Terms Of Compression Rate Research Articles

Exploiting Prior Knowledge in Compressed Sensing Wireless ECG Systems

Recent results in telecardiology show that compressed sensing (CS) is a promising tool to lower energy consumption in wireless body area networks for electrocardiogram (ECG) monitoring. However, the performance of current CS-based algorithms, in terms of compression rate and reconstruction quality of the ECG, still falls short of the performance attained by state-of-the-art wavelet-based algorithms. In this paper, we propose to exploit the structure of the wavelet representation of the ECG signal to boost the performance of CS-based methods for compression and reconstruction of ECG signals. More precisely, we incorporate prior information about the wavelet dependencies across scales into the reconstruction algorithms and exploit the high fraction of common support of the wavelet coefficients of consecutive ECG segments. Experimental results utilizing the MIT-BIH Arrhythmia Database show that significant performance gains, in terms of compression rate and reconstruction quality, can be obtained by the proposed algorithms compared to current CS-based methods.

Evaluating a File Fragmentation System for Multi-Provider Cloud Storage

The Internet of Things (IoT) refers to the interconnection of billions of constrained devices, denoted as ``smart objects'', in an Internet-like structure. Smart objects typically feature limited capabilities in terms of computation and memory and operate in constrained environments, such as low-power lossy networks. As the Internet Protocol (IP) has been foreseen as the standard for communications in IoT, an effort to bring IP connectivity to smart objects and define suitable communication protocols (i.e. Constrained Application Protocol (CoAP)) is being carried out within standardization organizations, such as the Internet Engineering Task Force (IETF). In this paper, we propose a constrained version of the Session Initiation Protocol (SIP), named ``CoSIP'', whose intent is to allow constrained devices to instantiate communication sessions in a lightweight and standard fashion. Session instantiation can include a negotiation phase of some parameters which will be used for all subsequent communication. CoSIP can be adopted in several application scenarios, such as service discovery and publish/subscribe applications, which are detailed. An evaluation of the proposed protocol is also presented, based on a Java implementation of CoSIP, to show the benefits that its adoption can bring about, in terms of compression rate with the existing SIP protocol and message overhead compared with the use of CoAP.

POMAR: Compression of progressive oriented meshes accessible randomly

This paper presents a new random accessible and progressive lossless manifold triangle mesh compression algorithm named POMAR. It allows to extract different parts of the input mesh at different levels of detail during the decompression. A smooth transition without artefacts is generated between adjacent regions decompressed at different levels of detail. Our approach allows selective decompression with low delay and compares favourably to previous random accessible and progressive schemes in terms of compression rates.

Triangle mesh compression along the Hamiltonian cycle

This paper proposes a novel and efficient algorithm for single-rate compression of triangle meshes. The input mesh is traversed along its greedy Hamiltonian cycle in O(n) time. Based on the Hamiltonian cycle, the mesh connectivity can be encoded by a face label sequence with low entropy containing only four kinds of labels (HETS) and the transmission delay at the decoding end that frequently occurs in the conventional single-rate approaches is obviously reduced. The mesh geometry is compressed with a global coordinate concentration strategy and a novel local parallelogram error prediction scheme. Experiments on realistic 3D models demonstrate the effectiveness of our approach in terms of compression rates and run time performance compared to the leading single-rate and progressive mesh compression methods.

Motion detection technology as a tool for cardiopulmonary resuscitation (CPR) quality training: A randomised crossover mannequin pilot study

IntroductionOutcome after cardiac arrest is dependent on the quality of chest compressions (CC). A great number of devices have been developed to provide guidance during CPR. The present study evaluates a new CPR feedback system (Mini-VREM: Mini-Virtual Reality Enhanced Mannequin) designed to improve CC during training. MethodsMini-VREM system consists of a Kinect® (Microsoft, Redmond, WA, USA) motion sensing device and specifically developed software to provide audio–visual feedback. Mini-VREM was connected to a commercially available mannequin (Laerdal Medical, Stavanger, Norway). Eighty trainees (healthcare professionals and lay people) volunteered in this randomised crossover pilot study. All subjects performed a 2min CC trial, 1h pause and a second 2min CC trial. The first group (FB/NFB, n=40) performed CC with Mini-VREM feedback (FB) followed by CC without feedback (NFB). The second group (NFB/FB, n=40) performed vice versa. Primary endpoints: adequate compression (compression rate between 100 and 120min−1 and compression depth between 50 and 60mm); compressions rate within 100–120min−1; compressions depth within 50–60mm. ResultsWhen compared to the performance without feedback, with Mini-VREM feedback compressions were more adequate (FB 35.78% vs. NFB 7.27%, p<0.001) and more compressions achieved target rate (FB 72.04% vs. 31.42%, p<0.001) and target depth (FB 47.34% vs. 24.87%, p=0.002). The participants perceived the system to be easy to use with effective feedback. ConclusionsThe Mini-VREM system was able to improve significantly the CC performance by healthcare professionals and by lay people in a simulated CA scenario, in terms of compression rate and depth.

