Compressed Sensing and Reconstruction of Unstructured Mesh Datasets: Optimal Compression.
Abstract not provided.
Salloum, Maher↗
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First Results of Dynamic X-Ray Diffraction and Shock Compression on CaF2 and Comparison with Static Compression.
Abstract not provided.
Kalita, Patrycja E↗
What to Support when You're Compressing: The State of Practice, Gaps, and Opportunities for Scientific Data Compression
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Computer science↗
Lossless Astronomical Image Compression and the Effects of Random Noise
In this paper we compare a variety of modern image compression methods on a large sample of astronomical images. We begin by demonstrating from first principles how the amount of noise in the image pixel values sets a theoretical upper limit on the lossless compression ratio of the image. We derive simple procedures for measuring the amount of noise in an image and for quantitatively predicting how much compression will be possible. We then compare the traditional technique of using the GZIP utility to externally compress the image, with a newer technique of dividing the image into tiles, and then compressing and storing each tile in a FITS binary table structure. This tiled-image compression technique offers a choice of other compression algorithms besides GZIP, some of which are much better suited to compressing astronomical images. Our tests on a large sample of images show that the Rice algorithm provides the best combination of speed and compression efficiency. In particular, Rice typically produces 1.5 times greater compression and provides much faster compression speed than GZIP. Floating point images generally contain too much noise to be effectively compressed with any lossless algorithm. We have developed a compression technique which discards some of the useless noise bits by quantizing the pixel values as scaled integers. The integer images can then be compressed by a factor of 4 or more. Our image compression and uncompression utilities (called fpack and funpack) that were used in this study are publicly available from the HEASARC web site.Users may run these stand-alone programs to compress and uncompress their own images.
Pence, William↗
Low-Complexity Lossless and Near-Lossless Data Compression Technique for Multispectral Imagery
This work extends the lossless data compression technique described in Fast Lossless Compression of Multispectral- Image Data, (NPO-42517) NASA Tech Briefs, Vol. 30, No. 8 (August 2006), page 26. The original technique was extended to include a near-lossless compression option, allowing substantially smaller compressed file sizes when a small amount of distortion can be tolerated. Near-lossless compression is obtained by including a quantization step prior to encoding of prediction residuals. The original technique uses lossless predictive compression and is designed for use on multispectral imagery. A lossless predictive data compression algorithm compresses a digitized signal one sample at a time as follows: First, a sample value is predicted from previously encoded samples. The difference between the actual sample value and the prediction is called the prediction residual. The prediction residual is encoded into the compressed file. The decompressor can form the same predicted sample and can decode the prediction residual from the compressed file, and so can reconstruct the original sample. A lossless predictive compression algorithm can generally be converted to a near-lossless compression algorithm by quantizing the prediction residuals prior to encoding them. In this case, since the reconstructed sample values will not be identical to the original sample values, the encoder must determine the values that will be reconstructed and use these values for predicting later sample values. The technique described here uses this method, starting with the original technique, to allow near-lossless compression. The extension to allow near-lossless compression adds the ability to achieve much more compression when small amounts of distortion are tolerable, while retaining the low complexity and good overall compression effectiveness of the original algorithm.
