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

High Level Design of adaptive distributed controller for Partial Dynamic reconfiguration in FPGA

International audienceControlling dynamic and partial reconfigurations becomes one of the most important key issues in modern embedded systems design. In fact, in such systems, the reconfiguration controller can significantly affect the system performances. Indeed, the controller has to handle efficiently three major tasks during runtime: observation (monitoring), taking reconfiguration decisions and notify decisions to the rest of the system in order to realize it. We present in this paper a novel high level approach permitting to model, using MARTE UML profile, modular and flexible distributed controllers for dynamic reconfiguration management. This approach permits components/ models reuse and allows systematic code generation. It consequently makes reconfigurable systems design less tedious and reduces time to market

Source video rate allocation and scheduling policy design for wireless networks

In this work we address the problem of designing an efficient algorithm that allows high quality real time video streaming, from the source coding and scheduling perspectives. We take into account some key quantities like frame correlation, channel constraints and QoS metrics such as distortion and PSNR. We first formalize the problem in a mathematical fashion and then propose a Markov-chain based solution that applies to quantized values of the metrics involved in the framewor

Adapting Computer Vision Models To Limitations On Input Dimensionality And Model Complexity

When considering instances of distributed systems where visual sensors communicate with remote predictive models, data traffic is limited to the capacity of communication channels, and hardware limits the processing of collected data prior to transmission. We study novel methods of adapting visual inference to limitations on complexity and data availability at test time, wherever the aforementioned limitations exist. Our contributions detailed in this thesis consider both task-specific and task-generic approaches to reducing the data requirement for inference, and evaluate our proposed methods on a wide range of computer vision tasks. This thesis makes four distinct contributions: (i) We investigate multi-class action classification via two-stream convolutional neural networks that directly ingest information extracted from compressed video bitstreams. We show that selective access to macroblock motion vector information provides a good low-dimensional approximation of the underlying optical flow in visual sequences. (ii) We devise a bitstream cropping method by which AVC/H.264 and H.265 bitstreams are reduced to the minimum amount of necessary elements for optical flow extraction, while maintaining compliance with codec standards. We additionally study the effect of codec rate-quality control on the sparsity and noise incurred on optical flow derived from resulting bitstreams, and do so for multiple coding standards. (iii) We demonstrate degrees of variability in the amount of data required for action classification, and leverage this to reduce the dimensionality of input volumes by inferring the required temporal extent for accurate classification prior to processing via learnable machines. (iv) We extend the Mixtures-of-Experts (MoE) paradigm to adapt the data cost of inference for any set of constituent experts. We postulate that the minimum acceptable data cost of inference varies for different input space partitions, and consider mixtures where each expert is designed to meet a different set of constraints on input dimensionality. To take advantage of the flexibility of such mixtures in processing different input representations and modalities, we train biased gating functions such that experts requiring less information to make their inferences are favoured to others. We finally note that, our proposed data utility optimization solutions include a learnable component which considers specified priorities on the amount of information to be used prior to inference, and can be realized for any combination of tasks, modalities, and constraints on available data

Towards flexible hardware/software encoding using H.264

As the electronics world continues to expand, bringing smaller and more portable devices to consumers, demands for media access continue to rise. Consumers are seeking the ability to view the wealth of information available on the Internet from devices such as smart phones, tablets, and music players. In addition to Internet browsing, smart phones and tablets in particular look to reinvent phone communication by adding video chat through services such as Skype and FaceTime. Bringing video to mobile platforms requires trade-offs between size, channel capacity, hardware cost, quality, loading times and power consumption. H.264, the current standard for video encoding specifies multiple profiles to support different modes of operation and environments. Creating an H.264 video encoder for a mobile platform requires a proper balance between the aforementioned trade-offs while maintaining flexibility in a real time environment such as video chatting. The goal of this thesis was to investigate the trade-offs of implementing the H.264 Baseline encoding process specifically at low bit rates in hardware and software using Field Programmable Gate Array (FPGA) reconfigurable resources with an embedded processor core on the same chip. To further preserve encoding flexibility, existing encoding parameters were left intact. The Joint Model (JM) Reference encoder modified to include only the Baseline Profile was used as an initial reference point to evaluate the efficacy of the finished encoder. To improve upon the initial software implementation, major software bottlenecks were identified and hardware accelerators were designed aimed at producing a speedup capable of encoding 176x144 or Quarter Common Intermediate Format (QCIF) videos in real-time at 24 Frames Per Second (FPS) or greater. Finally, the hardware/software implementation was analyzed in comparison with the original JM Reference software encoder. This analysis included FPS, bit rate, encoding time, luminance Peak Signal-to-Noise Ratio (Y-PSNR) and associated hardware costs

