Publications de Pascal Frossard

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This paper adresses the transmission of VBR MPEG-2 video streams on top of both Network Adaptation (NAL) and ATM Adaptation Layers (AAL) for real-time multimedia applications. The NAL, specific to MPEG-2, provides a selective data protection mechanism based on syntactic criteria. The AAL provides a reliable transmission by applying per-cell sequence numbering combined with a selective Forward Error Correction (FEC) mechanism based on Burst Erasure codes. Studies carried out with Constant Bit Rate (CBR) vide

We consider the problem of distributed delivery of correlated data from sensors in ad hoc network topologies. We propose to use network coding in order to exploit the path diversity in the network for efficient delivery of the sensor information. We further show that the correlation between the data sources can be exploited at receivers for efficient approximate decoding when the number of received data packets is not sufficient for perfect decoding. We analyze how the decoding performance is influenced by

We consider the transmission of variable bit rate (VBR) video over a network offering a guaranteed service such as ATM VBR or the guaranteed service of the IETF. The guaranteed service requires that the flow accepted by the network has to be conforming with a traffic envelope sigma. In this context, the output of the video encoder is constrained by the traffic envelope defined at the network entry point, the playback delay budget and the decoding buffer size. In previous works, the constraints are satisfied

We consider the delivery of video assets over a best-effort network, possibly through a caching proxy located close to the clients generating the requests. We are interested in the joint server scheduling and prefix/partial caching strategy that minimizes the aggregate transmission rate over the backbone network (i.e., average output server rate) under a cache of given capacity. We present multiple schemes to address various service levels and client resources by enabling bandwidth and cache space tradeoffs

In presence of sparse noise we propose kernel regression for predicting output vectors which are smooth over a given graph. Sparse noise models the training outputs being corrupted either with missing samples or large perturbations. The presence of sparse ...

Deep neural networks (DNNs) provide state-of-the-art accuracy for vision tasks, but they require significant resources for training. Thus, they are trained on cloud servers far from the edge devices that acquire the data. This issue increases communication ...

Feature extraction based on different types of signal filters has received a lot of attention in the context of face recognition. It generally results into extremely high dimensional feature vectors, and sampling of the coefficients is required to reduce t ...

Despite their impressive performance on image classification tasks, deep networks have a hard time generalizing to unforeseen corruptions of their data. To fix this vulnerability, prior works have built complex data augmentation strategies, combining multi ...

Vision foundation models (FMs) are accelerating the development of digital pathology algorithms and transforming biomedical research. These models learn, in a self-supervised manner, to represent histological features in highly heterogeneous tiles extracted from whole-slide images (WSIs) of real-world patient samples. The performance of these FMs is significantly influenced by the size, diversity, and balance of the pre-training data. Yet, data selection has been primarily guided by expert knowledge at the

This paper addresses the problem of rate distortion analysis in the context of multi-view image coding, where images are predicted via disparity compensation based on depth map. We first present an an- alytical model for the variance of the residual error ...

Viewers of 360-degree videos are provided with both visual modality to characterize their surrounding views and audio modality to indicate the sound direction. Though both modalities are important for saliency prediction, little work has been done by joint ...

We propose Grab-UCB, a graph-kernelmulti-arms bandit algorithm to learn online the optimal source placement in large scale networks, such that the reward obtained from a priori unknown network processes is maximized. The uncertainty calls for online learni ...

This paper addresses the problem of choosing the best streaming policy for distortion optimal multipath video delivery, under delay constraints. The streaming policy consists in a joint selection of the video packets to be transmitted, as well as their sen ...

This paper deals with the optimal allocation of MPEG-2 encoding and media-independent Forward Error Correction (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 exponen

With the increasing amount of available data and advances in computing capabilities, deep neural networks (DNNs) have been successfully employed to solve challenging tasks in various areas, including healthcare, climate, and finance. Nevertheless, state-of ...

HTTP Adaptive Streaming (HAS) permits to efficiently deliver video to multiple heterogenous users in a fully distributed way. This might however lead to unfair bandwidth utilization among HAS users. Therefore, network-assisted HAS systems have been propose ...

Cardiovascular disease is the leading global cause of mortality, with coronary artery disease (CAD) as its most prevalent form, necessitating early risk prediction. While 3D coronary artery digital twins reconstructed from imaging offer detailed anatomy for personalized assessment, their analysis relies on computationally intensive computational fluid dynamics (CFD), limiting scalability. Data-driven approaches are hindered by scarce labeled data and lack of physiological priors. To address this, we present

Adaptive streaming addresses the increasing and heterogenous demand of multimedia content over the Internet by offering several encoded versions for each video sequence. Each version (or representation) has a different resolution and bit rate, aimed at a s ...

In communication systems, there are many tasks, like modulation classification, for which Deep Neural Networks (DNNs) have obtained promising performance. However, these models have been shown to be susceptible to adversarial perturbations, namely impercep ...

Pascal Frossard

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Reliable Transmission of MPEG-2 VBR Video Streams over New Network Adaptation and ATM Adaptation Layers

Network coding of correlated data with approximate decoding

Joint Smoothing and Source Rate Selection for Guaranteed Service Networks

Joint Server Scheduling and Proxy Caching for Video Delivery

Kernel Regression for Graph Signal Prediction in Presence of Sparse Noise

Hierarchical Training of Deep Neural Networks Using Early Exiting

Joint Server Scheduling and Proxy Caching for Video Delivery

Classification-Specific Feature Sampling for Face Recognition

PRIME: A Few Primitives Can Boost Robustness to Common Corruptions

Revisiting Automatic Data Curation for Vision Foundation Models in Digital Pathology

Rate distortion analysis in a disparity compensated scheme

SVGC-AVA: 360-Degree Video Saliency Prediction With Spherical Vector-Based Graph Convolution and Audio-Visual Attention

Online Network Source Optimization with Graph-Kernel MAB

Packet Selection and Scheduling for Multipath Video Streaming

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

Adversarial training with informed data selection

Price-based Controller for Utility-aware HTTP Adaptive Streaming

Physics-informed self-supervised learning for predictive modeling of coronary artery digital twins

Optimal Selection of Adaptive Streaming Representations

SafeAMC: Adversarial training for robust modulation classification models