Joint Learning of Fully Connected Network Models in Lifting Based Image Coders-Reference-Cited by-同舟云学术

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Joint Learning of Fully Connected Network Models in Lifting Based Image Coders

Published:2024 Issue: Volume:33 Page:134-148
ISSN:1057-7149
Container-title:IEEE Transactions on Image Processing
language:
Short-container-title:IEEE Trans. on Image Process.

Author:

Dardouri Tassnim¹^ORCID,Kaaniche Mounir²^ORCID,Benazza-Benyahia Amel³^ORCID,Dauphin Gabriel²^ORCID,Pesquet Jean-Christophe⁴^ORCID

Affiliation:

1. Novelis, Paris, France

2. L2TI, UR 3043, Université Sorbonne Paris Nord, Villetaneuse, France

3. SUP’COM, LR11TIC01, COSIM Laboratory, University of Carthage, El Ghazala, Carthage, Tunisia

4. Centre de Vision Numérique, Centrale-Supélec, Inria, Université Paris-Saclay, Gif-sur-Yvette, France

Funder

Agence nationale de la recherche (ANR) Research and Teaching Chair BRIDinG thE gAp Between iterative proximaL methods and nEural networks (BRIDGEABLE) in Artificial Intelligence

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Graphics and Computer-Aided Design,Software

Link

http://xplorestaging.ieee.org/ielx7/83/10346232/10325422.pdf?arnumber=10325422

Reference56 articles.

1. A two-dimensional lifting scheme of integer wavelet transform for lossless image compression;sun;Proc Int Conf Image Process,2004

2. Factoring wavelet transforms into lifting steps

3. Efficient joint multiscale decomposition for color stereo image coding;dhifallah;Proc 22nd Eur Signal Process Conf (EUSIPCO),2014

4. M-band nonlinear subband decompositions with perfect reconstruction

5. Two-dimensional non separable adaptive lifting scheme for still and stereo image coding

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