646 research outputs found
Scalable video/image transmission using rate compatible PUM turbo codes
The robust delivery of video over emerging wireless networks poses many challenges due to the heterogeneity of access networks, the variations in streaming devices, and the expected variations in network conditions caused by interference and coexistence. The proposed approach exploits the joint optimization of a wavelet-based scalable video/image coding framework and a forward error correction method based on PUM turbo codes. The scheme minimizes the reconstructed image/video distortion at the decoder subject to a constraint on the overall transmission bitrate budget. The minimization is achieved by exploiting the rate optimization technique and the statistics of the transmission channel
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
OPTIMISED COMPRESSION STRATEGY IN WAVELET-BASED VIDEO CODING USING IMPROVED CONTEXT MODELS
ABSTRACT Accurate probability estimation is a key to efficient compression in entropy coding phase of state-of-the-art video coding systems. Probability estimation can be enhanced if contexts in which symbols occur are used during the probability estimation phase. However, these contexts have to be carefully designed in order to avoid negative effects. Methods that use tree structures to model contexts of various syntax elements have been proven efficient in image and video coding. In this paper we use such structure to build optimised contexts for application in scalable wavelet-based video coding. With the proposed approach context are designed separately for intra-coded frames and motion-compensated frames considering varying statistics across different spatio-temporal subbands. Moreover, contexts are separately designed for different bit-planes. Comparison with compression using fixed contexts from Embedded ZeroBlock Coding (EZBC) has been performed showing improvements when context modelling on tree structures is applied
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
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
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
Optimising Spatial and Tonal Data for PDE-based Inpainting
Some recent methods for lossy signal and image compression store only a few
selected pixels and fill in the missing structures by inpainting with a partial
differential equation (PDE). Suitable operators include the Laplacian, the
biharmonic operator, and edge-enhancing anisotropic diffusion (EED). The
quality of such approaches depends substantially on the selection of the data
that is kept. Optimising this data in the domain and codomain gives rise to
challenging mathematical problems that shall be addressed in our work.
In the 1D case, we prove results that provide insights into the difficulty of
this problem, and we give evidence that a splitting into spatial and tonal
(i.e. function value) optimisation does hardly deteriorate the results. In the
2D setting, we present generic algorithms that achieve a high reconstruction
quality even if the specified data is very sparse. To optimise the spatial
data, we use a probabilistic sparsification, followed by a nonlocal pixel
exchange that avoids getting trapped in bad local optima. After this spatial
optimisation we perform a tonal optimisation that modifies the function values
in order to reduce the global reconstruction error. For homogeneous diffusion
inpainting, this comes down to a least squares problem for which we prove that
it has a unique solution. We demonstrate that it can be found efficiently with
a gradient descent approach that is accelerated with fast explicit diffusion
(FED) cycles. Our framework allows to specify the desired density of the
inpainting mask a priori. Moreover, is more generic than other data
optimisation approaches for the sparse inpainting problem, since it can also be
extended to nonlinear inpainting operators such as EED. This is exploited to
achieve reconstructions with state-of-the-art quality.
We also give an extensive literature survey on PDE-based image compression
methods
Efficient reconfigurable architectures for 3D medical image compression
This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Recently, the more widespread use of three-dimensional (3-D) imaging modalities,
such as magnetic resonance imaging (MRI), computed tomography (CT), positron
emission tomography (PET), and ultrasound (US) have generated a massive amount
of volumetric data. These have provided an impetus to the development of other
applications, in particular telemedicine and teleradiology. In these fields, medical
image compression is important since both efficient storage and transmission of data
through high-bandwidth digital communication lines are of crucial importance.
Despite their advantages, most 3-D medical imaging algorithms are computationally intensive with matrix transformation as the most fundamental operation involved in the transform-based methods. Therefore, there is a real need for high-performance systems, whilst keeping architectures exible to allow
for quick upgradeability with real-time applications. Moreover, in order to obtain
efficient solutions for large medical volumes data, an efficient implementation of
these operations is of significant importance. Reconfigurable hardware, in the form of field programmable gate arrays (FPGAs) has been proposed as viable system
building block in the construction of high-performance systems at an economical price.