Learning from mobile contexts to minimize the mobile location search latency

We propose to learn an extremely compact visual descriptor from the mobile contexts towards low bit rate mobile location search. Our scheme combines location related side information from the mobile devices to adaptively supervise the compact visual descriptor design in a flexible manner, which is very suitable to search locations or landmarks within a bandwidth constraint wireless link. Along with the proposed compact descriptor learning, a large-scale, contextual aware mobile visual search benchmark dataset PKUBench is also introduced, which serves as the first comprehensive benchmark for the quantitative evaluation of how the cheaply available mobile contexts can help the mobile visual search systems. Our proposed contextual learning based compact descriptor has shown to outperform the existing works in terms of compression rate and retrieval effectiveness.

Progressive compression of manifold polygon meshes

This paper presents a new algorithm for the progressive compression of manifold polygon meshes. The input surface is decimated by several traversals that generate successive levels of detail through a specific patch decimation operator which combines vertex removal and local remeshing. The mesh connectivity is encoded by two lists of Boolean error predictions based on the mesh geometry: one for the inserted edges and the other for the faces with a removed center vertex. The mesh geometry is encoded with a barycentric error prediction of the removed vertex coordinates and a local curvature prediction. We also include two methods that improve the rate-distortion performance: a wavelet formulation with a lifting scheme and an adaptive quantization technique. Experimental results demonstrate the effectiveness of our approach in terms of compression rates and rate-distortion performance.

Coherent vorticity extraction in resistive drift-wave turbulence: Comparison of orthogonal wavelets versus proper orthogonal decomposition

We assess two techniques for extracting coherent vortices out of turbulent flows: the wavelet based Coherent Vorticity Extraction (CVE) and the Proper Orthogonal Decomposition (POD). The former decomposes the flow field into an orthogonal wavelet representation and subsequent thresholding of the coefficients allows one to split the flow into organized coherent vortices with non-Gaussian statistics and an incoherent random part which is structureless. POD is based on the singular value decomposition and decomposes the flow into basis functions which are optimal with respect to the retained energy for the ensemble average. Both techniques are applied to direct numerical simulation data of two-dimensional drift-wave turbulence governed by Hasegawa-Wakatani equation, considering two limit cases: the quasi-hydrodynamic and the quasi-adiabatic regimes. The results are compared in terms of compression rate, retained energy, retained enstrophy and retained radial flux, together with the enstrophy spectrum and higher order statistics.

Enabling energy-efficient and lossy-aware data compression in wireless sensor networks by multi-objective evolutionary optimization

Nodes of wireless sensor networks (WSNs) are typically powered by batteries with a limited capacity. Thus, energy is a primary constraint in the design and deployment of WSNs. Since radio communication is in general the main cause of power consumption, the different techniques proposed in the literature to improve energy efficiency have mainly focused on limiting transmission/reception of data, for instance, by adopting data compression and/or aggregation. The limited resources available in a sensor node demand, however, the development of specifically designed algorithms. To this aim, we propose an approach to perform lossy compression on single node based on a differential pulse code modulation scheme with quantization of the differences between consecutive samples. Since different combinations of the quantization process parameters determine different trade-offs between compression performance and information loss, we exploit a multi-objective evolutionary algorithm to generate a set of combinations of these parameters corresponding to different optimal trade-offs. The user can therefore choose the combination with the most suitable trade-off for the specific application. We tested our lossy compression approach on three datasets collected by real WSNs. We show that our approach can achieve significant compression ratios despite negligible reconstruction errors. Further, we discuss how our approach outperforms LTC, a lossy compression algorithm purposely designed to be embedded in sensor nodes, in terms of compression rate and complexity.

Compression-unimpaired batch-image encryption combining vector quantization and index compression

As the network bandwidth has grown rapidly, it has become common to share a large number of still images via the Internet by means of batch transmission. Unfortunately, most existing methods encrypt only a single image, so there is room for improvement in our ability to send batches of images at one time. A complete reconstruction of the methods is sometimes necessary, especially when considering performance criteria. This paper presents a novel batch-image encryption algorithm that combines Vector Quantization (VQ) and additional index-compression process to benefit from their computational efficiency and low transmission bandwidth without affecting the original compression rate. The experimental results show the performance of this new scheme in terms of compression rate and computational cost.

