Gastaldo, P.[Paolo],
Zunino, R.[Rodolfo],
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Vicario, E.[Elena],
Objective quality assessment of displayed images by using neural
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SP:IC(20), No. 7, August 2005, pp. 643-661.
Elsevier DOI
0508
BibRef
Brankov, J.G.,
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Wei, L.,
El Naqa, I.,
Wernick, M.N.,
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MedImg(28), No. 7, July 2009, pp. 991-999.
IEEE DOI
0906
BibRef
Charrier, C.[Christophe],
Lézoray, O.[Olivier],
Lebrun, G.[Gilles],
Machine learning to design full-reference image quality assessment
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SP:IC(27), No. 3, March 2012, pp. 209-219.
Elsevier DOI
1203
FR-IQA algorithm; Classification; Theory of evidence; SVM
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See also Color VQ-Based Image Compression by Manifold Learning.
BibRef
Gastaldo, P.[Paolo],
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DOI Link
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BibRef
Hu, A.Z.[An-Zhou],
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Image quality assessment using a SVD-based structural projection,
SP:IC(29), No. 3, 2014, pp. 293-302.
Elsevier DOI
1403
Image quality assessment (IQA)
BibRef
Hu, A.Z.[An-Zhou],
Zhang, R.[Rong],
Yin, D.[Dong],
Hu, W.L.[Wen-Long],
Machine learning-based multi-channel evaluation pooling strategy for
image quality assessment,
ICIP13(427-430)
IEEE DOI
1402
Databases
BibRef
Narwaria, M.[Manish],
Lin, W.S.[Wei-Si],
Cetin, A.E.[A. Enis],
Scalable image quality assessment with 2D mel-cepstrum and machine
learning approach,
PR(45), No. 1, 2012, pp. 299-313.
Elsevier DOI
1410
Image quality assessment
BibRef
Guha, T.[Tanaya],
Nezhadarya, E.[Ehsan],
Ward, R.K.[Rabab K.],
Sparse representation-based image quality assessment,
SP:IC(29), No. 10, 2014, pp. 1138-1148.
Elsevier DOI
1411
Dictionary learning
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
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
Hu, B.,
Li, L.,
Liu, H.,
Lin, W.,
Qian, J.,
Pairwise-Comparison-Based Rank Learning for Benchmarking Image
Restoration Algorithms,
MultMed(21), No. 8, August 2019, pp. 2042-2056.
IEEE DOI
1908
image restoration, learning (artificial intelligence),
advanced image restoration techniques,
image structure
BibRef
Chen, Z.L.[Zhuo-Lun],
Wu, X.W.[Xiao-Wei],
Research on regional energy efficiency based on GIS technology and
image quality processing,
JVCIR(62), 2019, pp. 410-417.
Elsevier DOI
1908
Deep learning, GIS technology, Image quality analysis, Model, Quality
BibRef
Chen, X.[Xue],
Zhang, L.[Lanyong],
Liu, T.[Tong],
Kamruzzaman, M.M.,
Research on deep learning in the field of mechanical equipment fault
diagnosis image quality,
JVCIR(62), 2019, pp. 402-409.
Elsevier DOI
1908
Deep learning, Mechanical equipment, Equipment maintenance, Image quality
BibRef
Wei, G.H.[Guang-Hui],
Sheng, Z.[Zhou],
Image quality assessment for intelligent emergency application based
on deep neural network,
JVCIR(63), 2019, pp. 102581.
Elsevier DOI
1909
Entropy theory, Big data, Wisdom emergency, Quality model, Neural network
BibRef
Ko, K.M.[Kuo-Min],
Ko, P.C.[Po-Chang],
Lin, S.Y.[Shih-Yang],
Hong, Z.[Zhen],
Quality-guided image classification toward information management
applications,
JVCIR(63), 2019, pp. 102594.
Elsevier DOI
1909
Deep learning, Convolutional neural network, Image retrieval,
Image quality assessment
BibRef
Chen, G.B.[Guo-Bin],
Zhai, M.[Maotong],
Quality assessment on remote sensing image based on neural networks,
JVCIR(63), 2019, pp. 102580.
