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Learning graph convolutional network for blind mesh visual quality assessment

Ilyass Abouelaziz, Aladine Chetouani, Mohammed El Hassouni, Hocine Cherifi, Longin Jan Latecki
2021 IEEE Access  
24 mesh quality assessment.  ...  CONCLUSION 605 In this work, we rely on a shallow graph convolutional 606 network to accurately estimate distorted meshes perceived 607 visual quality.  ...  For more information, see https://creativecommons.org/licenses/by/4.0/  ... 
doi:10.1109/access.2021.3094663 fatcat:mb5gsbpojbclhcbamepncq36cy

2021 Index IEEE Transactions on Multimedia Vol. 23

2021 IEEE transactions on multimedia  
The Author Index contains the primary entry for each item, listed under the first author's name.  ...  ., +, TMM 2021 3306-3317 Anisotropic Graph Convolutional Network for Semi-Supervised Learning.  ...  ., +, TMM 2021 320-332 Re-Visiting Discriminator for Blind Free-Viewpoint Image Quality Assess-ment.  ... 
doi:10.1109/tmm.2022.3141947 fatcat:lil2nf3vd5ehbfgtslulu7y3lq

2020 Index IEEE Transactions on Image Processing Vol. 29

2020 IEEE Transactions on Image Processing  
., +, TIP 2020 1139-1151 KonIQ-10k: An Ecologically Valid Database for Deep Learning of Blind Image Quality Assessment.  ...  ., +, TIP 2020 5877-5888 Screen Content Video Quality Assessment: Subjective and Objective Study. Sparse Graph Regularized Mesh Color Edit Propagation.  ... 
doi:10.1109/tip.2020.3046056 fatcat:24m6k2elprf2nfmucbjzhvzk3m

Blind Mesh Assessment Based on Graph Spectral Entropy and Spatial Features

Lin, Yu, Chen, Jiang, Chen, Peng
2020 Entropy  
Therefore, how to evaluate the visual quality of 3D mesh is becoming an important problem and it is necessary to design effective tools for blind 3D mesh quality assessment.  ...  In this paper, we propose a new Blind Mesh Quality Assessment method based on Graph Spectral Entropy and Spatial features, called as BMQA-GSES. 3D mesh can be represented as graph signal, in the graph  ...  [17] proposed a blind MQA (BMQA) method using a convolutional neural network (CNN) based on the extraction of visual representative features.  ... 
doi:10.3390/e22020190 pmid:33285965 pmcid:PMC7516613 fatcat:w7jfkjxru5hnxcy7lg4p236ike

2020 Index IEEE Transactions on Multimedia Vol. 22

2020 IEEE transactions on multimedia  
., +, TMM Aug. 2020 1969-1984 Deep Multimodality Learning for UAV Video Aesthetic Quality Assessment.  ...  ., +, TMM July 2020 1785-1795 Blind Night-Time Image Quality Assessment: Subjective and Objective Approaches.  ...  Image watermarking Blind Watermarking for 3-D Printed Objects by Locally Modifying Layer Thickness. 2780 -2791 Low-Light Image Enhancement With Semi-Decoupled Decomposition.  ... 
doi:10.1109/tmm.2020.3047236 fatcat:llha6qbaandfvkhrzpe5gek6mq

2021 Index IEEE Transactions on Image Processing Vol. 30

2021 IEEE Transactions on Image Processing  
The Author Index contains the primary entry for each item, listed under the first author's name.  ...  Ding, Q., +, TIP 2021 6459-6472 Cubemap-Based Perception-Driven Blind Quality Assessment for tion.  ...  ., +, TIP 2021 5819-5834 UGC-VQA: Benchmarking Blind Video Quality Assessment for User Gen-erated Content.  ... 
doi:10.1109/tip.2022.3142569 fatcat:z26yhwuecbgrnb2czhwjlf73qu

Entropy-Based Algorithms for Signal Processing

Gwanggil Jeon, Abdellah Chehri
2020 Entropy  
Acknowledgments: We would like to express our appreciation to all the authors for their informative contributions and the reviewers for their support and constructive critiques in making this Special Issue  ...  Finally, we would like to express our sincere gratitude to journal staff and the Assistant Editor, for providing us with this unique opportunity to present our works in MDPI Entropy.  ...  Therefore, how to evaluate the visual quality of the 3D mesh is becoming a crucial problem, and it is necessary to design practical tools for blind 3D mesh quality assessment.  ... 
doi:10.3390/e22060621 pmid:33286393 fatcat:mj3ol3tqivbk5o7r7nogdv3f7m

