Speech Compression Using Discrete Wavelet Transform

Speech compression is an area of digital processing that is focusing on reducing bit rate of the speech signal for transmission or storage without significant loss of quality. Wavelet transform has been recently proposed for signal analysis. Speech signal compression using wavelet transform is given a considerable attention in this thesis. Speech coding is a lossy scheme and is implemented here to compress onedimensional speech signal. Basically, this scheme consists of four operations which are the transform, threshold techniques (by level and global threshold), quantization, and entropy encoding operations. The reconstruction of the compressed signal as well as the detailed steps needed are discussed.The performance of wavelet compression is compared against linear Productive Coding and Global System for Mobile Communication (GSM) algorithms using SNR, PSNR, NRMSE and compression ratio. Software simulating the lossy compression scheme is developed using Matlab 6. This software provides the basic speech analysis as well as the compression and decompression operations. The results obtained show reasonably high compression ratio and good signal quality

Optimization of a new digital image compression algorithm based on nonlinear dynamical systems

In this paper we discuss the formulation, research and development of an optimization process for a new compression algorithm known as DYNAMAC, which has its basis in the nonlinear systems theory. We establish that by increasing the measure of randomness of the signal, the peak signal to noise ratio and in turn the quality of compression can be improved to a great extent. This measure, entropy, through exhaustive testing, will be linked to peak signal to noise ratio (PSNR, a measure of quality) and by various discussions and inferences we will establish that this measure would independently drive the compression process towards optimization. We will also introduce an Adaptive Huffman Algorithm to add to the compression ratio of the current algorithm without incurring any losses during transmission (Huffman being a lossless scheme)

ECG compression for Holter monitoring

Cardiologists can gain useful insight into a patient's condition when they are able to correlate the patent's symptoms and activities. For this purpose, a Holter Monitor is often used - a portable electrocardiogram (ECG) recorder worn by the patient for a period of 24-72 hours. Preferably, the monitor is not cumbersome to the patient and thus it should be designed to be as small and light as possible; however, the storage requirements for such a long signal are very large and can significantly increase the recorder's size and cost, and so signal compression is often employed. At the same time, the decompressed signal must contain enough detail for the cardiologist to be able to identify irregularities. "Lossy" compressors may obscure such details, where a "lossless" compressor preserves the signal exactly as captured.The purpose of this thesis is to develop a platform upon which a Holter Monitor can be built, including a hardware-assisted lossless compression method in order to avoid the signal quality penalties of a lossy algorithm. The objective of this thesis is to develop and implement a low-complexity lossless ECG encoding algorithm capable of at least a 2:1 compression ratio in an embedded system for use in a Holter Monitor. Different lossless compression techniques were evaluated in terms of coding efficiency as well as suitability for ECG waveform application, random access within the signal and complexity of the decoding operation. For the reduction of the physical circuit size, a System On a Programmable Chip (SOPC) design was utilized. A coder based on a library of linear predictors and Rice coding was chosen and found to give a compression ratio of at least 2:1 and as high as 3:1 on real-world signals tested while having a low decoder complexity and fast random access to arbitrary parts of the signal. In the hardware-assisted implementation, the speed of encoding was a factor of between four and five faster than a software encoder running on the same CPU while allowing the CPU to perform other tasks during the encoding process

Efficient ECG Compression and QRS Detection for E-Health Applications

Current medical screening and diagnostic procedures have shifted toward recording longer electrocardiogram (ECG) signals, which have traditionally been processed on personal computers (PCs) with high-speed multi-core processors and efficient memory processing. Battery-driven devices are now more commonly used for the same purpose and thus exploring highly efficient, low-power alternatives for local ECG signal collection and processing is essential for efficient and convenient clinical use. Several ECG compression methods have been reported in the current literature with limited discussion on the performance of the compressed and the reconstructed ECG signals in terms of the QRS complex detection accuracy. This paper proposes and evaluates different compression methods based not only on the compression ratio (CR) and percentage root-mean-square difference (PRD), but also based on the accuracy of QRS detection. In this paper, we have developed a lossy method (Methods III) and compared them to the most current lossless and lossy ECG compression methods (Method I and Method II, respectively). The proposed lossy compression method (Method III) achieves CR of 4.5×, PRD of 0.53, as well as an overall sensitivity of 99.78% and positive predictivity of 99.92% are achieved (when coupled with an existing QRS detection algorithm) on the MIT-BIH Arrhythmia database and an overall sensitivity of 99.90% and positive predictivity of 99.84% on the QT database.This work was made possible by NPRP grant #7-684-1-127 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors.Scopu

Proceedings of the Scientific Data Compression Workshop

Continuing advances in space and Earth science requires increasing amounts of data to be gathered from spaceborne sensors. NASA expects to launch sensors during the next two decades which will be capable of producing an aggregate of 1500 Megabits per second if operated simultaneously. Such high data rates cause stresses in all aspects of end-to-end data systems. Technologies and techniques are needed to relieve such stresses. Potential solutions to the massive data rate problems are: data editing, greater transmission bandwidths, higher density and faster media, and data compression. Through four subpanels on Science Payload Operations, Multispectral Imaging, Microwave Remote Sensing and Science Data Management, recommendations were made for research in data compression and scientific data applications to space platforms