activity
20202025
most citedCNN-based driving of block partitioning for intra slices encoding

41 citations · 41 across the 4 of their papers we have counts for

collaborators

5 papers

cs.CV2025

Exploiting Latent Properties to Optimize Neural Codecs

Muhammet Balcilar, Bharath Bhushan Damodaran, Karam Naser +2

End-to-end image and video codecs are becoming increasingly competitive, compared to traditional compression techniques that have been developed through decades of manual engineeri…

eess.IV2022

Neural Network based Inter bi-prediction Blending

Franck Galpin, Philippe Bordes, Thierry Dumas +2

This paper presents a learning-based method to improve bi-prediction in video coding. In conventional video coding solutions, the motion compensation of blocks from already decoded…

eess.IV2021

Combined neural network-based intra prediction and transform selection

Thierry Dumas, Franck Galpin, Philippe Bordes

The interactions between different tools added successively to a block-based video codec are critical to its rate-distortion efficiency. In particular, when deep neural network-bas…

cs.MM202041 cited

CNN-based driving of block partitioning for intra slices encoding

Franck Galpin, Fabien Racapé, Sunil Jaiswal +3

This paper provides a technical overview of a deep-learning-based encoder method aiming at optimizing next generation hybrid video encoders for driving the block partitioning in in…

eess.IV2020

Iterative training of neural networks for intra prediction

Thierry Dumas, Franck Galpin, Philippe Bordes

This paper presents an iterative training of neural networks for intra prediction in a block-based image and video codec. First, the neural networks are trained on blocks arising f…