High Performance Multiview Video Coding

Following the standardization of the latest video coding standard High Efficiency Video Coding in 2013, in 2014, multiview extension of HEVC (MV-HEVC) was published and brought significantly better compression performance of around 50% for multiview and 3D videos compared to multiple independent single-view HEVC coding. However, the extremely high computational complexity of MV-HEVC demands significant optimization of the encoder. To tackle this problem, this work investigates the possibilities of using modern parallel computing platforms and tools such as single-instruction-multiple-data (SIMD) instructions, multi-core CPU, massively parallel GPU, and computer cluster to significantly enhance the MVC encoder performance. The aforementioned computing tools have very different computing characteristics and misuse of the tools may result in poor performance improvement and sometimes even reduction. To achieve the best possible encoding performance from modern computing tools, different levels of parallelism inside a typical MVC encoder are identified and analyzed. Novel optimization techniques at various levels of abstraction are proposed, non-aggregation massively parallel motion estimation (ME) and disparity estimation (DE) in prediction unit (PU), fractional and bi-directional ME/DE acceleration through SIMD, quantization parameter (QP)-based early termination for coding tree unit (CTU), optimized resource-scheduled wave-front parallel processing for CTU, and workload balanced, cluster-based multiple-view parallel are proposed. The result shows proposed parallel optimization techniques, with insignificant loss to coding efficiency, significantly improves the execution time performance. This , in turn, proves modern parallel computing platforms, with appropriate platform-specific algorithm design, are valuable tools for improving the performance of computationally intensive applications

Architectures for Adaptive Low-Power Embedded Multimedia Systems

This Ph.D. thesis describes novel hardware/software architectures for adaptive low-power embedded multimedia systems. Novel techniques for run-time adaptive energy management are proposed, such that both HW & SW adapt together to react to the unpredictable scenarios. A complete power-aware H.264 video encoder was developed. Comparison with state-of-the-art demonstrates significant energy savings while meeting the performance constraint and keeping the video quality degradation unnoticeable

Towards Computational Efficiency of Next Generation Multimedia Systems

To address throughput demands of complex applications (like Multimedia), a next-generation system designer needs to co-design and co-optimize the hardware and software layers. Hardware/software knobs must be tuned in synergy to increase the throughput efficiency. This thesis provides such algorithmic and architectural solutions, while considering the new technology challenges (power-cap and memory aging). The goal is to maximize the throughput efficiency, under timing- and hardware-constraints

Traitement des signaux et images en temps réel ("implantation de H.264 sur MPSoC")

Cette thèse est élaborée en cotutelle entre l université Badji Mokhtar (Laboratoire LERICA) et l université de bourgogne (Laboratoire LE2I, UMR CNRS 5158). Elle constitue une contribution à l étude et l implantation de l encodeur H.264/AVC. Durent l évolution des normes de compression vidéo, une réalité sure est vérifiée de plus en plus : avoir une bonne performance du processus de compression nécessite l élaboration d équipements beaucoup plus performants en termes de puissance de calcul, de flexibilité et de portabilité et ceci afin de répondre aux exigences des différents traitements et satisfaire au critère Temps Réel . Pour assurer un temps réel pour ce genre d applications, une solution reste possible est l utilisation des systèmes sur puce (SoC) ou bien des systèmes multiprocesseurs sur puce (MPSoC) implantés sur des plateformes reconfigurables à base de circuit FPGA. L objective de cette thèse consiste à l étude et l implantation des algorithmes de traitement des signaux et images et en particulier la norme H.264/AVC, et cela dans le but d assurer un temps réel pour le cycle codage-décodage. Nous utilisons deux plateformes FPGA de Xilinx (ML501 et XUPV5). Dans la littérature, il existe déjà plusieurs implémentations du décodeur. Pour l encodeur, malgré les efforts énormes réalisés, il reste toujours du travail pour l optimisation des algorithmes et l extraction des parallélismes possibles surtout avec une variété de profils et de niveaux de la norme H.264/AVC.Dans un premier temps de cette thèse, nous proposons une implantation matérielle d un contrôleur mémoire spécialement pour l encodeur H.264/AVC. Ce contrôleur est réalisé en ajoutant, au contrôleur mémoire DDR2 des deux plateformes de Xilinx, une couche intelligente capable de calculer les adresses et récupérer les données nécessaires pour les différents modules de traitement de l encodeur. Ensuite, nous proposons des implantations matérielles (niveau RTL) des modules de traitement de l encodeur H.264. Sur ces implantations, nous allons exploiter les deux principes de parallélisme et de pipelining autorisé par l encodeur en vue de la grande dépendance inter-blocs. Nous avons ainsi proposé plusieurs améliorations et nouvelles techniques dans les modules de la chaine Intra et le filtre anti-blocs. A la fin de cette thèse, nous utilisons les modules réalisés en matériels pour la l implantation Matérielle/logicielle de l encodeur H.264/AVC. Des résultats de synthèse et de simulation, en utilisant les deux plateformes de Xilinx, sont montrés et comparés avec les autres implémentations existantesThis thesis has been carried out in joint supervision between the Badji Mokhtar University (LERICA Laboratory) and the University of Burgundy (LE2I laboratory, UMR CNRS 5158). It is a contribution to the study and implementation of the H.264/AVC encoder. The evolution in video coding standards have historically demanded stringent performances of the compression process, which imposes the development of platforms that perform much better in terms of computing power, flexibility and portability. Such demands are necessary to fulfill requirements of the different treatments and to meet "Real Time" processing constraints. In order to ensure real-time performances, a possible solution is to made use of systems on chip (SoC) or multiprocessor systems on chip (MPSoC) built on platforms based reconfigurable FPGAs. The objective of this thesis is the study and implementation of algorithms for signal and image processing (in particular the H.264/AVC standard); especial attention was given to provide real-time coding-decoding cycles. We use two FPGA platforms (ML501 and XUPV5 from Xilinx) to implement our architectures. In the literature, there are already several implementations of the decoder. For the encoder part, despite the enormous efforts made, work remains to optimize algorithms and extract the inherent parallelism of the architecture. This is especially true with a variety of profiles and levels of H.264/AVC. Initially, we proposed a hardware implementation of a memory controller specifically targeted to the H.264/AVC encoder. This controller is obtained by adding, to the DDR2 memory controller, an intelligent layer capable of calculating the addresses and to retrieve the necessary data for several of the processing modules of the encoder. Afterwards, we proposed hardware implementations (RTL) for the processing modules of the H.264 encoder. In these implementations, we made use of principles of parallelism and pipelining, taking into account the constraints imposed by the inter-block dependency in the encoder. We proposed several enhancements and new technologies in the channel Intra modules and the deblocking filter. At the end of this thesis, we use the modules implemented in hardware for implementing the H.264/AVC encoder in a hardware/software design. Synthesis and simulation results, using both platforms for Xilinx, are shown and compared with other existing implementationsDIJON-BU Doc.électronique (212319901) / SudocSudocFranceF