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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
NASA NTRS1993

High performance compression of science data

In the future, NASA expects to gather over a tera-byte per day of data requiring space for levels of archival storage. Data compression will be a key component in systems that store this data (e.g., optical disk and tape) as well as in communications systems (both between space and Earth and between scientific locations on Earth). We propose to develop algorithms that can be a basis for software and hardware systems that compress a wide variety of scientific data with different criteria for fidelity/bandwidth tradeoffs. The algorithmic approaches we consider are specially targeted for parallel computation where data rates of over 1 billion bits per second are achievable with current technology.

Storer, James A.↗
NASA NTRS1975

Compression of contour data through exploiting curve-to-curve dependence

An approach to exploiting curve-to-curve dependencies in order to achieve high data compression is presented. One of the approaches to date of along curve compression through use of cubic spline approximation is taken and extended by investigating the additional compressibility achievable through curve-to-curve structure exploitation. One of the models under investigation is reported on.

Yalabik, N.↗
DOE OSTI2016

Parallel compression of data chunks of a shared data object using a log-structured file system

Techniques are provided for parallel compression of data chunks being written to a shared object. A client executing on a compute node or a burst buffer node in a parallel computing system stores a data chunk generated by the parallel computing system to a shared data object on a storage node by compressing the data chunk; and providing the data compressed data chunk to the storage node that stores the shared object. The client and storage node may employ Log-Structured File techniques. The compressed data chunk can be de-compressed by the client when the data chunk is read. A storage node stores a data chunk as part of a shared object by receiving a compressed version of the data chunk from a compute node; and storing the compressed version of the data chunk to the shared data object on the storage node.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1972

Channel coding/decoding alternatives for compressed TV data on advanced planetary missions.

The compatibility of channel coding/decoding schemes with a specific TV compressor developed for advanced planetary missions is considered. Under certain conditions, it is shown that compressed data can be transmitted at approximately the same rate as uncompressed data without any loss in quality. Thus, the full gains of data compression can be achieved in real-time transmission.

Rice, R. F.↗
NASA NTRS1993

Compression for an effective management of telemetry data

A Technological DataBase (T.D.B.) records all the values taken by the physical on-board parameters of a satellite since launch time. The amount of temporal data is very large (about 15 Gbytes for the satellite TDF1) and an efficient system must allow users to have a fast access to any value. This paper presents a new solution for T.D.B. management. The main feature of our new approach is the use of lossless data compression methods. Several parametrizable data compression algorithms based on substitution, relative difference and run-length encoding are available. Each of them is dedicated to a specific type of variation of the parameters' values. For each parameter, an analysis of stability is performed at decommutation time, and then the best method is chosen and run. A prototype intended to process different sorts of satellites has been developed. Its performances are well beyond the requirements and prove that data compression is both time and space efficient. For instance, the amount of data for TDF1 has been reduced to 1.05 Gbytes (compression ratio is 1/13) and access time for a typical query has been reduced from 975 seconds to 14 seconds.

Arcangeli, J.-P.↗
NASA NTRS1989

Hybrid LZW compression

The Science Data Management and Science Payload Operations subpanel reports from the NASA Conference on Scientific Data Compression (Snowbird, Utah in 1988) indicate the need for both lossless and lossy image data compression systems. The ranges developed by the subpanel suggest ratios of 2:1 to 4:1 for lossless coding and 2:1 to 6:1 for lossy predictive coding. For the NASA Freedom Science Video Processing Facility it would be highly desirable to implement one baseline compression system which would meet both of these criteria. Presented here is such a system, utilizing an LZW hybrid coding scheme which is adaptable to either type of compression. Simulation results are presented with the hybrid LZW algorithm operating in each of its modes.

Lewis, H. Garton, Jr.↗
DOE OSTI2019

TTHRESH: Tensor Compression for Multidimensional Visual Data

Memory and network bandwidth are decisive bottlenecks when handling high-resolution multidimensional data sets in visualization applications, and they increasingly demand suitable data compression strategies. In this paper, we introduce a novel lossy compression algorithm for multidimensional data over regular grids. It leverages the higher-order singular value decomposition (HOSVD), a generalization of the SVD to three dimensions and higher, together with bit-plane, run-length and arithmetic coding to compress the HOSVD transform coefficients. Our scheme degrades the data particularly smoothly and achieves lower mean squared error than other state-of-the-art algorithms at low-to-medium bit rates, as it is required in data archiving and management for visualization purposes. Further advantages of the proposed algorithm include very fine bit rate selection granularity and the ability to manipulate data at very small cost in the compression domain, for example to reconstruct filtered and/or subsampled versions of all (or selected parts) of the data set.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1974

Outer planet Pioneer imaging communications system study

The effects of different types of imaging data compression on the elements of the Pioneer end-to-end data system were studied for three imaging transmission methods. These were: no data compression, moderate data compression, and the advanced imaging communications system. It is concluded that: (1) the value of data compression is inversely related to the downlink telemetry bit rate; (2) the rolling characteristics of the spacecraft limit the selection of data compression ratios; and (3) data compression might be used to perform acceptable outer planet mission at reduced downlink telemetry bit rates.

Source record↗
NASA NTRS1982

On-board image compression

Image data compression schemes are discussed with regard to achieving maximum compression ratio. It is demonstrated that the compression ratio must be traded-off against the allowable residual distortion in the reconstructed image.

