Etude et mise en place d'une plateforme d'adaptation multiservice embarquée pour la gestion de flux multimédia à différents niveaux logiciels et matériels

Les avancées technologiques ont permis la commercialisation à grande échelle de terminaux mobiles. De ce fait, l homme est de plus en plus connecté et partout. Ce nombre grandissant d usagers du réseau ainsi que la forte croissance du contenu disponible, aussi bien d un point de vue quantitatif que qualitatif saturent les réseaux et l augmentation des moyens matériels (passage à la fibre optique) ne suffisent pas. Pour surmonter cela, les réseaux doivent prendre en compte le type de contenu (texte, vidéo, ...) ainsi que le contexte d utilisation (état du réseau, capacité du terminal, ...) pour assurer une qualité d expérience optimum. A ce sujet, la vidéo fait partie des contenus les plus critiques. Ce type de contenu est non seulement de plus en plus consommé par les utilisateurs mais est aussi l un des plus contraignant en terme de ressources nécéssaires à sa distribution (taille serveur, bande passante, ). Adapter un contenu vidéo en fonction de l état du réseau (ajuster son débit binaire à la bande passante) ou des capacités du terminal (s assurer que le codec soit nativement supporté) est indispensable. Néanmoins, l adaptation vidéo est un processus qui nécéssite beaucoup de ressources. Cela est antinomique à son utilisation à grande echelle dans les appareils à bas coûts qui constituent aujourd hui une grande part dans l ossature du réseau Internet. Cette thèse se concentre sur la conception d un système d adaptation vidéo à bas coût et temps réel qui prendrait place dans ces réseaux du futur. Après une analyse du contexte, un système d adaptation générique est proposé et évalué en comparaison de l état de l art. Ce système est implémenté sur un FPGA afin d assurer les performances (temps-réels) et la nécessité d une solution à bas coût. Enfin, une étude sur les effets indirects de l adaptation vidéo est menée.On the one hand, technology advances have led to the expansion of the handheld devices market. Thanks to this expansion, people are more and more connected and more and more data are exchanged over the Internet. On the other hand, this huge amound of data imposes drastic constrains in order to achieve sufficient quality. The Internet is now showing its limits to assure such quality. To answer nowadays limitations, a next generation Internet is envisioned. This new network takes into account the content nature (video, audio, ...) and the context (network state, terminal capabilities ...) to better manage its own resources. To this extend, video manipulation is one of the key concept that is highlighted in this arising context. Video content is more and more consumed and at the same time requires more and more resources. Adapting videos to the network state (reducing its bitrate to match available bandwidth) or to the terminal capabilities (screen size, supported codecs, ) appears mandatory and is foreseen to take place in real time in networking devices such as home gateways. However, video adaptation is a resource intensive task and must be implemented using hardware accelerators to meet the desired low cost and real time constraints.In this thesis, content- and context-awareness is first analyzed to be considered at the network side. Secondly, a generic low cost video adaptation system is proposed and compared to existing solutions as a trade-off between system complexity and quality. Then, hardware conception is tackled as this system is implemented in an FPGA based architecture. Finally, this system is used to evaluate the indirect effects of video adaptation; energy consumption reduction is achieved at the terminal side by reducing video characteristics thus permitting an increased user experience for End-Users.BORDEAUX1-Bib.electronique (335229901) / SudocSudocFranceF

Etude et mise en place d’une plateforme d’adaptation multiservice embarquée pour la gestion de flux multimédia à différents niveaux logiciels et matériels

