902 research outputs found
Distributed video coding for wireless video sensor networks: a review of the state-of-the-art architectures
Distributed video coding (DVC) is a relatively new video coding architecture originated from two fundamental theorems namely, SlepianâWolf and WynerâZiv. Recent research developments have made DVC attractive for applications in the emerging domain of wireless video sensor networks (WVSNs). This paper reviews the state-of-the-art DVC architectures with a focus on understanding their opportunities and gaps in addressing the operational requirements and application needs of WVSNs
Efficient algorithms for scalable video coding
A scalable video bitstream specifically designed for the needs of various client terminals,
network conditions, and user demands is much desired in current and future video transmission
and storage systems. The scalable extension of the H.264/AVC standard (SVC) has
been developed to satisfy the new challenges posed by heterogeneous environments, as
it permits a single video stream to be decoded fully or partially with variable quality, resolution,
and frame rate in order to adapt to a specific application. This thesis presents
novel improved algorithms for SVC, including: 1) a fast inter-frame and inter-layer coding
mode selection algorithm based on motion activity; 2) a hierarchical fast mode selection
algorithm; 3) a two-part Rate Distortion (RD) model targeting the properties of different
prediction modes for the SVC rate control scheme; and 4) an optimised Mean Absolute
Difference (MAD) prediction model.
The proposed fast inter-frame and inter-layer mode selection algorithm is based on the
empirical observation that a macroblock (MB) with slow movement is more likely to be
best matched by one in the same resolution layer. However, for a macroblock with fast
movement, motion estimation between layers is required. Simulation results show that
the algorithm can reduce the encoding time by up to 40%, with negligible degradation in
RD performance.
The proposed hierarchical fast mode selection scheme comprises four levels and makes
full use of inter-layer, temporal and spatial correlation aswell as the texture information of
each macroblock. Overall, the new technique demonstrates the same coding performance
in terms of picture quality and compression ratio as that of the SVC standard, yet produces
a saving in encoding time of up to 84%. Compared with state-of-the-art SVC fast mode
selection algorithms, the proposed algorithm achieves a superior computational time reduction
under very similar RD performance conditions.
The existing SVC rate distortion model cannot accurately represent the RD properties of
the prediction modes, because it is influenced by the use of inter-layer prediction. A separate
RD model for inter-layer prediction coding in the enhancement layer(s) is therefore
introduced. Overall, the proposed algorithms improve the average PSNR by up to 0.34dB
or produce an average saving in bit rate of up to 7.78%. Furthermore, the control accuracy
is maintained to within 0.07% on average.
As aMADprediction error always exists and cannot be avoided, an optimisedMADprediction
model for the spatial enhancement layers is proposed that considers the MAD from
previous temporal frames and previous spatial frames together, to achieve a more accurateMADprediction.
Simulation results indicate that the proposedMADprediction model
reduces the MAD prediction error by up to 79% compared with the JVT-W043 implementation
MASCOT : metadata for advanced scalable video coding tools : final report
The goal of the MASCOT project was to develop new video coding schemes and tools that provide both an increased coding efficiency as well as extended scalability features compared to technology that was available at the beginning of the project. Towards that goal the following tools would be used: - metadata-based coding tools; - new spatiotemporal decompositions; - new prediction schemes. Although the initial goal was to develop one single codec architecture that was able to combine all new coding tools that were foreseen when the project was formulated, it became clear that this would limit the selection of the new tools. Therefore the consortium decided to develop two codec frameworks within the project, a standard hybrid DCT-based codec and a 3D wavelet-based codec, which together are able to accommodate all tools developed during the course of the project
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
Motion Scalability for Video Coding with Flexible Spatio-Temporal Decompositions
PhDThe research presented in this thesis aims to extend the scalability range of the
wavelet-based video coding systems in order to achieve fully scalable coding with a
wide range of available decoding points. Since the temporal redundancy regularly
comprises the main portion of the global video sequence redundancy, the techniques
that can be generally termed motion decorrelation techniques have a central role in
the overall compression performance. For this reason the scalable motion modelling
and coding are of utmost importance, and specifically, in this thesis possible
solutions are identified and analysed.
The main contributions of the presented research are grouped into two
interrelated and complementary topics. Firstly a flexible motion model with rateoptimised
estimation technique is introduced. The proposed motion model is based
on tree structures and allows high adaptability needed for layered motion coding. The
flexible structure for motion compensation allows for optimisation at different stages
of the adaptive spatio-temporal decomposition, which is crucial for scalable coding
that targets decoding on different resolutions. By utilising an adaptive choice of
wavelet filterbank, the model enables high compression based on efficient mode
selection. Secondly, solutions for scalable motion modelling and coding are
developed. These solutions are based on precision limiting of motion vectors and
creation of a layered motion structure that describes hierarchically coded motion.
The solution based on precision limiting relies on layered bit-plane coding of motion
vector values. The second solution builds on recently established techniques that
impose scalability on a motion structure. The new approach is based on two major
improvements: the evaluation of distortion in temporal Subbands and motion search
in temporal subbands that finds the optimal motion vectors for layered motion
structure.
Exhaustive tests on the rate-distortion performance in demanding scalable video
coding scenarios show benefits of application of both developed flexible motion
model and various solutions for scalable motion coding
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