Wavelet-based Data Compression Research Articles

WaveRange: wavelet-based data compression for three-dimensional numerical simulations on regular grids

A wavelet-based method for compression of three-dimensional simulation data is presented and its software framework is described. It uses wavelet decomposition and subsequent range coding with quantization suitable for floating-point data. The effectiveness of this method is demonstrated by applying it to example numerical tests, ranging from idealized configurations to realistic global-scale simulations. The novelty of this study is in its focus on assessing the impact of compression on post-processing and restart of numerical simulations.Graphical abstract

Role Of Pattern Characteristics In Cross Correlation Based Motion Estimation

Objectives: To establish a pattern tracking based motion estimation algorithm for the stereovision based system and to investigate the effect of threshold value (Thv), size and population of patterns on the pattern tracking value. Methods: Proposed motion estimation algorithm correlates the set of motion frames captured from two high speed cameras configured in stereovision system. The correlation scheme was based on grayscale pattern tracking in moving frames. Pattern development and correlation algorithms were developed. A spherical object was given small random displacements and the motion was captured using stereovision system. The effectiveness of algorithm is evaluated with the pattern tracking value which should be close to 1 for perfect pattern match. Findings: The correlation results indicate that pattern tracking value were found to be 0.920 and 0.899 for left and right cameras respectively when the threshold value (>10) and size 10 pixels are considered with high population (200 patterns). With the increase in pattern size, the pattern tracking value decreased. Another study revealed that pattern tracking value was comparatively higher when the pattern population was maximum (200 patterns). The pattern tracking value again decreased when the threshold value (>15) is considered. It was concluded that pattern size of 10 pixels with threshold value (>10) is more pronounced for motion estimation. The proposed algorithm is verified for the 3D displacements of a rectangular plate mounted on XYZ translation stage. The pattern tracking values were 0.97, 0.95, 0.96 and 0.96, 0.94, 0.95 for X, Y and Z displacements respectively. The correlation algorithm is also coupled with the compression technique using wavelet based data compression. Novelty/Applications: The proposed algorithm can be efficiently applied for both in plane and out of plane motion estimation. The algorithm can provide constructive outcomes for small motion prediction with proper selection of pattern size and threshold value. Keywords: Camera calibration; Image acquisition; Image correlation; Motion estimation; Pattern tracking; Compression

Wavelet-based data compression for wide-area measurement data of oscillations

This paper proposes a wavelet-based data compression method to compress the recorded data of oscillations in power systems for wide-area measurement systems. Actual recorded oscillations and simulated oscillations are compressed and reconstructed by the wavelet-based data compression method to select the best wavelet functions and decomposition scales according to the criterion of the minimum compression distortion composite index, for a balanced consideration of compression performance and reconstruction accuracy. Based on the selections, the relationship between the oscillation frequency and the corresponding optimal wavelet and scale is discussed, and a piecewise linear model of the base-2 logarithm of the frequency and the order of the wavelet is developed, in which different pieces represent different scales. As a result, the wavelet function and decomposition scale can be selected according to the oscillation frequency. Compared with the wavelet-based data compression method with a fixed wavelet scale for disturbance signals and the real-time data compression method based on exception compression and swing door trending for oscillations, the proposed method can provide high compression ratios and low distortion rates.

Block sparsity-based joint compressed sensing recovery of multi-channel ECG signals.

In recent years, compressed sensing (CS) has emerged as an effective alternative to conventional wavelet based data compression techniques. This is due to its simple and energy-efficient data reduction procedure, which makes it suitable for resource-constrained wireless body area network (WBAN)-enabled electrocardiogram (ECG) telemonitoring applications. Both spatial and temporal correlations exist simultaneously in multi-channel ECG (MECG) signals. Exploitation of both types of correlations is very important in CS-based ECG telemonitoring systems for better performance. However, most of the existing CS-based works exploit either of the correlations, which results in a suboptimal performance. In this work, within a CS framework, the authors propose to exploit both types of correlations simultaneously using a sparse Bayesian learning-based approach. A spatiotemporal sparse model is employed for joint compression/reconstruction of MECG signals. Discrete wavelets transform domain block sparsity of MECG signals is exploited for simultaneous reconstruction of all the channels. Performance evaluations using Physikalisch-Technische Bundesanstalt MECG diagnostic database show a significant gain in the diagnostic reconstruction quality of the MECG signals compared with the state-of-the art techniques at reduced number of measurements. Low measurement requirement may lead to significant savings in the energy-cost of the existing CS-based WBAN systems.

