41 citations · 71 across the 11 of their papers we have counts for
11 papers
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…
Designs and Implementations in Neural Network-based Video Coding
Yue Li, Junru Li, Chaoyi Lin +9
The past decade has witnessed the huge success of deep learning in well-known artificial intelligence applications such as face recognition, autonomous driving, and large language…
Latent-Shift: Gradient of Entropy Helps Neural Codecs
Muhammet Balcilar, Bharath Bhushan Damodaran, Karam Naser +2
End-to-end image/video codecs are getting competitive compared to traditional compression techniques that have been developed through decades of manual engineering efforts. These t…
RQAT-INR: Improved Implicit Neural Image Compression
Bharath Bhushan Damodaran, Muhammet Balcilar, Franck Galpin +1
Deep variational autoencoders for image and video compression have gained significant attraction in the recent years, due to their potential to offer competitive or better compress…
Entropy Coding Improvement for Low-complexity Compressive Auto-encoders
Franck Galpin, Muhammet Balcilar, Frédéric Lefebvre +2
End-to-end image and video compression using auto-encoders (AE) offers new appealing perspectives in terms of rate-distortion gains and applications. While most complex models are…
Revisiting the Sample Adaptive Offset post-filter of VVC with Neural-Networks
Philippe Bordes, Franck Galpin, Thierry Dumas +1
The Sample Adaptive Offset (SAO) filter has been introduced in HEVC to reduce general coding and banding artefacts in the reconstructed pictures, in complement to the De-Blocking F…
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…
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…
Machine Learning based Efficient QT-MTT Partitioning Scheme for VVC Intra Encoders
Alexandre Tissier, Wassim Hamidouche, Souhaiel Belhadj Dit Mdalsi +3
The next-generation Versatile Video Coding (VVC) standard introduces a new Multi-Type Tree (MTT) block partitioning structure that supports Binary-Tree (BT) and Ternary-Tree (TT) s…
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…
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…