Level Of Data Compression Research Articles

Pulsewidth Modulation-Based Algorithm for Spike Phase Encoding and Decoding of Time-Dependent Analog Data.

This article proposes a new spike encoding and decoding algorithm for analog data. The algorithm uses the pulsewidth modulation principles to achieve a high reconstruction accuracy of the signal, along with a high level of data compression. Two benchmark data sets are used to illustrate the method: stock index time series and human voice data. Applications of the method for spiking neural network (SNN) modeling and neuromorphic implementations are discussed. The proposed method would allow the development of new applications of SNNs as regression techniques for predictive time-series modeling.

A workflow for seismic imaging with quantified uncertainty

The interpretation of seismic images faces challenges due to the presence of several uncertainty sources. Uncertainties exist in data measurements, source positioning, and subsurface geophysical properties. Understanding uncertainties’ role and how they influence the outcome is fundamental in the earth sciences and essential in the oil and gas industry decision-making process. Geophysical imaging is time-consuming. When we add uncertainty quantification, it becomes both time and data-intensive. In this work, we propose a workflow for seismic imaging with quantified uncertainty. We build the workflow upon Bayesian tomography, reverse time migration, and image interpretation based on statistical information. The workflow explores an efficient hybrid parallel computational strategy to decrease the reverse time migration execution time. High levels of data compression are applied to reduce data transfer among workflow activities and data storage. We capture and analyze provenance data at runtime to improve workflow execution, monitoring, and debugging with negligible overhead. Numerical experiments on the Marmousi2 Velocity Model Benchmark demonstrate the workflow capabilities. We observe excellent weak and strong scalability, and results suggest that lossy data compression does not hamper the seismic imaging uncertainty quantification. We explore the propagation of the velocity uncertainties to the seismic images by confidence maps and probability density functions in control points. The multi-modal character revealed by such high-order statistics reinforces the need for the machinery we have put together in our workflow.

Compressed-Domain Detection and Estimation for Colocated MIMO Radar

This article proposes a compressed-domain signal processing (CSP) multiple-input multiple-output (MIMO) radar, a MIMO radar approach that achieves substantial sample complexity reduction by exploiting the idea of CSP. CSP MIMO radar involves two levels of data compression followed by target detection at the compressed domain. First, compressive sensing is applied at the receive antennas, followed by a Capon beamformer, which is designed to suppress clutter. Exploiting the sparse nature of the beamformer output, a second compression is applied to the filtered data. Target detection is subsequently conducted by formulating and solving a hypothesis testing problem at each grid point of the discretized angle space. The proposed approach enables an eightfold reduction of the sample complexity in some settings as compared to a conventional compressed sensing (CS) MIMO radar, thus enabling faster target detection. Receiver operating characteristic curves of the proposed detector are provided. Simulation results show that the proposed approach outperforms recovery-based CS algorithms.

Exploring publish/subscribe, multilevel cloud elasticity, and data compression in telemedicine

Background and Objective: Multiple medical specialties rely on image data, typically following the Digital Imaging and Communications in Medicine (DICOM) ISO 12052 standard, to support diagnosis through telemedicine. Remote analysis by different physicians requires the same image to be transmitted simultaneously to different destinations in real-time. This scenario poses a need for a large number of resources to store and transmit DICOM images in real-time, which has been explored using some cloud-based solutions. However, these solutions lack strategies to improve the performance through the cloud elasticity feature. In this context, this article proposes a cloud-based publish/subscribe (PubSub) model, called PS2DICOM, which employs multilevel resource elasticity to improve the performance of DICOM data transmissions.Methods: A prototype is implemented to evaluate PS2DICOM. A PubSub communication model is adopted, considering the coexistence of two classes of users: (i) image data producers (publishers); and (ii) image data consumers (subscribers). PS2DICOM employs a cloud infrastructure to guarantee service availability and performance through resource elasticity in two levels of the cloud: (i) brokers and (ii) data storage. In addition, images are compressed prior to the transmission to reduce the demand for network resources using one of three different algorithms: (i) DEFLATE, (ii) LZMA, and (iii) BZIP2. PS2DICOM employs dynamic data compression levels at the client side to improve network performance according to the current available network throughput.Results: Results indicate that PS2DICOM can improve transmission quality, storage capabilities, querying, and retrieving of DICOM images. The general efficiency gain is approximately 35% in data sending and receiving operations. This gain is resultant from the two levels of elasticity, allowing resources to be scaled up or down automatically in a transparent manner.Conclusions: The contributions of PS2DICOM are twofold: (i) multilevel cloud elasticity to adapt the computing resources on demand; (ii) adaptive data compression to meet the network quality and optimize data transmission. Results suggest that the use of compression in medical image data using PS2DICOM can improve the transmission efficiency, allowing the team of specialists to communicate in real-time, even when they are geographically distant.

