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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
NASA NTRS1988

Image segmentation by iterative parallel region growing with application to data compression and image analysis

Image segmentation can be a key step in data compression and image analysis. However, the segmentation results produced by most previous approaches to region growing are suspect because they depend on the order in which portions of the image are processed. An iterative parallel segmentation algorithm avoids this problem by performing globally best merges first. Such a segmentation approach, and two implementations of the approach on NASA's Massively Parallel Processor (MPP) are described. Application of the segmentation approach to data compression and image analysis is then described, and results of such application are given for a LANDSAT Thematic Mapper image.

Tilton, James C.↗
NASA NTRS1991

Photorefractive Crystal Compresses Dynamic Range Of Image

Experiment shows dynamic range of spatial variations of illumination within image compressed by use of photorefractive crystal. In technique, photorefractive crystal placed in optical path at some stage preceding video camera, photographic camera, or final photodetector stage. Provided brightness of parts of scene vary as slowly as or more slowly than photorefractive crystal responds, effect exploited to provide real-time dynamic-range compression to prevent saturation of bright areas in video or photographic images of scene, helping to preserve spatial-variation information in such images.

Liu, Hua-Kuang↗
NASA NTRS1979

Block adaptive rate controlled image data compression

A block adaptive rate controlled (BARC) image data compression algorithm is described. It is noted that in the algorithm's principal rate controlled mode, image lines can be coded at selected rates by combining practical universal noiseless coding techniques with block adaptive adjustments in linear quantization. Compression of any source data at chosen rates of 3.0 bits/sample and above can be expected to yield visual image quality with imperceptible degradation. Exact reconstruction will be obtained if the one-dimensional difference entropy is below the selected compression rate. It is noted that the compressor can also be operated as a floating rate noiseless coder by simply not altering the input data quantization. Here, the universal noiseless coder ensures that the code rate is always close to the entropy. Application of BARC image data compression to the Galileo orbiter mission of Jupiter is considered.

Rice, R. F.↗
NASA NTRS1995

Model-based VQ for image data archival, retrieval and distribution

An ideal image compression technique for image data archival, retrieval and distribution would be one with the asymmetrical computational requirements of Vector Quantization (VQ), but without the complications arising from VQ codebooks. Codebook generation and maintenance are stumbling blocks which have limited the use of VQ as a practical image compression algorithm. Model-based VQ (MVQ), a variant of VQ described here, has the computational properties of VQ but does not require explicit codebooks. The codebooks are internally generated using mean removed error and Human Visual System (HVS) models. The error model assumed is the Laplacian distribution with mean, lambda-computed from a sample of the input image. A Laplacian distribution with mean, lambda, is generated with uniform random number generator. These random numbers are grouped into vectors. These vectors are further conditioned to make them perceptually meaningful by filtering the DCT coefficients from each vector. The DCT coefficients are filtered by multiplying by a weight matrix that is found to be optimal for human perception. The inverse DCT is performed to produce the conditioned vectors for the codebook. The only image dependent parameter used in the generation of codebook is the mean, lambda, that is included in the coded file to repeat the codebook generation process for decoding.

Manohar, Mareboyana↗
NASA NTRS1994

Learning random networks for compression of still and moving images

Image compression for both still and moving images is an extremely important area of investigation, with numerous applications to videoconferencing, interactive education, home entertainment, and potential applications to earth observations, medical imaging, digital libraries, and many other areas. We describe work on a neural network methodology to compress/decompress still and moving images. We use the 'point-process' type neural network model which is closer to biophysical reality than standard models, and yet is mathematically much more tractable. We currently achieve compression ratios of the order of 120:1 for moving grey-level images, based on a combination of motion detection and compression. The observed signal-to-noise ratio varies from values above 25 to more than 35. The method is computationally fast so that compression and decompression can be carried out in real-time. It uses the adaptive capabilities of a set of neural networks so as to select varying compression ratios in real-time as a function of quality achieved. It also uses a motion detector which will avoid retransmitting portions of the image which have varied little from the previous frame. Further improvements can be achieved by using on-line learning during compression, and by appropriate compensation of nonlinearities in the compression/decompression scheme. We expect to go well beyond the 250:1 compression level for color images with good quality levels.