Xie, Hua↗
Pilot Field Test: Use of a Compression Garment During a Stand Test After Long-Duration Space Flight
Orthostatic intolerance (OI) is a concern for astronauts returning from long-duration space flight. One countermeasure that has been used to protect against OI after short-duration bed rest and space flight is the use of lower body and abdominal compression garments. However, since the end of the Space Shuttle era we have not been able to test crewmembers during the first 24 hours after landing on Earth. NASA's Pilot Field Test provided us the opportunity to test cardiovascular responses of crewmembers wearing the Russian Kentavr compression garment during a stand test at multiple time points throughout the first 24 hours after landing. HYPOTHESIS We hypothesized that the Kentavr compression garment would prevent an increase in heart rate (HR) >15 bpm during a 3.5-min stand test. METHODS: The Pilot Field Test was conducted up to 3 times during the first 24 hours after crewmembers returned to Earth: (1) either in a tent adjacent to the Soyuz landing site in Kazakhstan (approx.1 hr) or after transportation to the Karaganda airport (approx. 4 hr); (2) during a refueling stop in Scotland (approx.12 hr); and (3) upon return to NASA Johnson Space Center (JSC) (approx.24 hr). We measured HR and arterial pressure (finger photoplethysmography) for 2 min while the crewmember was prone and throughout 3.5 min of quiet standing. Eleven crewmembers consented to participate; however, 2 felt too ill to start the test and 1 stopped 30 sec into the stand portion of the test. Of the remaining 8 crewmembers, 2 did not wear the Russian Kentavr compression garment. Because of inclement weather at the landing site, 5 crewmembers were flown by helicopter to the Karaganda airport before initial testing and received intravenous saline before completing the stand test. One of these crewmembers wore only the portion of the Russian Kentavr compression garment that covered the lower leg and thus lacked thigh and abdominal compression. All crewmembers continued wearing the Russian Kentavr compression garment during the second testing session in Scotland, but none wore it during testing at JSC. RESULTS: The mean Delta HR from the supine to standing position in the 8 crewmembers measured pre-flight or 60 days after return from long-duration space flight was 9.8 bpm. During the first few hours after landing from long-duration space flight, the mean Delta HR of the 6 crewmembers who wore the Russian Kentavr compression garment in Kazakhstan or Karaganda was +14 bpm and the change in mean arterial pressure (Delta MAP) was +0.8 mmHg, while the 2 crewmembers who did not wear the Russian Kentavr compression garment had a Delta HR of +38 bpm and a Delta MAP of +1.1 mmHg. In Scotland, 4 crewmembers wore the Russian Kentavr compression garment and had a Delta HR of +7.4 bpm while the 3 crewmembers who did not wear it had a Delta HR of +25.0 bpm. Seven crewmembers were tested upon return to JSC approx. 24 hr after landing, but none wore the Russian Kentavr compression garment and their Delta HR was 16.0 bpm. CONCLUSIONS: These are the first stand-test data to be collected from long-duration crewmembers during the first 24 hr of re-adaptation to gravity on Earth. The Delta HR measured in crewmembers who completed the stand-test while wearing Kentavr within the first approx.4 hours after returning to Earth was only slightly elevated from pre-flight Delta HR, while the few subjects who did not wear the Russian Kentavr compression garment had a much larger increase in HR in order to maintain arterial pressure throughout 3.5-min of standing. These data demonstrate the effectiveness of a compression garment in preventing large increases in HR during a 3.5 min stand test after long-duration space flight. However, the fact that three crewmembers were too ill to complete the test or was not able to complete 3.5 min of standing despite wearing the Russian Kentavr compression garment indicates that wearing a compression garment does not resolve all problems crewmembers face during the period of re-adaptation immediately after return to Earth's gravity.
Laurie, S. S.↗
Performance of a Discrete Wavelet Transform for Compressing Plasma Count Data and its Application to the Fast Plasma Investigation on NASA's Magnetospheric Multiscale Mission
Plasma measurements in space are becoming increasingly faster, higher resolution, and distributed over multiple instruments. As raw data generation rates can exceed available data transfer bandwidth, data compression is becoming a critical design component. Data compression has been a staple of imaging instruments for years, but only recently have plasma measurement designers become interested in high performance data compression. Missions will often use a simple lossless compression technique yielding compression ratios of approximately 2:1, however future missions may require compression ratios upwards of 10:1. This study aims to explore how a Discrete Wavelet Transform combined with a Bit Plane Encoder (DWT/BPE), implemented via a CCSDS standard, can be used effectively to compress count information common to plasma measurements to high compression ratios while maintaining little or no compression error. The compression ASIC used for the Fast Plasma Investigation (FPI) on board the Magnetospheric Multiscale mission (MMS) is used for this study. Plasma count data from multiple sources is examined: resampled data from previous missions, randomly generated data from distribution functions, and simulations of expected regimes. These are run through the compression routines with various parameters to yield the greatest possible compression ratio while maintaining little or no error, the latter indicates that fully lossless compression is obtained. Finally, recommendations are made for future missions as to what can be achieved when compressing plasma count data and how best to do so.
