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NASA NTRS1996

Communications and information research: Improved space link performance via concatenated forward error correction coding

With the development of new advanced instruments for remote sensing applications, sensor data will be generated at a rate that not only requires increased onboard processing and storage capability, but imposes demands on the space to ground communication link and ground data management-communication system. Data compression and error control codes provide viable means to alleviate these demands. Two types of data compression have been studied by many researchers in the area of information theory: a lossless technique that guarantees full reconstruction of the data, and a lossy technique which generally gives higher data compaction ratio but incurs some distortion in the reconstructed data. To satisfy the many science disciplines which NASA supports, lossless data compression becomes a primary focus for the technology development. While transmitting the data obtained by any lossless data compression, it is very important to use some error-control code. For a long time, convolutional codes have been widely used in satellite telecommunications. To more efficiently transform the data obtained by the Rice algorithm, it is required to meet the a posteriori probability (APP) for each decoded bit. A relevant algorithm for this purpose has been proposed which minimizes the bit error probability in the decoding linear block and convolutional codes and meets the APP for each decoded bit. However, recent results on iterative decoding of 'Turbo codes', turn conventional wisdom on its head and suggest fundamentally new techniques. During the past several months of this research, the following approaches have been developed: (1) a new lossless data compression algorithm, which is much better than the extended Rice algorithm for various types of sensor data, (2) a new approach to determine the generalized Hamming weights of the algebraic-geometric codes defined by a large class of curves in high-dimensional spaces, (3) some efficient improved geometric Goppa codes for disk memory systems and high-speed mass memory systems, and (4) a tree based approach for data compression using dynamic programming.

Rao, T. R. N.↗

NASA NTRS1994

Advanced End-to-end Simulation for On-board Processing (AESOP)

Developers of data compression algorithms typically use their own software together with commercial packages to implement, evaluate and demonstrate their work. While convenient for an individual developer, this approach makes it difficult to build on or use another's work without intimate knowledge of each component. When several people or groups work on different parts of the same problem, the larger view can be lost. What's needed is a simple piece of software to stand in the gap and link together the efforts of different people, enabling them to build on each other's work, and providing a base for engineers and scientists to evaluate the parts as a cohesive whole and make design decisions. AESOP (Advanced End-to-end Simulation for On-board Processing) attempts to meet this need by providing a graphical interface to a developer-selected set of algorithms, interfacing with compiled code and standalone programs, as well as procedures written in the IDL and PV-Wave command languages. As a proof of concept, AESOP is outfitted with several data compression algorithms integrating previous work on different processors (AT&T DSP32C, TI TMS320C30, SPARC). The user can specify at run-time the processor on which individual parts of the compression should run. Compressed data is then fed through simulated transmission and uncompression to evaluate the effects of compression parameters, noise and error correction algorithms. The following sections describe AESOP in detail. Section 2 describes fundamental goals for usability. Section 3 describes the implementation. Sections 4 through 5 describe how to add new functionality to the system and present the existing data compression algorithms. Sections 6 and 7 discuss portability and future work.

Mazer, Alan S.↗

NASA NTRS1991

Recent advances in coding theory for near error-free communications

Channel and source coding theories are discussed. The following subject areas are covered: large constraint length convolutional codes (the Galileo code); decoder design (the big Viterbi decoder); Voyager's and Galileo's data compression scheme; current research in data compression for images; neural networks for soft decoding; neural networks for source decoding; finite-state codes; and fractals for data compression.

Cheung, K.-M.↗

NASA NTRS1993

Applications of wavelet-based compression to multidimensional Earth science data

A data compression algorithm involving vector quantization (VQ) and the discrete wavelet transform (DWT) is applied to two different types of multidimensional digital earth-science data. The algorithms (WVQ) is optimized for each particular application through an optimization procedure that assigns VQ parameters to the wavelet transform subbands subject to constraints on compression ratio and encoding complexity. Preliminary results of compressing global ocean model data generated on a Thinking Machines CM-200 supercomputer are presented. The WVQ scheme is used in both a predictive and nonpredictive mode. Parameters generated by the optimization algorithm are reported, as are signal-to-noise (SNR) measurements of actual quantized data. The problem of extrapolating hydrodynamic variables across the continental landmasses in order to compute the DWT on a rectangular grid is discussed. Results are also presented for compressing Landsat TM 7-band data using the WVQ scheme. The formulation of the optimization problem is presented along with SNR measurements of actual quantized data. Postprocessing applications are considered in which the seven spectral bands are clustered into 256 clusters using a k-means algorithm and analyzed using the Los Alamos multispectral data analysis program, SPECTRUM, both before and after being compressed using the WVQ program.

