Discrete Wavelet Transform Coefficients Research Articles

Efficient Image Compression based on Seam Carving for Arbitrary Resolution Display Devices

In mobile communications, image retargeting is generally required at the user end. The existing coding techniques do not support content-aware spatial scalability. The work presented in this paper addresses the increasing demand of compression techniques for arbitrary resolution display devices. This paper throws light on efficient content-aware compression of medical images. The principle of seam carving is incorporated into a wavelet codec. In the meantime, discrete wavelet transform (DWT) is performed and DWT coefficients are grouped. The coefficients are encoded and decoded using SPIHT. The bit stream is then transmitted in energy descending order and the image can be reconstructed in a content-aware manner. Experimental results show that the retargeted image generated by the proposed technique preserve sensitive image content and has been proven efficient for medical images by achieving excellent compression performances.

Efficient VLSI Architecture for ECG Data Compression

This paper presents an efficient ECG signals compression techniques using a 2D DWT coefficient thresholding and its design implementation of an efficient JPEG2000 encoder that employs the Distributed Arithmetic (DA) technique for the complex computation of Discrete Wavelet Transform (DWT).2D approaches exploit the fact that redundancy of ECG signal occurs between adjacent beats and adjacent samples. Cut and aligned error signal to form 2D matrix, then 2D matrix is transformed. Ultimately wavelet coefficients are segmented into groups and thresholded. DA-DWT is used for reducing the complex computations, which can increase the speed and throughput as well. Architecture is based on the principles of pipelining and parallelism to obtain the optimal speed and throughput. Architecture is simple, modular and cascadable for computing a DA-DWT. This technique is faster when ROM table in on chip memory and memory size is reduced by splitting ROM table .The description and functionalities of the design is modeled by Verilog HDL. The simulation and synthesis methodology are used to target it on Virtex-II Pro FPGA (xc4v1x25-12sf363) that consumes 6% resources of FPGA and shows the clock frequency 310.207 MHz by DADWT using Sym20. Experiments on selected records from MIT-BIH arrhythmia database revealed that the proposed design is significantly more efficient.

An evaluation of the effects of wavelet coefficient quantisation in transform based EEG compression

In recent years, there has been a growing interest in the compression of electroencephalographic (EEG) signals for telemedical and ambulatory EEG applications. Data compression is an important factor in these applications as a means of reducing the amount of data required for transmission. Allowing for a carefully controlled level of loss in the compression method can provide significant gains in data compression. Quantisation is easy to implement method of data reduction that requires little power expenditure. However, it is a relatively simple, non-invertible operation, and reducing the bit-level too far can result in the loss of too much information to reproduce the original signal to an appropriate fidelity. Other lossy compression methods allow for finer control over compression parameters, generally relying on discarding signal components the coder deems insignificant. SPIHT is a state of the art signal compression method based on the Discrete Wavelet Transform (DWT), originally designed for images but highly regarded as a general means of data compression. This paper compares the approaches of compression by changing the quantisation level of the DWT coefficients in SPIHT, with the standard thresholding method used in SPIHT, to evaluate the effects of each on EEG signals. The combination of increasing quantisation and the use of SPIHT as an entropy encoder has been shown to provide significantly improved results over using the standard SPIHT algorithm alone.

Damage Detection in Static Beams via Wavelet-Based Technique

A wavelet transform (WT) has been proposed to detect the presence of damage and identify its location using the deflection response of the damaged beams alone. The static deflection responses of Euler-Bernoulli beams are measured by considered a concentrated loads and cracks at various locations on the beam. The static deflection from the beam is used directly as the input signal for discrete wavelet transform (DWT) analysis. The location of damage is indicated by the peak of DWT coefficient plot. Although the deflection responses are at different loads and cracks location, cracks still can be detected. The results show that the sign of DWT coefficients can be used in predicting the location of damage/crack on the beams alone without any baseline data. Effect of crack depth, crack position and load location is discussed which can be extended to quantify damage.

