333 research outputs found
Zero-padding Network Coding and Compressed Sensing for Optimized Packets Transmission
Ubiquitous Internet of Things (IoT) is destined to connect everybody and everything on a never-before-seen scale. Such networks, however, have to tackle the inherent issues created by the presence of very heterogeneous data transmissions over the same shared network. This very diverse communication, in turn, produces network packets of various sizes ranging from very small sensory readings to comparatively humongous video frames. Such a massive amount of data itself, as in the case of sensory networks, is also continuously captured at varying rates and contributes to increasing the load on the network itself, which could hinder transmission efficiency. However, they also open up possibilities to exploit various correlations in the transmitted data due to their sheer number. Reductions based on this also enable the networks to keep up with the new wave of big data-driven communications by simply investing in the promotion of select techniques that efficiently utilize the resources of the communication systems. One of the solutions to tackle the erroneous transmission of data employs linear coding techniques, which are ill-equipped to handle the processing of packets with differing sizes. Random Linear Network Coding (RLNC), for instance, generates unreasonable amounts of padding overhead to compensate for the different message lengths, thereby suppressing the pervasive benefits of the coding itself. We propose a set of approaches that overcome such issues, while also reducing the decoding delays at the same time. Specifically, we introduce and elaborate on the concept of macro-symbols and the design of different coding schemes. Due to the heterogeneity of the packet sizes, our progressive shortening scheme is the first RLNC-based approach that generates and recodes unequal-sized coded packets. Another of our solutions is deterministic shifting that reduces the overall number of transmitted packets. Moreover, the RaSOR scheme employs coding using XORing operations on shifted packets, without the need for coding coefficients, thus favoring linear encoding and decoding complexities.
Another facet of IoT applications can be found in sensory data known to be highly correlated, where compressed sensing is a potential approach to reduce the overall transmissions. In such scenarios, network coding can also help. Our proposed joint compressed sensing and real network coding design fully exploit the correlations in cluster-based wireless sensor networks, such as the ones advocated by Industry 4.0. This design focused on performing one-step decoding to reduce the computational complexities and delays of the reconstruction process at the receiver and investigates the effectiveness of combined compressed sensing and network coding
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
Non-Orthogonal Signal and System Design for Wireless Communications
The thesis presents research in non-orthogonal multi-carrier signals, in which: (i) a new signal format termed truncated orthogonal frequency division multiplexing (TOFDM) is proposed to improve data rates in wireless communication systems, such as those used in mobile/cellular systems and wireless local area networks (LANs), and (ii) a new design and experimental implementation of a real-time spectrally efficient frequency division multiplexing (SEFDM) system are reported. This research proposes a modified version of the orthogonal frequency division multiplexing (OFDM) format, obtained by truncating OFDM symbols in the time-domain. In TOFDM, subcarriers are no longer orthogonally packed in the frequency-domain as time samples are only partially transmitted, leading to improved spectral efficiency. In this work, (i) analytical expressions are derived for the newly proposed TOFDM signal, followed by (ii) interference analysis, (iii) systems design for uncoded and coded schemes, (iv) experimental implementation and (v) performance evaluation of the new proposed signal and system, with comparisons to conventional OFDM systems. Results indicate that signals can be recovered with truncated symbol transmission. Based on the TOFDM principle, a new receiving technique, termed partial symbol recovery (PSR), is designed and implemented in software de ned radio (SDR), that allows efficient operation of two users for overlapping data, in wireless communication systems operating with collisions. The PSR technique is based on recovery of collision-free partial OFDM symbols, followed by the reconstruction of complete symbols to recover progressively the frames of two users suffering collisions. The system is evaluated in a testbed of 12-nodes using SDR platforms. The thesis also proposes channel estimation and equalization technique for non-orthogonal signals in 5G scenarios, using an orthogonal demodulator and zero padding. Finally, the implementation of complete SEFDM systems in real-time is investigated and described in detail
Cross-Layer Optimization of DVB-T2 System for Mobile Services
Mobile broadcast services have experienced a strong boost in recent years through the standardization of several mobile broadcast systems such as DVB-H, ATSC-M/H, DMB-T/H, and CMMB. However, steady need for higher quality services is projected to surpass the capabilities of the existing mobile broadcast systems. Consequently, work on new generations of mobile broadcast technology is starting under the umbrella of different industry consortia, such as DVB.