Surveillance centric coding

PhDThe research work presented in this thesis focuses on the development of techniques specific to surveillance videos for efficient video compression with higher processing speed. The Scalable Video Coding (SVC) techniques are explored to achieve higher compression efficiency. The framework of SVC is modified to support Surveillance Centric Coding (SCC). Motion estimation techniques specific to surveillance videos are proposed in order to speed up the compression process of the SCC. The main contributions of the research work presented in this thesis are divided into two groups (i) Efficient Compression and (ii) Efficient Motion Estimation. The paradigm of Surveillance Centric Coding (SCC) is introduced, in which coding aims to achieve bit-rate optimisation and adaptation of surveillance videos for storing and transmission purposes. In the proposed approach the SCC encoder communicates with the Video Content Analysis (VCA) module that detects events of interest in video captured by the CCTV. Bit-rate optimisation and adaptation are achieved by exploiting the scalability properties of the employed codec. Time segments containing events relevant to surveillance application are encoded using high spatiotemporal resolution and quality while the irrelevant portions from the surveillance standpoint are encoded at low spatio-temporal resolution and / or quality. Thanks to the scalability of the resulting compressed bit-stream, additional bit-rate adaptation is possible; for instance for the transmission purposes. Experimental evaluation showed that significant reduction in bit-rate can be achieved by the proposed approach without loss of information relevant to surveillance applications. In addition to more optimal compression strategy, novel approaches to performing efficient motion estimation specific to surveillance videos are proposed and implemented with experimental results. A real-time background subtractor is used to detect the presence of any motion activity in the sequence. Different approaches for selective motion estimation, GOP based, Frame based and Block based, are implemented. In the former, motion estimation is performed for the whole group of pictures (GOP) only when a moving object is detected for any frame of the GOP. iii While for the Frame based approach; each frame is tested for the motion activity and consequently for selective motion estimation. The selective motion estimation approach is further explored at a lower level as Block based selective motion estimation. Experimental evaluation showed that significant reduction in computational complexity can be achieved by applying the proposed strategy. In addition to selective motion estimation, a tracker based motion estimation and fast full search using multiple reference frames has been proposed for the surveillance videos. Extensive testing on different surveillance videos shows benefits of application of proposed approaches to achieve the goals of the SCC

