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PubMed2003

Text compression using hybrids of BWT and GBAM.

In this paper we considered a theoretical evaluation of data and text compression algorithm based on the Burrows-Wheeler Transform (BWT) and General Bidirectional Associative Memory (GBAM). A new data and text lossless compression method, based on the combination of BWT1 and GBAM2 approaches, is presented. The algorithm was tested on many texts in different formats (ASCII and RTF). The compression ratio achieved is fairly good, on average 28-36%. Decompression is fast.

Algorithms↗
PubMed2005

Wavelet compression of three-dimensional time-lapse biological image data.

The use of multifocal-plane, time-lapse recordings of living specimens has allowed investigators to visualize dynamic events both within ensembles of cells and individual cells. Recordings of such four-dimensional (4D) data from digital optical sectioning microscopy produce very large data sets. We describe a wavelet-based data compression algorithm that capitalizes on the inherent redunancies within multidimensional data to achieve higher compression levels than can be obtained from single images. The algorithm will permit remote users to roam through large 4D data sets using communication channels of modest bandwidth at high speed. This will allow animation to be used as a powerful aid to visualizing dynamic changes in three-dimensional structures.

Algorithms↗
PubMed1997

Image data compression.

The tutorial will expand on all of the topics outlined above and will provide additional information on other compression techniques. The practical implications of data compression and the important considerations in choosing a compression scheme will also be discussed. Development of new compression algorithms remains an active area of investigation and will continue to increase the degree of compression possible.

Algorithms↗
PubMed2005

Application of compression-based distance measures to protein sequence classification: a methodological study.

MOTIVATION: Distance measures built on the notion of text compression have been used for the comparison and classification of entire genomes and mitochondrial genomes. The present study was undertaken in order to explore their utility in the classification of protein sequences. RESULTS: We constructed compression-based distance measures (CBMs) using the Lempel-Zlv and the PPMZ compression algorithms and compared their performance with that of the Smith-Waterman algorithm and BLAST, using nearest neighbour or support vector machine classification schemes. The datasets included a subset of the SCOP protein structure database to test distant protein similarities, a 3-phosphoglycerate-kinase sequences selected from archaean, bacterial and eukaryotic species as well as low and high-complexity sequence segments of the human proteome, CBMs values show a dependence on the length and the complexity of the sequences compared. In classification tasks CBMs performed especially well on distantly related proteins where the performance of a combined measure, constructed from a CBM and a BLAST score, approached or even slightly exceeded that of the Smith-Waterman algorithm and two hidden Markov model-based algorithms.

Algorithms↗
PubMed2004

Gridding and compression of microarray images.

With the recent explosion of interest in microarray technology, massive amounts of microarray images are currently being produced. The storage and the transmission of this type of data are becoming increasingly challenging. Here we propose lossless and lossy compression algorithms for microarray images originally digitized at 16 bpp (bits per pixels) that achieve an average of 9.5 - 11.5 bpp (lossless) and 4.6 - 6.7 bpp (lossy, with a PSNR of 63 dB). The lossy compression is applied only on the background of the image, thereby preserving the regions of interest. The methods are based on a completely automatic gridding procedure of the image.

Algorithms↗
PubMed1997

Iterative function system and genetic algorithm-based EEG compression.

A method for EEG compression is proposed, using Iterative Function System (IFS) and Genetic Algorithms (GAs) with elitist model, keeping the quality sufficiently good for clinical purposes. Compression using IFS is usually called fractal compression. The self transformability property of the EEG signals is assumed and is exploited in the fractal compression technique. To ascertain the self transformability of the EEG signal, some isometric transformations have been applied. The technique described here utilizes Genetic Algorithm that decreases the search space for finding the self similarities in the given signal. This article presents theory and implementation of the proposed method. The fidelity of the reconstructed signal obtained by the present compression algorithm has been assessed both qualitatively and quantitatively. The compression ratios, for the EEG signals in various states, are found to be comparable to the other available techniques for EEG compression. In our method at least 85% data reduction has been achieved.