Elsevier DOI
1909
Image quality assessment, Remote sensing image, Deep learning,
Information entropy
BibRef
Zhang, Y.J.[Yan-Jun],
Gong, S.[Shuai],
Luo, M.J.[Ming-Jiu],
Image quality guided biology application for genetic analysis,
JVCIR(64), 2019, pp. 102606.
Elsevier DOI
1911
Image quality assessment, Low-level features, Deep learning, BP neural network
BibRef
He, T.[Tao],
Li, X.F.[Xiao-Feng],
Image quality recognition technology based on deep learning,
JVCIR(65), 2019, pp. 102654.
Elsevier DOI
1912
Low quality image, Deep learning, Image recognition,
Support vector machines(SVM)
BibRef
Kong, Y.Q.[Yan-Qiang],
Cui, L.[Liu],
Hou, R.[Rui],
Full-reference IPTV image quality assessment by deeply learning
structural cues,
SP:IC(83), 2020, pp. 115779.
Elsevier DOI
2003
Full-reference IQA, IPTV, Distance metric,
Structural information, Deep model
BibRef
Zhu, M.L.[Min-Ling],
Ge, D.Y.[Dong-Yuan],
Image quality assessment based on deep learning with FPGA
implementation,
SP:IC(83), 2020, pp. 115780.
Elsevier DOI
2003
Image quality assessment, Deep learning, FPGA, CNN, Image feature learning
BibRef
Huang, J.C.[Jui-Chan],
Huang, H.C.[Hao-Chen],
Liu, H.H.[Hsin-Hung],
Research on the parallelization of image quality analysis algorithm
based on deep learning,
JVCIR(71), 2020, pp. 102709.
Elsevier DOI
2009
Deep learning, Image distortion, Image quality analysis, Parallelization
BibRef
Huang, Z.W.[Zhi-Wei],
Li, Y.[Yan],
Luo, S.G.[Shi-Guang],
Hierarchical Learning-Guided human motion quality assessment in big
data environment,
JVCIR(71), 2020, pp. 102700.
Elsevier DOI
2009
Applied quality assessment. How good to determine human motion.
Quality assessment, Reinforcement learning, Human activities,
Big data, Hierarchical networks
BibRef
Hadizadeh, H.,
Heravi, A.R.,
Bajic, I.V.,
Karami, P.,
A Perceptual Distinguishability Predictor For JND-Noise-Contaminated
Images,
IP(28), No. 5, May 2019, pp. 2242-2256.
IEEE DOI
1903
feature extraction, image classification, neural nets,
perceptual distinguishability predictor, reference image,
neural network
BibRef
Zhang, X.[Xikun],
Hou, J.[Jie],
Quality assessment towards cell diffraction image based on
multi-channel feature fusion,
JVCIR(64), 2019, pp. 102632.
Elsevier DOI
1911
Image quality assessment, Cell diffraction image, Deep neural network
BibRef
Takagi, M.[Motohiro],
Sakurai, A.[Akito],
Hagiwara, M.[Masafumi],
Discriminative Convolutional Neural Network for Image Quality
Assessment with Fixed Convolution Filters,
IEICE(E102-D), No. 11, November 2019, pp. 2265-2266.
WWW Link.
1912
BibRef
Huang, Y.[Ying],
Zhang, Q.P.[Qing-Ping],
Research on image screening model of ancient villages,
JVCIR(61), 2019, pp. 33-41.
Elsevier DOI
1906
Select images for quality to use in later analysis.
Ancient villages, Image screening, SIFT, Convolutional neural network
BibRef
Ieremeiev, O.[Oleg],
Lukin, V.[Vladimir],
Okarma, K.[Krzysztof],
Egiazarian, K.O.[Karen O.],
Full-Reference Quality Metric Based on Neural Network to Assess the
Visual Quality of Remote Sensing Images,
RS(12), No. 15, 2020, pp. xx-yy.
DOI Link
2008
See also JPEG-Based Perceptual Image Coding with Block-Based Image Quality Metric.