Selected topics on distributed video coding

Distributed Video Coding (DVC) is a new paradigm for video compression based on the information theoretical results of Slepian and Wolf (SW), and Wyner and Ziv (WZ). While conventional coding has a rigid complexity allocation as most of the complex tasks are performed at the encoder side, DVC enables a flexible complexity allocation between the encoder and the decoder. The most novel and interesting case is low complexity encoding and complex decoding, which is the opposite of conventional coding. While the latter is suitable for applications where the cost of the decoder is more critical than the encoder's one, DVC opens the door for a new range of applications where low complexity encoding is required and the decoder's complexity is not critical. This is interesting with the deployment of small and battery-powered multimedia mobile devices all around in our daily life. Further, since DVC operates as a reversed-complexity scheme when compared to conventional coding, DVC also enables the interesting scenario of low complexity encoding and decoding between two ends by transcoding between DVC and conventional coding. More specifically, low complexity encoding is possible by DVC at one end. Then, the resulting stream is decoded and conventionally re-encoded to enable low complexity decoding at the other end. Multiview video is attractive for a wide range of applications such as free viewpoint television, which is a system that allows viewing the scene from a viewpoint chosen by the viewer. Moreover, multiview can be beneficial for monitoring purposes in video surveillance. The increased use of multiview video systems is mainly due to the improvements in video technology and the reduced cost of cameras. While a multiview conventional codec will try to exploit the correlation among the different cameras at the encoder side, DVC allows for separate encoding of correlated video sources. Therefore, DVC requires no communication between the cameras in a multiview scenario. This is an advantage since communication is time consuming (i.e. more delay) and requires complex networking. Another appealing feature of DVC is the fact that it is based on a statistical framework. Moreover, DVC behaves as a natural joint source-channel coding solution. This results in an improved error resilience performance when compared to conventional coding. Further, DVC-based scalable codecs do not require a deterministic knowledge of the lower layers. In other words, the enhancement layers are completely independent from the base layer codec. This is called the codec-independent scalability feature, which offers a high flexibility in the way the various layers are distributed in a network. This thesis addresses the following topics: First, the theoretical foundations of DVC as well as the practical DVC scheme used in this research are presented. The potential applications for DVC are also outlined. DVC-based schemes use conventional coding to compress parts of the data, while the rest is compressed in a distributed fashion. Thus, different conventional codecs are studied in this research as they are compared in terms of compression efficiency for a rich set of sequences. This includes fine tuning the compression parameters such that the best performance is achieved for each codec. Further, DVC tools for improved Side Information (SI) and Error Concealment (EC) are introduced for monoview DVC using a partially decoded frame. The improved SI results in a significant gain in reconstruction quality for video with high activity and motion. This is done by re-estimating the erroneous motion vectors using the partially decoded frame to improve the SI quality. The latter is then used to enhance the reconstruction of the finally decoded frame. Further, the introduced spatio-temporal EC improves the quality of decoded video in the case of erroneously received packets, outperforming both spatial and temporal EC. Moreover, it also outperforms error-concealed conventional coding in different modes. Then, multiview DVC is studied in terms of SI generation, which differentiates it from the monoview case. More specifically, different multiview prediction techniques for SI generation are described and compared in terms of prediction quality, complexity and compression efficiency. Further, a technique for iterative multiview SI is introduced, where the final SI is used in an enhanced reconstruction process. The iterative SI outperforms the other SI generation techniques, especially for high motion video content. Finally, fusion techniques of temporal and inter-view side informations are introduced as well, which improves the performance of multiview DVC over monoview coding. DVC is also used to enable scalability for image and video coding. Since DVC is based on a statistical framework, the base and enhancement layers are completely independent, which is an interesting property called codec-independent scalability. Moreover, the introduced DVC scalable schemes show a good robustness to errors as the quality of decoded video steadily decreases with error rate increase. On the other hand, conventional coding exhibits a cliff effect as the performance drops dramatically after a certain error rate value. Further, the issue of privacy protection is addressed for DVC by transform domain scrambling, which is used to alter regions of interest in video such that the scene is still understood and privacy is preserved as well. The proposed scrambling techniques are shown to provide a good level of security without impairing the performance of the DVC scheme when compared to the one without scrambling. This is particularly attractive for video surveillance scenarios, which is one of the most promising applications for DVC. Finally, a practical DVC demonstrator built during this research is described, where the main requirements as well as the observed limitations are presented. Furthermore, it is defined in a setup being as close as possible to a complete real application scenario. This shows that it is actually possible to implement a complete end-to-end practical DVC system relying only on realistic assumptions. Even though DVC is inferior in terms of compression efficiency to the state of the art conventional coding for the moment, strengths of DVC reside in its good error resilience properties and the codec-independent scalability feature. Therefore, DVC offers promising possibilities for video compression with transmission over error-prone environments requirement as it significantly outperforms conventional coding in this case

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