Gelenbe, Erol↗
NASA NTRS2003

A High Performance Image Data Compression Technique for Space Applications

A highly performing image data compression technique is currently being developed for space science applications under the requirement of high-speed and pushbroom scanning. The technique is also applicable to frame based imaging data. The algorithm combines a two-dimensional transform with a bitplane encoding; this results in an embedded bit string with exact desirable compression rate specified by the user. The compression scheme performs well on a suite of test images acquired from spacecraft instruments. It can also be applied to three-dimensional data cube resulting from hyper-spectral imaging instrument. Flight qualifiable hardware implementations are in development. The implementation is being designed to compress data in excess of 20 Msampledsec and support quantization from 2 to 16 bits. This paper presents the algorithm, its applications and status of development.

Yeh, Pen-Shu↗
NASA NTRS1987

SAR image data compression for an on-line archive system

This paper summarizes the investigation of SAR image data compression for an on-line archive data distribution system. This system is planned for the ground processing system of Alaska SAR Facility (ASF) and Shuttle Imaging Radar (SIR-C). The objective of the SAR image data compression is to enable the data archive system to provide the remote users a large data base with good image quality, short response time, low transfer cost, and minimal decoding complexity. The requirements and limitations of the on-line archive data distribution system are presented. The effects of SAR image data characteristics on data compression are addressed. The users' survey results suggest that compression ratios between 10:1 and 20:1 appear suitable. Based on the algorithm evaluation results, the two-level tree-searched vector quantization technique has been recommended as the SAR image data compression algorithm for the on-line archive data distribution system.

Chang, C. Y.↗
NASA NTRS1981

Compressing TV-image data

Compressing technique calculates activity estimator for each segment of image line. Estimator is used in conjunction with allowable bits per line, N, to determine number of bits necessary to code each segment and which segments can tolerate truncation. Preprocessed line data are then passed to adaptive variable-length coder, which selects optimum transmission code. Method increases capacity of broadcast and cable television transmissions and helps reduce size of storage medium for video and digital audio recordings.

Hilbert, E. E.↗
NASA NTRS2010

Reconfigurable Hardware for Compressing Hyperspectral Image Data

High-speed, low-power, reconfigurable electronic hardware has been developed to implement ICER-3D, an algorithm for compressing hyperspectral-image data. The algorithm and parts thereof have been the topics of several NASA Tech Briefs articles, including Context Modeler for Wavelet Compression of Hyperspectral Images (NPO-43239) and ICER-3D Hyperspectral Image Compression Software (NPO-43238), which appear elsewhere in this issue of NASA Tech Briefs. As described in more detail in those articles, the algorithm includes three main subalgorithms: one for computing wavelet transforms, one for context modeling, and one for entropy encoding. For the purpose of designing the hardware, these subalgorithms are treated as modules to be implemented efficiently in field-programmable gate arrays (FPGAs). The design takes advantage of industry- standard, commercially available FPGAs. The implementation targets the Xilinx Virtex II pro architecture, which has embedded PowerPC processor cores with flexible on-chip bus architecture. It incorporates an efficient parallel and pipelined architecture to compress the three-dimensional image data. The design provides for internal buffering to minimize intensive input/output operations while making efficient use of offchip memory. The design is scalable in that the subalgorithms are implemented as independent hardware modules that can be combined in parallel to increase throughput. The on-chip processor manages the overall operation of the compression system, including execution of the top-level control functions as well as scheduling, initiating, and monitoring processes. The design prototype has been demonstrated to be capable of compressing hyperspectral data at a rate of 4.5 megasamples per second at a conservative clock frequency of 50 MHz, with a potential for substantially greater throughput at a higher clock frequency. The power consumption of the prototype is less than 6.5 W. The reconfigurability (by means of reprogramming) of the FPGAs makes it possible to effectively alter the design to some extent to satisfy different requirements without adding hardware. The implementation could be easily propagated to future FPGA generations and/or to custom application-specific integrated circuits.

