Publications de Pascal Frossard

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This paper proposes a rate-distortion optimal a posteriori quantization scheme for Matching Pursuit coefficients. The a posteriori quantization applies to a Matching Pursuit expansion that has been generated off-line, and cannot benefit of any feedback loop to the encoder in order to compensate for the quantization noise. The redundancy of the Matching Pursuit dictionary provides an indicator of the relative importance of coefficients and atom indices, and subsequently on the quantization error. It is used

This paper addresses the problem of streaming packetized media over a lossy packet network, with sender-driven (re)transmissions and receiver acknowledgments. It extends the markovian formulation of the rate- distortion optimized (RaDiO) streaming framewor ...

This paper addresses the reconstruction of high resolution omnidirectional images from a low resolution video acquired by an omnidirectional camera moving in a static scene. In order to exploit the additional information provided by the side images in the ...

The papers in this special issue are intended to address some of the main research challenges in Graph Signal Processing by presenting a collection of the latest advances in the domain. These papers examine key representation, learning and processing aspec ...

We consider streaming video content over an overlay network of peer nodes. Each of the nodes employs a mesh-pull mechanism to organize the download of data units from its neighbours. We propose a novel algorithm for constructing the distribution overlay, w ...

In this paper, we consider the problem of streaming video information on lossy packet networks with path and source diversity, such as wireless mesh networks or more generally overlay architectures. We propose to distribute the video information between se ...

This paper proposes a new method for reconstruction of star-shaped 3D surfaces from scattered datasets, where such surfaces are considered as signals living in the space of square integrable functions on the unit sphere. We first propose a generalization of the Fourier transform on the sphere. A practical reconstruction method is then presented, which interpolates a spherical signal on an equiangular grid, from non-uniformly sampled dataset representing a 3D point cloud. The experiments show that the propos

The amount of transmitted data in computer networks is expected to grow considerably in the future, putting more and more pressure on the network infrastructures. In order to guarantee a good service, it then becomes fundamental to use the network resource ...

We address the problem of rate allocation and network/path selection for multiple users, running simultaneous applications over multiple parallel access networks. Our joint optimization problem consists of finding the appropriate application rate allocatio ...

This paper describes a novel video coding scheme based on a three-dimensional Matching Pursuit algorithm. In addition to good compression performance at low bit rate, the proposed coder allows for flexible spatial, temporal and rate scalability thanks to its progressive coding structure. The Matching Pursuit algorithm generates a sparse decomposition of a video sequence in a series of spatio-temporal atoms, taken from an overcomplete dictionary of three-dimensional basis functions. The dictionary is generat

Accurate forecasting of photovoltaic (PV) and wind production is crucial for the integration of more renewable energy sources into the power grid. To address the limited resolution and costs of methods based on numerical weather predictions (NWP), we take ...

State-of-the-art 2D image compression schemes rely on the power of convolutional neural networks (CNNs). Although CNNs offer promising perspectives for 2D image compression, extending such models to omnidirectional images is not straightforward. First, omn ...

Graph diffusion models have emerged as state-of-the-art techniques in graph generation; yet, integrating domain knowledge into these models remains challenging. Domain knowledge is particularly important in real-world scenarios, where invalid generated graphs hinder deployment in practical applications. Unconstrained and conditioned graph diffusion models fail to guarantee such domain-specific structural properties. We present ConStruct, a novel framework that enables graph diffusion models to incorporate h

Graph neural networks (GNNs) are commonly divided into message-passing neural networks (MPNNs) and spectral graph neural networks, reflecting two largely separate research traditions in machine learning and signal processing. This paper argues that this divide is mostly artificial, hindering progress in the field. We propose a viewpoint in which both MPNNs and spectral GNNs are understood as different parametrizations of permutation-equivariant operators acting on graph signals. From this perspective, many

Bulging is a medical characteristic of the eardrum that is crucial for the diagnosis of acute otitis media. This work proposes a novel classification method for distinguishing bulged eardrums from non-bulged ones. The method uses novel key features extract ...

This paper deals with the optimal allocation of MPEG-2 encoding and media-independent FEC rates under a total given bandwidth. The optimality is defined in terms of minimum perceptual end-to-end distortion given a set of video and network parameters. We first derive the set of equations leading to the residual loss process parameters. That is, the packet loss ratio and the average burst length after FEC decoding. We then show that the perceptual source distortion decreases exponentially with the MPEG-2 sour

In free viewpoint applications, the images are cap- tured by an array of cameras that acquire a scene of interest from different perspectives. Any intermediate viewpoint not included in the camera array can be virtually synthesized by the decoder, at a qua ...

In this paper, we propose a scalable algorithm for spectral embedding. The latter is a standard tool for graph clustering. However, its computational bottleneck is the eigendecomposition of the graph Laplacian matrix, which prevents its application to larg ...

Driven by massive amounts of data and important advances in computational resources, new deep learning systems have achieved outstanding results in a large spectrum of applications. Nevertheless, our current theoretical understanding on the mathematical fo ...

This paper presents a distributed coding scheme for the representation of 3D scenes captured by omnidirectional cameras. We consider a scenario with a pair of similar cameras that benefit from equivalent bandwidth and computational resources. The images ar ...

Pascal Frossard

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A Posteriori Quantization of Progressive Matching Pursuit Streams

Dependent packet transmission policies in rate-distortion optimized media scheduling

Plenoptic based super-resolution for omnidirectional image sequences

Introduction to the IEEE Journal on Selected Topics in Signal Processing and IEEE Transactions on Signal and Information Processing Over Networks Joint Special Issue on Graph Signal Processing

Delay-based overlay construction in P2P video broadcast

Raptor Network Video Coding

FST-based Reconstruction of 3D-models from Non-Uniformly Sampled Datasets on the Sphere

Online Resource Inference in Network Utility Maximization Problems

Joint Network and Rate Allocation for Simultaneous Wireless Applications

MP3D: Highly Scalable Video Coding Scheme Based on Matching Pursuit

Interpretable temporal-spatial graph attention network for multi-site PV power forecasting

OSLO: On-the-Sphere Learning for Omnidirectional Images and Its Application to 360-Degree Image Compression

Generative Modelling of Structurally Constrained Graphs

Position: Message-passing and spectral GNNs are two sides of the same coin

Bulged Eardrum Detection From 3D Data

Joint Source/FEC Rate Selection for Optimal MPEG-2 Video Delivery

Interactive Free Viewpoint Video Streaming Using Prioritized Network Coding

Stochastic Gradient Descent for Spectral Embedding with Implicit Orthogonality Constraint

Optimism in the Face of Adversity: Understanding and Improving Deep Learning Through Adversarial Robustness

Symmetric Distributed Coding of Stereo Omnidirectional Images