Frédéric Dufaux
15
Documents
Présentation
Dr. Frederic Dufaux is a CNRS Research Director at Université Paris-Saclay, CNRS, CentraleSupélec, Laboratoire des Signaux et Systèmes (L2S, UMR 8506), where he is head of the Telecom and Networking hub. He is a Fellow of IEEE.
Frederic received the M.Sc. in physics and Ph.D. in electrical engineering from the Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland, in 1990 and 1994 respectively.
He has over 20 years of experience in research, previously holding positions at EPFL, Emitall Surveillance, Genimedia, Compaq, Digital Equipment, and MIT. He was also Editor-in-Chief of Signal Processing: Image Communication from 2010 until 2019.
Frederic served as Chair of the IEEE SPS Multimedia Signal Processing (MMSP) Technical Committee in 2018 and 2019. He is a member of the IEEE SPS Technical Directions Board. He was Vice General Chair of ICIP 2014, General Chair of MMSP 2018, and Technical Program co-Chair of ICIP 2019. He will be Technical Program co-Chair of ICIP 2021. He is also a founding member and the Chair of the EURASIP Technical Area Committee on Visual Information Processing.
He has been involved in the standardization of digital video and imaging technologies for more than 15 years, participating both in the MPEG and JPEG committees. He was co-chairman of JPEG 2000 over wireless (JPWL) and co-chairman of JPSearch. He is the recipient of two ISO awards for these contributions.
His research interests include image and video coding, 3D video, high dynamic range imaging, visual quality assessment, video surveillance, privacy protection, image and video analysis, multimedia content search and retrieval, video transmission over wireless network. He is author or co-author of 3 books, more than 200 research publications (h-index=44, 8000+ citations) and 20 patents issued or pending.
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Survey on Deep Learning-based Point Cloud CompressionFrontiers in Signal Processing, 2022, 2, ⟨10.3389/frsip.2022.846972⟩
Article dans une revue
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Learning-based 3D point cloud quality assessment using a support vector regressorImage Quality and System Performance, IS&T International Symposium on Electronic Imaging (EI 2022), Jan 2022, San Francisco, United States. ⟨10.2352/EI.2022.34.9.IQSP-385⟩
Communication dans un congrès
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Combination of Deep Learning-based and Handcrafted Features for Blind Image Quality Assessment9th European Workshop on Visual Information Processing (EUVIP 2021), Jun 2021, Paris (virtual), France. pp.1-6, ⟨10.1109/EUVIP50544.2021.9484013⟩
Communication dans un congrès
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A deep perceptual metric for 3D point cloudsImage Quality and System Performance, IS&T International Symposium on Electronic Imaging (EI 2021), Jan 2021, San Francisco, United States. pp.257-1-257-7, ⟨10.2352/ISSN.2470-1173.2021.9.IQSP-257⟩
Communication dans un congrès
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A Deep Point Cloud Geometry Coding ToolboxIEEE International Conference on Multimedia & Expo Workshops (ICMEW), Jul 2021, Shenzhen, China. pp.1-2, ⟨10.1109/ICMEW53276.2021.9455986⟩
Communication dans un congrès
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Deep learning-based blind quality assessment of 3D point clouds without referenceIEEE International Conference on Multimedia & Expo Workshops (ICMEW), Jul 2021, Shenzhen (virtual), China. pp.1-6, ⟨10.1109/ICMEW53276.2021.9455967⟩
Communication dans un congrès
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Convolutional Neural Network for 3D Point Cloud Quality Assessment with ReferenceIEEE International Workshop on Multimedia Signal Processing (MMSP'2021), Oct 2021, Tampere, Finland. pp.1-6, ⟨10.1109/MMSP53017.2021.9733565⟩
Communication dans un congrès
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Folding-based Compression of Point Cloud AttributesIEEE International Conference on Image Processing (ICIP’2020), Oct 2020, Abu Dhabi, United Arab Emirates. pp.3309-3313, ⟨10.1109/ICIP40778.2020.9191180⟩
Communication dans un congrès
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Improved Deep Point Cloud Geometry CompressionIEEE International Workshop on Multimedia Signal Processing (MMSP'2020), Sep 2020, Tampere, Finland. pp.1-6, ⟨10.1109/MMSP48831.2020.9287077⟩
Communication dans un congrès
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Learning Convolutional Transforms for Lossy Point Cloud Geometry Compression26th IEEE International Conference on Image Processing (ICIP 2019), Sep 2019, Taipei, Taiwan. pp.4320-4324, ⟨10.1109/ICIP.2019.8803413⟩
Communication dans un congrès
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Point cloud compressionImmersive Video Technologies, Elsevier, 2022, 978-0-323-91755-1. ⟨10.1016/B978-0-32-391755-1.00019-5⟩
Chapitre d'ouvrage
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Learning-based PCC via Unfolding of 3D Point CloudsUnited States, Patent n° : United States Patent Application No. 63/213,654. 2021
Brevet
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Learning-based Point Cloud Compression via Tearing TransformUnited States, Patent n° : United States Patent Application No. 63/181,270. 2021
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State Summarization for Binary Voxel Grid CodingUnited States, Patent n° : United States Patent Application No. 63/275,511. 2021
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Outlier Grouping based Point Cloud CompressionUnited States, Patent n° : United States Patent Application No. 63/281,803. 2021
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