In this paper, we address the question of how DVB-T2 transmission can be optimized for improved mobile broadcast reception. We investigate cross-layer optimization techniques with a focus on the transport of scalable video (SVC) streams over DVB-T2 Physical Layer Pipes (PLP). Throughout the paper, we propose different optimization options and verify their utility
Object-based coding for plenoptic videos
A new object-based coding system for a class of dynamic image-based representations called plenoptic videos (PVs) is proposed. PVs are simplified dynamic light fields, where the videos are taken at regularly spaced locations along line segments instead of a 2-D plane. In the proposed object-based approach, objects at different depth values are segmented to improve the rendering quality. By encoding PVs at the object level, desirable functionalities such as scalability of contents, error resilience, and interactivity with an individual image-based rendering (IBR) object can be achieved. Besides supporting the coding of texture and binary shape maps for IBR objects with arbitrary shapes, the proposed system also supports the coding of grayscale alpha maps as well as depth maps (geometry information) to respectively facilitate the matting and rendering of the IBR objects. Both temporal and spatial redundancies among the streams in the PV are exploited to improve the coding performance, while avoiding excessive complexity in selective decoding of PVs to support fast rendering speed. Advanced spatial/temporal prediction methods such as global disparity-compensated prediction, as well as direct prediction and its extensions are developed. The bit allocation and rate control scheme employing a new convex optimization-based approach are also introduced. Experimental results show that considerable improvements in coding performance are obtained for both synthetic and real scenes, while supporting the stated object-based functionalities. © 2006 IEEE.published_or_final_versio
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Research and developments of Dirac video codec
This thesis was submitted for the degree of Doctor of Philosophy and was awarded by Brunel University.In digital video compression, apart from storage, successful transmission of the compressed video
data over the bandwidth limited erroneous channels is another important issue. To enable a video
codec for broadcasting application, it is required to implement the corresponding coding tools (e.g.
error-resilient coding, rate control etc.). They are normally non-normative parts of a video codec and
hence their specifications are not defined in the standard. In Dirac as well, the original codec is
optimized for storage purpose only and so, several non-normative part of the encoding tools are still
required in order to be able to use in other types of application.
Being the "Research and Developments of the Dirac Video Codec" as the research title, phase I of
the project is mainly focused on the error-resilient transmission over a noisy channel. The error-resilient
coding method used here is a simple and low complex coding scheme which provides the
error-resilient transmission of the compressed video bitstream of Dirac video encoder over the packet
erasure wired network. The scheme combines source and channel coding approach where error-resilient
source coding is achieved by data partitioning in the wavelet transformed domain and
channel coding is achieved through the application of either Rate-Compatible Punctured
Convolutional (RCPC) Code or Turbo Code (TC) using un-equal error protection between header plus
MV and data. The scheme is designed mainly for the packet-erasure channel, i.e. targeted for the
Internet broadcasting application.
But, for a bandwidth limited channel, it is still required to limit the amount of bits generated from
the encoder depending on the available bandwidth in addition to the error-resilient coding. So, in the
2nd phase of the project, a rate control algorithm is presented. The algorithm is based upon the Quality
Factor (QF) optimization method where QF of the encoded video is adaptively changing in order to
achieve average bitrate which is constant over each Group of Picture (GOP). A relation between the
bitrate, R and the QF, which is called Rate-QF (R-QF) model is derived in order to estimate the
optimum QF of the current encoding frame for a given target bitrate, R.
In some applications like video conferencing, real-time encoding and decoding with minimum
delay is crucial, but, the ability to do real-time encoding/decoding is largely determined by the
complexity of the encoder/decoder. As we all know that motion estimation process inside the encoder
is the most time consuming stage. So, reducing the complexity of the motion estimation stage will
certainly give one step closer to the real-time application. So, as a partial contribution toward realtime
application, in the final phase of the research, a fast Motion Estimation (ME) strategy is designed
and implemented. It is the combination of modified adaptive search plus semi-hierarchical way of
motion estimation. The same strategy was implemented in both Dirac and H.264 in order to
investigate its performance on different codecs. Together with this fast ME strategy, a method which
is called partial cost function calculation in order to further reduce down the computational load of the
cost function calculation was presented. The calculation is based upon the pre-defined set of patterns
which were chosen in such a way that they have as much maximum coverage as possible over the
whole block.
In summary, this research work has contributed to the error-resilient transmission of compressed
bitstreams of Dirac video encoder over a bandwidth limited error prone channel. In addition to this,
the final phase of the research has partially contributed toward the real-time application of the Dirac
video codec by implementing a fast motion estimation strategy together with partial cost function
calculation idea.BBC R&D and Brunel University
Trick play on Audiovisual Information for Tape, Disk and Solid-State based Digital Recording Systems
Object-driven block-based algorithm for the compression of stereo image pairs
In this paper, we propose a novel object driven, block based algorithm for the compression of stereo image pairs. The
algorithm effectively combines the simplicity and adaptability of the existing block based stereo image compression
techniques [1-6] with an edge/contour based object extraction technique to determine appropriate compression strategy for
various areas of the right image. Extensive experiments carried out support that significant improvements of up to 20% in
compression ratio can be achieved by the proposed algorithm, compared with the existing stereo image compression
techniques. Yet the reconstructed image quality is maintained at an equivalent level in terms of PSNR values. In terms of
visual quality, the right image reconstructed by the proposed algorithm does not incur any noticeable effect compared with
the outputs of the best algorithms.
The proposed algorithm performs object extraction and matching between the reconstructed left frame and the original right
frame to identify those objects that match but are displaced by varying amounts due to binocular parallax. Different coding
strategies are then applied separately to internal areas and the bounding areas for each identified object. Based on the mean
squared matching error of the internal blocks and a selected threshold, a decision is made whether or not to encode the
predictive errors inside these objects. The output bit stream includes entropy coding of object disparity, block disparity and
possibly some errors, which fail to meet the threshold requirement in the proposed algorith
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