A Low-Complexity ECG Feature Extraction Algorithm for Mobile Healthcare Applications

This paper introduces a low-complexity algorithm for the extraction of the fiducial points from the Electrocardiogram (ECG). The application area we consider is that of remote cardiovascular monitoring, where continuous sensing and processing takes place in low-power, computationally constrained devices, thus the power consumption and complexity of the processing algorithms should remain at a minimum level. Under this context, we choose to employ the Discrete Wavelet Transform (DWT) with the Haar function being the mother wavelet, as our principal analysis method. From the modulus-maxima analysis on the DWT coefficients, an approximation of the ECG fiducial points is extracted. These initial findings are complimented with a refinement stage, based on the time-domain morphological properties of the ECG, which alleviates the decreased temporal resolution of the DWT. The resulting algorithm is a hybrid scheme of time and frequency domain signal processing. Feature extraction results from 27 ECG signals from QTDB, were tested against manual annotations and used to compare our approach against the state-of-the art ECG delineators. In addition, 450 signals from the 15-lead PTBDB are used to evaluate the obtained performance against the CSE tolerance limits. Our findings indicate that all but one CSE limits are satisfied. This level of performance combined with a complexity analysis, where the upper bound of the proposed algorithm, in terms of arithmetic operations, is calculated as 2:423N + 214 additions and 1:093N + 12 multiplications for N 861 or 2:553N + 102 additions and 1:093N +10 multiplications for N > 861 (N being the number of input samples), reveals that the proposed method achieves an ideal trade-off between computational complexity and performance, a key requirement in remote CVD monitoring systems.

Secure hybrid multibit multiplicative watermarking for media distribution over mobile Internet

AbstractWith the rapid development and rich applications of mobile Internet, security issues become urgent. For example, piracy of media content (image, audio, or video) becomes easier because mobile Internet provides more ways to re‐distribute media contents. Watermarking has been regarded as a potential solution for identifying pirate copies. However, the watermarking algorithms' security and robustness are still not ideal enough for practical applications, especially in mobile scenarios with more attacks. In this paper, a novel hybrid multiplicative watermarking algorithm is proposed to obtain higher security and good robustness. In this algorithm, two embedding rules are changed according to the secret key, which embed the watermark into Discrete Wavelet Transform (DWT) coefficients. The secret key improves the watermark system's security. Additionally, the optimum and locally optimum hybrid multiplicative watermarking decoders are proposed, respectively, to detect the watermark. Because it works with two rules, it obtains a tradeoff in robustness. Various comparative experiments are carried out, and results are given to prove the valid of theoretical analysis. Copyright © 2013 John Wiley & Sons, Ltd.

Robust Watermarking Scheme for Multispectral Images Using Discrete Wavelet Transform and Tucker Decomposition

Watermarking represents a potentially effective tool for the protection and verification of ownership rights in remote sensing images. Multispectral images (MSIs) are the main type of images acquired by remote sensing radiometers. In this paper, a robust multispectral image watermarking technique based on the discrete wavelet transform (DWT) and the tucker decomposition (TD) is proposed. The core idea behind our proposed technique is to apply TD on the DWT coefficients of spectral bands of multispectral images. We use DWT to effectively separate multispectral images into different sub-images and TD to efficiently compact the energy of sub-images. Then watermark is embedded in the elements of the last frontal slices of the core tensor with the smallest absolute value. The core tensor has a good stability and represents the multispectral image properties. The experimental results on LANDSAT images show the proposed approach is robust against various types of attacks such as lossy compression, cropping, addition of noise etc.

Improved Data Embedding Method for Digital Image with Human Visual Systems

In this paper, we consider a data-embedding method in the discrete wavelet transform (DWT) domain of the digital images. A bit-pattern embedding method with Human visual system (HVS) and correlation-based detection have been proposed by Kino and Wada based on the watermarking scheme, which is referred to as the conventional method. In the conventional method, the capacity of embedding data depends on the ‘potential’ correlation between the DWT coefficients of the host image and watermark signal. We propose the new embedding and detection algorithm without affected by the ‘potential’ correlation in order to increase the capacity of embedding data. The data-embedding and detection in the proposed method are performed by considering the absolute value of the correlation between the watermarked DWT coefficients and watermark signal. In the simulation, we show that the capacity of the embedding data in the proposed method is increased as compared with the conventional method under the same PSNR value (image quality).