BibRef
Zhou, Z.[Zihan],
Li, J.[Jing],
Quan, Y.H.[Yu-Hui],
Xu, R.[Ruotao],
Image Quality Assessment Using Kernel Sparse Coding,
MultMed(23), 2021, pp. 1592-1604.
IEEE DOI
2106
Image coding, Kernel, Dictionaries, Measurement, Encoding,
Visualization, Mathematical model, Image quality assessment,
dictionary learning
BibRef
Sim, K.[Kyohoon],
Yang, J.C.[Jia-Chen],
Lu, W.[Wen],
Gao, X.B.[Xin-Bo],
MaD-DLS: Mean and Deviation of Deep and Local Similarity for Image
Quality Assessment,
MultMed(23), 2021, pp. 4037-4048.
IEEE DOI
2112
Feature extraction, Visualization, Distortion, Image quality,
Convolution, Standards, Neurons, Image quality assessment,
standard deviation pooling
BibRef
Jain, P.[Parima],
Shikkenawis, G.[Gitam],
Mitra, S.K.[Suman K.],
Natural Scene Statistics and CNN Based Parallel Network for Image
Quality Assessment,
ICIP21(1394-1398)
IEEE DOI
2201
Image quality, Databases, Social networking (online),
Feature extraction, Robustness, Convolutional neural networks,
Convolutional Neural Networks
BibRef
Chen, P.F.[Peng-Fei],
Li, L.[Leida],
Wu, Q.B.[Qing-Bo],
Wu, J.J.[Jin-Jian],
SPIQ: A Self-Supervised Pre-Trained Model for Image Quality
Assessment,
SPLetters(29), 2022, pp. 513-517.
IEEE DOI
2202
Distortion, Feature extraction, Task analysis, Transformers,
Training, Predictive models, Image quality,
contrastive learning
BibRef
Xu, M.[Mai],
Jiang, L.[Lai],
Li, C.[Chen],
Wang, Z.[Zulin],
Tao, X.M.[Xiao-Ming],
Viewport-Based CNN:
A Multi-Task Approach for Assessing 360° Video Quality,
PAMI(44), No. 4, April 2022, pp. 2198-2215.
IEEE DOI
2203
Task analysis, Visualization, Cameras, Proposals, Motion detection,
Quality assessment, Visual quality assessment, 360° video, CNN
BibRef
Ou, F.Z.[Fu-Zhao],
Wang, Y.G.[Yuan-Gen],
Li, J.[Jin],
Zhu, G.P.[Guo-Pu],
Kwong, S.[Sam],
A Novel Rank Learning Based No-Reference Image Quality Assessment
Method,
MultMed(24), 2022, pp. 4197-4211.
IEEE DOI
2209
Distortion, Feature extraction, Training, Image quality,
Convolutional neural networks, Predictive models,
authentic distortion
BibRef
Sendjasni, A.[Abderrezzaq],
Larabi, M.C.[Mohamed-Chaker],
Cheikh, F.A.[Faouzi Alaya],
Convolutional Neural Networks for Omnidirectional Image Quality
Assessment: A Benchmark,
CirSysVideo(32), No. 11, November 2022, pp. 7301-7316.
IEEE DOI
2211
BibRef
Earlier:
Convolutional Neural Networks for Omnidirectional Image Quality
Assessment: Pre-Trained or Re-Trained?,
ICIP21(3413-3417)
IEEE DOI
2201
Feature extraction, Databases, Convolutional neural networks,
Visualization, Training, Task analysis, Predictive models, Benchmark,
image quality assessment.
Image quality, Correlation, Databases, Image processing,
Transfer learning, Performance gain, Quality assessment, benchmark
BibRef
Lan, T.Y.[Tian-Yi],
Riaz, S.[Saleem],
Zhang, X.D.[Xuan-De],
Mirza, A.[Alina],
Afzal, F.[Farkhanda],
Iqbal, Z.[Zeshan],
Khan, M.A.[Muhammad Attique],
Alhaisoni, M.[Majed],
Alqahtani, A.[Abdullah],
Federated learning based nonlinear two-stage framework for
full-reference image quality assessment: An application for biometric,
IVC(128), 2022, pp. 104588.
