Search NASA⌕ Search

SEARCH · Search NASA

Results for “lossless compression”

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.

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

At least 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS1994

Radiometric resolution enhancement by lossy compression as compared to truncation followed by lossless compression

Recent advances in imaging technology make it possible to obtain imagery data of the Earth at high spatial, spectral and radiometric resolutions from Earth orbiting satellites. The rate at which the data is collected from these satellites can far exceed the channel capacity of the data downlink. Reducing the data rate to within the channel capacity can often require painful trade-offs in which certain scientific returns are sacrificed for the sake of others. In this paper we model the radiometric version of this form of lossy compression by dropping a specified number of least significant bits from each data pixel and compressing the remaining bits using an appropriate lossless compression technique. We call this approach 'truncation followed by lossless compression' or TLLC. We compare the TLLC approach with applying a lossy compression technique to the data for reducing the data rate to the channel capacity, and demonstrate that each of three different lossy compression techniques (JPEG/DCT, VQ and Model-Based VQ) give a better effective radiometric resolution than TLLC for a given channel rate.

Tilton, James C.↗
DOE OSTI2024

Investigation of fast and efficient lossless compression algorithms for macromolecular crystallography experiments

Structural biology experiments benefit significantly from state-of-the-art synchrotron data collection. One can acquire macromolecular crystallography (MX) diffraction data on large-area photon-counting pixel-array detectors at framing rates exceeding 1000 frames per second, using 200 Gbps network connectivity, or higher when available. In extreme cases this represents a raw data throughput of about 25 GB s −1 , which is nearly impossible to deliver at reasonable cost without compression. Our field has used lossless compression for decades to make such data collection manageable. Many MX beamlines are now fitted with DECTRIS Eiger detectors, all of which are delivered with optimized compression algorithms by default, and they perform well with current framing rates and typical diffraction data. However, better lossless compression algorithms have been developed and are now available to the research community. Here one of the latest and most promising lossless compression algorithms is investigated on a variety of diffraction data like those routinely acquired at state-of-the-art MX beamlines.

36 MATERIALS SCIENCE↗
NASA NTRS2006

Fast Lossless Compression of Multispectral-Image Data

An algorithm that effects fast lossless compression of multispectral-image data is based on low-complexity, proven adaptive-filtering algorithms. This algorithm is intended for use in compressing multispectral-image data aboard spacecraft for transmission to Earth stations. Variants of this algorithm could be useful for lossless compression of three-dimensional medical imagery and, perhaps, for compressing image data in general.

Klimesh, Matthew↗
NASA NTRS1992

Lossless compression of AVIRIS data: Comparison of methods and instrument constraints

A family of lossless compression methods, allowing exact image reconstruction, are evaluated for compressing Airborne Visible/Infrared Imaging Spectrometers (AVIRIS) image data. The methods are used on Differential Pulse Code Modulation (DPCM). The compressed data have an entropy of order 6 bits/pixel. A theoretical model indicates that significantly better lossless compression is unlikely to be achieved because of limits caused by the noise in the AVIRIS channels. AVIRIS data differ from data produced by other visible/near-infrared sensors, such as LANDSAT-TM or SPOT, in several ways. Firstly, the data are recorded at a greater resolution (12 bits, though packed into 16-bit words). Secondly, the spectral channels are relatively narrow and provide continuous coverage of the spectrum so that the data in adjacent channels are generally highly correlated. Thirdly, the noise characteristics of the AVIRIS are defined by the channels' Noise Equivalent Radiances (NER's), and these NER's show that, at some wavelengths, the least significant 5 or 6 bits of data are essentially noise.