Aranki, Nazeeh↗
NASA NTRS2010

NASA Tech Briefs, February 2010

Topics covered include: Insulation-Testing Cryostat With Lifting Mechanism; Optical Testing of Retroreflectors for Cryogenic Applications; Measuring Cyclic Error in Laser Heterodyne Interferometers; Self-Referencing Hartmann Test for Large-Aperture Telescopes; Measuring a Fiber-Optic Delay Line Using a Mode-Locked Laser; Reconfigurable Hardware for Compressing Hyperspectral Image Data; Spatio-Temporal Equalizer for a Receiving-Antenna Feed Array; High-Speed Ring Bus; Nanoionics-Based Switches for Radio-Frequency Applications; Lunar Dust-Tolerant Electrical Connector; Compact, Reliable EEPROM Controller; Quad-Chip Double-Balanced Frequency Tripler; Ka-Band Waveguide Two-Way Hybrid Combiner for MMIC Amplifiers; Radiation-Hardened Solid-State Drive; Use of Nanofibers to Strengthen Hydrogels of Silica, Other Oxides, and Aerogels; Two Concepts for Deployable Trusses; Concentric Nested Toroidal Inflatable Structures; Investigating Dynamics of Eccentricity in Turbomachines; Improved Low-Temperature Performance of Li-Ion Cells Using New Electrolytes; Integrity Monitoring of Mercury Discharge Lamps; White-Light Phase-Conjugate Mirrors as Distortion Correctors; Biasable, Balanced, Fundamental Submillimeter Monolithic Membrane Mixer; ICER-3D Hyperspectral Image Compression Software; and Context Modeler for Wavelet Compression of Spectral Hyperspectral Images.

Source record↗
DOE OSTI2024

Integrated photonic encoder for low power and high-speed image processing

Abstract Modern lens designs are capable of resolving greater than 10 gigapixels, while advances in camera frame-rate and hyperspectral imaging have made data acquisition rates of Terapixel/second a real possibility. The main bottlenecks preventing such high data-rate systems are power consumption and data storage. In this work, we show that analog photonic encoders could address this challenge, enabling high-speed image compression using orders-of-magnitude lower power than digital electronics. Our approach relies on a silicon-photonics front-end to compress raw image data, foregoing energy-intensive image conditioning and reducing data storage requirements. The compression scheme uses a passive disordered photonic structure to perform kernel-type random projections of the raw image data with minimal power consumption and low latency. A back-end neural network can then reconstruct the original images with structural similarity exceeding 90%. This scheme has the potential to process data streams exceeding Terapixel/second using less than 100 fJ/pixel, providing a path to ultra-high-resolution data and image acquisition systems.

47 OTHER INSTRUMENTATION↗
NASA NTRS1981

An image-data-compression algorithm

Cluster Compression Algorithm (CCA) preprocesses Landsat image data immediately following satellite data sensor (receiver). Data are reduced by extracting pertinent image features and compressing this result into concise format for transmission to ground station. This results in narrower transmission bandwidth, increased data-communication efficiency, and reduced computer time in reconstructing and analyzing image. Similar technique could be applied to other types of recorded data to cut costs of transmitting, storing, distributing, and interpreting complex information.

Hilbert, E. E.↗
DOE OSTI2022

Quantum pixel representations and compression for N-dimensional images

Abstract We introduce a novel and uniform framework for quantum pixel representations that overarches many of the most popular representations proposed in the recent literature, such as (I)FRQI, (I)NEQR, MCRQI, and (I)NCQI. The proposed QPIXL framework results in more efficient circuit implementations and significantly reduces the gate complexity for all considered quantum pixel representations. Our method scales linearly in the number of pixels and does not use ancilla qubits. Furthermore, the circuits only consist of $$R_y$$ R y gates and $$\text {CNOT}$$ CNOT gates making them practical in the NISQ era. Additionally, we propose a circuit and image compression algorithm that is shown to be highly effective, being able to reduce the necessary gates to prepare an FRQI state for example scientific images by up to 90% without sacrificing image quality. Our algorithms are made publicly available as part of , a Quantum Image Pixel Library.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1993

Compressing subbanded image data with Lempel-Ziv-based coders

A method of improving the compression of image data using Lempel-Ziv-based coding is presented. Image data is first processed with a simple transform, such as the Walsh Hadamard Transform, to produce subbands. The subbanded data can be rounded to eight bits or it can be quantized for higher compression at the cost of some reduction in the quality of the reconstructed image. The data is then run-length coded to take advantage of the large runs of zeros produced by quantization. Compression results are presented and contrasted with a subband compression method using quantization followed by run-length coding and Huffman coding. The Lempel-Ziv-based coding in conjunction with run-length coding produces the best compression results at the same reconstruction quality (compared with the Huffman-based coding) on the image data used.

