Fast Predictive Wavelet Transform for lossless image compression

This article presents an advanced method for monolithic 3-D integrated circuits (ICs)’ test compression based on 3-D Haar wavelet transforms. The main purpose of this study is to reduce the test response data consumption while achieving higher fault coverage using new Haar wavelet transforms. Considering the faultiness of test compression, this article would like to introduce the “3-D Haar wavelet transforms,” which would update part of 2-D butterfly unit into 3-D butterfly unit based on the traditional Haar wavelet transforms to simplify the test response output nodes. To better achieve the purpose, a new no-X-unknown-carry adder is applied to build 3-D no-X-unknown-carry Haar wavelet transform (3-D_NxUC_HWT), which not only could solve X-masking but also could transmit an X-bit efficiently to make a delay fault diagnosis. In addition, the constructed reuse architecture makes it possible to plan reasonably the test compression structure by making full use of monolithic interlayer vias to shorten the parallel transmission length. In this study, the test point data are output to the test compressor in parallel through interlayer vias and the compressed results are shifted out by serial scan chain to shorten the test time. The experimental results clearly demonstrate that this method has superiority of higher test coverage, faster test time, fewer test analysis data, and smaller hardware overhead.

Human iris is one of the most reliable biometrics because of its uniqueness, stability and noninvasive nature.Thus it has attracted the attention of biometrics based identification and verification research and development community.Iris recognition has been a fast growing, challenging and interesting area in real-time applications.A large number of iris recognition algorithms have been developed for decades.This paper presents a comparative study of the performance from the iris authentication using Discrete Cosine Transform (DCT), Haar wavelet transform and a proposed Column Mean Method based features.Here iris recognition is done using the image feature set extracted from DCT, Haar Wavelet transform and Column Mean.Analysis was performed with the mentioned methods, consisting of the False Acceptance Rate (FAR) and the Genuine Acceptance Rate (GAR).The proposed technique is tested on an iris image database having 384 images.The iris recognition systems that produce very low error rates were successfully designed.DCT and Column Mean Methods give better performance with the accuracy of 98.44% and Haar Wavelet Transform gives a performance with the accuracy of 97.66%.The proposed Column Mean Method is superior in having faster recognition rate.

Objectives: Image Denoising refers to the recovery of an image that has been corrupted by noise due to poor quality of image acquisition and transmission. Accordingly, there is a need to reduce the noise present in the image as a consequence of the denoised image formed. Methods/Statistical Analysis: This paper presents Image denoising using Wavelet transforms, Contourlet transforms and Block Matching Transforms governed by bivariate shrinkage (Bi-shrink) filter techniques. The Wavelet transform uses up-sampling, down-sampling, low pass filter and high pass filter to perform denoising operation, the Contourlet transform uses up-sampling, down-sampling, low pass filter and high pass filter and directional filter banks to perform denoising operation, the Block Matching Transform uses Haar Transforms, Discrete cosine transforms and Karhunen Loeve transform to perform denoising operation. Findings: The performance of wavelet transforms, Contourlet transforms and Block Matching Transforms are evaluated for Reference images (such as towers, shades and ruler images) corrupted by gaussian noise and salt and pepper noise, by computing two error metrics Peak Signal to Noise Ratio (PSNR) and Execution Time (ET) with help of shrinkage function. Programming these using MATLAB R2014a by exploring its wavelet transform, Contourlet transform, image processing and signal processing toolboxes and the values are presented in tabular forms and discussed in the section 6. In this paper the block matching haar discrete cosine transform is proposed for denoising of images (especially for those images possessing detailed textures) that works through haar transform and discrete cosine transform outstrips the basic transform discrete wavelet transform and semi translation invariant contourlet transform. For the images corrupted by Gaussian noise and denoised by the proposed transform outstrips the basic transform “Discrete Wavelet Transform by PSNR = 6.71 dB, ET = 25.89 sec” and “Semi Translation Invariant Contourlet Transforms by PSNR = 5.49 dB, ET = 5.89 sec”. For the images corrupted by Salt and Pepper noise and denoised by the proposed transform outstrips the basic transform “Discrete Wavelet Transform by PSNR = 21.15 dB, ET = 0.27 sec” and “Semi Translation Invariant Contourlet Transforms by PSNR = 20.05 dB, ET = 5.80 sec”. Application/Improvements: In this paper Block Matching Haar Discrete cosine transform is proposed to overcome the limitations of wavelet transforms and Contourlet transforms, hence to attain the trade-off between high peak signal to noise ratio and less execution time. Results and Discussion section illustrates the efficacy of the proposed transform in terms of peak signal to noise ratio, execution time and visual quality of images.

