5.3.10.1 Learning for Image Quality Evaluation, CNN, GAN

Gastaldo, P.[Paolo], Zunino, R.[Rodolfo], Heynderickx, I.[Ingrid], Vicario, E.[Elena],
Objective quality assessment of displayed images by using neural networks,
SP:IC(20), No. 7, August 2005, pp. 643-661.
Elsevier DOI 0508
BibRef

Brankov, J.G., Yang, Y., Wei, L., El Naqa, I., Wernick, M.N.,
Learning a Channelized Observer for Image Quality Assessment,
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 algorithm,
SP:IC(27), No. 3, March 2012, pp. 209-219.
Elsevier DOI 1203
FR-IQA algorithm; Classification; Theory of evidence; SVM classification; SVM regression
See also Color VQ-Based Image Compression by Manifold Learning. BibRef

Gastaldo, P.[Paolo], Zunino, R.[Rodolfo], Redi, J.[Judith],
Supporting visual quality assessment with machine learning,
JIVP(2013), No. 1, 2013, pp. 54.
DOI Link 1311
BibRef

Hu, A.Z.[An-Zhou], Zhang, R.[Rong], Yin, D.[Dong], Zhan, Y.B.[Yi-Bing],
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.[Yuhui], 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

Tianyi, L.[Lan], Riaz, S.[Saleem], Xuande, Z.[Zhang], 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

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 .