A multi-sensor sub-Nyquist power spectrum blind sampling approach for low-power wireless sensors in operational modal analysis applications

A novel multi-sensor power spectrum blind sampling (PSBS) approach is proposed supporting low-power wireless sensor networks (WSN) for Operational Modal Analysis (OMA) applications. The developed approach relies on arrays of wireless sensors, employing deterministic non-uniform in time multi-coset sampling to acquire structural response acceleration signals at sub-Nyquist sampling rates, treated as realizations of stationary random processes without making any assumption about the average signal frequency content and spectral support. The acquired compressed measurements are transmitted to a central server and collectively processed via a PSBS technique, herein extended to the multi-sensor case, to estimate the power spectral density matrix of an underlying spatially correlated stationary response acceleration random process directly from the compressed measurements. Structural modal properties are then extracted through standard frequency domain decomposition (FDD). The efficacy of the proposed approach to resolve closely-spaced modes is numerically tested for various data compression levels using noisy response acceleration signals of a white-noise excited finite element model of a space truss as well as field-recorded acceleration time-histories of an instrumented bridge under operational loading. It is shown that accurate mode shapes based on the modal assurance criterion can be obtained from as low as 89% less measurements compared to conventional non-compressive FDD at Nyquist sampling rate. Further, significant gains in energy consumption and battery lifetime prolongation of the order of years are estimated, assuming wireless sensors operating on multi-coset sampling at different data compression levels. It is, therefore, concluded that the proposed PSBS approach could provide long-term structural health monitoring systems with low-maintenance cost once wireless sensors with multi-coset sampling capabilities become commercially available.

A framework for data compression and damage detection in structural health monitoring applied on a laboratory three-story structure

Structural Health Monitoring (SHM) is an important technique used to preserve many types of structures in the short and long run, using sensor networks to continuously gather the desired data. However, this causes a strong impact in the data size to be stored and processed. A common solution to this is using compression algorithms, where the level of data compression should be adequate enough to allow the correct damage identification. In this work, we use the data sets from a laboratory three-story structure to evaluate the performance of common compression algorithms which, then, are combined with damage detection algorithms used in SHM. We also analyze how the use of Independent Component Analysis, a common technique to reduce noise in raw data, can assist the detection performance. The results showed that Piecewise Linear Histogram combined with Nonlinear PCA have the best trade-off between compression and detection for small error thresholds while Adaptive PCA with Principal Component Analysis perform better with higher values.

Model-Based Edge Detector for Spectral Imagery Using Sparse Spatiospectral Masks

Two model-based algorithms for edge detection in spectral imagery are developed that specifically target capturing intrinsic features such as isoluminant edges that are characterized by a jump in color but not in intensity. Given prior knowledge of the classes of reflectance or emittance spectra associated with candidate objects in a scene, a small set of spectral-band ratios, which most profoundly identify the edge between each pair of materials, are selected to define a edge signature. The bands that form the edge signature are fed into a spatial mask, producing a sparse joint spatiospectral nonlinear operator. The first algorithm achieves edge detection for every material pair by matching the response of the operator at every pixel with the edge signature for the pair of materials. The second algorithm is a classifier-enhanced extension of the first algorithm that adaptively accentuates distinctive features before applying the spatiospectral operator. Both algorithms are extensively verified using spectral imagery from the airborne hyperspectral imager and from a dots-in-a-well midinfrared imager. In both cases, the multicolor gradient (MCG) and the hyperspectral/spatial detection of edges (HySPADE) edge detectors are used as a benchmark for comparison. The results demonstrate that the proposed algorithms outperform the MCG and HySPADE edge detectors in accuracy, especially when isoluminant edges are present. By requiring only a few bands as input to the spatiospectral operator, the algorithms enable significant levels of data compression in band selection. In the presented examples, the required operations per pixel are reduced by a factor of 71 with respect to those required by the MCG edge detector.