Roger, R. E.↗
NASA NTRS1993

Lossless compression of image data products on th e FIFE CD-ROM series

How do you store enough of the key data sets, from a total of 120 gigabytes of data collected for a scientific experiment, on a collection of CD-ROM's, small enough to distribute to a broad scientific community? In such an application where information loss in unacceptable, lossless compression algorithms are the only choice. Although lossy compression algorithms can provide an order of magnitude improvement in compression ratios over lossless algorithms the information that is lost is often part of the key scientific precision of the data. Therefore, lossless compression algorithms are and will continue to be extremely important in minimizing archiving storage requirements and distribution of large earth and space (ESS) data sets while preserving the essential scientific precision of the data.

Newcomer, Jeffrey A.↗
NASA NTRS2006

Low-Complexity Adaptive Lossless Compression of Hyperspectral Imagery

A low-complexity, adaptive predictive technique for lossless compression of hyperspectral imagery is described. This technique is designed to be suitable for implementation in hardware such as a field programmable gate array (FPGA); such an implementation could be used for high-speed compression of hyperspectral imagery onboard a spacecraft. The predictive step of the technique makes use of the sign algorithm, which is a relative of the least mean square (LMS) algorithm from the field of low-complexity adaptive filtering. The compressed data stream consists of prediction residuals encoded using a method similar to that of the JPEG-LS lossless image compression standard. Compression results are presented for several datasets including some raw Airborne Visible/ Infrared Imaging Spectrometer (AVIRIS) datasets and raw Atmospheric Infrared Sounder (AIRS) datasets. The compression effectiveness obtained with the technique is competitive with that of the best of previously described techniques with similar complexity.

lossless data compression↗
NASA NTRS2005

Low-Complexity Lossless Compression of Hyperspectral Imagery Via Adaptive Filtering

A low-complexity, adaptive predictive technique for lossless compression of hyperspectral data is presented. The technique relies on the sign algorithm from the repertoire of adaptive filtering. The compression effectiveness obtained with the technique is competitive with that of the best of previously described techniques with similar complexity.

lossless data compression↗
NASA NTRS1994

Lossless compression of NOAA-AVHRR satellite data

A high-performance lossless compression system for satellite NOAA data is developed. The data is called 'high resolution picture transmission' (HRPT) data, and consists of around 93 percent advanced very high resolution radiometer (AVHRR) multi-channel image data and 7 percent of miscellaneous data. In compressing the image portion, we classify each pixel into 10 different groups and apply a multi-channel prediction and a non-linear error conversion. The entropy coder is an arithmetic coder which is adaptive and regenerates the approximation of the statistical properties of the source as an initial probability table. To compress the non-image part, we used the general compressor (gzip). From experimental results, the original information is compressed down to 25 percent to approx. 40 percent.

Takamura, Seishi↗
NASA NTRS2009

Lossless Compression of Data into Fixed-Length Packets

A computer program effects lossless compression of data samples from a one-dimensional source into fixed-length data packets. The software makes use of adaptive prediction: it exploits the data structure in such a way as to increase the efficiency of compression beyond that otherwise achievable. Adaptive linear filtering is used to predict each sample value based on past sample values. The difference between predicted and actual sample values is encoded using a Golomb code.

Kiely, Aaron B.↗
NASA NTRS2010

Algorithm for Lossless Compression of Calibrated Hyperspectral Imagery

A two-stage predictive method was developed for lossless compression of calibrated hyperspectral imagery. The first prediction stage uses a conventional linear predictor intended to exploit spatial and/or spectral dependencies in the data. The compressor tabulates counts of the past values of the difference between this initial prediction and the actual sample value. To form the ultimate predicted value, in the second stage, these counts are combined with an adaptively updated weight function intended to capture information about data regularities introduced by the calibration process. Finally, prediction residuals are losslessly encoded using adaptive arithmetic coding. Algorithms of this type are commonly tested on a readily available collection of images from the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) hyperspectral imager. On the standard calibrated AVIRIS hyperspectral images that are most widely used for compression benchmarking, the new compressor provides more than 0.5 bits/sample improvement over the previous best compression results. The algorithm has been implemented in Mathematica. The compression algorithm was demonstrated as beneficial on 12-bit calibrated AVIRIS images.

