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All sourcesNASA NTRSDOE OSTI
NASA NTRS1988

The design and performance of a real-time self excited vocoder

This paper is concerned with a generic class of predictive speech coders that includes the newly proposed Self Excited Vocoder (SEV) and the well known Code-Excited Linear Predictive Coder (CELPC). All members of this class form an excitation sequence for a linear predictive model filter using the same general model for the excitation signal. The general excitation model is based on a block coding technique where each sequence is drawn from an ensemble of sequences. This paper reports on two developments related to this general model. The first development is a new type of excitation ensemble that can in general be populated by many different types of sequences. The second development is a means of populating this new type of ensemble based on a vector quantizer design procedure using a new distortion measure.

Rose, Richard C.↗
NASA NTRS1988

Spatial compression of Seasat SAR imagery

The results of a study of techniques for spatial compression of synthetic-aperture-radar (SAR) imagery are summarized. Emphasis is on image-data volume reduction for archive and online storage applications while preserving the image resolution and radiometric fidelity. A quantitative analysis of various techniques, including vector quantization (VQ) and adaptive discrete cosine transform (ADCT), is presented. Various factors such as compression ratio, algorithm complexity, and image quality are considered in determining the optimal algorithm. The compression system requirements are established for electronic access of an online archive system based on the results of a survey of the science community. The various algorithms are presented and their results evaluated considering the effects of speckle noise and the wide dynamic range inherent in SAR imagery. The conclusion is that although the ADCT produces the best signal-to-distortion-noise ratio for a given compression ratio, the two-level tree-searched VQ technique is preferred due to its simplicity of decoding and near-optimal performance.

Chang, C. Y.↗
NASA NTRS1989

Data compression experiments with LANDSAT thematic mapper and Nimbus-7 coastal zone color scanner data

A case study is presented where an image segmentation based compression technique is applied to LANDSAT Thematic Mapper (TM) and Nimbus-7 Coastal Zone Color Scanner (CZCS) data. The compression technique, called Spatially Constrained Clustering (SCC), can be regarded as an adaptive vector quantization approach. The SCC can be applied to either single or multiple spectral bands of image data. The segmented image resulting from SCC is encoded in small rectangular blocks, with the codebook varying from block to block. Lossless compression potential (LDP) of sample TM and CZCS images are evaluated. For the TM test image, the LCP is 2.79. For the CZCS test image the LCP is 1.89, even though when only a cloud-free section of the image is considered the LCP increases to 3.48. Examples of compressed images are shown at several compression ratios ranging from 4 to 15. In the case of TM data, the compressed data are classified using the Bayes' classifier. The results show an improvement in the similarity between the classification results and ground truth when compressed data are used, thus showing that compression is, in fact, a useful first step in the analysis.

Tilton, James C.↗
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 NTRS1988

Compression of high spectral resolution imagery

The Earth Orbiting Satellite (EOS), scheduled for launch in the mid-1990s, will include a next-generation multispectral imaging system (HIRIS) having unprecedented spatial and spectral resolution. Its high resolution, however, comes at the cost of a raw data rate which exceeds the communication channel capacity assigned to the entire EOS mission. This paper explores noisy compression algorithms which may compress multispectral data by up to 30:1 or more. Algorithm performance is measured using both traditional and mission-oriented criteria (e.g., feature classification consistency). It is shown that vector quantization, merged with suitable preprocessing techniques, is the most viable candidate.

Baker, Richard L.↗
NASA NTRS1989

Advances in image compression and automatic target recognition; Proceedings of the Meeting, Orlando, FL, Mar. 30, 31, 1989

Various papers on image compression and automatic target recognition are presented. Individual topics addressed include: target cluster detection in cluttered SAR imagery, model-based target recognition using laser radar imagery, Smart Sensor front-end processor for feature extraction of images, object attitude estimation and tracking from a single video sensor, symmetry detection in human vision, analysis of high resolution aerial images for object detection, obscured object recognition for an ATR application, neural networks for adaptive shape tracking, statistical mechanics and pattern recognition, detection of cylinders in aerial range images, moving object tracking using local windows, new transform method for image data compression, quad-tree product vector quantization of images, predictive trellis encoding of imagery, reduced generalized chain code for contour description, compact architecture for a real-time vision system, use of human visibility functions in segmentation coding, color texture analysis and synthesis using Gibbs random fields.

Tescher, Andrew G.↗
NASA NTRS1990

Distributed memory approaches for robotic neural controllers

The suitability is explored of two varieties of distributed memory neutral networks as trainable controllers for a simulated robotics task. The task requires that two cameras observe an arbitrary target point in space. Coordinates of the target on the camera image planes are passed to a neural controller which must learn to solve the inverse kinematics of a manipulator with one revolute and two prismatic joints. Two new network designs are evaluated. The first, radial basis sparse distributed memory (RBSDM), approximates functional mappings as sums of multivariate gaussians centered around previously learned patterns. The second network types involved variations of Adaptive Vector Quantizers or Self Organizing Maps. In these networks, random N dimensional points are given local connectivities. They are then exposed to training patterns and readjust their locations based on a nearest neighbor rule. Both approaches are tested based on their ability to interpolate manipulator joint coordinates for simulated arm movement while simultaneously performing stereo fusion of the camera data. Comparisons are made with classical k-nearest neighbor pattern recognition techniques.