ECG compression using non-recursive wavelet transform with quality control

ABSTRACTWhile wavelet-based electrocardiogram (ECG) data compression using scalar quantisation (SQ) yields excellent compression performance, a wavelet’s SQ scheme, however, must select a set of multilevel quantisers for each quantisation process. As a result of the properties of multiple-to-one mapping, however, this scheme is not conducive for reconstruction error control. In order to address this problem, this paper presents a single-variable control SQ scheme able to guarantee the reconstruction quality of wavelet-based ECG data compression. Based on the reversible round-off non-recursive discrete periodised wavelet transform (RRO-NRDPWT), the SQ scheme is derived with a three-stage design process that first uses genetic algorithm (GA) for high compression ratio (CR), followed by a quadratic curve fitting for linear distortion control, and the third uses a fuzzy decision-making for minimising data dependency effect and selecting the optimal SQ. The two databases, Physikalisch-Technische Bundesanstalt (PTB) and Massachusetts Institute of Technology (MIT) arrhythmia, are used to evaluate quality control performance. Experimental results show that the design method guarantees a high compression performance SQ scheme with statistically linear distortion. This property can be independent of training data and can facilitate rapid error control.

Fusion of Thresholding Rules During Wavelet-Based Noisy Image Compression

The new method for combining semisoft thresholding rules during wavelet-based data compression of images with multiplicative noise is suggested. The method chooses the best thresholding rule and the threshold value using the proposed criteria which provide the best nonlinear approximations and take into consideration errors of quantization. The results of computer modeling have shown that the suggested method provides relatively good image quality after restoration in the sense of some criteria such as PSNR, SSIM, etc.

Onboard joint nondestructive despeckling and wavelet-based data compression of multilook synthetic aperture radar images using reordered spatial oriented trees

The suggested method of joint despeckling and set partitioning in hierarchical trees–based compression for degraded multilook synthetic aperture radar (SAR) images uses a fused image as a reference during compression. The fused image itself is formed from intermediate frames (looks) by the logical operation on wavelet coefficients, which are presented in the form of spatial oriented trees (SOTs) of wavelet decomposition. This decomposition is calculated for one and the same region of the Earth’s surface during SAR spacecraft flight. The high-frequency subbands (details) of the fused images contain the smallest wavelet coefficients from different SOTs. The wavelet coefficients related to the low-frequency subband (approximation) are processed by another special logical operation providing a good smoothing. Based on the improved fused image, the best bit rate (optimal operation point) is found by observing the output mean square errors and cross-correlation coefficients between the compressed and reference images. The results of computer modeling and comparison with a few well-known despeckling and compression procedures have shown the superb quality of the proposed method in the sense of different criteria such as peak signal-to-noise ratio, structural similarity, etc.

Application of a Real-Time Data Compression and Adapted Protocol Technique for WAMS

This paper proposes a real-time data compression and adapted protocol technique for wide-area measurement systems (WAMS). The compression algorithm combines exception compression (EC) with swing door trending (SDT) compression. The compression logic is designed to perform this algorithm in real time. Selection of compression parameters and data reconstruction are presented. An adapted protocol is introduced by improving the format of data frames defined by IEEE standard C37.118 for compressed data packets. The proposed compression technique and protocol were applied to the phasor measurement units (PMUs) of a hydropower plant in Guizhou Power Grid in Southwest China. A low-frequency oscillation incident was recorded by this technique. The raw, compressed and reconstructed data were analyzed to verify the compression and determine the accuracy of the proposed technique. Also, the wavelet-based data compression, the standalone EC and SDT are compared with the proposed compression technique. Our results demonstrated that this compression can reach the compression ratios in the range of 6 to 11. Also, this compression and adapted protocol technique can reduce the size of data packets by approximately 75% with high accuracy in both dynamic and steady states.

A MOBILE BASED MICROSCOPE FOR SAMPLE RECOGNITION

Mobile phones provide important services like GPS, short-range wireless communications using infrared or Bluetooth, business applications, mobile banking etc. Mobile hard-ware add--ons are also popular like, handheld keyboard with mobile phone, mobile with handheld miniature microscope (phonoscope) and others. Imaging is one of the most important features of mobile technology. Magnified images are providing in depth recognition capabilities of object. Mobile based microscope is used for Geology, Biological, Medicine, Horticulture and others areas. Objective of this paper is to present a method for rock identification using mobile based microscope camera imaging of surface parameter. Rock surface parameters are color, grain, texture. The development in the area of mobile application has opened new challenges in mobile image processing. In this paper we demonstrate a method that adopts microscope optics into mobile camera optics and developed java based (J2ME) mobile application for recognizing rock type. This consists of feature extraction algorithm using wavelet based data compression and neural network based feature classification. Rock surface parameters used in this work is grain. The signature extracted from grain parameter is used to identify the rock type.