Kiely, Aaron B.↗
NASA NTRS2005

Low-Complexity Lossless Compression of Hyperspectral Imagery via Adaptive Filtering

A low-complexity, adaptive predictive technique for lossless compression of hyperspectral data is presented. The technique relies on the sign algorithm from the repertoire of adaptive filtering. The compression effectiveness obtained with the technique is competitive with that of the best of previously described techniques with similar complexity.

Klimesh, M.↗
DOE OSTI2024

Accelerating Lossy and Lossless Compression on Emerging BlueField DPU Architectures

Data compression has become a crucial technique in addressing performance bottlenecks caused by increasing data volumes in High-Performance Computing (HPC), Big Data, and Deep Learning (DL). Despite its potential to boost system performance, recent studies have identified significant challenges with existing compression methods, mainly due to their high computational demands amidst continuously growing data sizes. Concurrently, the advent of Data Processing Units (DPUs), equipped with programmable System-on-Chip (SoC) and specialized compression accelerators, offers a promising opportunity to alter the landscape of data compression. This paper explores the complexities and potential of leveraging NVIDIA BlueField DPUs to accelerate lossy and lossless compression. Towards this, we introduce PEDAL, an innovative library that leverages the hardware capabilities of DPUs to unify and optimize data compression designs. Moreover, we seamlessly co-design PEDAL with the popular MPICH MPI library, demonstrating up to 101x speedup in compression time and 88x decrease in communication latency. Drawing on these achievements, we share our experience with various research communities about accelerating data compression on DPUs in communication-oriented HPC scenarios.

Li, Yuke↗
NASA NTRS1995

High Order Entropy-Constrained Residual VQ for Lossless Compression of Images

High order entropy coding is a powerful technique for exploiting high order statistical dependencies. However, the exponentially high complexity associated with such a method often discourages its use. In this paper, an entropy-constrained residual vector quantization method is proposed for lossless compression of images. The method consists of first quantizing the input image using a high order entropy-constrained residual vector quantizer and then coding the residual image using a first order entropy coder. The distortion measure used in the entropy-constrained optimization is essentially the first order entropy of the residual image. Experimental results show very competitive performance.

Kossentini, Faouzi↗
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↗
NASA NTRS2011

Freeing Space for NASA: Incorporating a Lossless Compression Algorithm into NASA's FOSS System

NASA's Fiber Optic Strain Sensing (FOSS) system can gather and store up to 1,536,000 bytes (1.46 megabytes) per second. Since the FOSS system typically acquires hours - or even days - of data, the system can gather hundreds of gigabytes of data for a given test event. To store such large quantities of data more effectively, NASA is modifying a Lempel-Ziv-Oberhumer (LZO) lossless data compression program to compress data as it is being acquired in real time. After proving that the algorithm is capable of compressing the data from the FOSS system, the LZO program will be modified and incorporated into the FOSS system. Implementing an LZO compression algorithm will instantly free up memory space without compromising any data obtained. With the availability of memory space, the FOSS system can be used more efficiently on test specimens, such as Unmanned Aerial Vehicles (UAVs) that can be in flight for days. By integrating the compression algorithm, the FOSS system can continue gathering data, even on longer flights.

Fiechtner, Kaitlyn↗
NASA NTRS1994

Some practical aspects of lossless and nearly-lossless compression of AVHRR imagery

Compression of Advanced Very high Resolution Radiometers (AVHRR) imagery operating in a lossless or nearly-lossless mode is evaluated. Several practical issues are analyzed including: variability of compression over time and among channels, rate-smoothing buffer size, multi-spectral preprocessing of data, day/night handling, and impact on key operational data applications. This analysis is based on a DPCM algorithm employing the Universal Noiseless Coder, which is a candidate for inclusion in many future remote sensing systems. It is shown that compression rates of about 2:1 (daytime) can be achieved with modest buffer sizes (less than or equal to 2.5 Mbytes) and a relatively simple multi-spectral preprocessing step.

Hogan, David B.↗