Lifting-Based Wavelet Transform Research Articles

An Iterative Filtering Based ECG Denoising Using Lifting Wavelet Transform Technique

This research article explores a hybrid strategy that combines an adaptive iterative filtering (IF) method and the fast discrete lifting-based wavelet transform (LWT) to eliminate power-line noise (PLI) and baseline wander from an electrocardiogram (ECG) signal. Due to its correct mathematical basis and its guaranteed a priori convergence, the iterative filtering approach was preferred over empirical mode decomposition (EMD). The noisy modes generated from the IF are fed to an LWT system so as to be disintegrated into the detail and the approximation coefficients. These coefficients are then scaled using a threshold method to generate a noise-free signal. The proposed strategy improves the quality and allows us to precisely preserve the vital components of the signal. The method’s potency has been established empirically by calculating the improvement in signal-to-noise ratio, cross-correlation coefficient and percent root-mean-square difference for different recordings available on the MIT-BIH arrhythmia database and then compared to numerous existing methods.

Performance Analysis of Watermarking on Indian Classical Songs using Lifting Wavelet Transform and Singular Value Decomposition

Digital audio watermarking performs the process of embedding a perceptually transparent digital signature carrying a message about the host signal into a host audio signal. A technique of digital audio watermarking using lifting based wavelet transform (LWT) and singular value decomposition (SVD) is applied to watermark Indian classical songs. In order to confirm the security and improve the robustness of the audio watermarking scheme, binary watermark image encrypted using Arnold transform is used. The encrypted watermark bits are embedded into the singular values of the coefficient matrix of LWT low frequency subband through quantization index modulation (QIM). In order to evaluate the performance of the LWT-SVD based audio watermarking method, subjective and objective quality tests are conducted. Investigations are performed using different wavelets and the performance parameters show that Daubechies (Db4) wavelet is more suited for watermarking of Indian classical songs. Robustness of the algorithm is analyzed by including additive white gaussian noise, denoising, and resampling.

High performance FPGA architecture for dual mode processor of Integer Haar Lifting-based Wavelet Transform

Discrete Wavelet Transform (DWT) becomes a major part for many applications. Fast, low area, and low power consumption hardware for DWT is necessary for some new technologies such as OFDM transceiver and wireless multimedia sensor networks. This paper presents efficient dual mode (decomposition and reconstruction) Integer Haar Lifting Wavelet Transform (IHLWT) architecture. The proposed architecture reduces the hardware requirements by exploiting the arithmetic operations redundancy which is involved in IHLWT computations. It is multiplier-free and it performs IHLWT with only a single adder and subtractor which have reconfigurable input buses to perform decomposition and reconstruction transformations. IEEE standard VHDL has been used to develop the proposed processor. This makes the design vendor independent and therefore easily portable across FPGA devices from multiple vendors. The generic design is flexible and can perform any arbitrary signal length. The synthesis of the processor showed that it requires low number of CLB-slices and low power consumption with high operating-frequency for various Xilinx FPGA devices. The processor has been successfully implemented and tested on Xilinx Spartan6-SP601 Evaluation Board. The implemented hardware has been tested in real time by using many recording audio signals. All the implemented hardware results were identical 100% with IHLWT software results.

A real-time hyper-accuracy integrative approach to peak identification using lifting-based wavelet and Gaussian model for field mobile mass spectrometer

Field mobile mass spectrometer is pivotal apparatus for real-time qualitative and quantitative analyses of chemical substances in situ environment pollution detection. To solve spectrum peak signal interfered by complicated noise, and to recognize irregular peak shape as well as quick monitoring, a real-time denoising and hyper-accuracy peak identification integrative approach for field mobile mass spectrometer using lifting-based wavelet transform (LWT) and Gaussian model has been developed. First, LWT was applied to eliminate the noise and to search for mass peak parameters in raw spectral peak data. Then, fitting the irregular mass peaks with Gaussian multi-peaks, a regular spectrum signal was obtained for further processing. Both of synthetic and apparatus experiment results show that LWT is a fast and effective denoising and peak identification method and retained the original peak features. The denoising effect (SNR/RMSE) by LWT was superior to Savitzky–Golay method used widely by experimental mass spectrometer, and the processing time was shortened obviously. Moreover integrated with Gaussian fitting algorithm, the peak parameters (the peak area A, centroid c, and half peak's width w) had been optimized. As the result, qualitative and quantitative accuracies of FMMS increased consequently. In addition, the approach achieved data compression.

Retrieval Oriented Robust Audio Hashing

Aiming at content-based audio retrieval (CBAR) applications, a robust audio hashing scheme is proposed. First the audio is divided to frame by fixed length and then low-frequent and high-frequent components are obtained by three-level lifting-based wavelet transformation in every frame. Secondly the audio frame is approximately represented as a product of a base matrix and an encoding matrix, or coefficient matrix, using non-negative matrix factorization (NMF). Finally the sum of each column in the coefficient matrix is calculated, which is then quantized to produce one bit of the hash sequence. Experiment results show that the proposed scheme is robust against Mp3 compression, Real compression, filtering, amplitude compression, equalization, echo, etc. It is insensitive to small local change, and therefore is suitable for distinguishing different audios.