Elsevier DOI
2212
Image quality assessment, Activation function, Nonlinearity,
Two-stage framework, Deep learning
BibRef
Sang, Q.B.[Qing-Bing],
Shu, Z.[Ziru],
Liu, L.X.[Li-Xiong],
Hu, C.[Cong],
Wu, Q.[Qin],
Image quality assessment based on self-supervised learning and
knowledge distillation,
JVCIR(90), 2023, pp. 103708.
Elsevier DOI
2301
Knowledge distillation, Self-supervised learning, Image quality evaluation
BibRef
Boral, S.[Subhadip],
Sarkar, M.[Mainak],
Ghosh, A.[Ashish],
MEQA: Manifold embedding quality assessment via anisotropic scaling
and Kolmogorov-Smirnov test,
PR(139), 2023, pp. 109447.
Elsevier DOI
2304
Manifold learning, Anisotropic scaling, Gradient descent,
Global scaling, Singular value decomposition, Kolmogorov-Smirnov test
BibRef
Zhou, S.[Siwang],
Deng, X.N.[Xiao-Ning],
Li, C.Q.[Cheng-Qing],
Liu, Y.H.[Yong-He],
Jiang, H.B.[Hong-Bo],
Recognition-Oriented Image Compressive Sensing With Deep Learning,
MultMed(25), 2023, pp. 2022-2032.
IEEE DOI
2306
Image reconstruction, Image recognition, Image quality,
Reconstruction algorithms, Imaging, Deep learning, Measurement,
machine recognition
BibRef
Prabhakaran, V.[Vishnu],
Swamy, G.[Gokul],
Image Quality Assessment using Semi-Supervised Representation
Learning,
VAQuality23(538-547)
IEEE DOI
2302
Image quality, Representation learning, Conferences,
Computational modeling, Predictive models, Task analysis
BibRef
Xu, T.[Tongda],
Shao, Y.F.[Yi-Fan],
Wang, Y.[Yan],
Qin, H.W.[Hong-Wei],
Spatial Moment Pooling Improves Neural Image Assessment,
ICIP22(271-275)
IEEE DOI
2211
Training, Image quality, Switched mode power supplies,
Neural networks, Feature extraction,
convolutional neural networks
BibRef
Yue, G.H.[Guang-Hui],
Cheng, D.[Di],
Wu, H.[Honglv],
Jiang, Q.P.[Qiu-Ping],
Wang, T.F.[Tian-Fu],
Improving IQA Performance Based on Deep Mutual Learning,
ICIP22(2182-2186)
IEEE DOI
2211
Training, Image quality, Neural networks, Network architecture,
Feature extraction, Task analysis, Image quality assessment,
convolutional neural networks
BibRef
Cao, Y.[Yue],
Wan, Z.L.[Zhao-Lin],
Ren, D.W.[Dong-Wei],
Yan, Z.[Zifei],
Zuo, W.M.[Wang-Meng],
Incorporating Semi-Supervised and Positive-Unlabeled Learning for
Boosting Full Reference Image Quality Assessment,
CVPR22(5841-5851)
IEEE DOI
2210
Image quality, Training, Visualization, Computational modeling,
Training data, Semisupervised learning, Boosting, Low-level vision
BibRef
Lao, S.S.[Shan-Shan],
Gong, Y.[Yuan],
Shi, S.W.[Shu-Wei],
Yang, S.[Sidi],
Wu, T.[Tianhe],
Wang, J.H.[Jia-Hao],
Xia, W.H.[Wei-Hao],
Yang, Y.J.[Yu-Jiu],
Attentions Help CNNs See Better: Attention-based Hybrid Image Quality
Assessment Network,
NTIRE22(1139-1148)
IEEE DOI
2210
Image quality, Convolution, Semantics, Predictive models,
Feature extraction, Generative adversarial networks, Distortion
BibRef
Cong, H.[Heng],
Fu, L.Z.[Ling-Zhi],
Zhang, R.Y.[Rong-Yu],
Zhang, Y.S.[Yu-Sheng],
Wang, H.[Hao],
He, J.R.[Jia-Rong],
Gao, J.[Jin],
