225 research outputs found
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
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.
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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
Scalable Video Streaming with Prioritised Network Coding on End-System Overlays
PhDDistribution over the internet is destined to become a standard approach for live broadcasting
of TV or events of nation-wide interest. The demand for high-quality live video
with personal requirements is destined to grow exponentially over the next few years. Endsystem
multicast is a desirable option for relieving the content server from bandwidth bottlenecks
and computational load by allowing decentralised allocation of resources to the users
and distributed service management. Network coding provides innovative solutions for a
multitude of issues related to multi-user content distribution, such as the coupon-collection
problem, allocation and scheduling procedure. This thesis tackles the problem of streaming
scalable video on end-system multicast overlays with prioritised push-based streaming.
We analyse the characteristic arising from a random coding process as a linear channel
operator, and present a novel error detection and correction system for error-resilient decoding,
providing one of the first practical frameworks for Joint Source-Channel-Network
coding. Our system outperforms both network error correction and traditional FEC coding
when performed separately. We then present a content distribution system based on endsystem
multicast. Our data exchange protocol makes use of network coding as a way to
collaboratively deliver data to several peers. Prioritised streaming is performed by means
of hierarchical network coding and a dynamic chunk selection for optimised rate allocation
based on goodput statistics at application layer. We prove, by simulated experiments, the
efficient allocation of resources for adaptive video delivery. Finally we describe the implementation
of our coding system. We highlighting the use rateless coding properties, discuss
the application in collaborative and distributed coding systems, and provide an optimised
implementation of the decoding algorithm with advanced CPU instructions. We analyse
computational load and packet loss protection via lab tests and simulations, complementing
the overall analysis of the video streaming system in all its components
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
Description-driven Adaptation of Media Resources
The current multimedia landscape is characterized by a significant diversity in terms of available media formats, network technologies, and device properties. This heterogeneity has resulted in a number of new challenges, such as providing universal access to multimedia content. A solution for this diversity is the use of scalable bit streams, as well as the deployment of a complementary system that is capable of adapting scalable bit streams to the constraints imposed by a particular usage environment (e.g., the limited screen resolution of a mobile device). This dissertation investigates the use of an XML-driven (Extensible Markup Language) framework for the format-independent adaptation of scalable bit streams. Using this approach, the structure of a bit stream is first translated into an XML description. In a next step, the resulting XML description is transformed to reflect a desired adaptation of the bit stream. Finally, the transformed XML description is used to create an adapted bit stream that is suited for playback in the targeted usage environment. The main contribution of this dissertation is BFlavor, a new tool for exposing the syntax of binary media resources as an XML description. Its development was inspired by two other technologies, i.e. MPEG-21 BSDL (Bitstream Syntax Description Language) and XFlavor (Formal Language for Audio-Visual Object Representation, extended with XML features). Although created from a different point of view, both languages offer solutions for translating the syntax of a media resource into an XML representation for further processing. BFlavor (BSDL+XFlavor) harmonizes the two technologies by combining their strengths and eliminating their weaknesses. The expressive power and performance of a BFlavor-based content adaptation chain, compared to tool chains entirely based on either BSDL or XFlavor, were investigated by several experiments. One series of experiments targeted the exploitation of multi-layered temporal scalability in H.264/AVC, paying particular attention to the use of sub-sequences and hierarchical coding patterns, as well as to the use of metadata messages to communicate the bit stream structure to the adaptation logic. BFlavor was the only tool to offer an elegant and practical solution for XML-driven adaptation of H.264/AVC bit streams in the temporal domain
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