Publishing large DNA sequence data in reduced spaces and lasting formats, in paper or PDF

Scientific publications carry a practical moral duty: they must last. Along that line of thinking, some methods are proposed to allow economically and structurally viable publication of DNA sequence data of any size in printed matter and PDFs. The proposal is primarily aimed at contributing for preserving information for the future, while allowing authors to avoid information splitting and complement storage ex situ, that is, in server machines, outside the publication proper. The technique may also help to solve the impasse between the ICZN Code requirement that a new nomen be associated to diagnostic characters for the taxon vs. the phylogenetic definition of taxa, based on cladograms only: sequence data are characters, and can now be easily and comfortably included in taxonomic publications, with direct textual mention to their diagnostic sections. The compression level achieved allows the inclusion of all wanted DNA or RNA sequences in the same printed matter or PDF publications where the sequences are cited and discussed. Reduced font sizes, invisible fonts, and original 2D black & white and color barcodes are illustrated and briefly discussed. The level of data compression achieved can allow each full page of sequence data, or about 5000 characters, to be precisely coded into a color barcode as small as a square of 1.5 mm. A practical example is provided with Taeniogonalos woodorum Smith (Hymenoptera, Trigonalidae). Free software to generate publishable barcodes from txt or FASTA files is provided at www.systaxon.ufes.br/dna.

Wavelet Approximation of GRID Fields: Application to Quantitative Structure-Activity Relationships.

Molecular interaction fields such as those computed by the GRID program are widely used in applications such as virtual screening, molecular docking and 3D-QSAR modelling. They characterise molecules according to their favourable interaction sites and therefore enable predictions to be made on how molecules might interact. The fields are, however, comprised of a very large number of data points which presents difficulties for many applications. For example, there are likely to be high degrees of correlation between the variables which can lead to misleading results in 3D-QSAR. We describe the use of wavelet methods for approximating such data into a much smaller number of variables. We present a number of validation experiments, including use of the approximated GRIDs in 3D-QSAR, and demonstrate that wavelet approximation at high levels of data compression preserves the information content in GRID fields while significantly reducing computational requirements.

Abstract 4957: Comparison of commercial and custom microarrays: which is the best choice for copy-number analysis in tumor samples

Abstract Microarray technologies are powerful assays, which can identify copy-number abnormalities (CNA) underlying the pathogenesis of cancer. Several commercial arrays are available but it is unclear which is the best platform. We did a side-by-side comparison between two leading arrays; the Affymetrix SNP6 and Agilent 1M, to compare data compression, noise, gene and exon coverage, and CNA detection. Five samples were run on both platforms and analyzed as recommended by the manufacturer. We first performed an in silico comparison to define the number of probes per serial 5k interval, number of probes per gene and exon, and the coefficient of variation of different smoothing windows. Comparing the CV of the two arrays showed a similar level of variation between a 3 and 5 probe averaging on the 1M and SNP6, respectively. Using these smoothing levels the 1M interrogates 81.2% of the genes in the genome compared to 69.3% on the SNP6. At the exon level the 1M has at least one probe in 64.8% compared to 41.2% on the SNP6. We observed a similar level of data compression (∼10%) on the two arrays but the SNP6 had nearly twice the noise (SD 0.38 versus 0.21). Finally, there was a marked increment in the number of CNA identified by 1M (mean of 78.5) than SNP6.0 (59.5). In this comparison the Agilent 1M out performed the Affymetrix SNP6. Even with half the number of probes, the 1M was superior in CNA detection, gene/exon coverage and showed less signal variation. A comparison with previous platforms showed that these two arrays did not identify new gene amplifications but did detect new deletions, however, neither platform detected several known deletions detected by other methods. Therefore, we designed a custom, exon-centered, 2×400K array (Agilent) with the goal of identifying additional deletions. The design outline was: a) all the probes from the Agilent CGH 44K array; b) 3 probes per exon across the human exome; c) high resolution tiling of 1450 cancer implicated genes; and d) tiling the 4kb flanking all MIRs. To improve performance, we only included probes with performance scores higher than 0.8 and 0.85 in queries b) and c), respectively. Lastly, 15000 probes from the 1M array that map to exons missed by our design parameters were also included. Our custom array has half the probes (415K) of the 1M (960K) and 4.5x less than the SNP6 (1.85M) but shows the best gene and exon coverage. When the minimum number of probes needed for calculating a CNA is considered, 86% of genes are interrogated compared to 81.2% (1M) and 69.3% (SNP6). The difference is even more marked at the exon level with 84% covered by at least one probe and at least 63.2% interrogated by two probes compared to 5.3% (1M) and 18.0% (SNP6). The promising in silico comparison of this exon-centered array, its more stringent design parameters in combination with significantly reducing the cost per run should make this a very promising array design for CNA analysis of tumor samples in the future Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 4957.