Bradley, Jonathan N.↗

NASA NTRS2004

The New CCSDS Image Compression Recommendation

The Consultative Committee for Space Data Systems (CCSDS) data compression working group has recently adopted a recommendation for image data compression, with a final release expected in 2005. The algorithm adopted in the recommendation consists a two dimensional discrete wavelet transform of the image, followed by progressive bit-plane coding of the transformed data. The algorithm can provide both lossless and lossy compression, and allows a user to directly control the compressed data volume or the fidelity with which the wavelet-transformed data can be reconstructed. The algorithm is suitable for both frame-based image data and scan-based sensor data, and has applications for near-earth and deep-space missions. The standard will be accompanied by free software sources on a future web site. An ASIC implementation of the compressor is currently under development. This paper describes the compression algorithm along with the requirements that drove the selection of the algorithm.

image compression↗

NASA NTRS2005

The New CCSDS Image Compression Recommendation

The Consultative Committee for Space Data Systems (CCSDS) data compression working group has recently adopted a recommendation for image data compression, with a final release expected in 2005. The algorithm adopted in the recommendation consists of a two-dimensional discrete wavelet transform of the image, followed by progressive bit-plane coding of the transformed data. The algorithm can provide both lossless and lossy compression, and allows a user to directly control the compressed data volume or the fidelity with which the wavelet-transformed data can be reconstructed. The algorithm is suitable for both frame-based image data and scan-based sensor data, and has applications for near-Earth and deep-space missions. The standard will be accompanied by free software sources on a future web site. An Application-Specific Integrated Circuit (ASIC) implementation of the compressor is currently under development. This paper describes the compression algorithm along with the requirements that drove the selection of the algorithm. Performance results and comparisons with other compressors are given for a test set of space images.

Yeh, Pen-Shu↗

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 NTRS1992

Vector quantization for volume rendering

Volume rendering techniques typically process volumetric data in raw, uncompressed form. As algorithmic and architectural advances improve rendering speeds, however, larger data sets will be evaluated requiring consideration of data storage and transmission issues. In this paper, we analyze the data compression requirements for volume rendering applications and present a solution based on vector quantization. The proposed system compresses volumetric data and then renders images directly from the new data format. Tests on a fluid flow data set demonstrate that good image quality may be achieved at a compression ratio of 17:1 with only a 5 percent cost in additional rendering time.

Ning, Paul↗

NASA NTRS1991

Scientific requirements for space science data systems

In the 1990's space plasma physics studies will increasingly involve correlative analysis of observations from multiple instruments and multiple spacecraft. The solar terrestrial physics missions in the 1990's will be designed around simultaneous observations from spacecraft monitoring the solar wind, the polar magnetosphere and the near and distant magnetotail. Within these regions, clusters of spacecraft flying in formation will increasingly involve comparative magnetospheric studies. No single lab will have the expertise to process and analyze all of the different types of data so the data repositories will be distributed. Catalog and browse systems will be required to help select events for study. Data compression techniques may be useful in designing the data bases used for selecting events for study. Data compression onboard the spacecraft will be necessary, since instrument data rates will be much larger than available telemetry rates. However, considerable care will be necessary to avoid losing valuable data when applying data compression algorithms.

Walker, Raymond J.↗

NASA NTRS2005

Improved Compression of Wavelet-Transformed Images

A recently developed data-compression method is an adaptive technique for coding quantized wavelet-transformed data, nominally as part of a complete image-data compressor. Unlike some other approaches, this method admits a simple implementation and does not rely on the use of large code tables. A common data compression approach, particularly for images, is to perform a wavelet transform on the input data, and then losslessly compress a quantized version of the wavelet-transformed data. Under this compression approach, it is common for the quantized data to include long sequences, or runs, of zeros. The new coding method uses prefixfree codes for the nonnegative integers as part of an adaptive algorithm for compressing the quantized wavelet-transformed data by run-length coding. In the form of run-length coding used here, the data sequence to be encoded is parsed into strings consisting of some number (possibly 0) of zeros, followed by a nonzero value. The nonzero value and the length of the run of zeros are encoded. For a data stream that contains a sufficiently high frequency of zeros, this method is known to be more effective than using a single variable length code to encode each symbol. The specific prefix-free codes used are from two classes of variable-length codes: a class known as Golomb codes, and a class known as exponential-Golomb codes. The codes within each class are indexed by a single integer parameter. The present method uses exponential-Golomb codes for the lengths of the runs of zeros, and Golomb codes for the nonzero values. The code parameters within each code class are determined adaptively on the fly as compression proceeds, on the basis of statistics from previously encoded values. In particular, a simple adaptive method has been devised to select the parameter identifying the particular exponential-Golomb code to use. The method tracks the average number of bits used to encode recent runlengths, and takes the difference between this average length and the code parameter. When this difference falls outside a fixed range, the code parameter is updated (increased or decreased). The Golomb code parameter is selected based on the average magnitude of recently encoded nonzero samples. The coding method requires no floating- point operations, and more readily adapts to local statistics than other methods. The method can also accommodate arbitrarily large input values and arbitrarily long runs of zeros. In practice, this means that changes in the dynamic range or size of the input data set would not require a change to the compressor. The algorithm has been tested in computational experiments on test images. A comparison with a previously developed algorithm that uses large code tables (generated via Huffman coding on training data) suggests that the data-compression effectiveness of the present algorithm is comparable to the best performance achievable by the previously developed algorithm.