Particles↗
Hardware Implementation of Lossless Adaptive Compression of Data From a Hyperspectral Imager
Efficient onboard data compression can reduce the data volume from hyperspectral imagers on NASA and DoD spacecraft in order to return as much imagery as possible through constrained downlink channels. Lossless compression is important for signature extraction, object recognition, and feature classification capabilities. To provide onboard data compression, a hardware implementation of a lossless hyperspectral compression algorithm was developed using a field programmable gate array (FPGA). The underlying algorithm is the Fast Lossless (FL) compression algorithm reported in Fast Lossless Compression of Multispectral- Image Data (NPO-42517), NASA Tech Briefs, Vol. 30, No. 8 (August 2006), p. 26 with the modification reported in Lossless, Multi-Spectral Data Comressor for Improved Compression for Pushbroom-Type Instruments (NPO-45473), NASA Tech Briefs, Vol. 32, No. 7 (July 2008) p. 63, which provides improved compression performance for data from pushbroom-type imagers. An FPGA implementation of the unmodified FL algorithm was previously developed and reported in Fast and Adaptive Lossless Onboard Hyperspectral Data Compression System (NPO-46867), NASA Tech Briefs, Vol. 36, No. 5 (May 2012) p. 42. The essence of the FL algorithm is adaptive linear predictive compression using the sign algorithm for filter adaption. The FL compressor achieves a combination of low complexity and compression effectiveness that exceeds that of stateof- the-art techniques currently in use. The modification changes the predictor structure to tolerate differences in sensitivity of different detector elements, as occurs in pushbroom-type imagers, which are suitable for spacecraft use. The FPGA implementation offers a low-cost, flexible solution compared to traditional ASIC (application specific integrated circuit) and can be integrated as an intellectual property (IP) for part of, e.g., a design that manages the instrument interface. The FPGA implementation was benchmarked on the Xilinx Virtex IV LX25 device, and ported to a Xilinx prototype board. The current implementation has a critical path of 29.5 ns, which dictated a clock speed of 33 MHz. The critical path delay is end-to-end measurement between the uncompressed input data and the output compression data stream. The implementation compresses one sample every clock cycle, which results in a speed of 33 Msample/s. The implementation has a rather low device use of the Xilinx Virtex IV LX25, making the total power consumption of the implementation about 1.27 W.
Keymeulen, Didlier↗
Dynamic Virtual Chunks: On Supporting Efficient Accesses to Compressed Scientific Data
Data compression could ameliorate the I/O pressure of data-intensive scientific applications. Unfortunately, the conventional wisdom of naively applying data compression to the file or block brings the dilemma between efficient random accesses and high compression ratios. File-level compression barely supports efficient random accesses to the compressed data: any retrieval request need trigger the decompression from the beginning of the compressed file. Block-level compression provides flexible random accesses to the compressed blocks, but introduces extra overhead when applying the compressor to each and every block that results in a degraded overall compression ratio. This paper extends our prior work that introduces virtual chunks offering efficient random accesses to the compressed scientific data without sacrificing the compression ratio. Virtual chunks are logical blocks pointed at by appended references without breaking the physical continuity of the file content. These references allow the decompression to start from an arbitrary position (efficient random accesses), while no per-block overhead is introduced because the file’s physical entirety is retained (high compression ratio). One limitation of virtual chunk is it only supports static references. This paper presents the algorithms, analysis, and evaluations of dynamic virtual chunks to deal with the cases where the references are updated dynamically.
I/O compression performance optimization↗
4D-CT scans reveal reduced magnitude of respiratory liver motion achieved by different abdominal compression plate positions in patients with intrahepatic tumors undergoing helical tomotherapy
Purpose: While abdominal compression (AC) can be used to reduce respiratory liver motion in patients receiving helical tomotherapy for hepatocellular carcinoma, the nature and extent of this effect is not well described. The purpose of this study was to evaluate the changes in magnitude of three-dimensional liver motion with abdominal compression using four-dimensional (4D) computed tomography (CT) images of several plate positions. Methods: From January 2012 to October 2015, 72 patients with intrahepatic carcinoma and divided into four groups underwent 4D-CT scans to assess respiratory liver motion. Of the 72 patients, 19 underwent abdominal compression of the cephalic area between the subxiphoid and umbilicus (group A), 16 underwent abdominal compression of the caudal region between the subxiphoid area and the umbilicus (group B), 11 patients underwent abdominal compression of the caudal umbilicus (group C), and 26 patients remained free breathing (group D). 4D-CT images were sorted into ten-image series, according to the respiratory phase from the end inspiration to the end expiration, and then transferred to treatment planning software. All liver contours were drawn by a single physician and confirmed by a second physician. Liver relative coordinates were automatically generated to calculate the liver respiratory motion in different axial directions to compile the 10 ten contours into a single composite image. Differences in respiratory liver motion were assessed with a one-way analysis of variance test of significance. Results: The average respiratory liver motion in the Y axial direction was 4.53 ± 1.16, 7.56 ± 1.30, 9.95 ± 2.32, and 9.53 ± 2.62 mm in groups A, B, C, and D, respectively, with a significant change among the four groups (p < 0.001). Abdominal compression was most effective in group A (compression plate on the subxiphoid area), with liver displacement being 2.53 ± 0.93, 4.53 ± 1.16, and 2.14 ± 0.92 mm on the X-, Y-, and Z-axes, respectively. There was no significant difference in respiratory liver motion between group C (displacement: 3.23 ± 1.47, 9.95 ± 2.32, and 2.92 ± 1.10 mm on the X-, Y-, and Z-axes, respectively) and group D (displacement: 3.35 ± 1.55, 9.53 ± 2.62, and 3.35 ± 1.73 mm on the X-, Y-, and Z-axes, respectively). Abdominal compression was least effective in group C (compression on caudal umbilicus), with liver motion in this group similar to that of free-breathing patients (group D). Conclusions: 4D-CT scans revealed significant liver motion control via abdominal compression of the subxiphoid area; however, this control of liver motion was not observed with compression of the caudal umbilicus. The authors, therefore, recommend compression of the subxiphoid area in patients undergoing external radiotherapy for intrahepatic carcinoma.