Multimodal Medical Image registration using Discrete Wavelet Transform

In image processing, image registration develops a relationship between two images using optimal transformation where the images could have been taken at various times, sources or devices, or from different perspectives. It aligns the reference and moving image using geometric transformations. This research study evaluates the performance of multimodal(images acquired from different sources)image registration technique using Discrete Wavelet Transform (DWT).The reference and the target images are decomposed into their respective DWT coefficients and then are processed for image registration. After registration, the resultant DWT coefficients are transformed back using Inverse DWT into their spatial coordinates in order to retrieve the registered image. The similarity of the two input images for image registration is calculated and investigated using a similarity metric known as Mutual Information (MI) which is maximized. The quality of registration is measured using cross-correlation coefficient (CCC) of the registered image with respect to the reference image. Finally the time taken for image registration in wavelet domain is analyzed and compared with the image registration taking place in spatial domain.

LOGO EXTRACTION USING COMBINED DISCRETE WAVELET TRANSFORM AND DYNAMIC STOCHASTIC RESONANCE

In this paper, we have introduced a new method for watermark (logo) extraction from distorted watermarked images. The method is based on combined discrete wavelet transform (DWT) and dynamic stochastic resonance (DSR). In this method, the image property such as variance corresponding to the DWT coefficients of the image is tuned with the dynamic stochastic resonance parameters which causes resonance to the DWT coefficients. That is, the signal amplitude enhances and noise amplitude degraded in the DWT coefficients. This approach extracts hided logo from the distorted watermarked image which is almost very similar to the original logo. The experimental results have been compared with the existing techniques and were found to be superior.

Adaptive audio watermarking via the optimization point of view on the wavelet-based entropy

This study aims to present an adaptive audio watermarking method using ideas of wavelet-based entropy (WBE). The method converts low-frequency coefficients of discrete wavelet transform (DWT) into the WBE domain, followed by the calculations of mean values of each audio as well as derivation of some essential properties of WBE. A characteristic curve relating the WBE and DWT coefficients is also presented. The foundation of the embedding process lies on the approximately invariant property demonstrated from the mean of each audio and the characteristic curve. Besides, the quality of the watermarked audio is optimized. In the detecting process, the watermark can be extracted using only values of the WBE. Finally, the performance of the proposed watermarking method is analyzed in terms of signal to noise ratio, mean opinion score and robustness. Experimental results confirm that the embedded data are robust to resist the common attacks like re-sampling, MP3 compression, low-pass filtering, and amplitude scaling.

Snort IDS Hybrid ADS Preprocessor

Abstract The paper presents hybrid anomaly detection preprocessor for SNORT IDS - Intrusion Detection System [1] base on statistical test and DWT - Discrete Wavelet Transform coefficient analysis. Preprocessor increases functionality of SNORT IDS system and has complementary properties. Possibility of detection network anomalies is increased by using two different algorithms. SNORT captures network traffic features which are used by ADS (Anomaly Detection System) preprocessor for detecting anomalies. Chi-square statistical test and DWT subband coefficients energy values are used for calculating of normal network traffic profiles. We evaluated proposed SNORT extension with the use of test network.