Lynch, T. J.↗
DOE OSTI2023

Classical and quantum compression for edge computing: the ubiquitous data dimensionality reduction

Edge computing aims to address the challenges associated with communicating and transferring large amounts of data generated remotely to a data center in a timely and efficient manner. A central pillar of edge computing is local (i.e., at- or near-source) data processing capability so that data transfer to a data center for processing can be minimized. Data compression at the edge is therefore a natural component of edge workflows. Here we present a survey of data compression algorithms with a focus on edge computing. Not all compression algorithms can accommodate the data type heterogeneity, tight processing and communication time constraints, or energy efficiency requirement characteristics of edge computing. We discuss specific examples of compression algorithms that are being explored in the context of edge computing. We end our review with a brief survey of emerging quantum compression techniques that are of importance in quantum information processing, including the proposed concept of quantum edge computing.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2009

Lossless Compression of Classification-Map Data

A lossless image-data-compression algorithm intended specifically for application to classification-map data is based on prediction, context modeling, and entropy coding. The algorithm was formulated, in consideration of the differences between classification maps and ordinary images of natural scenes, so as to be capable of compressing classification- map data more effectively than do general-purpose image-data-compression algorithms. Classification maps are typically generated from remote-sensing images acquired by instruments aboard aircraft (see figure) and spacecraft. A classification map is a synthetic image that summarizes information derived from one or more original remote-sensing image(s) of a scene. The value assigned to each pixel in such a map is the index of a class that represents some type of content deduced from the original image data for example, a type of vegetation, a mineral, or a body of water at the corresponding location in the scene. When classification maps are generated onboard the aircraft or spacecraft, it is desirable to compress the classification-map data in order to reduce the volume of data that must be transmitted to a ground station.

Hua, Xie↗
DOE OSTI2020

Extreme Compression for Large Scale Data Store

For the last 5 years Accelogic pioneered and perfected a radically new theory of numerical computing codenamed “Compressive Computing”, which has an extremely profound impact on real-world computer science [1]. At the core of this new theory is the discovery of one of its fundamental theorems which states that, under very general conditions, the vast majority (typically between 70% and 80%) of the bits used in modern large-scale numerical computations are absolutely irrelevant for the accuracy of the end result. This theory of Compressive Computing provides mechanisms able to identify (with high intelligence and surgical accuracy) the number of bits (i.e., the precision) that can be used to represent numbers without affecting the substance of the end results, as they are computed and vary in real time. The bottom line outcome would be to provide a state-of-the-art compression algorithm that surpasses those currently available in the ROOT framework, with the purpose of enabling substantial economic and operational gains (including speedup) for High Energy and Nuclear Physics data storage/analysis. In our initial studies, a factor of nearly x4 (3.9) compression was achieved with RHIC/STAR data where ROOT compression managed only x1.4.In this contribution, we will present our concepts of “functionally lossless compression”, have a glance at examples and achievements in other communities, present the results and outcome of our current, ongoing R&D, as well as present a high-level view of our plan to move forward with a ROOT implementation that would deliver a basic solution readily integrated into HENP applications. As a collaboration of experimental scientists, private industry, and the ROOT Team, our aim is to capitalize on the substantial success delivered by the initial effort and produce a robust technology properly packaged as an open-source tool that could be used by virtually every experiment around the world as means for improving data management and accessibility.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2018

About a method for compressing X-ray computed microtomography data

The management of scientific data is of high importance especially for experimental techniques that produce big data volumes. Such a technique is x-ray computed tomography (CT) and its community has introduced advanced data formats which allow for better management of experimental data. Rather than the organization of the data and the associated meta-data, the main topic on this work is data compression and its applicability to experimental data collected from a synchrotron-based CT beamline at the Elettra-Sincrotrone Trieste facility (Italy) and studies images acquired from various types of samples. This study covers parallel beam geometry, but it could be easily extended to a cone-beam one. The reconstruction workflow used is the one currently in operation at the beamline. Contrary to standard image compression studies, this manuscript proposes a systematic framework and workflow for the critical examination of different compression techniques and does so by applying it to experimental data. Beyond the methodology framework, this study presents and examines the use of JPEG-XR in combination with HDF5 and TIFF formats providing insights and strategies on data compression and image quality issues that can be used and implemented at other synchrotron facilities and laboratory systems. In conclusion, projection data compression using JPEG-XR appears as a promising, efficient method to reduce data file size and thus to facilitate data handling and image reconstruction

data compression↗
DOE OSTI2016

Graphics processing unit-assisted lossless decompression

Systems and methods for decompressing compressed data that has been compressed by way of a lossless compression algorithm are described herein. In a general embodiment, a graphics processing unit (GPU) is programmed to receive compressed data packets and decompress such packets in parallel. The compressed data packets are compressed representations of an image, and the lossless compression algorithm is a Rice compression algorithm.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Enhancing ZFP: A Statistical Approach to Understanding and Reducing Error Bias in a Lossy Floating-Point Compression Algorithm

The amount of data generated and gathered in scientific simulations and data collection applications is continuously growing, putting mounting pressure on storage and bandwidth concerns. A means of reducing such issues is data compression; but, lossless data compression is typically ineffective when applied to floating-point data. Thus, users tend to apply a lossy data compressor, which allows for small deviations from the original data. It is essential to understand how the error from lossy compression impacts the accuracy of the data analytics. Thus, we must analyze not only the compression properties but the error as well. In this paper, we provide a statistical analysis of the error caused by ZFP compression, a state-of-the-art, lossy compression algorithm explicitly designed for floating-point data. We show that the error is indeed biased and propose simple modifications to the algorithm to neutralize the bias and further reduce the resulting error.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Electronic device and method for compressing sampled data

An electronic device for compressing sampled data comprises a memory element and a processing element. The memory element is configured to store sampled data points and sampled times. The processing element is in electronic communication with the memory element and is configured to receive a plurality of sampled data points, a slope for each sampled data point in succession, the slope being a value of a change between the sampled data point and its successive sampled data point, and store the sampled data point in the memory element when the slope changes in value from a previous sampled data point.

97 MATHEMATICS AND COMPUTING↗