60 APPLIED LIFE SCIENCES↗
SU-D-BRA-06: Duodenal Interfraction Motion with Abdominal Compression
Purpose: To quantify the effect of abdominal compression on duodenal motion during pancreatic radiotherapy. Methods: Seven patients treated for pancreatic cancer were selected for analysis. Four patients were treated with abdominal compression and three without. The duodenum was contoured by the same physician on each CBCT (five CBCTs for patients with compression, four for non-compression patients). CBCTs were rigidly registered using a soft tissue match and contours were copied to the delivered plans which were all radical (BED > 50 Gy). The distance between the duodenum on the planning CT and each CBCT was quantified by calculating the root mean square (RMS) distance. The DVHs of each abdominal compression patient was converted to an EQD2 DVH (alpha/beta = 10) using an in-house tool and volumes receiving at least 25, 35, 45, and 50 Gy were recorded. Results: The maximum variation in duodenal volumes on the CBCTs for the four abdominal compression patients were 19.1 cm{sup 3} (32.8%), 19.1 cm{sup 3} (20.6%), 19.9 cm{sup 3} (14.3%), and 12.9 cm{sup 3} (27.3%) compared to 15.2 cm{sup 3} (17.6%), 34.7 cm{sup 3} (83.4%), and 56 cm{sup 3} (60.2%) for non-compression patients. The average RMS distance between the duodenum on the planning CT and each CBCT for all abdominal compression patients was 0.3 cm compared to 0.7 cm for non-compressed patients. The largest (and average) difference between the planning CT and CBCTs in volume of duodenum receiving more than 25, 35, 45 and 50 Gy for abdominal compression patients was 11% (5%), 9% (3%), 9% (2%), and 6% (1%). Conclusion: Abdominal compression reduces variation in volume and absolute position of the duodenum throughout treatment. This is seen as an improvement but does not eliminate the need to consider dosimetric effects of motion. Abdominal compression is particularly useful in SBRT when only a few fractions are delivered. Alon Witztum is supported by an MRC/Gray Institute DPhil Studentship. Daniel Holyoake is supported by a CRUK/Nuffield Clinical Research Fellowship. Sam Warren and Mike Partridge are supported by CRUK grant C5255/A15935. Maria Hawkins received an MRC Fellowship MC-PC-12001/2.
60 APPLIED LIFE SCIENCES↗
Streaming Compression of Scientific Data via Weak-SINDy
Here, in this paper, a streaming weak-SINDy algorithm is developed specifically for compressing streaming scientific data. The production of scientific data, either via simulation or experiments, is undergoing a stage of exponential growth, which makes data compression important and often necessary for storing and utilizing large scientific data sets. As opposed to classical “offline” compression algorithms that perform compression on a readily available data set, streaming compression algorithms compress data “online” while the data generated from simulation or experiments is still flowing through the system. This feature makes streaming compression algorithms well suited for scientific data compression, where storing the full data set offline is often infeasible. This work proposes a new streaming compression algorithm, streaming weak-SINDy, which takes advantage of the underlying data characteristics during compression. The streaming weak-SINDy algorithm constructs feature matrices and target vectors in the online stage via a streaming integration method in a memory efficient manner. The feature matrices and target vectors are then used in the offline stage to build a model through a regression process that aims to recover equations that govern the evolution of the data. For compressing high-dimensional streaming data, we adopt a streaming proper orthogonal decomposition (POD) process to reduce the data dimension and then use the streaming weak-SINDy algorithm to compress the temporal data of the POD expansion. We propose modifications to the streaming weak-SINDy algorithm to accommodate the dynamically updated POD basis. By combining the built model from the streaming weak-SINDy algorithm and a small amount of data samples, the full data flow could be reconstructed accurately at a low memory cost, as shown in the numerical tests.
