Distributed joint source channel coding on a multiple access channel with side information

We consider the problem of transmission of several discrete sources over a multiple access channel (MAC) with side information at the sources and the decoder. Source-channel separation does not hold for this channel. Sufficient conditions are provided for transmission of sources with a given distortion. The channel could have continuous alphabets (Gaussian MAC is a special case). Various previous results are obtained as special cases.

Guessing under source uncertainty with side information

We study the problem of guessing the realization of a finite alphabet source, when some side information is provided, in a setting where the only knowledge the guesser has about the source and the correlated side information is that the joint source is one among a family. We define a notion of redundancy, identify a quantity that measures this redundancy, and study its properties. We then identify good guessing strategies that minimize the supremum redundancy (over the family). The minimum value measures the richness of the uncertainty class.

Distributed Coding for Multiple Access Communication with Side Information

In a typical sensor network scenario a goal is to monitor a spatio-temporal process through a number of inexpensive sensing nodes, the key parameter being the fidelity at which the process has to be estimated at distant locations. We study such a scenario in which multiple encoders transmit their correlated data at finite rates to a distant, common decoder over a discrete time multiple access channel under various side information assumptions. In particular, we derive an achievable rate region for this communication problem.

Lossy distribution source coding with side information

In a typical sensor network scenario a goal is to monitor a spatio-temporal process through a number of inexpensive sensing nodes, the key parameter being the fidelity at which the process has to be estimated at distant locations. We study such a scenario in which multiple encoders transmit their correlated data at finite rates to a distant and common decoder. In particular, we derive inner and outer bounds on the rate region for the random field to be estimated with a given mean distortion.

Analysis of LDPC Codes for Compression of Nonuniform Sources with Side Information Using Density Evolution

In this paper, we explore the use of LDPC codes for nonuniform sources under distributed source coding paradigm. Our analysis reveals that several capacity approaching LDPC codes indeed do approach the Slepian-Wolf bound for nonuniform sources as well. The Monte Carlo simulation results show that highly biased sources can be compressed to 0.049 bits/sample away from Slepian-Wolf bound for moderate block lengths.

Error Correcting Functional Source Coding with Decoder Side Information using Row-Latin Rectangles

The functional source coding problem in which the receiver side information (Has-set) and demands (Want-set) include functions of source messages is studied using row-Latin rectangle. The source transmits encoded messages, called the functional source code, in order to satisfy the receiver's demands. We obtain a minimum length using the row-Latin rectangle. Next, we consider the case of transmission errors and provide a necessary and sufficient condition that a functional source code must satisfy so that the receiver can correctly decode the values of the functions in its Want-set.

Compression Efficiency Analysis of Wyner-Ziv Video Coding with Motion Compensated Side Information Interpolation

The Wyner-Ziv video coding (WZVC) rate distortion performance is highly dependent on the quality of the side information, an estimation of the original frame, created at the decoder. This paper, characterizes the WZVC efficiency when motion compensated frame interpolation (MCFI) techniques are used to generate the side information, a difficult problem in WZVC especially because the decoder only has available some reference decoded frames. The proposed WZVC compression efficiency rate model relates the power spectral of the estimation error to the accuracy of the MCFI motion field. Then, some interesting conclusions may be derived related to the impact of the motion field smoothness and the correlation to the true motion trajectories on the compression performance.

Advanced side information creation techniques and framework for Wyner-Ziv video coding

Recently, several distributed video coding (DVC) solutions based on the distributed source coding (DSC) paradigm have appeared in the literature. Wyner-Ziv (WZ) video coding, a particular case of DVC where side information is made available at the decoder, enable to achieve a flexible distribution of the computational complexity between the encoder and decoder, promising to fulfill novel requirements from applications such as video surveillance, sensor networks and mobile camera phones. The quality of the side information at the decoder has a critical role in determining the WZ video coding rate-distortion (RD) performance, notably to raise it to a level as close as possible to the RD performance of standard predictive video coding schemes. Towards this target, efficient motion search algorithms for powerful frame interpolation are much needed at the decoder. In this paper, the RD performance of a Wyner-Ziv video codec is improved by using novel, advanced motion compensated frame interpolation techniques to generate the side information. The development of these type of side information estimators is a difficult problem in WZ video coding, especially because the decoder only has available some reference, decoded frames. Based on the regularization of the motion field, novel side information creation techniques are proposed in this paper along with a new frame interpolation framework able to generate higher quality side information at the decoder. To illustrate the RD performance improvements, this novel side information creation framework has been integrated in a transform domain turbo coding based Wyner-Ziv video codec. Experimental results show that the novel side information creation solution leads to better RD performance than available state-of-the-art side information estimators, with improvements up to 2 dB: moreover, it allows outperforming H.264/AVC Intra by up to 3 dB with a lower encoding complexity.

