Search PubMed⌕ Search

SEARCH · Search PubMed

Results for “Compression Algorithms”

Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 235 records · Page 13Linked to original sources
PubMed2000

Optimal zonal wavelet-based ECG data compression for a mobile telecardiology system.

A new integrated design approach for an optimal zonal wavelet-based ECG data compression (OZWC) method for a mobile telecardiology model is presented. The hybrid implementation issues of this wavelet method with a GSM-based mobile telecardiology system are also introduced. The performance of the mobile system with compressed ECG data segments selected from the MIT-BIH arrhythmia data base is evaluated in terms of bit error rate (BER), percent rms difference (PRD), and visual clinical inspection. The compression performance analysis of the OZWC is compared with another wavelet-based (Discrete Symmetric Wavelet Compression) approach. The optimal wavelet algorithm achieved a maximum compression ratio of 18: 1 with low PRD ratios. The mobile telemedical simulation results show the successful compressed ECG transmission at speeds of 100 (km/h) with BER rates of less than 10(-15), providing a 73% reduction in total mobile transmission time with clinically acceptable reconstruction of the received signals. This approach will provide a framework for the design and functionality issues of GSM-based wireless telemedicine systems with wavelet compression techniques and their future integration for the next generation of mobile telecardiology systems.

Cardiology↗
PubMed2006

A novel ECG data compression method based on nonrecursive discrete periodized wavelet transform.

In this paper, a novel electrocardiogram (ECG) data compression method with full wavelet coefficients is proposed. Full wavelet coefficients involve a mean value in the termination level and the wavelet coefficients of all octaves. This new approach is based on the reversible round-off nonrecursive one-dimensional (1-D) discrete periodized wavelet transform (1-D NRDPWT), which performs overall stages decomposition with minimum register word length and resists truncation error propagation. A nonlinear word length reduction algorithm with high compression ratio (CR) is also developed. This algorithm supplies high and low octave coefficients with small and large decimal quantization scales, respectively. This quantization process can be performed without an extra divider. The two performance parameters, CR and percentage root mean square difference (PRD), are evaluated using the MIT-BIH arrhythmia database. Compared with the SPIHT scheme, the PRD is improved by 14.95% for 4 < or = CR < or = 12 and 17.6% for 14 < or = CR < or = 20.

Algorithms↗
PubMed2001

Comparison of image compression viability for lossy and lossless JPEG and Wavelet data reduction in coronary angiography.

BACKGROUND: Lossless or lossy compression of coronary angiogram data can reduce the enormous amounts of data generated by coronary angiographic imaging. The recent International Study of Angiographic Data Compression (ISAC) assessed the clinical viability of lossy Joint Photographic Expert Group (JPEG) compression but was unable to resolve two related questions: (A) the performance of lossless modes of compression in coronary angiography and (B) the performance of newer lossy wavelet algorithms. This present study seeks to supply some of this information. METHODS: The performance of several lossless image compression methods was measured in the same set of images as used in the ISAC study. For the assessment of the relative image quality of lossy JPEG and wavelet compression, the observers ranked the perceived image quality of computer-generated coronary angiograms compressed with wavelet compression relative to the same images with JPEG compression. This ranking allowed the matching of compression ratios for wavelet compression with the clinically viable compression ratios for the JPEG method as obtained in the ISAC study. RESULTS: The best lossless compression scheme (LOCO-I) offered a mean compression ratio (CR) of 3.80:1. The quality of images compressed with the lossy wavelet-based method at CR = 10:1 and 20:1 was comparable to JPEG compression at CR = 6:1 and 10:1, respectively. CONCLUSION: The study has shown that lossless compression can exceed the CR of 2:1 usually quoted. For lossy compression, the range of clinically viable compression ratios can probably be extended by 50 to 100% when applying wavelet compression algorithms as compared to JPEG compression. These results can motivate a larger clinical study.

Algorithms↗
PubMed1997

Oncologic image compression using both wavelet and masking techniques.