Evoked EMG M-responses obtained from the thenar muscle in the palm by electrical stimulation of the median nerve demonstrate a well-established smooth bipolar shape for normal healthy subjects while kinks are observed in certain neurological disorders, particularly in cervical spondylotic neuropathy. A first differentiation failed to identify these kinks because of comparable values obtained for normally rising and falling segments of the smooth regions, and due to noise. In this study, the usefulness of the wavelet transform (WT), that provides localized measures of non-stationary signals is investigated. The Haar WT was used to analyze a total of 36 M-responses recorded from the median nerves of 6 normal subjects (having smooth shape) and 12 subjects with assumed neurological disorders (having kinks), for two points of stimulation on the same nerve. Features in the time-scale representation of the M-responses were studied using WT to distinguish smooth M-responses from ones with kinks. Variations in the coefficient line of the WT were also studied to allow visualization of WT at different scales (inverse of frequency). The high and low frequency regions in the WT came out distinctively which helped identifications of kinks even of very subtle ones in the M-responses which were difficult to obtain using the differentiated signal. In conclusion, the wavelet analysis may be a technique of choice in identifying kinks in M-responses in relation to time, thus enhancing the accuracy of neurological diagnosis. DOI: http://dx.doi.org/10.3329/bjmp.v5i1.14667 Bangladesh Journal of Medical Physics Vol.5 No.1 2012 41-51

We present a new method for image compression in all-optical domain. The new hardware architecture is suitable for directly integrating with digital cameras for image processing. The proposed architecture is based on the optical Haar wavelet transform (HWT) using only one 4x4 multimode interference (MMI) coupler cascaded with a 2x2 MMI coupler. The processing of images therefore is at very high speed.

A new idea to control of distributed parameter systems is to induct the boundary control idea into predictive control of distributed parameter systems. Because of complexity of distributed parameter systems, control system design is the difficulty of control theory. In this paper, discrete-time boundary predictive control algorithm of first-order linear modulus-varying distributed parameter systems based on orthogonal wavelets transformation is proposed. First-order linear modulus-varying distributed parameter systems in boundary control is approximated in Haar wavelets transformation. So the predictive control proposition of distributed parameter systems has been transformed into the predictive control issue of lumped parameter systems. The boundary predictive controller is designed for the input sequence returning to the boundary predictive control rule of the original system. Simulation studies of the proposed algorithm, as well as the system robustness under uncertainty such as the parameters perturbations, and a disturb occurring to the system output are showed. The results have verified the control effectiveness of the proposed algorithm.

Discrete Wavelet Transform, (DWT), is known to be one of the best compression techniques. It provides a mathematical way of encoding information in such a way that it is layered according to level of detail. In this paper, we used Haar wavelets as the basis of transformation functions. Haar wavelet transformation is composed of a sequence of low pass and high pass filters, known as filter bank. The redundancy of the DWT detail coefficients are reduced through thresholding and further through Huffman encoding. The proposed threshold algorithm is based upon the statistics of the DWT coefficients. The quality of the compressed images has been evaluated using some factors like Compression Ratio, (CR), and Peak Signal to Noise Ratio, (PSNR). Experimental results demonstrate that the proposed technique provides sufficient higher compression ratio compared to other compression thresholding techniques.

Recently the protection and privacy of information or data have become a major concern to deal with. Developed coding for secure transmission and decrypting the image are steadily more needed for specific military, corrective, country defense, and various applications. Variance sorts of security information or data increment strategies are being created as of now, cryptographic systems are one basic path. Keep the message private by translating data or information into an alternative frame, such that the message is not perceivable. This study aims to expect the security image arrangement to be upgraded by following the Haar wavelet transformation and the RC4 encryption algorithm. For simplicity, the Image compression executed by Haar wavelet transformation to compress the image and to calculate high-speed performance. For image protection process, the RC4 encryption algorithm is applied that is also for secure image transformation.

This paper compared noise removal using the Fourier series and the Haar wavelet transformations. The results showed that noise from the measured data can be filtered by neglecting high-order terms of Fourier coefficients. It also showed that signal denoising can be achieved by Haar wavelet transformation by filtering the noise before inverting the transformed data back to time domain. A further comparison using a set of data with variation 6.3mV from five measurements of a sample showed that the variations after denoising can be reduced to 3.8mV by the Fourier series and to 2.3mV by 3-level Haar wavelet. Both methods can filter noise in signal and keep the predicted curve consistent with the measured data. The signal becomes smooth if denoised by the Fourier series but the variation of signal, however, can be reduced more if denoised by the Haar wavelet. Moreover, from the computation complexity viewpoint, signal denoising by Haar wavelet is much better than that by Fourier series.

This study proposed concealment of information through watermarking techniques using a combination of Haar Wavelet Transform (HWT) and Chinese Remainder Theorem (CRT) algorithms. The use of HWT can improve the imperceptibility of the watermarked image. While the CRT has a shorter operation and minimal distortion and provides security to copyright because of its insertion technique that indirectly also encrypts the copyright itself. Watermark insertion is done by using HWT on subband LL followed by CRT process. The evaluation standard that will be used in this research is Peak Signal to Noise Ratio (PSNR) and Normalized Correlation (NC). From the measurement results obtained the average value of PSNR 63.55dB, which shows the very high imperceptible quality of the watermarked image. While copyright extraction can be recovered perfectly with the value of NC 1.