Kiely, Aaron↗

NASA NTRS1984

Studies of optical and biological properties of terrestrial land cover using multispectral linear array technology

A series of experiments to study the optical and biological properties of terrestrial land cover are planned for late 1987 using a six-channel imaging spectroradiometer based on newly developed multispectral linear array (MLA) detector technology. Data from selected portions of the Sahel and rain forests of Africa and South America will be used to delineate biomass classes and estimate spherical albedos. A spatial resolution of 15 meters in the four visible-near IR channels and 30 meters in two shortwave IR channels, including a 'new' channel centered at 1.24 micrometers when combined with a spectral width of 20 nm for all channels, will be used to investigate possible improvements in land cover classification. Technology demonstrations include a test of data compression on data quality, the first spaceborne utilization of short wave infrared Schottky barrier Pd2Si detector arrays, and the use of close-butted, multi-array modules with attached spectral filters.

Barnes, W. L.↗

NASA NTRS2003

Compressing Aviation Data in XML Format

Design, operations and maintenance activities in aviation involve analysis of variety of aviation data. This data is typically in disparate formats making it difficult to use with different software packages. Use of a self-describing and extensible standard called XML provides a solution to this interoperability problem. XML provides a standardized language for describing the contents of an information stream, performing the same kind of definitional role for Web content as a database schema performs for relational databases. XML data can be easily customized for display using Extensible Style Sheets (XSL). While self-describing nature of XML makes it easy to reuse, it also increases the size of data significantly. Therefore, transfemng a dataset in XML form can decrease throughput and increase data transfer time significantly. It also increases storage requirements significantly. A natural solution to the problem is to compress the data using suitable algorithm and transfer it in the compressed form. We found that XML-specific compressors such as Xmill and XMLPPM generally outperform traditional compressors. However, optimal use of Xmill requires of discovery of optimal options to use while running Xmill. This, in turn, depends on the nature of data used. Manual disc0ver.y of optimal setting can require an engineer to experiment for weeks. We have devised an XML compression advisory tool that can analyze sample data files and recommend what compression tool would work the best for this data and what are the optimal settings to be used with a XML compression tool.

Patel, Hemil↗

NASA NTRS1993

Transform coding for space applications

Data compression coding requirements for aerospace applications differ somewhat from the compression requirements for entertainment systems. On the one hand, entertainment applications are bit rate driven with the goal of getting the best quality possible with a given bandwidth. Science applications are quality driven with the goal of getting the lowest bit rate for a given level of reconstruction quality. In the past, the required quality level has been nothing less than perfect allowing only the use of lossless compression methods (if that). With the advent of better, faster, cheaper missions, an opportunity has arisen for lossy data compression methods to find a use in science applications as requirements for perfect quality reconstruction runs into cost constraints. This paper presents a review of the data compression problem from the space application perspective. Transform coding techniques are described and some simple, integer transforms are presented. The application of these transforms to space-based data compression problems is discussed. Integer transforms have an advantage over conventional transforms in computational complexity. Space applications are different from broadcast or entertainment in that it is desirable to have a simple encoder (in space) and tolerate a more complicated decoder (on the ground) rather than vice versa. Energy compaction with new transforms are compared with the Walsh-Hadamard (WHT), Discrete Cosine (DCT), and Integer Cosine (ICT) transforms.

Glover, Daniel↗

NASA NTRS1993

Subband/transform functions for image processing

Functions for image data processing written for use with the MATLAB(TM) software package are presented. These functions provide the capability to transform image data with block transformations (such as the Walsh Hadamard) and to produce spatial frequency subbands of the transformed data. Block transforms are equivalent to simple subband systems. The transform coefficients are reordered using a simple permutation to give subbands. The low frequency subband is a low resolution version of the original image, while the higher frequency subbands contain edge information. The transform functions can be cascaded to provide further decomposition into more subbands. If the cascade is applied to all four of the first stage subbands (in the case of a four band decomposition), then a uniform structure of sixteen bands is obtained. If the cascade is applied only to the low frequency subband, an octave structure of seven bands results. Functions for the inverse transforms are also given. These functions can be used for image data compression systems. The transforms do not in themselves produce data compression, but prepare the data for quantization and compression. Sample quantization functions for subbands are also given. A typical compression approach is to subband the image data, quantize it, then use statistical coding (e.g., run-length coding followed by Huffman coding) for compression. Contour plots of image data and subbanded data are shown.

