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

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

Hou, W.L.[Wei-Long], Gao, X.B.[Xin-Bo],
Saliency-Guided Deep Framework for Image Quality Assessment,
MultMedMag(22), No. 2, April 2015, pp. 46-55.
IEEE DOI 1507
Adaptation models BibRef

Risnandar, Aritsugi, M.[Masayoshi],
Real-time deep satellite image quality assessment,
RealTimeIP(14), No. 3, October 2018, pp. 477-494.
WWW Link. 1811
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

Wang, J.C.[Ji-Cheng], Shen, L.[Li], Qiao, W.F.[Wen-Fan], Dai, Y.S.[Yan-Shuai], Li, Z.L.[Zhi-Lin],
Deep Feature Fusion with Integration of Residual Connection and Attention Model for Classification of VHR Remote Sensing Images,
RS(11), No. 13, 2019, pp. xx-yy.
DOI Link 1907
BibRef

Li, Z.P.[Zhi-Peng], Shen, L.[Li], Wu, L.M.[Lin-Mei],
Image Quality Assessment for VHR Remote Sensing Image Classification,
ISPRS16(B7: 11-16).
DOI Link 1610
BibRef

Bian, T.L.[Tian-Liang],
An ensemble image quality assessment algorithm based on deep feature clustering,
SP:IC(81), 2020, pp. 115703.
Elsevier DOI 1912
Feature extraction, Clustering, Image quality assessment, PSNR, SIMM, VIF BibRef

Ko, H., Lee, D.Y., Cho, S., Bovik, A.C.,
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 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

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 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

Tsubota, K.[Koki], Akutsu, H.[Hiroaki], Aizawa, K.[Kiyoharu],
Evaluating the Stability of Deep Image Quality Assessment with Respect to Image Scaling,
IEICE(E105-D), No. 10, October 2022, pp. 1829-1833.
WWW Link. 2210
BibRef

Liang, D.[Dong], Gao, X.B.[Xin-Bo], 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


Shukla, A.[Ankit], Upadhyay, A.[Avinash], Bhugra, S.[Swati], Sharma, M.[Manoj],
Opinion Unaware Image Quality Assessment via Adversarial Convolutional Variational Autoencoder,
WACV24(2142-2152)
IEEE DOI 2404
Measurement, Image quality, Training, Representation learning, Benchmark testing, Distortion, Algorithms, and algorithms 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 .


Last update:May 6, 2024 at 15:50:14