5.3.10.1 Learning for Image Quality Evaluation, CNN, GAN

Chapter Contents (Back)
Image Quality. Learning. Neural Networks.

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


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, Computer architecture, 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 .


Last update:Oct 11, 2021 at 11:04:06