Data-Smoothness based Preprocessing Strategy for Wavelet Data Processing in Wireless Sensor Networks

Wavelet based data compression in wireless sensor networks can reduce in-network data transmissions and gain better data approximation. To improve the performance of algorithms based on wavelet data compression, data-smoothness based preprocessing strategy for wavelet data processing is proposed. The strategy can adjust the order of data to be processed for better smoothness through sample mean and control the frequency of data order adjustment by a threshold, achieving better data reconstruction precision with acceptable network control overhead, and higher data compression degree under a given requirement of data reconstruction precision. Theoretical analysis and experiments prove the effectiveness of the strategy.

Prediction Method of El Nino Southern Oscillation: ENSO by Means of Wavelet Based Data Compression with Appropriate Support Length of Base Function

Method for El Nino/Southern Oscillation: ENSO by means of wavelet based data compression with appropriate support length of base function is proposed. Through the experiments with observed southern oscillation index, the proposed method is validated. Also a method for determination of appropriate support length is proposed and is validated.

Multiple-view fluorescence optical tomography reconstruction using compression of experimental data

We report on the experimental demonstration of a fast reconstruction method for multiview fluorescence diffuse optical tomography by using a wavelet-based data compression. We experimentally demonstrate that the use of data compression combined with the multiview approach makes it possible to perform a fast reconstruction of high quality. A structured illumination approach, guided by the compression scheme, has been adopted to further reduce the acquisition time. The reconstruction algorithm is based on the finite element method, and hence is suitable for samples of any arbitrary shape.

A Wavelet-Based Data Compression Technique for Smart Grid

This paper proposes a wavelet-based data compression approach for the smart grid (SG). In particular, wavelet transform (WT)-based multiresolution analysis (MRA), as well as its properties, are studied for its data compression and denoising capabilities for power system signals in SG. Selection of the Order 2 Daubechies wavelet and scale 5 as the best wavelet function and the optimal decomposition scale, respectively, for disturbance signals is demonstrated according to the criterion of the maximum wavelet energy of wavelet coefficients (WCs). To justify the proposed method, phasor data are simulated under disturbance circumstances in the IEEE New England 39-bus system. The results indicate that WT-based MRA can not only compress disturbance signals but also depress the sinusoidal and white noise contained in the signals.

Improving the scalability of hyperspectral imaging applications on heterogeneous platforms using adaptive run-time data compression

Latest generation remote sensing instruments (called hyperspectral imagers) are now able to generate hundreds of images, corresponding to different wavelength channels, for the same area on the surface of the Earth. In previous work, we have reported that the scalability of parallel processing algorithms dealing with these high-dimensional data volumes is affected by the amount of data to be exchanged through the communication network of the system. However, large messages are common in hyperspectral imaging applications since processing algorithms are pixel-based, and each pixel vector to be exchanged through the communication network is made up of hundreds of spectral values. Thus, decreasing the amount of data to be exchanged could improve the scalability and parallel performance. In this paper, we propose a new framework based on intelligent utilization of wavelet-based data compression techniques for improving the scalability of a standard hyperspectral image processing chain on heterogeneous networks of workstations. This type of parallel platform is quickly becoming a standard in hyperspectral image processing due to the distributed nature of collected hyperspectral data as well as its flexibility and low cost. Our experimental results indicate that adaptive lossy compression can lead to improvements in the scalability of the hyperspectral processing chain without sacrificing analysis accuracy, even at sub-pixel precision levels.

A Modified Run-Length Coding for the Realization of Wavelet-based ECG Data Compression System

With high compression performance, realization of wavelet-based data compression system is crucial for multi-lead ECG signal recording. In this paper, a modified run-length coding (MRLC) algorithm associated with an efficient quantization scheme is proposed for the realization of a RRO-NRDPWT-based ECG data compression system. The MRLC with regularity and low computational complexity is suitable for hardware implementation at a cost of compression performance. This sacrifice will be compensated by the new quantization scheme. By using the MIT-BIH arrhythmia database, the experimental results show that the proposed scheme can be competitive to other wavelet-based approaches in compression performance. In addition, the MRLC can improve traditional run-length coding by about 13%.

