11,642 research outputs found
Band Codes for Energy-Efficient Network Coding with Application to P2P Mobile Streaming
A key problem in random network coding (NC) lies in the complexity and energy
consumption associated with the packet decoding processes, which hinder its
application in mobile environments. Controlling and hence limiting such factors
has always been an important but elusive research goal, since the packet degree
distribution, which is the main factor driving the complexity, is altered in a
non-deterministic way by the random recombinations at the network nodes. In
this paper we tackle this problem proposing Band Codes (BC), a novel class of
network codes specifically designed to preserve the packet degree distribution
during packet encoding, ecombination and decoding. BC are random codes over
GF(2) that exhibit low decoding complexity, feature limited and controlled
degree distribution by construction, and hence allow to effectively apply NC
even in energy-constrained scenarios. In particular, in this paper we motivate
and describe our new design and provide a thorough analysis of its performance.
We provide numerical simulations of the performance of BC in order to validate
the analysis and assess the overhead of BC with respect to a onventional NC
scheme. Moreover, peer-to-peer media streaming experiments with a random-push
protocol show that BC reduce the decoding complexity by a factor of two, to a
point where NC-based mobile streaming to mobile devices becomes practically
feasible.Comment: To be published in IEEE Transacions on Multimedi
DyPS: Dynamic Processor Switching for Energy-Aware Video Decoding on Multi-core SoCs
In addition to General Purpose Processors (GPP), Multicore SoCs equipping
modern mobile devices contain specialized Digital Signal Processor designed
with the aim to provide better performance and low energy consumption
properties. However, the experimental measurements we have achieved revealed
that system overhead, in case of DSP video decoding, causes drastic
performances drop and energy efficiency as compared to the GPP decoding. This
paper describes DyPS, a new approach for energy-aware processor switching (GPP
or DSP) according to the video quality . We show the pertinence of our solution
in the context of adaptive video decoding and describe an implementation on an
embedded Linux operating system with the help of the GStreamer framework. A
simple case study showed that DyPS achieves 30% energy saving while sustaining
the decoding performanc
Increasing Compression Ratio of Low Complexity Compressive Sensing Video Encoder with Application-Aware Configurable Mechanism
With the development of embedded video acquisition nodes and wireless video
surveillance systems, traditional video coding methods could not meet the needs
of less computing complexity any more, as well as the urgent power consumption.
So, a low-complexity compressive sensing video encoder framework with
application-aware configurable mechanism is proposed in this paper, where novel
encoding methods are exploited based on the practical purposes of the real
applications to reduce the coding complexity effectively and improve the
compression ratio (CR). Moreover, the group of processing (GOP) size and the
measurement matrix size can be configured on the encoder side according to the
post-analysis requirements of an application example of object tracking to
increase the CR of encoder as best as possible. Simulations show the proposed
framework of encoder could achieve 60X of CR when the tracking successful rate
(SR) is still keeping above 90%.Comment: 5 pages with 6figures and 1 table,conferenc
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A content-aware quantisation mechanism for transform domain distributed video coding
The discrete cosine transform (DCT) is widely applied in modern codecs to remove spatial redundancies, with the resulting DCT coefficients being quantised to achieve compression as well as bit-rate control. In distributed video coding (DVC) architectures like DISCOVER, DCT coefficient quantisation is traditionally performed using predetermined quantisation matrices (QM), which means the compression is heavily dependent on the sequence being coded. This makes bit-rate control challenging, with the situation exacerbated in the coding of high resolution sequences due to QM scarcity and the non-uniform bit-rate gaps between them. This paper introduces a novel content-aware quantisation (CAQ) mechanism to overcome the limitations of existing quantisation methods in transform domain DVC. CAQ creates a frame-specific QM to reduce quantisation errors by analysing the distribution of DCT coefficients. In contrast to the predetermined QM that is applicable to only 4x4 block sizes, CAQ produces QM for larger block sizes to enhance compression at higher resolutions. This provides superior bit-rate control and better output quality by seeking to fully exploit the available bandwidth, which is especially beneficial in bandwidth constrained scenarios. In addition, CAQ generates superior perceptual results by innovatively applying different weightings to the DCT coefficients to reflect the human visual system. Experimental results corroborate that CAQ both quantitatively and qualitatively provides enhanced output quality in bandwidth limited scenarios, by consistently utilising over 90% of available bandwidth
Statistical framework for video decoding complexity modeling and prediction
Video decoding complexity modeling and prediction is an increasingly important issue for efficient resource utilization in a variety of applications, including task scheduling, receiver-driven complexity shaping, and adaptive dynamic voltage scaling. In this paper we present a novel view of this problem based on a statistical framework perspective. We explore the statistical structure (clustering) of the execution time required by each video decoder module (entropy decoding, motion compensation, etc.) in conjunction with complexity features that are easily extractable at encoding time (representing the properties of each module's input source data). For this purpose, we employ Gaussian mixture models (GMMs) and an expectation-maximization algorithm to estimate the joint execution-time - feature probability density function (PDF). A training set of typical video sequences is used for this purpose in an offline estimation process. The obtained GMM representation is used in conjunction with the complexity features of new video sequences to predict the execution time required for the decoding of these sequences. Several prediction approaches are discussed and compared. The potential mismatch between the training set and new video content is addressed by adaptive online joint-PDF re-estimation. An experimental comparison is performed to evaluate the different approaches and compare the proposed prediction scheme with related resource prediction schemes from the literature. The usefulness of the proposed complexity-prediction approaches is demonstrated in an application of rate-distortion-complexity optimized decoding
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
Demo : distributed video coding applications in wireless multimedia sensor networks
Novel distributed video coding (DVC) architectures developed by the IBBT DVC group realize state-of-the-art video coding efficiency under stringent energy restrictions, while supporting error-resilience and scalability. Therefore, these architectures are particularly attractive for application scenarios involving low-complexity energy-constrained wireless visual sensors. This demo presents the scenarios, which are considered to be the most promising areas of integration for IBBT's DVC systems, considering feasibility and commercial applicability
Source and Physical-Layer Network Coding for Correlated Two-Way Relaying
In this paper, we study a half-duplex two-way relay channel (TWRC) with
correlated sources exchanging bidirectional information. In the case, when both
sources have the knowledge of correlation statistics, a source compression with
physical-layer network coding (SCPNC) scheme is proposed to perform the
distributed compression at each source node. When only the relay has the
knowledge of correlation statistics, we propose a relay compression with
physical-layer network coding (RCPNC) scheme to compress the bidirectional
messages at the relay. The closed-form block error rate (BLER) expressions of
both schemes are derived and verified through simulations. It is shown that the
proposed schemes achieve considerable improvements in both error performance
and throughput compared with the conventional non-compression scheme in
correlated two-way relay networks (CTWRNs).Comment: 15 pages, 6 figures. IET Communications, 201
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