Automated diagnosis of Coronary Artery Disease affected patients using LDA, PCA, ICA and Discrete Wavelet Transform

Coronary Artery Disease (CAD) is the narrowing of the blood vessels that supply blood and oxygen to the heart. Electrocardiogram (ECG) is an important cardiac signal representing the sum total of millions of cardiac cell depolarization potentials. It contains important insights into the state of health and nature of the disease afflicting the heart. However, it is very difficult to perceive the subtle changes in ECG signals which indicate a particular type of cardiac abnormality. Hence, we have used the heart rate signals from the ECG for the diagnosis of cardiac health. In this work, we propose a methodology for the automatic detection of normal and Coronary Artery Disease conditions using heart rate signals. The heart rate signals are decomposed into frequency sub-bands using Discrete Wavelet Transform (DWT). Principle Component Analysis (PCA), Linear Discriminant Analysis (LDA) and Independent Component Analysis (ICA) were applied on the set of DWT coefficients extracted from particular sub-bands in order to reduce the data dimension. The selected sets of features were fed into four different classifiers: Support Vector Machine (SVM), Gaussian Mixture Model (GMM), Probabilistic Neural Network (PNN) and K-Nearest Neighbor (KNN). Our results showed that the ICA coupled with GMM classifier combination resulted in highest accuracy of 96.8%, sensitivity of 100% and specificity of 93.7% compared to other data reduction techniques (PCA and LDA) and classifiers. Overall, compared to previous techniques, our proposed strategy is more suitable for diagnosis of CAD with higher accuracy.

A New Ventricular Late Potential Classification System Using Ant Colony Optimization

Problem statement: Ventricular Late Potentials (VLPs) are low-level high frequency signals that are usually found within the terminal part of the QRS complex from patients after myocardial infarction. Patients with VLPs are at risk of developing ventricular tachycardia, which is the major cause of death if patients suffering from heart disease. Approach: Discrete Wavelet Transform was used to detect VLPs and then ANT Colony Optimization (ACO) was applied to classify subjects with and without VLPs. Results: A set of Discrete Wavelet Transform (DWT) coefficients was selected from the wavelet decomposition. Three standard parameters of VLPs such as QRST, D40 and V40 were established. After that a novel clustering algorithm based on Ant Colony Optimization was developed for classifying arrhythmia types. Conclusion: The wavelet decomposition enabled us to perform the task efficiently and produced reliable results.

Content-Based Image Compression for Arbitrary-Resolution Display Devices

The existing image coding methods cannot support content-based spatial scalability with high compression. In mobile multimedia communications, image retargeting is generally required at the user end. However, content-based image retargeting (e.g., seam carving) is with high computational complexity and is not suitable for mobile devices with limited computing power. The work presented in this paper addresses the increasing demand of visual signal delivery to terminals with arbitrary resolutions, without heavy computational burden to the receiving end. In this paper, the principle of seam carving is incorporated into a wavelet codec (i.e., SPIHT ). For each input image, block-based seam energy map is generated in the pixel domain. In the meantime, multilevel discrete wavelet transform (DWT) is performed. Different from the conventional wavelet-based coding schemes, DWT coefficients here are grouped and encoded according to the resultant seam energy map. The bitstream is then transmitted in energy descending order. At the decoder side, the end user has the ultimate choice for the spatial scalability without the need to examine the visual content; an image with arbitrary aspect ratio can be reconstructed in a content-aware manner based upon the side information of the seam energy map. Experimental results show that, for the end users, the received images with an arbitrary resolution preserve important content while achieving high coding efficiency for transmission.

Low-Power Architecture for Epileptic Seizure Detection Based on Reduced Complexity DWT

In this article, we present a low-power, user-programmable architecture for discrete wavelet transform (DWT) based epileptic seizure detection algorithm. A simplified, low-pass filter (LPF)-only-DWT technique is employed in which energy contents of different frequency bands are obtained by subtracting quasi-averaged, consecutive LPF outputs. Training phase is used to identify the range of critical DWT coefficients that are in turn used to set patient-specific system level parameters for minimizing power consumption. The proposed optimizations allow the design to work at significantly lower power in the normal operation mode. The system has been tested on neural data obtained from kainate-treated rats. The design was implemented in TSMC-65nm technology and consumes less than 550-nW power at 250-mV supply.