Glover, Daniel↗

NASA NTRS2008

Image Compression Algorithm Altered to Improve Stereo Ranging

A report discusses a modification of the ICER image-data-compression algorithm to increase the accuracy of ranging computations performed on compressed stereoscopic image pairs captured by cameras aboard the Mars Exploration Rovers. (ICER and variants thereof were discussed in several prior NASA Tech Briefs articles.) Like many image compressors, ICER was designed to minimize a mean-square-error measure of distortion in reconstructed images as a function of the compressed data volume. The present modification of ICER was preceded by formulation of an alternative error measure, an image-quality metric that focuses on stereoscopic-ranging quality and takes account of image-processing steps in the stereoscopic-ranging process. This metric was used in empirical evaluation of bit planes of wavelet-transform subbands that are generated in ICER. The present modification, which is a change in a bit-plane prioritization rule in ICER, was adopted on the basis of this evaluation. This modification changes the order in which image data are encoded, such that when ICER is used for lossy compression, better stereoscopic-ranging results are obtained as a function of the compressed data volume.

Kiely, Aaron↗

NASA NTRS1971

Effects of compression and random noise on multispectral data.

Two problem areas are often encountered in the use of multispectral scanner data: (1) large quantities of man and machine time are required to analyze and store the volumes of data gathered and (2) the quality of the data is usually reduced by the introduction of unwanted random noise. This paper will present some effects of data compression and random noise on multispectral data, particularly as they apply to pattern recognition and picture quality.

Ready, P. J.↗

NASA NTRS1988

A VLSI design for universal noiseless coding

The practical, noiseless and efficient data-compression technique presented involves a conceptual VLSI design which is capable of meeting real-time processing rates and meets low-power, low-weight, and small-volume requirements. This form of data compression is applicable to image data compression aboard future low-budget spaceflight missions, for such instruments as visual-IR mapping spectrometers and high-resolution imaging spectrometers.

Lee, Jun-Ji↗

NASA NTRS2019

Bounding Methods for Heterogeneous Lidar-derived Navigational Geofences

Safe Unmanned Aerial Vehicle (UAV) operations near the ground require navigation methods that avoid fixed obstacles such as buildings, power lines and trees. Aerial lidar surveys of ground structures are available with the precision and accuracy to geolocate obstacles, but the high volume of raw survey data can exceed the compute power of onboard processors and the rendering ability of ground-based flight planning maps. Representing ground structures with bounding polyhedra instead of point clouds greatly reduces the data size and can enable effective obstacle avoidance, as long as the bounding geometry envelopes the structures with high spatial fidelity. This report describes in detail four methods to compute bounding geometries of ground obstacles from lidar point clouds. The four methods are: 1) 2.5D Maximum Elevation Box, 2) 2.5D Ground Map Extrusion, 3) 3D Bounding Cylinder, and 4) 3D Bounding Box. The methods are applied to five point cloud datasets from lidar surveys of UAV flight research sites in Georgia and Virginia with an average point spacing that ranges from 0.1m to 0.6m. The methods are assessed using survey areas with geometrically heterogeneous ground structures: buildings, vegetation, power lines, and sub-meter structures such as road signs and guy wires. The 2.5D Maximum Elevation Box method is useful for simple structures. The 2.5D Ground Map Extrusion method efficiently encloses vegetation, but requires handdrawn ground footprints. The 3D Bounding Cylinder method excels at enclosing linear structures such as power lines and fences. The 3D Bounding Box method excels at enclosing planar structures such as buildings. The methods are compared on the basis of data compression and boundary fidelity on selected areas. The 2.5D methods yield the highest data compression but the polyhedra produced by them enclose significant amounts of empty space. Boundary fidelity is superior for the 3D methods, though this fidelity comes at the cost of a roughly thirtyfold lower data compression ratio than the 2.5D Maximum Elevation Box method. A mix of these output geometries is proposed for autonomous UAV navigation with limited on-board computing. Both the accuracy and spatial detail of emerging satellite-based survey technology lower than that of aerial lidar scanning survey technology. Sub-meter structures and thin linear structures are not reliably mapped at present by satellite-based surveys.

Moore, Andrew J.↗