Guest Editorial, Preface, Main Points, Glossary and Chapter 1

Digital techniques have the potential to improve the practice of radiology but they also risk the overuse of radiation. The main advantages of digital imaging, i.e. wide dynamic range, post processing, multiple viewing options, and electronic transfer and archiving possibilities, are clear but overexposures can occur without an adverse impact on image quality. In conventional radiography, excessive exposure produces a 'black' film. In digital systems, good images are obtained for a large range of doses. It is very easy to obtain (and delete) images with digital fluoroscopy systems, and there may be a tendency to obtain more images than necessary. In digital radiology, higher patient dose usually means improved image quality, so a tendency to use higher patient doses than necessary could occur. Different medical imaging tasks require different levels of image quality, and doses that have no additional benefit for the clinical purpose should be avoided. Image quality can be compromised by inappropriate levels of data compression and/or postprocessing techniques. All these new challenges should be part of the optimisation process and should be included in clinical and technical protocols. Local diagnostic reference levels should be re-evaluated for digital imaging, and patient dose parameters should be displayed at the operator console. Frequent patient dose audits should occur when digital techniques are introduced. Training in the management of image quality and patient dose in digital radiology is necessary. Digital radiology will involve new regulations and invoke new challenges for practitioners. As digital images are easier to obtain and transmit, the justification criteria should be reinforced. Commissioning of digital systems should involve clinical specialists, medical physicists, and radiographers to ensure that imaging capability and radiation dose management are integrated. Quality control requires new procedures and protocols (visualisation, transmission, and archiving of the images).

Detection of masses and clustered microcalcifications on data compressed mammograms: an observer performance study.

To evaluate observers' ability to detect breast masses and clustered microcalcifications depicted on data compressed mammograms, an observer performance study was performed. Eight observers assessed 60 mammographic images obtained in six modes, ranging from noncompressed to a maximum data compression level of 101:1. Observers were asked to rate the images on a scale of 0 to 100 for the likelihood of the presence of a mass and also independently for the likelihood of the presence of clustered microcalcifications. In addition, observers were asked to rate their subjective assessment of the quality of each image for the detection of a mass and separately for the detection of microcalcifications. Receiver operating characteristic analyses were performed. The average area under the receiver operating characteristic curve, A(z), for the detection of clustered microcalcifications decreases significantly at the highest data compression level when compared with the noncompressed and two lowest levels of data compression (p < 0.01), and a trend test of the average area under the receiver operating characteristic curve for all observers is statistically significant (p < 0.05). No statistically significant differences among or between any of the data compression level modes for the detection of masses were detected. At a high level of mammogram data compression, observer performance was degraded for the detection of clustered microcalcifications. Detection of masses was not affected by the data compression methods and levels used in this study.

American College of Cardiology/ European Society of Cardiology international study of angiographic data compression phase III: Measurement of image quality differences at varying levels of data compression

OBJECTIVESWe sought to investigate up to which level of Joint Photographic Experts Group (JPEG) data compression the perceived image quality and the detection of diagnostic features remain equivalent to the quality and detectability found in uncompressed coronary angiograms.BACKGROUNDDigital coronary angiograms represent an enormous amount of data and therefore require costly computerized communication and archiving systems. Earlier studies on the viability of medical image compression were not fully conclusive.METHODSTwenty-one raters evaluated sets of 91 cine runs. Uncompressed and compressed versions of the images were presented side by side on one monitor, and image quality differences were assessed on a scale featuring six scores. In addition, the raters had to detect pre-defined clinical features. Compression ratios (CR) were 6:1, 10:1 and 16:1. Statistical evaluation was based on descriptive statistics and on the equivalence t-test.RESULTSAt the lowest CR (CR 6:1), there was already a small (15%) increase in assigning the aesthetic quality score indicating “quality difference is barely discernible—the images are equivalent.” At CR 10:1 and CR 16:1, close to 10% and 55%, respectively, of the compressed images were rated to be “clearly degraded, but still adequate for clinical use” or worse. Concerning diagnostic features, at CR 10:1 and CR 16:1 the error rate was 9.6% and 13.1%, respectively, compared with 9% for the baseline error rate in uncompressed images.CONCLUSIONSCompression at CR 6:1 provides equivalence with the original cine runs. If CR 16:1 were used, one would have to tolerate a significant increase in the diagnostic error rate over the baseline error rate. At CR 10:1, intermediate results were obtained.

