Distributed compression and quality assessment (QA) are essential ingredients in the design and analysis of networked signal processing systems with voluminous data. Distributed source coding techniques enable the efficient utilization of available resources and are extremely important in a multitude of data intensive applications including image and video. The quality analysis of such systems is also equally important in providing benchmarks on performance leading to improved design and control. This dissertation approaches the complementary problems of distributed compression and quality assessment using information theoretic methods. While such an approach provides intuition on designing practical coding schemes for distributed compression, it directly yields image and video QA algorithms with excellent performance that can be employed in practice. This dissertation considers the information theoretic study of sophisticated problems in distributed compression including, multiterminal multiple description coding, multiterminal source coding through relays and joint source channel coding of correlated sources over wireless channels. Random and/or structured codes are developed and shown to be optimal or near optimal through novel bounds on performance. While lattices play an important role in designing near optimal codes for multiterminal source coding through relays and joint source channel coding over multiple access channels, time sharing random Gaussian codebooks is optimal for a wide range of system parameters in the multiterminal multiple description coding problem. The dissertation also addresses the challenging problem of reduced reference image and video QA. A family of novel reduced reference image and video QA algorithms are developed based on spatial and temporal entropic differences. While the QA algorithms for still images only compute spatial entropic differences, the video QA algorithms compute both spatial and temporal entropic differences and combine them in a perceptually relevant manner. These algorithms attain excellent performances in terms of correlation with human judgments of quality on large QA databases. The framework developed also enables the study of the degradation in performance of QA algorithms from full reference information to almost no information from the reference image or video. / text

abstract: An analysis is presented of a network of distributed receivers encumbered by strong in-band interference. The structure of information present across such receivers and how they might collaborate to recover a signal of interest is studied. Unstructured (random coding) and structured (lattice coding) strategies are studied towards this purpose for a certain adaptable system model. Asymptotic performances of these strategies and algorithms to compute them are developed. A jointly-compressed lattice code with proper configuration performs best of all strategies investigated. / Dissertation/Thesis / Doctoral Dissertation Electrical Engineering 2019

Consider a lossy compression system with l distributed encoders and a centralized decoder. Each encoder compresses its observed source and forwards the compressed data to the decoder for joint reconstruction of the target signals under the mean squared error distortion constraint. It is assumed that the observed sources can be expressed as the sum of the target signals and the corruptive noises, which are generated independently from two (possibly di erent) symmetric multivariate Gaussian distributions. Depending on the parameters of such Gaussian distributions, the rate-distortion limit of this lossy compression system is characterized either completely or for a subset of distortions (including, but not necessarily limited to, those su fficiently close to the minimum distortion achievable when the observed sources are directly available at the decoder). The results are further extended to the robust distributed compression setting, where the outputs of a subset of encoders may also be used to produce a non-trivial reconstruction of the corresponding target signals. In particular, we obtain in the high-resolution regime a precise characterization of the minimum achievable reconstruction distortion based on the outputs of k + 1 or more encoders when every k out of all l encoders are operated collectively in the same mode that is greedy in the sense of minimizing the distortion incurred by the reconstruction of the corresponding k target signals with respect to the average rate of these k encoders. / Thesis / Master of Applied Science (MASc)

Les réseaux de capteurs sans fils (RCSFs) suscitent un intérêt croissant depuis une vingtaine d'années. La première partie de cette thèse est consacré à l'étude de l'efficacité de compression de données corrélées provenant d'un RCSF et acheminées vers un point de collecte à l'aide du codage réseau linéaire aléatoire. Les conditions nécessaires et suffisantes sont obtenues pour récupérer parfaitement les données que les capteurs mesurent. Puis on considère les nœuds dans un RCSF collaborant afin d'exécuter une tâche donnée (acquisition, détection...), pour laquelle chaque nœud a potentiellement un niveau d'expertise différent. La seconde partie de cette thèse est dédiée à la conception et à l'analyse d'algorithmes d'auto-évaluation distribués (AED), qui permettent à chaque nœud d'auto-évaluer son niveau d’expert. Trois types de problèmes sont considérés: i) la détection distribuée des nœuds défaillants (DDD), qui permet d'identifier les nœuds équipés de capteurs défectueux dans un RCSF; ii) la DDD dans un réseau tolérant aux déconnections (RTD) dont la topologie est dynamique et le degré de connectivité très faible; iii) la AED avec interactions pair à pair. Les résultats théoriques sont utiles pour configurer les paramètres des algorithmes. / Wireless sensor networks (WSNs) have attracted much interests in the last decade. The first part of this thesis considers sparse random linear network coding is for data gathering and compression in WSNs. An information-theoretic approach is applied to demonstrate the necessary and sufficient conditions to realize the asymptotically perfect reconstruction under MAP estimation. The second part of the thesis concerns the distributed self-rating (DSR) problem, for WSNs with nodes that have different ability of performing some task (sensing, detection...). The main assumption is that each node does not know and needs to estimate its ability. Depending on the number of ability levels and the communication conditions, three sub-problems have been addressed: i) distributed faulty node detection (DFD) to identify the nodes equipped with defective sensors in dense WSNs; ii) DFD in delay tolerant networks (DTNs) with sparse and intermittent connectivity; iii) DSR using pairwise comparison. Distributed algorithms have been proposed and analyzed. Theoretical results assess the effectiveness of the proposed solution and give guidelines in the design of the algorithm.