ECG compression using the context modeling arithmetic coding with dynamic learning vector–scalar quantization

Electrocardiogram (ECG) compression can significantly reduce the storage and transmission burden for the long-term recording system and telemedicine applications. In this paper, an improved wavelet-based compression method is proposed. A discrete wavelet transform (DWT) is firstly applied to the mean removed ECG signal. DWT coefficients in a hierarchical tree order are taken as the component of a vector named tree vector (TV). Then, the TV is quantized with a vector–scalar quantizer (VSQ), which is composed of a dynamic learning vector quantizer and a uniform scalar dead-zone quantizer. The context modeling arithmetic coding is finally employed to encode those quantized coefficients from the VSQ. All tested records are selected from the Massachusetts Institute of Technology-Beth Israel Hospital arrhythmia database. Statistical results show that the compression performance of the proposed method outperforms several published compression algorithms.

Application of principal component analysis to ECG signals for automated diagnosis of cardiac health

Electrocardiogram (ECG) is the P, QRS, T wave indicating the electrical activity of the heart. The subtle changes in amplitude and duration of ECG cannot be deciphered precisely by the naked eye, hence imposing the need for a computer assisted diagnosis tool. In this paper we have automatically classified five types of ECG beats of MIT-BIH arrhythmia database. The five types of beats are Normal (N), Right Bundle Branch Block (RBBB), Left Bundle Branch Block (LBBB), Atrial Premature Contraction (APC) and Ventricular Premature Contraction (VPC). In this work, we have compared the performances of three approaches. The first approach uses principal components of segmented ECG beats, the second approach uses principal components of error signals of linear prediction model, whereas the third approach uses principal components of Discrete Wavelet Transform (DWT) coefficients as features. These features from three approaches were independently classified using feed forward neural network (NN) and Least Square-Support Vector Machine (LS-SVM). We have obtained the highest accuracy using the first approach using principal components of segmented ECG beats with average sensitivity of 99.90%, specificity of 99.10%, PPV of 99.61% and classification accuracy of 98.11%. The system developed is clinically ready to deploy for mass screening programs.

Robust Image Coding Based Upon Compressive Sensing

Multiple description coding (MDC) is one of the widely used mechanisms to combat packet-loss in non-feedback systems. However, the number of descriptions in the existing MDC schemes is very small (typically 2). With the number of descriptions increasing, the coding complexity increases drastically and many decoders would be required. In this paper, the compressive sensing (CS) principles are studied and an alternative coding paradigm with a number of descriptions is proposed based upon CS for high packet loss transmission. Two-dimentional discrete wavelet transform (DWT) is applied for sparse representation. Unlike the typical wavelet coders (e.g., JPEG 2000), DWT coefficients here are not directly encoded, but re-sampled towards equal importance of information instead. At the decoder side, by fully exploiting the intra-scale and inter-scale correlation of multiscale DWT, two different CS recovery algorithms are developed for the low-frequency subband and high-frequency subbands, respectively. The recovery quality only depends on the number of received CS measurements (not on which of the measurements that are received). Experimental results show that the proposed CS-based codec is much more robust against lossy channels, while achieving higher rate-distortion (R-D) performance compared with conventional wavelet-based MDC methods and relevant existing CS-based coding schemes.

Rotation Invariant Texture Classification using Fuzzy Logic

this paper, we develop a scale invariant texture classification method based on Fuzzy logic. It is applied for the classification of texture images. Texture is a common property of any surface having uncertainty. Two types of texture features are extracted one using Discrete Wavelet Transform (DWT) and other using Co-occurrence matrix. Co- occurrence features are obtained using DWT coefficients. Two features are obtained from each sub-band of DWT coefficients upto fifth level of decomposition and eight features are extracted from co-occurrence matrix of whole image and each sub-band of first level DWT decomposition. The fuzzy classification is achieved in two steps, fuzzification step, and rule generation step. The performance is measured in terms of Success Rate. This study showed that the proposed method offers excellent scale invariant texture classification Success Rate. Also wavelet features like standard deviation, combination of energy and standard deviation along with some proposed hybrid feature sets outperform the other feature sets. This success rate is comparatively high when compared with results published earlier.