Wavelet Decomposition-Based Approach for Fast Damage Detection of Civil Structures

This paper presents a method, which can be implemented under limited resources such as wireless sensor network devices, for damage detection of civil structures. The detection method is applied to transient waves from vibration responses, and it consists of Haar wavelet decomposition, thresholding, quantization, and differentiating metric. Before the differentiating metric, a target signal is compressed into the wavelet coefficients .signature.. The results indicate that a single measurement channel without time synchronization of other measurement points is effective for instant damage detection through the case study using the ASCE structural health monitoring (SHM) benchmark program. It is also shown that we can reduce the data to a small fraction of the original data volume using this method, while retaining enough information to detect structural damage and its severity. The approach illustrated in this paper implies a promise of wavelet-based data compression and analysis for the increasing volume of SHM data.

Wavelet Compression of Three-Dimensional Time-Lapse Biological Image Data

The use of multifocal-plane, time-lapse recordings of living specimens has allowed investigators to visualize dynamic events both within ensembles of cells and individual cells. Recordings of such four-dimensional (4D) data from digital optical sectioning microscopy produce very large data sets. We describe a wavelet-based data compression algorithm that capitalizes on the inherent redunancies within multidimensional data to achieve higher compression levels than can be obtained from single images. The algorithm will permit remote users to roam through large 4D data sets using communication channels of modest bandwidth at high speed. This will allow animation to be used as a powerful aid to visualizing dynamic changes in three-dimensional structures.

Wavelet-based data compression of power system disturbances using the minimum description length criterion

This paper introduces a compression technique for power disturbance data via discrete wavelet transform (DWT) and wavelet packet transform (WPT). The data compression leads to a potential application for remote power protection and power quality monitoring. The compression technique is performed through signal decomposition up to a certain level, thresholding of wavelet coefficients, and signal reconstruction. The choice of which wavelet to use for the compression is of critical importance, because the wavelet affects reconstructed signal quality and the design of the system as a whole. The minimum description length (MDL) criterion is proposed for the selection of an appropriate wavelet filter. This criterion permits selection not only of the suitable wavelet filter but also the best number of wavelet retained coefficients for signal reconstruction. The experimental study has been carried out for a single-phase to ground fault event, and the data compression results of using the suitable wavelet filter show that the compression ratios are less than 11 % and are reduced to more than a half of that percentage value by implementing an additional lossless coding.

Error based criterion for on-line wavelet data compression

Abstract Wavelet based data compression methods have demonstrated superior performance over the conventional interpolative methods. However, the wavelet based methods need thresholding on the wavelet domain coefficients. Since wavelet coefficients are not commonly intuitive to engineers, significant a priori knowledge of either the wavelet coefficients or process thresholds is required. So unless thresholds are pre-specified, this requirement makes wavelets unsuitable for on-line implementations. Furthermore, as the relation between the wavelet domain coefficients and the measures of the quality of compression [root mean square error (RMSE) and local point error (LPE)] is not straightforward, it is difficult to achieve good control over the quality of compression by specifying thresholds on the wavelet coefficients. In this paper, an error based criterion is proposed for online wavelet data compression. It uses semantically straightforward measures of the quality of the result to be obtained to adaptively calculate the thresholds. Given a bound on time domain error limits like the RMSE and LPE, this technique adaptively computes the threshold values in wavelet domain. Experiments show that the resulting algorithm gives superior compression as compared to other wavelet based methods. Most importantly, it can be used on-line and provides an effective way of controlling LPE and RMSE. Finally, this method can easily be extended to other on-line wavelet applications such as data rectification and de-noising.

Adaptive Error Based Thresholding for On-Line Wavelet Compression

Wavelet based data compression is superior to interpolative compression techniques like boxcar as wavelets provide an efficient joint time-scale representation of the signal in terms of wavelet coefficients. Thresholding over these coefficients is used to achieve compression. A recursive on-line wavelet based compression algorithm, which combines the high quality of compression of wavelets and on-line implementability of interpolative methods has already been developed. In this paper, an error based thresholding framework is introduced, which adaptively computes these thresholds, given the bounds on root mean square error and local point error. This framework is applied to the recursive on-line wavelet based compression using Haar wavelets. Experiments show that the resulting algorithm gives superior compression as compared to interpolative methods. Moreover, it can be used on-line and provides an effective way of controlling the local point error (LPE) and the root mean square error (RMSE).