Synchronization recovery and state model reduction for soft decoding of variable length codes

Variable length codes exhibit de-synchronization problems when transmitted over noisy channels. Trellis decoding techniques based on Maximum A Posteriori (MAP) estimators are often used to minimize the error rate on the estimated sequence. If the number of symbols and/or bits transmitted are known by the decoder, termination constraints can be incorporated in the decoding process. All the paths in the trellis which do not lead to a valid sequence length are suppressed. This paper presents an analytic method to assess the expected error resilience of a VLC when trellis decoding with a sequence length constraint is used. The approach is based on the computation, for a given code, of the amount of information brought by the constraint. It is then shown that this quantity as well as the probability that the VLC decoder does not re-synchronize in a strict sense, are not significantly altered by appropriate trellis states aggregation. This proves that the performance obtained by running a length-constrained Viterbi decoder on aggregated state models approaches the one obtained with the bit/symbol trellis, with a significantly reduced complexity. It is then shown that the complexity can be further decreased by projecting the state model on two state models of reduced size

Hidden Markov Model-Based Encoding for Time-Correlated IoT Sources

As the use of Internet of Things (IoT) devices for monitoring purposes becomes ubiquitous, the efficiency of sensor communication is a major issue for the modern Internet. Channel coding is less efficient for extremely short packets, and traditional techniques that rely on source compression require extensive signaling or pre-existing knowledge of the source dynamics. In this work, we propose an encoding and decoding scheme that learns source dynamics online using a Hidden Markov Model (HMM), puncturing a short packet code to outperform existing compression-based approaches. Our approach shows significant performance improvements for sources that are highly correlated in time, with no additional complexity on the sender side.Comment: Preprint version of the paper published in IEEE Communications Letter

Predictive data compression using adaptive arithmetic coding

The commonly used data compression techniques do not necessarily provide maximal compression and neither do they define the most efficient framework for transmission of data. In this thesis we investigate variants of the standard compression algorithms that use the strategy of partitioning of the data to be compressed. Doing so not only increases the compression ratio in many instances, it also reduces the maximum data block size for transmission. The partitioning of the data is made using a Markov model to predict if doing so would result in increased compression ratio. Experiments have been performed on text files comparing the new scheme to adaptive Huffman and arithmetic coding methods. The adaptive Huffman method has been implemented in a new way by combining the FGK method with Vitter’s implicit ordering of nodes

Codes robustes et codes joints source-canal pour transmission multimédia sur canaux mobiles

Some new error-resilient source coding and joint source/channel coding techniquesare proposed for the transmission of multimedia sources over error-prone channels.First, we introduce a class of entropy codes providing unequal error-resilience, i.e.providing some protection to the most sensitive information. These codes are thenextended to exploit the temporal dependencies. A new state model based on the aggregation of some states of the trellis is thenproposed and analyzed for soft source decoding of variable length codes with a lengthconstraint. It allows the weighting of the compromise between the estimation accuracyand the decoding complexity.Next, some paquetization methods are proposed to reduce the error propagationphenomenon of variable length codes.Finally, some re-writing rules are proposed to extend the binary codetree representationof entropy codes. The proposed representation allows in particular the designof codes with improved soft decoding performances.Cette thèse propose des codes robustes et des codes conjoints source/canal pourtransmettre des signaux multimédia sur des canaux bruités. Nous proposons des codesentropiques offrant une résistance intrinsèque aux données prioritaires. Ces codes sontétendus pour exploiter la dépendance temporelle du signal.Un nouveau modèle d’état est ensuite proposé et analysé pour le décodage souplede codes à longueur variable avec une contrainte de longueur. Il permet de réglerfinement le compromis performance de décodage/complexité.Nous proposons également de séparer, au niveau du codage entropique, les étapesde production des mots de codes et de paquétisation. Différentes stratégies de constructionde train binaire sont alors proposées.Enfin, la représentation en arbre binaire des codes entropiques est étendue enconsidérant des règles de ré-écriture. Cela permet en particulier d’obtenir des codesqui offrent des meilleures performances en décodage souple