97 MATHEMATICS AND COMPUTING↗
Understanding turbulence in compressing plasmas and its exploitation or prevention.
Unprecedented densities and temperatures are now achieved in compressions of plasma, by lasers and by pulsed power, in major experimental facilities. These compressions, carried out at the largest scale at the National Ignition Facility and at the Z Pulsed Power Facility, have important applications, including fusion, X-ray production, and materials research. Several experimental and simulation results suggest that the plasma in some of these compressions is turbulent. In fact, measurements suggest that in certain laboratory plasma compressions the turbulent energy is a dominant energy component. Similarly, turbulence is dominant in some compressing astrophysical plasmas, such as in molecular clouds. Turbulence need not be dominant to be important; even small quantities could greatly influence experiments that are sensitive to mixing of non-fuel into fuel, such as compressions seeking fusion ignition. Despite its important role in major settings, bulk plasma turbulence under compression is insufficiently understood to answer or even to pose some of the most fundamental questions about it. This thesis both identifies and answers key questions in compressing turbulent motion, while providing a description of the behavior of three-dimensional, isotropic, compressions of homogeneous turbulence with a plasma viscosity. This description includes a simple, but successful, new model for the turbulent energy of plasma undergoing compression. The unique features of compressing turbulence with a plasma viscosity are shown, including the sensitivity of the turbulence to plasma ionization, and a sudden viscous dissipation'' effect which rapidly converts plasma turbulent energy into thermal energy. This thesis then examines turbulence in both laboratory compression experiments and molecular clouds. It importantly shows: the possibility of exploiting turbulence to make fusion or X-ray production more efficient; conditions under which hot-spot turbulence can be prevented; and a lower bound on the growth of turbulence in molecular clouds. This bound raises questions about the level of dissipation in existing molecular cloud models. Finally, the observations originally motivating the thesis, Z-pinch measurements suggesting dominant turbulent energy, are reexamined by self-consistently accounting for the impact of the turbulence on the spectroscopic analysis. This is found to strengthen the evidence that the multiple observations describe a highly turbulent plasma state.
fast ignition↗
Optimising the processing and storage of visibilities using lossy compression
The next-generation radio astronomy instruments are providing a massive increase in sensitivity and coverage, largely through increasing the number of stations in the array and the frequency span sampled. The two primary problems encountered when processing the resultant avalanche of data are the need for abundant storage and the constraints imposed by I/O, as I/O bandwidths drop significantly on cold storage. An example of this is the data deluge expected from the SKA Telescopes of more than 60 PB per day, all to be stored on the buffer filesystem. While compressing the data is an obvious solution, the impacts on the final data products are hard to predict. In this paper, we chose an error-controlled compressor – MGARD – and applied it to simulated SKA-Mid and real pathfinder visibility data, in noise-free and noise-dominated regimes. As the data have an implicit error level in the system temperature, using an error bound in compression provides a natural metric for compression. MGARD ensures the compression incurred errors adhere to the user-prescribed tolerance. To measure the degradation of images reconstructed using the lossy compressed data, we proposed a list of diagnostic measures, exploring the trade-off between these error bounds and the corresponding compression ratios, as well as the impact on science quality derived from the lossy compressed data products through a series of experiments. We studied the global and local impacts on the output images for continuum and spectral line examples. We found relative error bounds of as much as 10%, which provide compression ratios of about 20, have a limited impact on the continuum imaging as the increased noise is less than the image RMS, whereas a 1% error bound (compression ratio of 8) introduces an increase in noise of about an order of magnitude less than the image RMS. For extremely sensitive observations and for very precious data, we would recommend a 0.1% error bound with compression ratios of about 4. These have noise impacts two orders of magnitude less than the image RMS levels. At these levels, the limits are due to instabilities in the deconvolution methods. We compared the results to the alternative compression tool DYSCO, in both the impacts on the images and in the relative flexibility. MGARD provides better compression for similar error bounds and has a host of potentially powerful additional features.