On the one hand, technology advances have led to the expansion of the handheld devices market. Thanks to this expansion, people are more and more connected and more and more data are exchanged over the Internet. On the other hand, this huge amound of data imposes drastic constrains in order to achieve sufficient quality. The Internet is now showing its limits to assure such quality. To answer nowadays limitations, a next generation Internet is envisioned. This new network takes into account the content nature (video, audio, ...) and the context (network state, terminal capabilities ...) to better manage its own resources. To this extend, video manipulation is one of the key concept that is highlighted in this arising context. Video content is more and more consumed and at the same time requires more and more resources. Adapting videos to the network state (reducing its bitrate to match available bandwidth) or to the terminal capabilities (screen size, supported codecs, …) appears mandatory and is foreseen to take place in real time in networking devices such as home gateways. However, video adaptation is a resource intensive task and must be implemented using hardware accelerators to meet the desired low cost and real time constraints.In this thesis, content- and context-awareness is first analyzed to be considered at the network side. Secondly, a generic low cost video adaptation system is proposed and compared to existing solutions as a trade-off between system complexity and quality. Then, hardware conception is tackled as this system is implemented in an FPGA based architecture. Finally, this system is used to evaluate the indirect effects of video adaptation; energy consumption reduction is achieved at the terminal side by reducing video characteristics thus permitting an increased user experience for End-Users.Les avancées technologiques ont permis la commercialisation à grande échelle de terminaux mobiles. De ce fait, l’homme est de plus en plus connecté et partout. Ce nombre grandissant d’usagers du réseau ainsi que la forte croissance du contenu disponible, aussi bien d’un point de vue quantitatif que qualitatif saturent les réseaux et l’augmentation des moyens matériels (passage à la fibre optique) ne suffisent pas. Pour surmonter cela, les réseaux doivent prendre en compte le type de contenu (texte, vidéo, ...) ainsi que le contexte d’utilisation (état du réseau, capacité du terminal, ...) pour assurer une qualité d’expérience optimum. A ce sujet, la vidéo fait partie des contenus les plus critiques. Ce type de contenu est non seulement de plus en plus consommé par les utilisateurs mais est aussi l’un des plus contraignant en terme de ressources nécéssaires à sa distribution (taille serveur, bande passante, …). Adapter un contenu vidéo en fonction de l’état du réseau (ajuster son débit binaire à la bande passante) ou des capacités du terminal (s’assurer que le codec soit nativement supporté) est indispensable. Néanmoins, l’adaptation vidéo est un processus qui nécéssite beaucoup de ressources. Cela est antinomique à son utilisation à grande echelle dans les appareils à bas coûts qui constituent aujourd’hui une grande part dans l’ossature du réseau Internet. Cette thèse se concentre sur la conception d’un système d’adaptation vidéo à bas coût et temps réel qui prendrait place dans ces réseaux du futur. Après une analyse du contexte, un système d’adaptation générique est proposé et évalué en comparaison de l’état de l’art. Ce système est implémenté sur un FPGA afin d’assurer les performances (temps-réels) et la nécessité d’une solution à bas coût. Enfin, une étude sur les effets indirects de l’adaptation vidéo est menée

Adaptive video delivery using semantics

The diffusion of network appliances such as cellular phones, personal digital assistants and hand-held computers has created the need to personalize the way media content is delivered to the end user. Moreover, recent devices, such as digital radio receivers with graphics displays, and new applications, such as intelligent visual surveillance, require novel forms of video analysis for content adaptation and summarization. To cope with these challenges, we propose an automatic method for the extraction of semantics from video, and we present a framework that exploits these semantics in order to provide adaptive video delivery. First, an algorithm that relies on motion information to extract multiple semantic video objects is proposed. The algorithm operates in two stages. In the first stage, a statistical change detector produces the segmentation of moving objects from the background. This process is robust with regard to camera noise and does not need manual tuning along a sequence or for different sequences. In the second stage, feedbacks between an object partition and a region partition are used to track individual objects along the frames. These interactions allow us to cope with multiple, deformable objects, occlusions, splitting, appearance and disappearance of objects, and complex motion. Subsequently, semantics are used to prioritize visual data in order to improve the performance of adaptive video delivery. The idea behind this approach is to organize the content so that a particular network or device does not inhibit the main content message. Specifically, we propose two new video adaptation strategies. The first strategy combines semantic analysis with a traditional frame-based video encoder. Background simplifications resulting from this approach do not penalize overall quality at low bitrates. The second strategy uses metadata to efficiently encode the main content message. The metadata-based representation of object's shape and motion suffices to convey the meaning and action of a scene when the objects are familiar. The impact of different video adaptation strategies is then quantified with subjective experiments. We ask a panel of human observers to rate the quality of adapted video sequences on a normalized scale. From these results, we further derive an objective quality metric, the semantic peak signal-to-noise ratio (SPSNR), that accounts for different image areas and for their relevance to the observer in order to reflect the focus of attention of the human visual system. At last, we determine the adaptation strategy that provides maximum value for the end user by maximizing the SPSNR for given client resources at the time of delivery. By combining semantic video analysis and adaptive delivery, the solution presented in this dissertation permits the distribution of video in complex media environments and supports a large variety of content-based applications