Leveraging noisy side information for disentangling of factors of variation in a supervised setting

Ce mémoire est composé de trois articles et présente les résultats de travaux de recherche effectués dans le but d'améliorer les techniques actuelles permettant d'utiliser des données associées à certaines tâches dans le but d'aider à l'entraînement de réseaux de neurones sur une tâche différente. Les deux premiers articles présentent de nouveaux ensembles de données créés pour permettre une meilleure évaluation de ce type de techniques d'apprentissage machine. Le premier article introduit une suite d'ensembles de données pour la tâche de reconnaissance automatique de chiffres écrits à la main. Ces ensembles de données ont été générés à partir d'un ensemble de données déjà existant, MNIST, auquel des nouveaux facteurs de variation ont été ajoutés. Le deuxième article introduit un ensemble de données pour la tâche de reconnaissance automatique d'expressions faciales. Cet ensemble de données est composé d'images de visages qui ont été collectées automatiquement à partir du Web et ensuite étiquetées. Le troisième et dernier article présente deux nouvelles approches, dans le contexte de l'apprentissage multi-tâches, pour tirer avantage de données pour une tâche donnée afin d'améliorer les performances d'un modèle sur une tâche différente. La première approche est une généralisation des neurones Maxout récemment proposées alors que la deuxième consiste en l'application dans un contexte supervisé d'une technique permettant d'inciter des neurones à apprendre des fonctions orthogonales, à l'origine proposée pour utilisation dans un contexte semi-supervisé.

Information-theoretic Studies and Capacity Bounds: Group Network Codes and Energy Harvesting Communication Systems

Network information theory and channels with memory are two important but difficult frontiers of information theory. In this two-parted dissertation, we study these two areas, each comprising one part. For the first area we study the so-called entropy vectors via finite group theory, and the network codes constructed from finite groups. In particular, we identify the smallest finite group that violates the Ingleton inequality, an inequality respected by all linear network codes, but not satisfied by all entropy vectors. Based on the analysis of this group we generalize it to several families of Ingleton-violating groups, which may be used to design good network codes. Regarding that aspect, we study the network codes constructed with finite groups, and especially show that linear network codes are embedded in the group network codes constructed with these Ingleton-violating families. Furthermore, such codes are strictly more powerful than linear network codes, as they are able to violate the Ingleton inequality while linear network codes cannot. For the second area, we study the impact of memory to the channel capacity through a novel communication system: the energy harvesting channel. Different from traditional communication systems, the transmitter of an energy harvesting channel is powered by an exogenous energy harvesting device and a finite-sized battery. As a consequence, each time the system can only transmit a symbol whose energy consumption is no more than the energy currently available. This new type of power supply introduces an unprecedented input constraint for the channel, which is random, instantaneous, and has memory. Furthermore, naturally, the energy harvesting process is observed causally at the transmitter, but no such information is provided to the receiver. Both of these features pose great challenges for the analysis of the channel capacity. In this work we use techniques from channels with side information, and finite state channels, to obtain lower and upper bounds of the energy harvesting channel. In particular, we study the stationarity and ergodicity conditions of a surrogate channel to compute and optimize the achievable rates for the original channel. In addition, for practical code design of the system we study the pairwise error probabilities of the input sequences.

The decoupling approach to quantum information theory

La théorie de l'information quantique étudie les limites fondamentales qu'imposent les lois de la physique sur les tâches de traitement de données comme la compression et la transmission de données sur un canal bruité. Cette thèse présente des techniques générales permettant de résoudre plusieurs problèmes fondamentaux de la théorie de l'information quantique dans un seul et même cadre. Le théorème central de cette thèse énonce l'existence d'un protocole permettant de transmettre des données quantiques que le receveur connaît déjà partiellement à l'aide d'une seule utilisation d'un canal quantique bruité. Ce théorème a de plus comme corollaires immédiats plusieurs théorèmes centraux de la théorie de l'information quantique. Les chapitres suivants utilisent ce théorème pour prouver l'existence de nouveaux protocoles pour deux autres types de canaux quantiques, soit les canaux de diffusion quantiques et les canaux quantiques avec information supplémentaire fournie au transmetteur. Ces protocoles traitent aussi de la transmission de données quantiques partiellement connues du receveur à l'aide d'une seule utilisation du canal, et ont comme corollaires des versions asymptotiques avec et sans intrication auxiliaire. Les versions asymptotiques avec intrication auxiliaire peuvent, dans les deux cas, être considérées comme des versions quantiques des meilleurs théorèmes de codage connus pour les versions classiques de ces problèmes. Le dernier chapitre traite d'un phénomène purement quantique appelé verrouillage: il est possible d'encoder un message classique dans un état quantique de sorte qu'en lui enlevant un sous-système de taille logarithmique par rapport à sa taille totale, on puisse s'assurer qu'aucune mesure ne puisse avoir de corrélation significative avec le message. Le message se trouve donc «verrouillé» par une clé de taille logarithmique. Cette thèse présente le premier protocole de verrouillage dont le critère de succès est que la distance trace entre la distribution jointe du message et du résultat de la mesure et le produit de leur marginales soit suffisamment petite.

