69 citations · 249 across the 23 of their papers we have counts for
26 papers
Light-weight CNN-based VVC Inter Partitioning Acceleration
Yiqun Liu, Mohsen Abdoli, Thomas Guionnet +2
The Versatile Video Coding (VVC) standard has been finalized by Joint Video Exploration Team (JVET) in 2020. Compared to the High Efficiency Video Coding (HEVC) standard, VVC offer…
Statistical Analysis of Inter Coding in VVC Test Model (VTM)
Yiqun Liu, Mohsen Abdoli, Thomas Guionnet +2
The promising improvement in compression efficiency of Versatile Video Coding (VVC) compared to High Efficiency Video Coding (HEVC) comes at the cost of a non-negligible encoder si…
Learning Kernel-Modulated Neural Representation for Efficient Light Field Compression
Jinglei Shi, Yihong Xu, Christine Guillemot
Light field is a type of image data that captures the 3D scene information by recording light rays emitted from a scene at various orientations. It offers a more immersive percepti…
Learning-based Spatial and Angular Information Separation for Light Field Compression
Jinglei Shi, Yihong Xu, Christine Guillemot
Light fields are a type of image data that capture both spatial and angular scene information by recording light rays emitted by a scene from different orientations. In this contex…
Distilled Low Rank Neural Radiance Field with Quantization for Light Field Compression
Jinglei Shi, Christine Guillemot
We propose in this paper a Quantized Distilled Low-Rank Neural Radiance Field (QDLR-NeRF) representation for the task of light field compression. While existing compression methods…
PnP-ReG: Learned Regularizing Gradient for Plug-and-Play Gradient Descent
Rita Fermanian, Mikael Le Pendu, Christine Guillemot
The Plug-and-Play (PnP) framework makes it possible to integrate advanced image denoising priors into optimization algorithms, to efficiently solve a variety of image restoration t…
Preconditioned Plug-and-Play ADMM with Locally Adjustable Denoiser for Image Restoration
Mikael Le Pendu, Christine Guillemot
Plug-and-Play optimization recently emerged as a powerful technique for solving inverse problems by plugging a denoiser into a classical optimization algorithm. The denoiser accoun…
A learning-based view extrapolation method for axial super-resolution
Zhaolin Xiao, Jinglei Shi, Xiaoran Jiang +1
Axial light field resolution refers to the ability to distinguish features at different depths by refocusing. The axial refocusing precision corresponds to the minimum distance in…
A Lightweight Neural Network for Monocular View Generation with Occlusion Handling
Simon Evain, Christine Guillemot
In this article, we present a very lightweight neural network architecture, trained on stereo data pairs, which performs view synthesis from one single image. With the growing succ…
Geometry-Aware Graph Transforms for Light Field Compact Representation
Mira Rizkallah, Xin Su, Thomas Maugey +1
The paper addresses the problem of energy compaction of dense 4D light fields by designing geometry-aware local graph-based transforms. Local graphs are constructed on super-rays t…
Prediction and Sampling with Local Graph Transforms for Quasi-Lossless Light Field Compression
Mira Rizkallah, Thomas Maugey, Christine Guillemot
Graph-based transforms have been shown to be powerful tools in terms of image energy compaction. However, when the support increases to best capture signal dependencies, the comput…
A Fourier Disparity Layer representation for Light Fields
Mikael Le Pendu, Christine Guillemot, Aljosa Smolic
In this paper, we present a new Light Field representation for efficient Light Field processing and rendering called Fourier Disparity Layers (FDL). The proposed FDL representation…
A Simple Framework to Leverage State-Of-The-Art Single-Image Super-Resolution Methods to Restore Light Fields
Reuben A. Farrugia, C. Guillemot
Plenoptic cameras offer a cost effective solution to capture light fields by multiplexing multiple views on a single image sensor. However, the high angular resolution is achieved…
Context-adaptive neural network based prediction for image compression
Thierry Dumas, Aline Roumy, Christine Guillemot
This paper describes a set of neural network architectures, called Prediction Neural Networks Set (PNNS), based on both fully-connected and convolutional neural networks, for intra…
Learning Discriminative Multilevel Structured Dictionaries for Supervised Image Classification
Jeremy Aghaei Mazaheri, Elif Vural, Claude Labit +1
Sparse representations using overcomplete dictionaries have proved to be a powerful tool in many signal processing applications such as denoising, super-resolution, inpainting, com…
Autoencoder based image compression: can the learning be quantization independent?
Thierry Dumas, Aline Roumy, Christine Guillemot
This paper explores the problem of learning transforms for image compression via autoencoders. Usually, the rate-distortion performances of image compression are tuned by varying t…
Light Field Super-Resolution using a Low-Rank Prior and Deep Convolutional Neural Networks
Reuben A. Farrugia, Christine Guillemot
Light field imaging has recently known a regain of interest due to the availability of practical light field capturing systems that offer a wide range of applications in the field…
Scalable image coding based on epitomes
Martin Alain, Christine Guillemot, Dominique Thoreau +1
In this paper, we propose a novel scheme for scalable image coding based on the concept of epitome. An epitome can be seen as a factorized representation of an image. Focusing on s…
Face Hallucination using Linear Models of Coupled Sparse Support
Reuben Farrugia, Christine Guillemot
Most face super-resolution methods assume that low-resolution and high-resolution manifolds have similar local geometrical structure, hence learn local models on the lowresolution…
A study of the classification of low-dimensional data with supervised manifold learning
Elif Vural, Christine Guillemot
Supervised manifold learning methods learn data representations by preserving the geometric structure of data while enhancing the separation between data samples from different cla…
Geometry-Aware Neighborhood Search for Learning Local Models for Image Reconstruction
Julio Cesar Ferreira, Elif Vural, Christine Guillemot
Local learning of sparse image models has proven to be very effective to solve inverse problems in many computer vision applications. To learn such models, the data samples are oft…
Partial light field tomographic reconstruction from a fixed-camera focal stack
A. Mousnier, E. Vural, C. Guillemot
This paper describes a novel approach to partially reconstruct high-resolution 4D light fields from a stack of differently focused photographs taken with a fixed camera. First, a f…
Out-of-sample generalizations for supervised manifold learning for classification
Elif Vural, Christine Guillemot
Supervised manifold learning methods for data classification map data samples residing in a high-dimensional ambient space to a lower-dimensional domain in a structure-preserving w…
Information-theoretic resolution of perceptual WSS watermarking of non i.i.d. Gaussian signals
Stéphane Pateux, Gaëtan Le Guelvouit, Christine Guillemot
The theoretical foundations of data hiding have been revealed by formulating the problem as message communication over a noisy channel. We revisit the problem in light of a more ge…