This work discusses the compression objects ratio for Macromedia Flash File (SWF) Image by Wavelet functions for compression and there effect for Macromedia Flash File (SWF) Images compression . We discusses classification objects in Macromedia Flash (SWF) image in to nine types objects Action, Font,Image, Sound, Text, Button, Frame, Shape and Sprite. The work is particularly targeted towards wavelet image compression best case by using Haar Wavelet Transformation with an idea to minimize the computational requirements by applying different compression thresholds for the waveletcoefficients and these results are obtained in fraction of seconds and thus to improve thequality of the reconstructed image. The promising results obtained concerning reconstructed images quality as well as preservation of significant image details, while, on the other hand achieving highcompression rates and better image quality while DB4 Wavelet Transformation higher compression rates ratio without kept for image quality .

Electroencephalogram (EEG) measures electrical activity of the brain and proffers valuable insight of the brain dynamics. Accurate and careful analysis of EEG signal plays a prominent role in the diagnosis of brain diseases like epilepsy, brain tumor. EEG is the most significant method used for epilepsy monitoring, diagnosis and rehabilitation. A patient-specific seizure detection model has been developed using Haar wavelet and Artificial Neural Network. HAAR Wavelet decomposition of multi-channel EEG with five scales is made and three frequency bands of EEG selected for the consequent process. The conventional Haar wavelet transform (HWT) is replaced by a modified Haar wavelet transform whereas the number of multiplications and additions are reduced. The Haar wavelet reduces computational complexity from the existing Haar wavelet structure which consumes only 1–3 ms based on the decomposition level to detect epilepsy.

Sea ice as a disaster has recently attracted a great deal of attention in China. Its monitoring has become a routine task for the maritime sector. Remote sensing, which depends mainly on SAR and optical sensors, has become the primary means for sea-ice research. Optical images contain abundant sea-ice multi-spectral information, whereas SAR images contain rich sea-ice texture information. If the characteristic advantages of SAR and optical images could be combined for sea-ice study, the ability of sea-ice monitoring would be improved. In this study, in accordance with the characteristics of sea-ice SAR and optical images, the transformation and fusion methods for these images were chosen. Also, a fusion method of optical and SAR images was proposed in order to improve sea-ice identification. Texture information can play an important role in sea-ice classification. Haar wavelet transformation was found to be suitable for the sea-ice SAR images, and the texture information of the sea-ice SAR image from Advanced Synthetic Aperture Radar (ASAR) loaded on ENVISAT was documented. The results of our studies showed that, the optical images in the hue-intensity-saturation (HIS) space could reflect the spectral characteristics of the sea-ice types more efficiently than in the red-green-blue (RGB) space, and the optical image from the China-Brazil Earth Resources Satellite (CBERS-02B) was transferred from the RGB space to the HIS space. The principal component analysis (PCA) method could potentially contain the maximum information of the sea-ice images by fusing the HIS and texture images. The fusion image was obtained by a PCA method, which included the advantages of both the sea-ice SAR image and the optical image. To validate the fusion method, three methods were used to evaluate the fused image, i.e., objective, subjective, and comprehensive evaluations. It was concluded that the fusion method proposed could improve the ability of image interpretation and sea-ice identification.

Video Steganography provides a secure way of communication by embedding the secret message inside video frames. Supported by a real-time environment, a significant growth in choosing video as a cover file has been noticed in the last decade. Large concealing capacity, more robustness to attacks, and indistinguishability of cover video and stego video are of major concern for any video steganography technique. In this paper, a novel technique has been proposed to achieve the above-mentioned objectives. Implementation of video steganography in the transform domain inherently contributes to improved robustness. We have embedded an image inside a chosen video file using the Lifting Scheme based on the Haar Wavelet Transform. To achieve increased concealing capacity, the Singular Value Decomposition technique has been applied before the actual embedding task. The proposed work is implemented using the MATLAB environment. The suggested method is then tested and analyzed for its robustness, capacity, and imperceptibility. It is concluded that proposed technique is robust against general video processing attacks, like low-pass filtering, scaling, rotation, transformation, and histogram equalization attacks. Additionally, the values for Peak Signal to Noise Ratio, Mean Square Error, Structure Similarity Index Matrix, and Correlation Coefficients were calculated and analyzed as a performance measure of the method.

One of the common methods for measuring distance is to use a camera and image processing algorithm, such as an eye and brain. Mechanical stereo vision uses two cameras to shoot the same object and analyzes the disparity of the stereo vision. One of the most robust methods to calculate disparity is the well-known census transform, which has the problem of conversion window selection. In this paper, three methods are proposed to improve the performance of the census transform. The first one uses a low-pass band of the wavelet to reduce the computation loading and a high-pass band of the wavelet to modify the disparity. The main idea of the second method is the adaptive size selection of the conversion window by edge information. The third proposed method is to apply the adaptive window size to the previous sparse census transform. In the experiments, two indexes, percentage of bad matching pixels (PoBMP) and root mean squared (RMS), are used to evaluate the performance with the known ground truth data. According to the results, the computation required can be reduced by the multiresolution feature of the wavelet transform. The accuracy is also improved with the modified disparity processing. Compared with previous methods, the number of operation points is reduced by the proposed adaptive window size method.