Consequently, FPGAs seem an ideal candidate to harness and exploit their inherent
advantages such as massive parallelism capabilities, multimillion gate counts, and
special low-power packages. The key achievements of the work presented in this thesis are summarised as follows. Two architectures for 3-D Haar wavelet transform (HWT) have been proposed based on transpose-based computation and partial reconfiguration suitable for 3-D medical imaging applications. These applications require continuous hardware servicing, and as a result dynamic partial reconfiguration (DPR) has been introduced. Comparative study for both non-partial and partial reconfiguration implementation has shown that DPR offers many advantages and leads to a compelling solution for implementing computationally intensive applications such as 3-D medical image compression. Using DPR, several large systems are mapped to small hardware resources, and the area, power consumption as well as maximum frequency are
optimised and improved. Moreover, an FPGA-based architecture of the finite Radon transform (FRAT)with three design strategies has been proposed: direct implementation of pseudo-code with a sequential or pipelined description, and block random access memory (BRAM)- based method. An analysis with various medical imaging modalities has been carried out. Results obtained for image de-noising implementation using FRAT exhibits
promising results in reducing Gaussian white noise in medical images. In terms of
hardware implementation, promising trade-offs on maximum frequency, throughput
and area are also achieved. Furthermore, a novel hardware implementation of 3-D medical image compression system with context-based adaptive variable length coding (CAVLC)
has been proposed. An evaluation of the 3-D integer transform (IT) and the discrete
wavelet transform (DWT) with lifting scheme (LS) for transform blocks reveal that
3-D IT demonstrates better computational complexity than the 3-D DWT, whilst
the 3-D DWT with LS exhibits a lossless compression that is significantly useful for
medical image compression. Additionally, an architecture of CAVLC that is capable
of compressing high-definition (HD) images in real-time without any buffer between
the quantiser and the entropy coder is proposed. Through a judicious parallelisation, promising results have been obtained with limited resources. In summary, this research is tackling the issues of massive 3-D medical volumes data that requires compression as well as hardware implementation to accelerate the
slowest operations in the system. Results obtained also reveal a significant achievement in terms of the architecture efficiency and applications performance.Ministry of Higher Education Malaysia (MOHE),
Universiti Tun Hussein Onn Malaysia (UTHM) and the British Counci
A credit-based approach to scalable video transmission over a peer-to-peer social network
PhDThe objective of the research work presented in this thesis is to study
scalable video transmission over peer-to-peer networks. In particular,
we analyse how a credit-based approach and exploitation of social networking
features can play a significant role in the design of such systems.
Peer-to-peer systems are nowadays a valid alternative to the traditional
client-server architecture for the distribution of multimedia content, as
they transfer the workload from the service provider to the final user,
with a subsequent reduction of management costs for the former. On
the other hand, scalable video coding helps in dealing with network
heterogeneity, since the content can be tailored to the characteristics
or resources of the peers. First of all, we present a study that evaluates
subjective video quality perceived by the final user under different
transmission scenarios. We also propose a video chunk selection algorithm
that maximises received video quality under different network
conditions. Furthermore, challenges in building reliable peer-to-peer
systems for multimedia streaming include optimisation of resource allocation
and design mechanisms based on rewards and punishments that
provide incentives for users to share their own resources. Our solution
relies on a credit-based architecture, where peers do not interact with
users that have proven to be malicious in the past. Finally, if peers
are allowed to build a social network of trusted users, they can share
the local information they have about the network and have a more
complete understanding of the type of users they are interacting with.
Therefore, in addition to a local credit, a social credit or social reputation
is introduced. This thesis concludes with an overview of future
developments of this research work
Colour image coding with wavelets and matching pursuit
This thesis considers sparse approximation of still images as the basis of a lossy compression system. The Matching Pursuit (MP) algorithm is presented as a method particularly suited for application in lossy scalable image coding. Its multichannel extension, capable of exploiting inter-channel correlations, is found to be an efficient way to represent colour data in RGB colour space. Known problems with MP, high computational complexity of encoding and dictionary design, are tackled by finding an appropriate partitioning of an image. The idea of performing MP in the spatio-frequency domain after transform such as Discrete Wavelet Transform (DWT) is explored. The main challenge, though, is to encode the image representation obtained after MP into a bit-stream. Novel approaches for encoding the atomic decomposition of a signal and colour amplitudes quantisation are proposed and evaluated. The image codec that has been built is capable of competing with scalable coders such as JPEG 2000 and SPIHT in terms of compression ratio
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