A new algorithm has been developed to compress oncologic images using both wavelet transform and field masking methods. A compactly supported wavelet transform is used to decompose the original image into high- and low-frequency subband images. The region-of-interest (ROI) inside an image, such as an irradiated field in an electronic portal image, is identified using an image segmentation technique and is then used to generate a mask. The wavelet transform coefficients outside the mask region are then ignored so that these coefficients can be efficiently coded to minimize the image redundancy. In this study, an adaptive uniform scalar quantization method and Huffman coding with a fixed code book are employed in subsequent compression procedures. Three types of typical oncologic images are tested for compression using this new algorithm: CT, MRI, and electronic portal images with 256 x 256 matrix size and 8-bit gray levels. Peak signal-to-noise ratio (PSNR) is used to evaluate the quality of reconstructed image. Effects of masking and image quality on compression ratio are illustrated. Compression ratios obtained using wavelet transform with and without masking for the same PSNR are compared for all types of images. The addition of masking shows an increase of compression ratio by a factor of greater than 1.5. The effect of masking on the compression ratio depends on image type and anatomical site. A compression ratio of greater than 5 can be achieved for a lossless compression of various oncologic images with respect to the region inside the mask. Examples of reconstructed images with compression ratio greater than 50 are shown.

Algorithms↗
PubMed2004

Embedded image compression based on wavelet pixel classification and sorting.

The method of modeling and ordering in wavelet domain is very important to design a successful algorithm of embedded image compression. In this paper, the modeling is limited to "pixel classification," the relationship between wavelet pixels in significance coding. Similarly, the ordering is limited to "pixel sorting," the coding order of wavelet pixels. We use pixel classification and sorting to provide a better understanding of previous works. The image pixels in wavelet domain are classified and sorted, either explicitly or implicitly, for embedded image compression. A new embedded image code is proposed based on a novel pixel classification and sorting (PCAS) scheme in wavelet domain. In PCAS, pixels to be coded are classified into several quantized contexts based on a large context template and sorted based on their estimated significance probabilities. The purpose of pixel classification is to exploit the intraband correlation in wavelet domain. Pixel sorting employs several fractional bit-plane coding passes to improve the rate-distortion performance. The proposed pixel classification and sorting technique is simple, yet effective, producing an embedded image code with excellent compression performance. In addition, our algorithm is able to provide either spatial or quality scalability with flexible complexity.

Algorithms↗
PubMed2006

Recursive versus sequential multiple error measures reduction: a curve simplification approach to ECG data compression.

Previous time domain compression methods have been tackled by sequential one point at a time sub-optimal selection strategies running in approximately O(N) or all points at a time optimal strategies running in approximately O(N3) temporal complexities. Yet basically, the selected dominant points (DPs) are locally only significant in these methods, which may lead to inaccurate reconstruction or even loss of clinical data. Alternatively, the recursive one point at a time selection strategy, through different variants of the Douglas-Peucker line simplification algorithm, computes globally significant DPs for an approximately O(N.log2(N)) temporal complexity. We illustrate that the recursive strategy performs numerically almost two times better than the sequential one. The piecewise linear approximation of the input ECG is formally expressed as a curve simplification problem, through reduction of the Hausdorff error measure. We also illustrate that reduction of two error measures performs better than reduction of one error measure. An additional compression option is proposed in the case of the recursive strategy through simplification of the sorted distances associated to the selected set of points. The outcome is a compression algorithm that yields compression ratios ranging from 8:1 to 22:1 for a perceptually good reconstruction quality and near linear execution time. The tests have been conducted on the MIT-BIH public ECG database. Results show that the proposed recursive algorithm is an excellent compromise on compression ratio - computational time - reconstruction quality.

Algorithms↗
PubMed2004

Bayesian resolution enhancement of compressed video.

Super-resolution algorithms recover high-frequency information from a sequence of low-resolution observations. In this paper, we consider the impact of video compression on the super-resolution task. Hybrid motion-compensation and transform coding schemes are the focus, as these methods provide observations of the underlying displacement values as well as a variable noise process. We utilize the Bayesian framework to incorporate this information and fuse the super-resolution and post-processing problems. A tractable solution is defined, and relationships between algorithm parameters and information in the compressed bitstream are established. The association between resolution recovery and compression ratio is also explored. Simulations illustrate the performance of the procedure with both synthetic and nonsynthetic sequences.

Algorithms↗
PubMed2005

No-reference quality assessment using natural scene statistics: JPEG2000.