Algorithms↗
PubMed1998

Clinical Validation of Digital Image Compression Levels for Fetal Echocardiograms for Telemedicine.

BACKGROUND: Digital telephone transmission of dynamic fetal echocardiographic sequences requires wide bandwidths. To reduce the transmission of nonessential information present in echocardiographic images, compression algorithms have been used and validated for adult echocardiography. The level of compression that retains all diagnostic clinical information must be determined and validated for fetal echocardiographic studies. The purpose of this study was to validate levels of image compression that are diagnostic for fetal echocardiography. METHODS: Twenty previously recorded fetal echocardiograms representing two normal and 18 congenitally abnormal hearts were looped, digitized, and compressed at differing ratios. Images were reviewed in a blinded manner by four experts. Images were scored for diagnosis, diagnostic quality, and technical quality. RESULTS: Forty-four images were viewed. Experts correctly diagnosed 87% of images. There were no images of unacceptable diagnostic quality at a compression ratio of 300 (0%) and 2% at compression ratio of 600:1. CONCLUSIONS: Digital image compression of 300:1 was diagnostic for fetal congenital heart diseases with a variety of defects using cine-looped images. These data support the use of compressed fetal images for telephone transmission with a low bandwidth.

Journal Article↗
PubMed2000

Rank-order polynomial subband decomposition for medical image compression.

In this paper, the problem of progressive lossless image coding is addressed. A nonlinear decomposition for progressive lossless compression is presented. The decomposition into subbands is called rank-order polynomial decomposition (ROPD) according to the polynomial prediction models used. The decomposition method presented here is a further development and generalization of the morphological subband decomposition (MSD) introduced earlier by the same research group. It is shown that ROPD provides similar or slightly better results than the compared coding schemes such as the codec based on set partitioning in hierarchical trees (SPIHT) and the codec based on wavelet/trellis-coded quantization (WTCQ). Our proposed method highly outperforms the standard JPEG. The proposed lossless compression scheme has the functionality of having a completely embedded bit stream, which allows for data browsing. It is shown that the ROPD has a better lossless rate than the MSD but it has also a much better browsing quality when only a part of the bit stream is decompressed. Finally, the possibility of hybrid lossy/lossless compression is presented using ultrasound images. As with other compression algorithms, considerable gain can be obtained if only the regions of interest are compressed losslessly.

Diagnostic Imaging↗
PubMed2002

Robust region of interest coding for improved sign language telecommunication.

More than 500,000 deaf people in North America use American Sign Language or a similar signed system as a first language. Long-distance communication of this visually based medium is hampered by its incompatibility with audio and text telecommunication systems. Movements associated with signed languages require a more consistent and higher frame rate than is available with residential video telephony. New video compression standards (JPEG 2000 and MPEG-4) allow optional region of interest coding in which areas within a frame can be assigned different levels of compression. This paper presents a novel skin color segmentation approach that identifies the hands and face each video frame. This method is robust in terms of variations in skin pigmentation in a single subject, in skin pigmentation across a population of potential users, subject clothing, and image background. Specifying these critical regions of interest to the compression algorithm maintains high visual quality in the regions of the hands and face, while allowing very lossy, high compression of the remainder of the video frame. This reduces the coded representation of each frame, and offers a potential increase in the frame rate for telecommunication.

Algorithms↗
PubMed2025

CREMSA: compressed indexing of (ultra) large multiple sequence alignments.