Techniques: interferometric↗
Exploring Autoencoder-based Error-bounded Compression for Scientific Data
Error-bounded lossy compression is becoming an indispensable technique for the success of today's scientific projects with vast volumes of data produced during the simulations or instrument data acquisitions. Not only can it significantly reduce data size, but it also can control the compression errors based on user-specified error bounds. Autoencoder (AE) models have been widely used in image compression, but few AE-based compression approaches support error-bounding features, which are highly required by scientific applications. To address this issue, we explore using convolutional autoencoders to improve error-bounded lossy compression for scientific data, with the following three key contributions. (1) We provide an in-depth investigation of the characteristics of various autoencoder models and develop an error-bounded autoencoder-based framework in terms of the SZ model. (2) We optimize the compression quality for main stages in our designed AE-based error-bounded compression framework, fine-tuning the block sizes and latent sizes and also optimizing the compression efficiency of latent vectors. (3) We evaluate our proposed solution using five real-world scientific datasets and comparing them with six other related works. Experiments show that our solution exhibits a very competitive compression quality from among all the compressors in our tests. In absolute terms, it can obtain a much better compression quality (100%similar to 800% improvement in compression ratio with the same data distortion) compared with SZ2.1 and ZFP in cases with a high compression ratio.
Liu, Jinyang↗
Fpack and Funpack Utilities for FITS Image Compression and Uncompression
Fpack is a utility program for optimally compressing images in the FITS (Flexible Image Transport System) data format (see http://fits.gsfc.nasa.gov). The associated funpack program restores the compressed image file back to its original state (as long as a lossless compression algorithm is used). These programs may be run from the host operating system command line and are analogous to the gzip and gunzip utility programs except that they are optimized for FITS format images and offer a wider choice of compression algorithms. Fpack stores the compressed image using the FITS tiled image compression convention (see http://fits.gsfc.nasa.gov/fits_registry.html). Under this convention, the image is first divided into a user-configurable grid of rectangular tiles, and then each tile is individually compressed and stored in a variable-length array column in a FITS binary table. By default, fpack usually adopts a row-by-row tiling pattern. The FITS image header keywords remain uncompressed for fast access by FITS reading and writing software. The tiled image compression convention can in principle support any number of different compression algorithms. The fpack and funpack utilities call on routines in the CFITSIO library (http://hesarc.gsfc.nasa.gov/fitsio) to perform the actual compression and uncompression of the FITS images, which currently supports the GZIP, Rice, H-compress, and PLIO IRAF pixel list compression algorithms.
Pence, W.↗
ICER-3D Hyperspectral Image Compression Software
Software has been developed to implement the ICER-3D algorithm. ICER-3D effects progressive, three-dimensional (3D), wavelet-based compression of hyperspectral images. If a compressed data stream is truncated, the progressive nature of the algorithm enables reconstruction of hyperspectral data at fidelity commensurate with the given data volume. The ICER-3D software is capable of providing either lossless or lossy compression, and incorporates an error-containment scheme to limit the effects of data loss during transmission. The compression algorithm, which was derived from the ICER image compression algorithm, includes wavelet-transform, context-modeling, and entropy coding subalgorithms. The 3D wavelet decomposition structure used by ICER-3D exploits correlations in all three dimensions of sets of hyperspectral image data, while facilitating elimination of spectral ringing artifacts, using a technique summarized in "Improving 3D Wavelet-Based Compression of Spectral Images" (NPO-41381), NASA Tech Briefs, Vol. 33, No. 3 (March 2009), page 7a. Correlation is further exploited by a context-modeling subalgorithm, which exploits spectral dependencies in the wavelet-transformed hyperspectral data, using an algorithm that is summarized in "Context Modeler for Wavelet Compression of Hyperspectral Images" (NPO-43239), which follows this article. An important feature of ICER-3D is a scheme for limiting the adverse effects of loss of data during transmission. In this scheme, as in the similar scheme used by ICER, the spatial-frequency domain is partitioned into rectangular error-containment regions. In ICER-3D, the partitions extend through all the wavelength bands. The data in each partition are compressed independently of those in the other partitions, so that loss or corruption of data from any partition does not affect the other partitions. Furthermore, because compression is progressive within each partition, when data are lost, any data from that partition received prior to the loss can be used to reconstruct that partition at lower fidelity. By virtue of the compression improvement it achieves relative to previous means of onboard data compression, this software enables (1) increased return of hyperspectral scientific data in the presence of limits on the rates of transmission of data from spacecraft to Earth via radio communication links and/or (2) reduction in spacecraft radio-communication power and/or cost through reduction in the amounts of data required to be downlinked and stored onboard prior to downlink. The software is also suitable for compressing hyperspectral images for ground storage or archival purposes.
Xie, Hua↗