Measurement of image or video quality is crucial for many image-processing algorithms, such as acquisition, compression, restoration, enhancement, and reproduction. Traditionally, image quality assessment (QA) algorithms interpret image quality as similarity with a "reference" or "perfect" image. The obvious limitation of this approach is that the reference image or video may not be available to the QA algorithm. The field of blind, or no-reference, QA, in which image quality is predicted without the reference image or video, has been largely unexplored, with algorithms focusing mostly on measuring the blocking artifacts. Emerging image and video compression technologies can avoid the dreaded blocking artifact by using various mechanisms, but they introduce other types of distortions, specifically blurring and ringing. In this paper, we propose to use natural scene statistics (NSS) to blindly measure the quality of images compressed by JPEG2000 (or any other wavelet based) image coder. We claim that natural scenes contain nonlinear dependencies that are disturbed by the compression process, and that this disturbance can be quantified and related to human perceptions of quality. We train and test our algorithm with data from human subjects, and show that reasonably comprehensive NSS models can help us in making blind, but accurate, predictions of quality. Our algorithm performs close to the limit imposed on useful prediction by the variability between human subjects.

Algorithms↗
PubMed2003

Parallel stored-integral and semidirect Hartree-Fock and DFT methods with data compression.

Recent developments in magnetic disk technology have made stored-integral techniques competitive with the currently more widely used direct methods, which involve the recalculation of the basic two-electron integrals. We present efficient conventional (all integrals stored) and semidirect Hartree-Fock and DFT algorithms with data compression for single-processor and distributed memory parallel computers, and compare them with the corresponding direct algorithms. On inexpensive modern personal computer-based hardware, the stored integral method is up to three times more efficient than the direct method in terms of total elapsed job time.

Journal Article↗
PubMed1992

Wavelet transform as a potential tool for ECG analysis and compression.

The recently introduced wavelet transform is a member of the class of time-frequency representations which include the Gabor short-time Fourier transform and Wigner-Ville distribution. Such techniques are of significance because of their ability to display the spectral content of a signal as time elapses. The value of the wavelet transform as a signal analysis tool has been demonstrated by its successful application to the study of turbulence and processing of speech and music. Since, in common with these subjects, both the time and frequency content of physiological signals are often of interest (the ECG being an obvious example), the wavelet transform represents a particularly relevant means of analysis. Following a brief introduction to the wavelet transform and its implementation, this paper describes a preliminary investigation into its application to the study of both ECG and heart rate variability data. In addition, the wavelet transform can be used to perform multiresolution signal decomposition. Since this process can be considered as a sub-band coding technique, it offers the opportunity for data compression, which can be implemented using efficient pyramidal algorithms. Results of the compression and reconstruction of ECG data are given which suggest that the wavelet transform is well suited to this task.

Electrocardiography↗
PubMed1999

Wavelet image compression--the quadtree coding approach.

Perfect reconstruction, quality scalability, and region-of-interest coding are basic features needed for the image compression schemes used in telemedicine applications. This paper proposes a new wavelet-based embedded compression technique that efficiently exploits the intraband dependencies and uses a quadtree-based approach to encode the significance maps. The algorithm produces a losslessly compressed embedded data stream, supports quality scalability, and permits region-of-interest coding. Moreover, experimental results obtained on various images show that the proposed algorithm provides competitive lossless/lossy compression results. The proposed technique is well suited for telemedicine applications that require fast interactive handling of large image sets, over networks with limited and/or variable bandwidth.

Algorithms↗
PubMed2006

A method for coordinating the distributed transmission of imagery.

Distributed imaging using sensor arrays is gaining popularity among various research and development communities. A common bottleneck within such an imaging sensor network is the large resulting data load. In applications for which transmission power and/or bandwidth are constrained, this can drastically decrease the sensor network lifetime. We present an algorithm that efficiently exploits inter- and intrasensor correlation for the purpose of power-constrained distributed transmission of sensor-network imagery. Gains in network lifetime up to 114% are obtained when using the suggested algorithm with lossless compression. Our results also demonstrate that when lossy compression is employed, much larger gains are achieved. For example, when a normalized root-mean-squared error of 0.78% can be tolerated in the received measurements, the network lifetime increases by a factor of 2.8, as compared to the (optimized) lossless case.

Algorithms↗
PubMed1993

FASTPAT: a fast and efficient algorithm for string searching in DNA sequences.