MOTIVATION: Recent viral outbreaks motivate the systematic collection of pathogenic genomes in order to accelerate their study and monitor the apparition/spread of variants. Due to their limited length and temporal proximity of their sequencing, viral genomes are usually organized, and analyzed as oversized Multiple Sequence Alignments (MSAs). Such MSAs are largely ungapped, and mostly homogeneous on a column-wise level but not at a sequential level due to local variations, hindering the performances of sequential compression algorithms. RESULTS: In order to enable an efficient handling of MSAs, including subsequent statistical analyses, we introduce CREMSA (Column-wise Run-length Encoding for MSAs), a new index that builds on sparse bitvector representations to compress an existing or streamed MSA, all the while allowing for an expressive set of accelerated requests to query the alignment without prior decompression. Using CREMSA, a 65 GB MSA consisting of 1.9M SARS-CoV 2 genomes could be compressed into 22 MB using less than half a gigabyte of main memory, while executing access requests in the order of 100 ns. Such a speed up enables a comprehensive analysis of covariation over this very large MSA. We further assess the impact of the sequence ordering on the compressibility of MSAs and propose a resorting strategy that, despite the proven NP-hardness of an optimal sort, induces greatly increased compression ratios at a marginal computational cost. AVAILABILITY AND IMPLEMENTATION: CREMSA is freely accessible at https://gitlab.univ-lille.fr/cremsa/cremsa. The Snakemake workflow for the benchmarks is available at: https://gitlab.univ-lille.fr/cremsa/bench. The data used in the paper is on Zenodo at https://zenodo.org/records/14698859 and https://zenodo.org/records/15100011.

SARS-CoV-2↗
PubMed2000

Effect of image compression on telepathology. A randomized clinical trial.

CONTEXT: For practitioners deploying store-and-forward telepathology systems, optimization methods such as image compression need to be studied. OBJECTIVE: To determine if Joint Photographic Expert Group (JPG or JPEG) compression, a glossy image compression algorithm, negatively affects the accuracy of diagnosis in telepathology. DESIGN: Double-blind, randomized, controlled trial. SETTING: University-based pathology departments. PARTICIPANTS: Resident and staff pathologists at the University of Illinois, Chicago, and University of Cincinnati, Cincinnati, Ohio. INTERVENTION: Compression of raw images using the JPEG algorithm. MAIN OUTCOME MEASURES: Image acceptability, accuracy of diagnosis, confidence level of pathologist, image quality. RESULTS: There was no statistically significant difference in the diagnostic accuracy between noncompressed (bit map) and compressed (JPG) images. There were also no differences in the acceptability, confidence level, and perception of image quality. Additionally, rater experience did not significantly correlate with degree of accuracy. CONCLUSIONS: For providers practicing telepathology, JPG image compression does not negatively affect the accuracy and confidence level of diagnosis. The acceptability and quality of images were also not affected.

Diagnosis, Computer-Assisted↗
PubMed1998

Suitability of minidisc (MD) recordings for voice perturbation analysis.

A new digital recording format, Minidisc (MD), shows promise for high-quality voice recordings. It is available in a portable size and uses magneto-optical recording techniques on a miniature compact disc. The disc can be recorded an unlimited number of times with essentially the same playback life span: however, the digital recording technique uses a data compression algorithm that may interfere with acoustic voice perturbation analysis. This study investigated what effects this compression may have and whether the MD format is viable for use in this application. The MD format was evaluated by traditional synthetic test signals used on recording devices. In addition, human phonation recorded on Digital Audio Tape (DAT) was used as the input to the MD. The output of the MD was then compared to the original DAT recording. The two signals were analyzed for long- and short-term perturbation measures, and their waveforms were visually inspected. The results indicated that the MD format performed as well as the DAT format in all areas of standard tests, with the exception of signal-to-noise (S/N) ratio. S/N ratio for the MD was approximately 10 dB less than for the DAT under normal operating conditions; however, in comparing perturbation measures on normal human vowels, there were no significant differences between the two formats, i.e., no distortions in voice perturbation were introduced by the MD record/playback process.

Female↗
PubMed2002

Lossless compression of otoneurological eye movement signals.

We studied the performance of several lossless compression algorithms on eye movement signals recorded in otoneurological balance and other physiological laboratories. Despite the wide use of these signals their compression has not been studied prior to our research. The compression methods were based on the common model of using a predictor to decorrelate the input and using an entropy coder to encode the residual. We found that these eye movement signals recorded at 400 Hz and with 13 bit amplitude resolution could losslessly be compressed with a compression ratio of about 2.7.

