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Dive into the research topics where Benoît Parrein is active.

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Featured researches published by Benoît Parrein.


wireless communications and networking conference | 2008

Simulation and Performance Analysis of MP-OLSR for Mobile Ad Hoc Networks

Jiazi Yi; Eddy Cizeron; Salima Hamma; Benoît Parrein

Mobile ad hoc networks (MANETs) consist of a collection of wireless mobile nodes which dynamically exchange data without reliance on a fixed base station or a wired backbone network, which makes routing a crucial issue for the design of a ad hoc networks. In this paper we discussed a hybrid multipath routing protocol named MP-OLSR. It is based on the link state algorithm and employs periodic exchange of messages to maintain topology information of the networks. In the mean time, it updates the routing table in an on-demand scheme and forwards the packets in multiple paths which have been determined at the source. If a link failure is detected, the algorithm recovers the route automatically. Concerning the instability of the wireless networks, the redundancy coding is used to improve the delivery ratio. The simulation in NS2 shows that the new protocol can effectively improve the performance of the networks.


international conference on image processing | 2009

Region-of-Interest intra prediction for H.264/AVC error resilience

Fadi Boulos; Wei Chen; Benoît Parrein; Patrick Le Callet

Packets in a video bitstream contain data with different levels of importance that yield unequal amounts of quality distortion when lost. In order to avoid sharp quality degradation due to packet loss, we propose in this paper an error resilience method that is applied to the region of interest (RoI) of the picture. This method protects the RoI while not yielding significant overhead. We perform an eye tracking test to determine the RoIs of a video sequence and we assess the performance of the proposed model in error-prone environments by means of a subjective quality test. Loss simulation results show that stopping the temporal error propagation in the RoIs of the pictures helps preserving an acceptable visual quality in the presence of packet loss.


international conference on image processing | 2004

Demosaicking and JPEG2000 compression of microscopy images

Benoît Parrein; Marc Tarin; Patrick Horain

This paper presents a comparison of original couplings between color filter array demosaicking methods and wavelet compression (JPEG2000). We focus on an application handling huge microscopy images (64 K/spl times/64 K pixels) for telediagnosis. Whereas coding is usually achieved after interpolation, we also consider demosaicking after decompression in order to optimize image quality for a given size of data. We also study the JPEG2000 stream structure for interactive visualization.


Computerized Medical Imaging and Graphics | 2008

Joint source–channel coding: Secured and progressive transmission of compressed medical images on the Internet

Marie Babel; Benoît Parrein; Olivier Déforges; Nicolas Normand; Jeanpierre Guédon; Véronique Coat

The joint source-channel coding system proposed in this paper has two aims: lossless compression with a progressive mode and the integrity of medical data, which takes into account the priorities of the image and the properties of a network with no guaranteed quality of service. In this context, the use of scalable coding, locally adapted resolution (LAR) and a discrete and exact Radon transform, known as the Mojette transform, meets this twofold requirement. In this paper, details of this joint coding implementation are provided as well as a performance evaluation with respect to the reference CALIC coding and to unequal error protection using Reed-Solomon codes.


international conference on acoustics, speech, and signal processing | 2006

Lossless Compression Based on A Discrete and Exact Radon Transform: A Preliminary Study

Florent Autrusseau; Benoît Parrein; Myriam Servieres

This article presents a new lossless compression algorithm based on a finite Radon transform. The baseline of this work consist in encoding the difference between several similar finite Radon projections. Two differential encoding techniques are needed to efficiently encode the Radon transformed data. An intra-projection encoding technique is first performed, this differential encoding process takes benefit of the redundancy present in such projections. The second step consists in encoding the similarities between Radon projections, this is the inter-projection encoding procedure. The used finite Radon transform (called Mojette transform), allows to ensure both compression and data redundancy. The main novelty of this work is the use of a secure distributed storage or transmission tool in a lossless compression context


electronic imaging | 2005

Secured and progressive transmission of compressed images on the Internet: application to telemedicine

Marie Babel; Benoît Parrein; Olivier Déforges; Nicolas Normand; Jeanpierre Guédon; Joseph Ronsin

Within the framework of telemedicine, the amount of images leads first to use efficient lossless compression methods for the aim of storing information. Furthermore, multiresolution scheme including Region of Interest (ROI) processing is an important feature for a remote access to medical images. What is more, the securization of sensitive data (e.g. metadata from DICOM images) constitutes one more expected functionality: indeed the lost of IP packets could have tragic effects on a given diagnosis. For this purpose, we present in this paper an original scalable image compression technique (LAR method) used in association with a channel coding method based on the Mojette Transform, so that a hierarchical priority encoding system is elaborated. This system provides a solution for secured transmission of medical images through low-bandwidth networks such as the Internet.


international conference on image processing | 2008

Redundant image representation via multi-scale digital Radon projections

Andrew Kingston; Benoît Parrein; Florent Autrusseau

A novel ordering of digital Radon projections coefficients is presented here that enables progressive image reconstruction from low resolution to full resolution. The digital Radon transform applied here is the Mojette transform first defined by Guedon et al. (1995). The Mojette transform is a natural way to generate redundancy to any specified degree and has been demonstrated to be useful for redundant representation for robust data storage and transmission. Combining this with the wavelet transform facilitates compression, i.e., joint source-channel coding, along with the additional property of scalability.


ITCom 2001: International Symposium on the Convergence of IT and Communications | 2001

Load-balancing and scalable multimedia distribution using the Mojette transform

Jeanpierre Guédon; Nicolas Normand; Pierre Verbert; Benoît Parrein; Florent Autrusseau

Video (and other multimedia sources) distribution starts to implement industrial solutions that supposes no quality of service (QoS) properties for the network. To overcome congestion problems in the core of a worldwide Internet network, mirrors sites at the edges of the network are dispatched. Thus the QoS problem is only relevant for the network extremities. Nevertheless, this strategy implies to replicate the multimedia database (denoted at MDB) at multiple edge points to meet the real-time constraints and to establish specific mechanisms between mirror sites to satisfy customer needs as for video distribution. For each of both kind of constraints, we propose a unique data/network representation.


arXiv: Networking and Internet Architecture | 2009

Implementation of Multipath and Multiple Description Coding in OLSR

Jiazi Yi; Eddy Cizeron; Salima Hamma; Benoît Parrein; Pascal Lesage


wireless communications and networking conference | 2010

Erasure Coding with the Finite Radon Transform

Nicolas Normand; Imants D. Svalbe; Benoît Parrein; Andrew Kingston

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Nicolas Normand

Centre national de la recherche scientifique

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Nicolas Normand

Centre national de la recherche scientifique

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Jiazi Yi

École Normale Supérieure

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