Fingerprint analysis using wavelet transform with application to compression and feature extraction

The main goal of this research is to design an efficient compression al~ gorithm for fingerprint images. The wavelet transform technique is the principal tool used to reduce interpixel redundancies and to obtain a parsimonious representation for these images. A specific fixed decomposition structure is designed to be used by the wavelet packet in order to save on the computation, transmission, and storage costs. This decomposition structure is based on analysis of information packing performance of several decompositions, two-dimensional power spectral density, effect of each frequency band on the reconstructed image, and the human visual sensitivities. This fixed structure is found to provide the "most" suitable representation for fingerprints, according to the chosen criteria. Different compression techniques are used for different subbands, based on their observed statistics. The decision is based on the effect of each subband on the reconstructed image according to the mean square criteria as well as the sensitivities in human vision. To design an efficient quantization algorithm, a precise model for distribution of the wavelet coefficients is developed. The model is based on the generalized Gaussian distribution. A least squares algorithm on a nonlinear function of the distribution model shape parameter is formulated to estimate the model parameters. A noise shaping bit allocation procedure is then used to assign the bit rate among subbands. To obtain high compression ratios, vector quantization is used. In this work, the lattice vector quantization (LVQ) is chosen because of its superior performance over other types of vector quantizers. The structure of a lattice quantizer is determined by its parameters known as truncation level and scaling factor. In lattice-based compression algorithms reported in the literature the lattice structure is commonly predetermined leading to a nonoptimized quantization approach. In this research, a new technique for determining the lattice parameters is proposed. In the lattice structure design, no assumption about the lattice parameters is made and no training and multi-quantizing is required. The design is based on minimizing the quantization distortion by adapting to the statistical characteristics of the source in each subimage. 11 Abstract Abstract Since LVQ is a multidimensional generalization of uniform quantizers, it produces minimum distortion for inputs with uniform distributions. In order to take advantage of the properties of LVQ and its fast implementation, while considering the i.i.d. nonuniform distribution of wavelet coefficients, the piecewise-uniform pyramid LVQ algorithm is proposed. The proposed algorithm quantizes almost all of source vectors without the need to project these on the lattice outermost shell, while it properly maintains a small codebook size. It also resolves the wedge region problem commonly encountered with sharply distributed random sources. These represent some of the drawbacks of the algorithm proposed by Barlaud [26). The proposed algorithm handles all types of lattices, not only the cubic lattices, as opposed to the algorithms developed by Fischer [29) and Jeong [42). Furthermore, no training and multiquantizing (to determine lattice parameters) is required, as opposed to Powell's algorithm [78). For coefficients with high-frequency content, the positive-negative mean algorithm is proposed to improve the resolution of reconstructed images. For coefficients with low-frequency content, a lossless predictive compression scheme is used to preserve the quality of reconstructed images. A method to reduce bit requirements of necessary side information is also introduced. Lossless entropy coding techniques are subsequently used to remove coding redundancy. The algorithms result in high quality reconstructed images with better compression ratios than other available algorithms. To evaluate the proposed algorithms their objective and subjective performance comparisons with other available techniques are presented. The quality of the reconstructed images is important for a reliable identification. Enhancement and feature extraction on the reconstructed images are also investigated in this research. A structural-based feature extraction algorithm is proposed in which the unique properties of fingerprint textures are used to enhance the images and improve the fidelity of their characteristic features. The ridges are extracted from enhanced grey-level foreground areas based on the local ridge dominant directions. The proposed ridge extraction algorithm, properly preserves the natural shape of grey-level ridges as well as precise locations of the features, as opposed to the ridge extraction algorithm in [81). Furthermore, it is fast and operates only on foreground regions, as opposed to the adaptive floating average thresholding process in [68). Spurious features are subsequently eliminated using the proposed post-processing scheme.

A Joint Source-Channel Coding Scheme for Transmission of Correlated Discrete Sources over a Gaussian Multiple Access Channel

We consider the problem of transmission of correlated discrete alphabet sources over a Gaussian Multiple Access Channel (GMAC). A distributed bit-to-Gaussian mapping is proposed which yields jointly Gaussian codewords. This can guarantee lossless transmission or lossy transmission with given distortions, if possible. The technique can be extended to the system with side information at the encoders and decoder.

Correlated Gaussian Sources over Orthogonal Gaussian Channels

We consider the transmission of correlated Gaussian sources over orthogonal Gaussian channels. It is shown that the Amplify and Forward (AF) scheme which simplifies the design of encoders and the decoder, performs close to the optimal scheme even at high SNR. Also, it outperforms a recently proposed scalar quantizer scheme both in performance and complexity. We also study AF when there is side information at the encoders and decoder.

Distributed Transmission of Functions of Correlated Sources over a Fading Multiple Access Channel

In this paper we address the problem of distributed transmission of functions of correlated sources over a fast fading multiple access channel (MAC). This is a basic building block in a hierarchical sensor network used in estimating a random field where the cluster head is interested only in estimating a function of the observations. The observations are transmitted to the cluster head through a fast fading MAC. We provide sufficient conditions for lossy transmission when the encoders and decoders are provided with partial information about the channel state. Furthermore signal side information maybe available at the encoders and the decoder. Various previous studies are shown as special cases. Efficient joint-source channel coding schemes are discussed for transmission of discrete and continuous alphabet sources to recover function values.