American College of Cardiology/ European Society of Cardiology international study of angiographic data compression phase II: The effects of varying JPEG data compression levels on the quantitative assessment of the degree of stenosis in digital coronary angiography

OBJECTIVESThis report describes whether lossy Joint Photographic Experts Group (JPEG) image compression/decompression has an effect on the quantitative assessment of vessel sizes by state-of-the-art quantitative coronary arteriography (QCA).BACKGROUNDThe Digital Imaging and Communications in Medicine (DICOM) digital exchange standard for angiocardiography prescribes that images must be stored loss free, thereby limiting JPEG compression to a maximum ratio of 2:1. For practical purposes it would be desirable to increase the compression ratio (CR), which would lead to lossy image compression.METHODSA series of 48 obstructed coronary segments were compressed/decompressed at CR 1:1 (uncompressed), 6:1, 10:1 and 16:1 and analyzed blindly and in random order using the QCA-CMS analytical software. Similar catheter and vessel start- and end-points were used within each image quartet, respectively. All measurements were repeated after several weeks using newly selected start- and end-points. Three different sub-analyses were carried out: the intra-observer, fixed inter-compression and variable inter-compression analyses, with increasing potential error sources, respectively.RESULTSThe intra-observer analysis showed significant systematic and random errors in the calibration factor at JPEG CR 10:1. The fixed inter-compression analysis demonstrated systematic errors in the calibration factor and recalculated vessel parameter results at CR 16:1 and for the random errors at CR 10:1 and 16:1. The variable inter-compression analysis presented systematic and random errors in the calibration factor and recalculated parameter results at CR 10:1 and 16:1. Any negative effect at CR 6:1 was found only for the calibration factor of the variable inter-compression analysis, which did not show up in the final vessel measurements.CONCLUSIONSCompression ratios of 10:1 and 16:1 affected the QCA results negatively and therefore should not be used in clinical research studies.

The Effect of Data Compression on Remote Sensing Image Classification

The effect of the Joint Photographic Coding Experts Group (JPEG) digital data compression technique on remote sensing image classification and accuracy is evaluated using two digital images of the town of Bishah taken in 1995 and 1997. The Earth Resources Data Analysis System (ERDAS)andan ALCHEMY software were employed for this purpose. Various data compression levels ranging from 10.8% to 18.2% were attempted. The results show that, for the 1995 image, GIS maps resulting from compression at the different rates are almost similar to the original uncompressed image. This is evident from comparison of standard deviation values at the various compression levels. However, there were noticeable differences between the 1997 original image and the GIS maps obtained from it after compression. Again this is clear from standard deviation of the various compressed images. It therefore seems that the effect of the JPEG compression technique is scene- and possibly time-dependent. It also seems that, for application areas where high accuracy of classification is not of paramount importance, the JPEG data compression technique allows data compression of up to 18% of the original scene without much affecting the overall quality of the data. This can serve a useful purpose on computer storage and data analysis for a wide range of earth science applications.

Telesurgery

To determine the clinical acceptability of various levels of video compression for remote proctoring of laparoscopic surgical procedures. Observational, controlled study. Community-based teaching hospital. Physician and nurse observers. Controlled surgical video scenes were subjected to various levels of data compression for digital transmission and display and shown to participant observers. Clinical acceptability of video scenes after application of video compression. Clinically acceptable video compression was achieved with a 1.25-megabit/second data rate, with the use of odd-screen 43.3:1 Joint Photographic Expert Group compression and a small screen for remote viewing. With proper video compression, remote proctoring of laparoscopic procedures may be performed with standard 1.5-megabit/second telecommunication data lines and services.