Image Quality Assessment with Gradient Siamese Network,
NTIRE22(1200-1209)
IEEE DOI
2210
Image quality, Convolution, Semantics,
Mean square error methods, Network architecture, Feature extraction
BibRef
Peng, Y.D.[Yan-Ding],
Xu, J.H.[Jia-Hua],
Luo, Z.Y.[Zi-Yuan],
Zhou, W.[Wei],
Chen, Z.B.[Zhi-Bo],
Multi-Metric Fusion Network for Image Quality Assessment,
CLIC21(1857-1860)
IEEE DOI
2109
Measurement, Training, Image quality, Adaptation models, Fuses,
Reliability theory, Feature extraction
BibRef
Guo, H.Y.[Hai-Yang],
Bin, Y.[Yi],
Hou, Y.Q.[Yu-Qing],
Zhang, Q.[Qing],
Luo, H.L.[Heng-Liang],
IQMA Network: Image Quality Multi-scale Assessment Network,
NTIRE21(443-452)
IEEE DOI
2109
Image quality, Image edge detection,
Predictive models, Feature extraction,
Generative adversarial networks
BibRef
Shi, S.[Shuwei],
Bai, Q.Y.[Qing-Yan],
Cao, M.D.[Ming-Deng],
Xia, W.H.[Wei-Hao],
Wang, J.H.[Jia-Hao],
Chen, Y.F.[Yi-Fan],
Yang, Y.J.[Yu-Jiu],
Region-Adaptive Deformable Network for Image Quality Assessment,
NTIRE21(324-333)
IEEE DOI
2109
Image quality, Visualization, Convolution,
Generative adversarial networks, Distortion, Pattern recognition
BibRef
Ahn, S.[Sewoong],
Choi, Y.[Yeji],
Yoon, K.[Kwangjin],
Deep Learning-based Distortion Sensitivity Prediction for
Full-Reference Image Quality Assessment,
NTIRE21(344-353)
IEEE DOI
2109
Image quality, Visualization, Sensitivity, Databases,
Superresolution, Transform coding, Predictive models
BibRef
Zhao, X.,
Lin, H.,
Guo, P.,
Saupe, D.,
Liu, H.,
Deep Learning vs. Traditional Algorithms for Saliency Prediction of
Distorted Images,
ICIP20(156-160)
IEEE DOI
2011
Distortion, Machine learning, Computational modeling, Databases,
Bars, Measurement, Image quality, Image quality assessment, saliency,
statistical analysis
BibRef
Hou, J.,
Lin, W.,
Zhao, B.,
Content-Dependency Reduction With Multi-Task Learning In Blind
Stitched Panoramic Image Quality Assessment,
ICIP20(3463-3467)
IEEE DOI
2011
Feature extraction, Training, Task analysis, Machine learning,
Image quality, Distortion, Quality assessment,
virtual reality
BibRef
Dong, Z.[Zhe],
Shen, X.[Xu],
Li, H.Q.[Hou-Qiang],
Tian, X.M.[Xin-Mei],
Photo Quality Assessment with DCNN that Understands Image Well,
MMMod15(II: 524-535).
Springer DOI
1501
BibRef
Mocanu, D.C.[Decebal Constantin],
Exarchakos, G.[Georgios],
Liotta, A.[Antonio],
Deep learning for objective quality assessment of 3D images,
ICIP14(758-762)
IEEE DOI
1502
Databases
BibRef
Huang, P.P.[Pi-Pei],
Qin, S.Y.[Shi-Yin],
Lu, D.H.[Dong-Huan],
A Novel Approach to Image Assessment by Seeking Unification of
Subjective and Objective Criteria Based on Supervised Learning,
MIRAGE11(274-285).
Springer DOI
1110
BibRef
Lahoulou, A.,
Viennet, E.,
Haddadi, M.,
Variable selection for image quality assessment using a Neural Network
based approach,
EUVIP10(45-49).
IEEE DOI
1110
BibRef
Chapter on Image Processing, Restoration, Enhancement, Filters, Image and Video Coding continues in
No-Reference Image Quality Evaluation .