Glover, Daniel↗
NASA NTRS1986

Catching the whispers from Uranus

Sophisticated telecommunications techniques are described that were used to acquire images of Uranus, its 14 moons and ten narrow rings darker than coal. The images, equal in quality to those transmitted from Saturn several years earlier despite the signal being weaker by 6 dB due to the increased distance, were received from Voyager 2 during its January 24, 1986 flyby of Uranus. Solutions to the problem of the weakening signal were found in modifications to Voyager's image processing system and NASA's ground tracking network. In April 1985, Voyager's prime flight data computer was reconfigured to accept only nonimaging science data, and its backup, only imaging data; the latter was reprogrammed to determine only arithmetic differences between adjacent pixel intensities rather than absolute intensities. By image compression, equivalent imaging information could be sent at lower bit rates. Instead of Golay coding, Reed-Solomon onboard encoding was used. These techniques gained the equivalent of 4-dB in imaging yield. Additional improvements were gained by using earth station antennas in pairs (the Parkes radio telescope and the Canberra ground station antenna). Moves under way to prepare for the Voyager encounter with Neptune in 1989 are described (using additional antennas and arrays, scaling up the Deep Space Network antennas from 64 m to 70 m, etc.) to assure almost Saturn-equivalent pictures despite a further 3.5-dB drop in signal strength.

Bartok, C. D.↗
NASA NTRS1992

Distance between images

Comparing two binary images and assigning a quantitative measure to this comparison finds its purpose in such tasks as image recognition, image compression, and image browsing. This quantitative measurement may be computed by utilizing the Hausdorff distance of the images represented as two-dimensional point sets. In this paper, we review two algorithms that have been proposed to compute this distance, and we present a parallel implementation of one of them on the MasPar parallel processor. We study their complexity and the results obtained by these algorithms for two different types of images: a set of displaced pairs of images of Gaussian densities, and a comparison of a Canny edge image with several edge images from a hierarchical region growing code.

Gualtieri, J. A.↗
NASA NTRS2007

Telerobotic Surgery: An Intelligent Systems Approach to Mitigate the Adverse Effects of Communication Delay

An extremely innovative approach has been presented, which is to have the surgeon operate through a simulator running in real-time enhanced with an intelligent controller component to enhance the safety and efficiency of a remotely conducted operation. The use of a simulator enables the surgeon to operate in a virtual environment free from the impediments of telecommunication delay. The simulator functions as a predictor and periodically the simulator state is corrected with truth data. Three major research areas must be explored in order to ensure achieving the objectives. They are: simulator as predictor, image processing, and intelligent control. Each is equally necessary for success of the project and each of these involves a significant intelligent component in it. These are diverse, interdisciplinary areas of investigation, thereby requiring a highly coordinated effort by all the members of our team, to ensure an integrated system. The following is a brief discussion of those areas. Simulator as a predictor: The delays encountered in remote robotic surgery will be greater than any encountered in human-machine systems analysis, with the possible exception of remote operations in space. Therefore, novel compensation techniques will be developed. Included will be the development of the real-time simulator, which is at the heart of our approach. The simulator will present real-time, stereoscopic images and artificial haptic stimuli to the surgeon. Image processing: Because of the delay and the possibility of insufficient bandwidth a high level of novel image processing is necessary. This image processing will include several innovative aspects, including image interpretation, video to graphical conversion, texture extraction, geometric processing, image compression and image generation at the surgeon station. Intelligent control: Since the approach we propose is in a sense predictor based, albeit a very sophisticated predictor, a controller, which not only optimizes end effector trajectory but also avoids error, is essential. We propose to investigate two different approaches to the controller design. One approach employs an optimal controller based on modern control theory; the other one involves soft computing techniques, i.e. fuzzy logic, neural networks, genetic algorithms and hybrids of these.

Cardullo, Frank M.↗