Hardware acceleration architectures for MPEG-Based mobile video platforms: a brief overview

This paper presents a brief overview of past and current hardware acceleration (HwA) approaches that have been proposed for the most computationally intensive compression tools of the MPEG-4 standard. These approaches are classified based on their historical evolution and architectural approach. An analysis of both evolutionary and functional classifications is carried out in order to speculate on the possible trends of the HwA architectures to be employed in mobile video platforms

Conception d'un micro capteur d'image CMOS à faible consommation d'énergie pour les réseaux de capteurs sans fil

This research aims to develop a vision system with low energy consumption for Wireless Sensor Networks (WSNs). The imager in question must meet the specific requirements of multimedia applications for Wireless Vision Sensor Networks. Indeed, a multimedia application requires intensive computation at the node and a considerable number of packets to be exchanged through the transceiver, and therefore consumes a lot of energy. An obvious solution to reduce the amount of transmitted data is to compress the images before sending them over WSN nodes. However, the severe constraints of nodes make ineffective in practice the implementation of standard compression algorithms (JPEG, JPEG2000, MJPEG, MPEG, H264, etc.). Desired vision system must integrate image compression techniques that are both effective and with low-complexity. Particular attention should be taken into consideration in order to best satisfy the compromise "Energy Consumption - Quality of Service (QoS)".Ce travail de recherche vise à concevoir un système de vision à faible consommation d'énergie pour les réseaux de capteurs sans fil. L'imageur en question doit respecter les contraintes spécifiques des applications multimédias pour les réseaux de capteurs de vision sans fil. En effet, de par sa nature, une application multimédia impose un traitement intensif au niveau du noeud et un nombre considérable de paquets à échanger à travers le lien radio, et par conséquent beaucoup d'énergie à consommer. Une solution évidente pour diminuer la quantité de données transmise, et donc la durée de vie du réseau, est de compresser les images avant de les transmettre. Néanmoins, les contraintes strictes des noeuds du réseau rendent inefficace en pratique l'exécution des algorithmes de compression standards (JPEG, JPEG2000, MJPEG, MPEG, H264, etc.). Le système de vision à concevoir doit donc intégrer des techniques de compression d'image à la fois efficaces et à faible complexité. Une attention particulière doit être prise en compte en vue de satisfaire au mieux le compromis "Consommation énergétique - Qualité de Service (QoS)"

Energy efficient enabling technologies for semantic video processing on mobile devices

Semantic object-based processing will play an increasingly important role in future multimedia systems due to the ubiquity of digital multimedia capture/playback technologies and increasing storage capacity. Although the object based paradigm has many undeniable benefits, numerous technical challenges remain before the applications becomes pervasive, particularly on computational constrained mobile devices. A fundamental issue is the ill-posed problem of semantic object segmentation. Furthermore, on battery powered mobile computing devices, the additional algorithmic complexity of semantic object based processing compared to conventional video processing is highly undesirable both from a real-time operation and battery life perspective. This thesis attempts to tackle these issues by firstly constraining the solution space and focusing on the human face as a primary semantic concept of use to users of mobile devices. A novel face detection algorithm is proposed, which from the outset was designed to be amenable to be offloaded from the host microprocessor to dedicated hardware, thereby providing real-time performance and reducing power consumption. The algorithm uses an Artificial Neural Network (ANN), whose topology and weights are evolved via a genetic algorithm (GA). The computational burden of the ANN evaluation is offloaded to a dedicated hardware accelerator, which is capable of processing any evolved network topology. Efficient arithmetic circuitry, which leverages modified Booth recoding, column compressors and carry save adders, is adopted throughout the design. To tackle the increased computational costs associated with object tracking or object based shape encoding, a novel energy efficient binary motion estimation architecture is proposed. Energy is reduced in the proposed motion estimation architecture by minimising the redundant operations inherent in the binary data. Both architectures are shown to compare favourable with the relevant prior art