Table of contents

2020 IEEE Transactions on Image Processing  
Chen 4027 KonIQ-10k: An Ecologically Valid Database for Deep Learning of Blind Image Quality Assessment .................. ..............................................................................  ...  Wan 2999 A Metric for Video Blending Quality Assessment ................................... Z. Zhu, H. Liu, J. Lu, and S.-M.  ... 
doi:10.1109/tip.2019.2940372 fatcat:h23ul2rqazbstcho46uv3lunku

Table of contents

2020 IEEE Transactions on Image Processing  
Guan 3805 KonIQ-10k: An Ecologically Valid Database for Deep Learning of Blind Image Quality Assessment .................. ..............................................................................  ...  Cao 4627 Sparse Graph Regularized Mesh Color Edit Propagation ................................ B. Li, Y.-K. Lai, and P. L.  ... 
doi:10.1109/tip.2019.2940373 fatcat:i7hktzn4wrfz5dhq7hj75u6esa

2021 Index IEEE Transactions on Pattern Analysis and Machine Intelligence Vol. 43

2022 IEEE Transactions on Pattern Analysis and Machine Intelligence  
The Author Index contains the primary entry for each item, listed under the first author's name.  ...  ., +, TPAMI Dec. 2021 4256-4271 Cascade R-CNN: High Quality Object Detection and Instance Segmenta-ChannelNets: Compact and Efficient Convolutional Neural Networks via Channel-Wise Convolutions.  ...  ., +, TPAMI Nov. 2021 3892-3903 Learning Channel-Wise Interactions for Binary Convolutional Neural Networks.  ... 
doi:10.1109/tpami.2021.3126216 fatcat:h6bdbf2tdngefjgj76cudpoyia

2020 Index IEEE Signal Processing Letters Vol. 27

2020 IEEE Signal Processing Letters  
., +, LSP 2020 1844-1848 Modeling Label Dependencies for Audio Tagging With Graph Convolutional Network.  ...  Huang, X., +, Reduced Reference Metric for Visual Quality Evaluation of Point Cloud Contents.  ... 
doi:10.1109/lsp.2021.3055468 fatcat:wfdtkv6fmngihjdqultujzv4by

Learning Graph-Convolutional Representations for Point Cloud Denoising [article]

Francesca Pistilli, Giulia Fracastoro, Diego Valsesia, Enrico Magli
2020 arXiv   pre-print
We propose a deep neural network based on graph-convolutional layers that can elegantly deal with the permutation-invariance problem encountered by learning-based point cloud processing methods.  ...  The network is fully-convolutional and can build complex hierarchies of features by dynamically constructing neighborhood graphs from similarity among the high-dimensional feature representations of the  ...  In this paper, we propose a deep graph-convolutional neural network for denoising of point cloud geometry.  ... 
arXiv:2007.02578v1 fatcat:zv2dyootkvasfg6txymp6fn2z4

Table of Contents

2020 IEEE Signal Processing Letters  
Ding 1655 A Reduced Reference Metric for Visual Quality Evaluation of Point Cloud Contents . . . . . . . . . . . . . I. Viola and P.  ...  Yi 231 Smooth Incremental Learning of Correlation Filters for Visual Tracking . . . . . . . . . . . . . . J. Guo, L. Zhuang, and P.  ...  Wu 1914 Multiscale Convolutional Fusion Network for Non-Lambertian Photometric Stereo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ... 
doi:10.1109/lsp.2020.3040844 fatcat:xpovskhrvfgctk3hhufuvpyyne

Table of contents

2019 2019 IEEE International Conference on Multimedia & Expo Workshops (ICMEW)  
Mesh Quality Assessment Method Based on Concave, Convex and Structural Features Analyses Yaoyao Lin (Ningbo University), Mei Yu (Ningbo University), Ken Chen (Ningbo University), Gangyi Jiang (Ningbo  ...  Visual Object Tracking via Graph Convolutional Representation of Southern California), Hanhan Li (Google Research), Shahab Kamali Zhengzheng Tu (Anhui University, China), Ajian Zhou (Anhui University,  ... 
doi:10.1109/icmew.2019.00004 fatcat:3iawlotcjraublx7puu6xcp44y

Editorial: Introduction to the Issue on Deep Learning for Image/Video Restoration and Compression

A. Murat Tekalp, Michele Covell, Radu Timofte, Chao Dong
2021 IEEE Journal on Selected Topics in Signal Processing  
The paper entitled "Learning robust graph-convolutional representations for point cloud denoising" proposes a deep learning method that can simultaneously denoise a point cloud and remove outliers in a  ...  Benefiting from a learnable ranker, RankSRGAN [25] can optimize the generative network in the direction of any image quality assessment (IQA) metrics and achieves state-of-the-art performance.  ... 
doi:10.1109/jstsp.2021.3053364 fatcat:hjo5pvw6lvgpfga2wfq4vpaq3q
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