Zero-Watermark Resistant to MP3 Compression

Most watermarking schemes in literatures are usually implemented by embedding a mark in a host media itself for intended purposes. The existing techniques for watermarking are confronted with the problems of perceptible quality degradation and the inherent conflict between imperceptibility and robustness, which introduced by the watermark embedding. In this paper, we propose a novel audio zero-watermarking scheme for content authentication. First the audio is divided to frame by fixed length and then low-frequent and high-frequent components are obtained by three-level lifting-based wavelet transformation in every frame. Secondly the audio frame is approximately represented as a product of a base matrix and a coefficient matrix using non-negative matrix factorization (NMF). Finally the sum of each column in the coefficient matrix is calculated which is quantized to produce a feature vector, and then the copyright information is obtained by calculating the watermark and feature vector. Experiment results show that the proposed scheme is robust against Mp3 compression and secure.

Audio watermarking scheme based on embedding strategy in low frequency components with a binary image

With the recent development of information technology and computer network, digital format of data has become more and more popular. However, a major problem faced by digital data providers and owners is protecting data from unauthorized copying and distribution. As a solution to the problem, digital watermark technology is now attracting attention as new method of protection against said unauthorized copying and distribution. The aim of the digital audio watermarking is to take prespecified data that carries certain information and hide it within the audio stream such that it is not audible to the human ear (i.e., transparent) but at the same time renders the file more resistant to removal (i.e., robust). In this paper, we propose a new method for embedding digital watermarks into audio signals in low frequency components, which method mitigates these and other related shortcomings. The proposed method uses the wavelet transform constructed by lifting-based wavelet transform (LBWT) in order to provide a fast implementation between watermark embedding and extraction parts. In the first stage of the proposed method, the original audio host signal is converted to a wavelet domain using LBWT. The signal is thus decomposed into low and high frequency components. Approximation coefficients correspond to low frequency components of the signal. Next, the watermark generated by pseudorandom numbers is embedded into wavelet approximation coefficients of the segmented host audio signal depending on the binary value of the binary image. The reason for embedding the watermark in the low frequency components is that these components' energy is greater than that of high frequency components in such a way that the watermark is inaudible; therefore, it should not alter the audible content and should not be easy to remove. The proposed method uses a binary image to decide whether or not the watermark generated by pseudorandom numbers is embedded in the audio host signal. To evaluate the performance of the proposed audio watermarking method, subjective and objective quality tests including bit error rate (BER) and signal-to-noise ratio (SNR) are conducted. The tests' results show that the proposed method yields a high recovery rate after attacks by commonly used audio data manipulations such as low-pass filtering, requantization, resampling and MP3 compression.

Weighted Adaptive Lifting-Based Wavelet Transform for Image Coding

In this paper, a new weighted adaptive lifting (WAL)-based wavelet transform is presented. The proposed WAL approach is designed to solve the problems existing in the previous adaptive directional lifting (ADL) approach, such as mismatch between the predict and update steps, interpolation favoring only horizontal or vertical direction, and invariant interpolation filter coefficients for all images. The main contribution of the proposed approach consists of two parts: one is the improved weighted lifting, which maintains the consistency between the predict and update steps as far as possible and preserves the perfect reconstruction at the same time; another is the directional adaptive interpolation, which improves the orientation property of the interpolated image and adapts to statistical property of each image. Experimental results show that the proposed WAL-based wavelet transform for image coding outperforms the conventional lifting-based wavelet transform up to 3.06 dB in PSNR and significant improvement in subjective quality is also observed. Compared with the ADL-based wavelet transform, up to 1.22-dB improvement in PSNR is reported.

Self-lifting scheme: new approach for generating and factoring wavelet filter bank

The authors presents a new lifting scheme, the self-lifting scheme, and prove that self-lifted wavelets based on orthogonal or biorthogonal wavelets remain biorthogonal. In contrast to self-lifting, the existing lifting scheme can be called cross-lifting. Compared with cross-lifting, the self-lifting scheme provides new approaches for constructing biorthogonal wavelets, as well as factorising wavelet filter bank (WFB). For constructing wavelets, the self-lifting-based method updates one part of a wavelet filter by the other part of the same filter and obtains two updated filters in one pass, whereas the cross-lifting based method updates one filter by another filter and obtains one updated filter in one pass. To factorise WFB, self-lifting takes one part of a filter as the factor to decompose the other part of the same filter and obtains two factorised filters in one pass, whereas cross-lifting based one takes one part of a filter as the factor to decompose the corresponding part of the other filter and obtains one factorised filter in one pass. Several examples show how to use self-lifting scheme to produce new wavelets with desirable properties, how to factorise complex WFBs into simple lifting filter banks, how to implement self-lifting-based discrete wavelet transform (WT) in z-domain and in time domain and why lifting-based WT is superior to convolution-based one.

Adaptive Directional Lifting-Based Wavelet Transform for Image Coding

We present a novel 2-D wavelet transform scheme of adaptive directional lifting (ADL) in image coding. Instead of alternately applying horizontal and vertical lifting, as in present practice, ADL performs lifting-based prediction in local windows in the direction of high pixel correlation. Hence, it adapts far better to the image orientation features in local windows. The ADL transform is achieved by existing 1-D wavelets and is seamlessly integrated into the global wavelet transform. The predicting and updating signals of ADL can be derived even at the fractional pixel precision level to achieve high directional resolution, while still maintaining perfect reconstruction. To enhance the ADL performance, a rate-distortion optimized directional segmentation scheme is also proposed to form and code a hierarchical image partition adapting to local features. Experimental results show that the proposed ADL-based image coding technique outperforms JPEG 2000 in both PSNR and visual quality, with the improvement up to 2.0 dB on images with rich orientation features.