Algorithms↗
PubMed2004

Testing time symmetry in time series using data compression dictionaries.

Time symmetry, often called statistical time reversibility, in a dynamical process means that any segment of time-series output has the same probability of occurrence in the process as its time reversal. A technique, based on symbolic dynamics, is proposed to distinguish such symmetrical processes from asymmetrical ones, given a time-series observation of the otherwise unknown process. Because linear stochastic Gaussian processes, and static nonlinear transformations of them, are statistically reversible, but nonlinear dynamics such as dissipative chaos are usually statistically irreversible, a test will separate large classes of hypotheses for the data. A general-purpose and robust statistical test procedure requires adapting to arbitrary dynamics which may have significant time correlation of undetermined form. Given a symbolization of the observed time series, the technology behind adaptive dictionary data compression algorithms offers a suitable estimate of reversibility, as well as a statistical likelihood test. The data compression methods create approximately independent segments permitting a simple and direct null test without resampling or surrogate data. We demonstrate the results on various time-series-reversible and irreversible systems.

Journal Article↗
PubMed2005

Three subsets of sequence complexity and their relevance to biopolymeric information.

Genetic algorithms instruct sophisticated biological organization. Three qualitative kinds of sequence complexity exist: random (RSC), ordered (OSC), and functional (FSC). FSC alone provides algorithmic instruction. Random and Ordered Sequence Complexities lie at opposite ends of the same bi-directional sequence complexity vector. Randomness in sequence space is defined by a lack of Kolmogorov algorithmic compressibility. A sequence is compressible because it contains redundant order and patterns. Law-like cause-and-effect determinism produces highly compressible order. Such forced ordering precludes both information retention and freedom of selection so critical to algorithmic programming and control. Functional Sequence Complexity requires this added programming dimension of uncoerced selection at successive decision nodes in the string. Shannon information theory measures the relative degrees of RSC and OSC. Shannon information theory cannot measure FSC. FSC is invariably associated with all forms of complex biofunction, including biochemical pathways, cycles, positive and negative feedback regulation, and homeostatic metabolism. The algorithmic programming of FSC, not merely its aperiodicity, accounts for biological organization. No empirical evidence exists of either RSC of OSC ever having produced a single instance of sophisticated biological organization. Organization invariably manifests FSC rather than successive random events (RSC) or low-informational self-ordering phenomena (OSC).

Algorithms↗
PubMed1997

Wavelet and wavelet packet compression of electrocardiograms.

Wavelets and wavelet packets have recently emerged as powerful tools for signal compression. Wavelet and wavelet packet-based compression algorithms based on embedded zerotree wavelet (EZW) coding are developed for electrocardiogram (ECG) signals, and eight different wavelets are evaluated for their ability to compress Holter ECG data. Pilot data from a blind evaluation of compressed ECG's by cardiologists suggest that the clinically useful information present in original ECG signals is preserved by 8:1 compression, and in most cases 16:1 compressed ECG's are clinically useful.

Algorithms↗
PubMed1995

ANN compression of morphologically similar ECG complexes.

A compression algorithm for electrocardiogram signals is presented, based on an auto-associative neural network. Issues of weight and activation coding are considered, and compression performances of various network sizes are compared. A unique feature is the performance improvement achieved using DC level removal. A comparison with existing techniques is provided.

Algorithms↗
PubMedDate not supplied

Introduction to wavelet-based compression of medical images.

Medical image compression can significantly enhance the performance of picture archiving and communication systems and may be considered an enabling technology for telemedicine. The wavelet transform is a powerful mathematical tool with many unique qualities that are useful for image compression and processing applications. Although wavelet concepts can be traced back to 1910, the mathematics of wavelets have only recently been formalized. By exploiting spatial and spectral information redundancy in images, wavelet-based methods offer significantly better results for compressing medical images than do compression algorithms based on Fourier methods, such as the discrete cosine transform used by the Joint Photographic Experts Group. Furthermore, wavelet-based compression does not suffer from blocking artifacts, and the restored image quality is generally superior at higher compression rates.

Humans↗