A novel 3D mesh compression using mesh segmentation with multiple principal plane analysis

This paper proposes a novel scheme for 3D model compression based on mesh segmentation using multiple principal plane analysis. This algorithm first performs a mesh segmentation scheme, based on fusion of the well-known k-means clustering and the proposed principal plane analysis to separate the input 3D mesh into a set of disjointed polygonal regions. The boundary indexing scheme for the whole object is created by assembling local regions. Finally, the current work proposes a triangle traversal scheme to encode the connectivity and geometry information simultaneously for every patch under the guidance of the boundary indexing scheme. Simulation results demonstrate that the proposed algorithm obtains good performance in terms of compression rate and reconstruction quality.

Reversible information hiding for VQ indices based on locally adaptive coding

Steganography is one of protective methods for secret communications over public networks such as the Internet. This paper proposes a novel reversible information hiding method for vector quantization (VQ) compressed images based on locally adaptive coding method. The proposed steganographic method embeds a secret message into VQ indices in an index table during the process of compressing the index table in the block-by-block manner. The experimental results show that, in average, the proposed method achieves the best visual quality of reconstructed images and the best embedding rate compared to two related works. In terms of compression rate and encoding execution time, in average, Yang et al.’s method is the best, followed by our proposed method, and then Lin and Chang’s method.

Search-order coding method with indicator-elimination property

Vector quantization (VQ) is a widely used technique for many applications especially for lossy image compression. Since VQ significantly reduces the size of a digital image, it can save the costs of storage space and image delivery. Search-order coding (SOC) was proposed for improving the performance of VQ in terms of compression rate. However, SOC requires extra data (i.e. indicators) to indicate source of codewords so the compression rate may be affected. To overcome such a drawback, in this paper, a search-order coding with the indicator-elimination property was proposed by using a technique of reversible data hiding. The proposed method is the first one using such a concept of data hiding to achieve a better compression rate of SOC. From experimental results, the performance of the SOC method can be successfully improved by the proposed indicator eliminated search-order coding method in terms of compression rate. In addition, compared with other relevant schemes, the proposed method is also more flexible than some existing schemes.

Context-dependent multilevel pattern matching for lossless image compression

In this paper, the multilevel pattern matching (MPM) code for compression of one-dimensional (1D) sequences is first generalized to compress two-dimensional (2D) images, resulting in a 2D multilevel pattern matching (MPM) code. It is shown that among all images of n pixels, the worst case redundancy of the 2D MPM code against any finite-template-based arithmetic code is O(1//spl radic/logn). This result contrasts unfavorably with the fact that among all 1D sequences of length n, the MPM code has a worst case redundancy of O(1/logn) against any finite-state arithmetic code; this is caused by the so-called 2D boundary effect. To alleviate the 2D boundary effect, we extend the 2D MPM code to the case of context modeling, yielding a context-dependent 2D MPM code. It is shown that among all images of n pixels, the context-dependent 2D MPM code has an O(1/logn) worst case redundancy against any finite-template-based arithmetic code satisfying a mild condition; this redundancy is better than that of the 2D MPM code without context models. Experimental results demonstrate that the context-dependent 2D MPM code significantly outperforms the 2D MPM code without context models for bi-level images. It is also demonstrated that, in terms of compression rates, the context-dependent 2D MPM code performs significantly better than the progressive coding mode of JBIG1 for both textual and bi-level images, and better than or comparably to the sequential coding mode of JBIG1 and JBIG2. In addition to its excellent compression performance, the context-dependent 2D MPM code allows progressive transmission of images.

Comparative Study of Image Compression Techniques for Digital Particle Image Velocimetry

A comparative, quantitative study of image compression techniques for use with digital particle image velocimetry has been performed. Several candidate compression algorithms were selected for the study including a lossless technique and a series of mathematical transform-based methods. Each of the compression algorithms was implemented using commercial off-the-shelf software packages. Three image sequences were selected to exercise the various compression methods. These sequences included a set of industry standard images and two sets of images obtained from experimental work conducted at NASA Langley. Evaluation of the various methods was accomplished using quantitative perceptual and metrological performance measures. The results of the study indicate that several methods of compression are applicable to digital particle image velocimetry images. For selected applications where thresholding of the image gray levels can be performed, lossless algorithms yield excellent performance in terms of compression rates and introduction of negligible errors to the images. For applications where thresholding is not appropriate, JPEG and wavelet-based algorithms yield high compression rates with minor spatial errors introduced to the images. Fractal-based algorithms were shown to be capable of providing high compression rates, but the technique must be guided via the use of a suitably selected template image if spatial errors are to be minimized in the compressed images.