A new string searching algorithm is presented aimed at searching for the occurrence of character patterns in longer character texts. The algorithm, specifically designed for nucleic acid sequence data, is essentially derived from the Boyer-Moore method (Comm. ACM, 20, 762-772, 1977). Both pattern and text data are compressed so that the natural 4-letter alphabet of nucleic acid sequences is considerably enlarged. The string search starts from the last character of the pattern and proceeds in large jumps through the text to be searched. The data compression and searching algorithm allows one to avoid searching for patterns not present in the text as well as to inspect, for each pattern, all text characters until the exact match with the text is found. These considerations are supported by empirical evidence and comparisons with other methods.

Algorithms↗
PubMed1997

Video sequence compression via supervised training on cellular neural networks.

In this paper, a novel approach for video sequence compression using Cellular Neural Networks (CNN's) is presented. CNN's are nets characterized by local interconnections between neurons (usually called cells), and can be modeled as dynamical systems. From among many different types, a CNN model operating in discrete-time (DT-CNN) has been chosen, its parameters being defined so that they are shared among all the cells in the network. The compression process proposed in this work is based on the possibility of replicating a given video sequence as a trajectory generated by the DT-CNN. In order for the CNN to follow a prescribed trajectory, a supervised training algorithm is implemented. Compression is achieved due to the fact that all the information contained in the sequence can be stored into a small number of parameters and initial conditions once training is stopped. Different improvements upon the basic formulation are analyzed and issues such as feasibility and complexity of the compression problem are also addressed. Finally, some examples with real video sequences illustrate the applicability of the method.

Algorithms↗
PubMed2006

System for deep venous thrombosis detection using objective compression measures.

A system for objective vessel compression assessment for deep venous thrombosis characterization using ultrasound image data and a sensorized ultrasound probe is presented. Two new objective measures calculated from applied force and transverse vessel area are also presented and used to describe vessel compressibility. A modified star-Kalman algorithm is used for feature detection in acquired ultrasound images, and objective measures of vessel compressibility are calculated from the detected features and acquired force and location data from the sensorized probe. A three-dimensional shape model of the examined vessel that includes compressibility measures mapped as colors to its surface is presented on the user interface, as well as a virtual representation of the image plane. The compressibility measures were validated using expert segmentation of healthy and diseased vessels and compared using paired t-tests, which showed a significant difference between healthy and diseased cases for both measures. 100% sensitivity and specificity were obtained for both measures. The system was implemented in real-time (16 Hz) and evaluated using a tissue phantom and on healthy human subjects. Sensitivity was 100% and 60%, while specificity was 97% for both measures when implemented. The initial results for the system and its components are promising.

Algorithms↗
PubMed1997

[Assessing indexes in the ECG data compression].

This paper describes various numerical assessing indexes in evaluating algorithms for the ECG data compression, such as numerical indexes of similarity (CC, RMS, PRD), indexes of error (amplitude error, peak error), efficiency of compression (Compression Ratio, bit rate), computing complexity, and so on. It also involves visual inspection which is more important in assessing algorithms.

Algorithms↗
PubMed1993

Real-time multiband dynamic compression and noise reduction for binaural hearing aids.

A multi-signal-processor set-up is introduced that is used for real-time implementation of digital hearing aid algorithms that operate on stereophonic (i.e., binaural) input signals and perform signal processing in the frequency domain. A multiband dynamic compression algorithm was implemented which operates in 24 critical band filter channels, allows for interaction between frequency bands and stereo channels, and is fitted to the hearing of the individual patient by a loudness scaling method. In addition, a binaural noise reduction algorithm was implemented that amplifies sound emanating from the front and suppresses lateral noise sources as well as reverberation. These algorithms were optimized with respect to their processing parameters and by minimizing the processing artifacts. Different versions of the algorithms were tested in six listeners with sensorineural hearing impairment using both subjective quality assessment methods and speech intelligibility measurements in different acoustical situations. For most subjects, linear frequency shaping was subjectively assessed to be negative, although it improved speech intelligibility in noise. Additional compression was assessed to be positive and did not deteriorate speech intelligibility as long as the processing parameters were fitted carefully. All noise reduction strategies employed here were subjectively assessed to be positive. Although the suppression of reverberation only slightly improved speech intelligibility, a combination of directional filtering and dereverberation provided a substantial improvement in speech intelligibility for most subjects and for a certain range of signal-to-noise ratios. The real-time implementation was very helpful in optimizing and testing the algorithms, and the overall results indicate that carefully designed and fitted binaural hearing aids might be very beneficial for a large number of patients.

Adult↗