Multimedia Forensics

This book is open access. Media forensics has never been more relevant to societal life. Not only media content represents an ever-increasing share of the data traveling on the net and the preferred communications means for most users, it has also become integral part of most innovative applications in the digital information ecosystem that serves various sectors of society, from the entertainment, to journalism, to politics. Undoubtedly, the advances in deep learning and computational imaging contributed significantly to this outcome. The underlying technologies that drive this trend, however, also pose a profound challenge in establishing trust in what we see, hear, and read, and make media content the preferred target of malicious attacks. In this new threat landscape powered by innovative imaging technologies and sophisticated tools, based on autoencoders and generative adversarial networks, this book fills an important gap. It presents a comprehensive review of state-of-the-art forensics capabilities that relate to media attribution, integrity and authenticity verification, and counter forensics. Its content is developed to provide practitioners, researchers, photo and video enthusiasts, and students a holistic view of the field

Multimedia Forensics

This book is open access. Media forensics has never been more relevant to societal life. Not only media content represents an ever-increasing share of the data traveling on the net and the preferred communications means for most users, it has also become integral part of most innovative applications in the digital information ecosystem that serves various sectors of society, from the entertainment, to journalism, to politics. Undoubtedly, the advances in deep learning and computational imaging contributed significantly to this outcome. The underlying technologies that drive this trend, however, also pose a profound challenge in establishing trust in what we see, hear, and read, and make media content the preferred target of malicious attacks. In this new threat landscape powered by innovative imaging technologies and sophisticated tools, based on autoencoders and generative adversarial networks, this book fills an important gap. It presents a comprehensive review of state-of-the-art forensics capabilities that relate to media attribution, integrity and authenticity verification, and counter forensics. Its content is developed to provide practitioners, researchers, photo and video enthusiasts, and students a holistic view of the field

Motion estimation based frame rate conversion hardware designs

Frame Rate Up-Conversion (FRC) is the conversion of a lower frame rate video signal to a higher frame rate video signal. FRC algorithms using Motion Estimation (ME) obtain better quality results. Among the block matching ME algorithms, Full Search (FS) achieves the best performance since it searches all search locations in a given search range. However, its computational complexity, especially for the recently available High Definition (HD) video formats, is very high. Therefore, in this thesis, we proposed new ME algorithms for real-time processing of HD video and designed efficient hardware architectures for implementing these ME algorithms. These algorithms perform very close to FS by searching much fewer search locations than FS algorithm. We implemented the proposed hardware architectures in VHDL and mapped them to a Xilinx FPGA. ME for FRC requires finding the true motion among consecutive frames. In order to find the true motion, Vector Median Filter (VMF) is used to smooth the motion vector field obtained by block matching ME. However, VMFs are difficult to implement in real-time due to their high computational complexity. Therefore, in this thesis, we proposed several techniques to reduce the computational complexity of VMFs by using data reuse methodology and by exploiting the spatial correlations in the vector field. In addition, we designed an efficient VMF hardware including the computation reduction techniques exploiting the spatial correlations in the motion vector field. We implemented the proposed hardware architecture in Verilog and mapped it to a Xilinx FPGA. ME based FRC requires interpolation of frames using the motion vectors found by ME. Frame interpolation algorithms also have high computational complexity. Therefore, in this thesis, we proposed a low cost hardware architecture for real-time implementation of frame interpolation algorithms. The proposed hardware architecture is reconfigurable and it allows adaptive selection of frame interpolation algorithms for each Macroblock. We implemented the proposed hardware architecture in VHDL and mapped it to a low cost Xilinx FPGA

