Image Quality Assessment With Few Training
Samples,
MultMed(25), 2023, pp. 8145-8156.
IEEE DOI
2312
BibRef
Hu, B.[Bo],
Zhu, G.[Guang],
Li, L.[Leida],
Gan, J.[Ji],
Li, W.S.[Wei-Sheng],
Gao, X.B.[Xin-Bo],
Blind Image Quality Index With Cross-Domain Interaction and
Cross-Scale Integration,
MultMed(26), 2024, pp. 2729-2739.
IEEE DOI
2402
Measurement, Feature extraction, Distortion, Image quality,
Transformers, Databases, Indexes, Image quality assessment,
cross-scale integration
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
Li, X.[Xiwen],
Wang, Z.H.[Zhi-Hua],
Xu, B.W.[Bin-Wei],
Blind image quality assessment with semi-supervised learning,
JVCIR(100), 2024, pp. 104100.
Elsevier DOI
2405
Blind image quality assessment, gMAD competition,
Learning to rank, Semi-supervised learning
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
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],
Cao, Y.Q.[Yu-Qin],
Liu, X.H.[Xiao-Hong],
Zhai, G.T.[Guang-Tao],
No-Reference Image Quality Assessment:
Obtain MOS From Image Quality Score Distribution,
CirSysVideo(35), No. 2, February 2025, pp. 1840-1854.
IEEE DOI
2502
Image quality, Feature extraction, Visualization, Correlation,
Training, Circuits and systems, Visual databases, Standards, loss functions
BibRef
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.D.[Yan-Ding],
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
Chahine, N.[Nicolas],
Ferradans, S.[Sira],
Vazquez-Corral, J.[Javier],
Ponce, J.[Jean],
Generalized portrait quality assessment,
PRL(189), 2025, pp. 122-128.
Elsevier DOI Code:
WWW Link.
2503
Image quality, Blind image quality
BibRef
Alsaafin, M.[Mohammed],
Alsheikh, M.[Musab],
Anwar, S.[Saeed],
Usman, M.[Muhammad],
Attention Down-Sampling Transformer, Relative Ranking and
Self-Consistency for Blind Image Quality Assessment,
ICIP24(1260-1266)
IEEE DOI Code:
WWW Link.
2411
Image quality, Degradation, Adaptation models, Visualization,
Image transformation, Transformers, Feature extraction, Relative Ranking
BibRef
Shin, N.H.[Nyeong-Ho],
Lee, S.H.[Seon-Ho],
Kim, C.S.[Chang-Su],
Blind Image Quality Assessment Based on Geometric Order Learning,
CVPR24(12799-12808)
IEEE DOI Code:
WWW Link.
2410
Image quality, Correlation, Computed tomography, Source coding,
Transformers, Vectors, Image quality assessment, order learning
BibRef
Roy, S.[Subhadeep],
Mitra, S.[Shankhanil],
Biswas, S.[Soma],
Soundararajan, R.[Rajiv],
Test Time Adaptation for Blind Image Quality Assessment,
ICCV23(16696-16705)
IEEE DOI
2401
BibRef
Zhou, W.[Wei],
Wang, Z.[Zhou],
Blind Omnidirectional Image Quality Assessment: Integrating Local
Statistics and Global Semantics,
ICIP23(1405-1409)
IEEE DOI
2312
BibRef
Yuan, K.[Kun],
Liu, H.B.[Hong-Bo],
Li, M.[Mading],
Sun, M.[Muyi],
Sun, M.[Ming],
Gong, J.C.[Jia-Chao],
Hao, J.H.[Jin-Hua],
Zhou, C.[Chao],
Tang, Y.S.[Yan-Song],
PTM-VQA: Efficient Video Quality Assessment Leveraging Diverse
PreTrained Models from the Wild,
CVPR24(2835-2845)
IEEE DOI
2410
Learning systems, Computational modeling, Benchmark testing,
Feature extraction, Distortion, Quality assessment,
metric learning
BibRef
Zhao, K.[Kai],
Yuan, K.[Kun],
Sun, M.[Ming],
Li, M.[Mading],
Wen, X.[Xing],
Quality-aware Pretrained Models for Blind Image Quality Assessment,
CVPR23(22302-22313)
IEEE DOI
2309
BibRef
Chen, Z.[Zewen],
Wang, J.[Juan],
Li, B.[Bing],
Yuan, C.F.[Chun-Feng],
Xiong, W.H.[Wei-Hua],
Cheng, R.[Rui],
Hu, W.M.[Wei-Ming],
Teacher-guided Learning for Blind Image Quality Assessment,
ACCV22(III:206-222).
Springer DOI
2307
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
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,
Computational efficiency
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
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
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, 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
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
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
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
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
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
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, 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
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
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
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
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 .