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Image quality assessment (IQA)
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Hu, A.Z.[An-Zhou],
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Machine learning-based multi-channel evaluation pooling strategy for
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ICIP13(427-430)
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Databases
BibRef
Narwaria, M.[Manish],
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Scalable image quality assessment with 2D mel-cepstrum and machine
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1410
Image quality assessment
BibRef
Guha, T.[Tanaya],
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Sparse representation-based image quality assessment,
SP:IC(29), No. 10, 2014, pp. 1138-1148.
Elsevier DOI
1411
Dictionary learning
BibRef
Hou, W.L.[Wei-Long],
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Saliency-Guided Deep Framework for Image Quality Assessment,
MultMedMag(22), No. 2, April 2015, pp. 46-55.
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Adaptation models
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Risnandar,
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Real-time deep satellite image quality assessment,
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Gao, F.[Fei],
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Blind image quality prediction by exploiting multi-level deep
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1806
Image quality assessment, Deep learning,
Convolutional Neural Networks (CNN),
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Oszust, M.,
Local Feature Descriptor and Derivative Filters for Blind Image
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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.,
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Pairwise-Comparison-Based Rank Learning for Benchmarking Image
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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],
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Research on regional energy efficiency based on GIS technology and
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JVCIR(62), 2019, pp. 410-417.
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1908
Deep learning, GIS technology, Image quality analysis, Model, Quality
BibRef
Chen, X.[Xue],
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Research on deep learning in the field of mechanical equipment fault
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JVCIR(62), 2019, pp. 402-409.
Elsevier DOI
1908
Deep learning, Mechanical equipment, Equipment maintenance, Image quality
BibRef
Wei, G.H.[Guang-Hui],
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Image quality assessment for intelligent emergency application based
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JVCIR(63), 2019, pp. 102581.
Elsevier DOI
1909
Entropy theory, Big data, Wisdom emergency, Quality model, Neural network
BibRef
Ko, K.M.[Kuo-Min],
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JVCIR(63), 2019, pp. 102594.
Elsevier DOI
1909
Deep learning, Convolutional neural network, Image retrieval,
Image quality assessment
BibRef
Chen, G.B.[Guo-Bin],
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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
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Zhang, Y.J.[Yan-Jun],
Gong, S.[Shuai],
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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],
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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)
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Wang, J.C.[Ji-Cheng],
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Deep Feature Fusion with Integration of Residual Connection and
Attention Model for Classification of VHR Remote Sensing Images,
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DOI Link
1907
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Li, Z.P.[Zhi-Peng],
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Image Quality Assessment for VHR Remote Sensing Image Classification,
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DOI Link
1610
BibRef
Bian, T.L.[Tian-Liang],
An ensemble image quality assessment algorithm based on deep feature
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SP:IC(81), 2020, pp. 115703.
Elsevier DOI
1912
Feature extraction, Clustering, Image quality assessment, PSNR, SIMM, VIF
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Ko, H.,
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Quality Prediction on Deep Generative Images,
IP(29), 2020, pp. 5964-5979.
IEEE DOI
2005
Image coding, Image quality,
Generative adversarial networks, Image databases,
subjective test
BibRef
Zhu, M.L.[Min-Ling],
Ge, D.Y.[Dong-Yuan],
Image quality assessment based on deep learning with FPGA
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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],
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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],
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Discriminative Convolutional Neural Network for Image Quality
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BibRef
Huang, Y.[Ying],
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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
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
Endo, K.[Kazuki],
Tanaka, M.[Masayuki],
Okutomi, M.[Masatoshi],
CNN-Based Classification of Degraded Images With Awareness of
Degradation Levels,
CirSysVideo(31), No. 10, October 2021, pp. 4046-4057.
IEEE DOI
2110
Degradation, Image restoration, Estimation, Transform coding,
Feature extraction, Distortion, Training, Degraded image,
restoration
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
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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
Tsubota, K.[Koki],
Akutsu, H.[Hiroaki],
Aizawa, K.[Kiyoharu],
Evaluating the Stability of Deep Image Quality Assessment with Respect
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IEICE(E105-D), No. 10, October 2022, pp. 1829-1833.
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2210
BibRef
Liang, D.[Dong],
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Lu, W.[Wen],
Li, J.[Jie],
Systemic distortion analysis with deep distortion directed image
quality assessment models,
SP:IC(109), 2022, pp. 116870.
Elsevier DOI
2210
Systemic distortion analysis, Image quality assessment,
Multi-label learning, Deep learning, Dictionary learning
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
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
Liu, L.X.[Li-Xiong],
Ma, P.C.[Ping-Chuan],
Wang, C.W.[Chong-Wen],
Xu, D.[Dong],
Omnidirectional Image Quality Assessment With Knowledge Distillation,
SPLetters(30), 2023, pp. 1562-1566.
IEEE DOI
2311
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
Chen, J.[Jian],
Li, S.Y.[Shi-Yun],
Lin, L.[Li],
Wan, J.[Jiaze],
Li, Z.Y.[Zuo-Yong],
No-reference blurred image quality assessment method based on
structure of structure features,
SP:IC(118), 2023, pp. 117008.
Elsevier DOI
2310
Image blur, No-reference image quality assessment, Log-gabor filter response,
Support vector regression, Local binary patterns (LBP)
BibRef
Yue, G.H.[Guang-Hui],
Cheng, D.[Di],
Li, L.[Leida],
Zhou, T.W.[Tian-Wei],
Liu, H.T.[Han-Tao],
Wang, T.F.[Tian-Fu],
Semi-Supervised Authentically Distorted Image Quality Assessment with
Consistency-Preserving Dual-Branch Convolutional Neural Network,
MultMed(25), 2023, pp. 6499-6511.
IEEE DOI
2311
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
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
Yu, Y.J.[Yi-Jiong],
Wang, T.[Tao],
Ran, K.[Kang],
Li, C.[Chang],
Wu, H.[Hao],
An intelligent remote sensing image quality inspection system,
IET-IPR(18), No. 3, 2024, pp. 678-693.
DOI Link
2402
image segmentation, image classification, deep learning,
image quality inspection, remote sensing
BibRef
Agnolucci, L.[Lorenzo],
Galteri, L.[Leonardo],
Bertini, M.[Marco],
del Bimbo, A.[Alberto],
ARNIQA: Learning Distortion Manifold for Image Quality Assessment,
WACV24(188-197)
IEEE DOI Code:
WWW Link.
2404
Manifolds, Image quality, Degradation, Training, Crops,
Image representation, Distortion, Algorithms, and algorithms
BibRef
Silbernagel, M.[Malte],
Wiegand, T.[Thomas],
Eisert, P.[Peter],
Bosse, S.[Sebastian],
Pre-Training with Fractal Images Facilitates Learned Image Quality
Estimation,
ICIP23(2625-2629)
IEEE DOI
2312
BibRef
Sendjasni, A.[Abderrezzaq],
Larabi, M.C.[Mohamed-Chaker],
Self Patch Labeling Using Quality Distribution Estimation for
CNN-Based 360-IQA Training,
ICIP23(2640-2644)
IEEE DOI
2312
BibRef
Saha, A.[Avinab],
Mishra, S.[Sandeep],
Bovik, A.C.[Alan C.],
Re-IQA: Unsupervised Learning for Image Quality Assessment in the
Wild,
CVPR23(5846-5855)
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
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
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.W.[Shu-Wei],
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
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
Full-Reference Image Quality Evaluation .