Design for energy-efficient and reliable fog-assisted healthcare IoT systems

Cardiovascular disease and diabetes are two of the most dangerous diseases as they are the leading causes of death in all ages. Unfortunately, they cannot be completely cured with the current knowledge and existing technologies. However, they can be effectively managed by applying methods of continuous health monitoring. Nonetheless, it is difficult to achieve a high quality of healthcare with the current health monitoring systems which often have several limitations such as non-mobility support, energy inefficiency, and an insufficiency of advanced services. Therefore, this thesis presents a Fog computing approach focusing on four main tracks, and proposes it as a solution to the existing limitations. In the first track, the main goal is to introduce Fog computing and Fog services into remote health monitoring systems in order to enhance the quality of healthcare. In the second track, a Fog approach providing mobility support in a real-time health monitoring IoT system is proposed. The handover mechanism run by Fog-assisted smart gateways helps to maintain the connection between sensor nodes and the gateways with a minimized latency. Results show that the handover latency of the proposed Fog approach is 10%-50% less than other state-of-the-art mobility support approaches. In the third track, the designs of four energy-efficient health monitoring IoT systems are discussed and developed. Each energy-efficient system and its sensor nodes are designed to serve a specific purpose such as glucose monitoring, ECG monitoring, or fall detection; with the exception of the fourth system which is an advanced and combined system for simultaneously monitoring many diseases such as diabetes and cardiovascular disease. Results show that these sensor nodes can continuously work, depending on the application, up to 70-155 hours when using a 1000 mAh lithium battery. The fourth track mentioned above, provides a Fog-assisted remote health monitoring IoT system for diabetic patients with cardiovascular disease. Via several proposed algorithms such as QT interval extraction, activity status categorization, and fall detection algorithms, the system can process data and detect abnormalities in real-time. Results show that the proposed system using Fog services is a promising approach for improving the treatment of diabetic patients with cardiovascular disease

Cinesonica: Sounding the Audiovisuality of Film and Video

The dissertation presents an exploration of neglected and under-theorised aspects of film and video sound. In doing so, the study proposes a sounding of the cinesonic; that is, it considers the deployment of sound within an audiovisual context. The key concern of this dissertation is how we might map and negotiate the materiality of film and video sound both beyond, and in relation to, its signitive dimensions, and what might be at stake in a critical engagement with that materiality. In particular, this sounding engages with the inscription of difference that is common to Saussurian linguistics, signitive formulations of sound-image relations, and notions of what might constitute the properly 'political' in an audiovisual poetics founded on modernist paradigms. The research demonstrates that any coming-to-terms with film and video's materiality needs to be informed by the idea that the material events we term 'the film' or 'the video' are marked by a relationship between sound and image. Thus the dissertation negotiates a sounding of these media in relation to that materiality best described as audiovisuality. The dissertation opens with a consideration of the way in which sound is commonly conceptualised in terms of its relationship with an object source, and how the formulation of sound as signifier militates against an engagement with its material dimensions. The following chapters explore neglected aspects of film and video sound by drawing on a range of theoretical resources predominantly - but not exclusively - derived from the work of Gilles Deleuze, with detailed case study analyses of specific film and video texts, and interviews with filmmakers. The topics covered in these chapters include the phenomenon of optical crackle, electronic sounds, the correspondence of sound and image pejoratively termed 'mickey-mousing', and the organisation and manipulation of sounds in British Scratch Video of the 1980s