Jorgensen, Charles C.↗
NASA NTRS1989

Data compression for Eos on-board SAR processor

A lightweight, low-power, real-time data compressor design for the Earth Observing System (Eos) onboard synthetic aperture radar (SAR) processor is presented. The implementation is based on VLSI design of a pipelined binary tree-searched vector quantizer (VQ), utilizing space-qualifiable 1.25-micron CMOS technology. The implementation exploits VLSI system design principles such as the modularity, regular data flow, simple interconnection, localized communication, simple global control, and parallel/pipelined processing. The overall system requires 30 chips with only one VLSI processing element design. The total weight is about 1.2 lbs, with an estimated power dissipation of approximately 4 watts operating at the maximum input data rate. The projected throughput rate exceeds 5 MHz.

Chang, C. Y.↗
NASA NTRS1991

SAR data compression: Application, requirements, and designs

The feasibility of reducing data volume and data rate is evaluated for the Earth Observing System (EOS) Synthetic Aperture Radar (SAR). All elements of data stream from the sensor downlink data stream to electronic delivery of browse data products are explored. The factors influencing design of a data compression system are analyzed, including the signal data characteristics, the image quality requirements, and the throughput requirements. The conclusion is that little or no reduction can be achieved in the raw signal data using traditional data compression techniques (e.g., vector quantization, adaptive discrete cosine transform) due to the induced phase errors in the output image. However, after image formation, a number of techniques are effective for data compression.

Curlander, John C.↗
NASA NTRS1991

A progressive data compression scheme based upon adaptive transform coding: Mixture block coding of natural images

A method for efficiently coding natural images using a vector-quantized variable-blocksized transform source coder is presented. The method, mixture block coding (MBC), incorporates variable-rate coding by using a mixture of discrete cosine transform (DCT) source coders. Which coders are selected to code any given image region is made through a threshold driven distortion criterion. In this paper, MBC is used in two different applications. The base method is concerned with single-pass low-rate image data compression. The second is a natural extension of the base method which allows for low-rate progressive transmission (PT). Since the base method adapts easily to progressive coding, it offers the aesthetic advantage of progressive coding without incorporating extensive channel overhead. Image compression rates of approximately 0.5 bit/pel are demonstrated for both monochrome and color images.

Rost, Martin C.↗
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 NTRS1993

Multi-rate, real time image compression for images dominated by point sources

An image compression system recently developed for compression of digital images dominated by point sources is presented. Encoding consists of minimum-mean removal, vector quantization, adaptive threshold truncation, and modified Huffman encoding. Simulations are presented showing that the peaks corresponding to point sources can be transmitted losslessly for low signal-to-noise ratios (SNR) and high point source densities while maintaining a reduced output bit rate. Encoding and decoding hardware has been built and tested which processes 552,960 12-bit pixels per second at compression rates of 10:1 and 4:1. Simulation results are presented for the 10:1 case only.

Huber, A. Kris↗
NASA NTRS1992

Image segmentation using fuzzy LVQ clustering networks

In this note we formulate image segmentation as a clustering problem. Feature vectors extracted from a raw image are clustered into subregions, thereby segmenting the image. A fuzzy generalization of a Kohonen learning vector quantization (LVQ) which integrates the Fuzzy c-Means (FCM) model with the learning rate and updating strategies of the LVQ is used for this task. This network, which segments images in an unsupervised manner, is thus related to the FCM optimization problem. Numerical examples on photographic and magnetic resonance images are given to illustrate this approach to image segmentation.

Tsao, Eric Chen-Kuo↗
NASA NTRS1993

Neurocomputing strategies in decomposition based structural design

The present paper explores the applicability of neurocomputing strategies in decomposition based structural optimization problems. It is shown that the modeling capability of a backpropagation neural network can be used to detect weak couplings in a system, and to effectively decompose it into smaller, more tractable, subsystems. When such partitioning of a design space is possible, parallel optimization can be performed in each subsystem, with a penalty term added to its objective function to account for constraint violations in all other subsystems. Dependencies among subsystems are represented in terms of global design variables, and a neural network is used to map the relations between these variables and all subsystem constraints. A vector quantization technique, referred to as a z-Network, can effectively be used for this purpose. The approach is illustrated with applications to minimum weight sizing of truss structures with multiple design constraints.

Szewczyk, Z.↗
NASA NTRS1990

Research on conceptual/innovative design for the life cycle

The goal of this research is developing and integrating qualitative and quantitative methods for life cycle design. The definition of the problem includes formal computer-based methods limited to final detailing stages of design; CAD data bases do not capture design intent or design history; and life cycle issues were ignored during early stages of design. Viewgraphs outline research in conceptual design; the SYMON (SYmbolic MONotonicity analyzer) algorithm; multistart vector quantization optimization algorithm; intelligent manufacturing: IDES - Influence Diagram Architecture; and 1st PRINCE (FIRST PRINciple Computational Evaluator).

Cagan, Jonathan↗
NASA NTRS1994

High performance compression of science data

Two papers make up the body of this report. One presents a single-pass adaptive vector quantization algorithm that learns a codebook of variable size and shape entries; the authors present experiments on a set of test images showing that with no training or prior knowledge of the data, for a given fidelity, the compression achieved typically equals or exceeds that of the JPEG standard. The second paper addresses motion compensation, one of the most effective techniques used in the interframe data compression. A parallel block-matching algorithm for estimating interframe displacement of blocks with minimum error is presented. The algorithm is designed for a simple parallel architecture to process video in real time.

Storer, James A.↗
NASA NTRS1993

LVQ and backpropagation neural networks applied to NASA SSME data

Feedfoward neural networks with backpropagation learning have been used as function approximators for modeling the space shuttle main engine (SSME) sensor signals. The modeling of these sensor signals is aimed at the development of a sensor fault detection system that can be used during ground test firings. The generalization capability of a neural network based function approximator depends on the training vectors which in this application may be derived from a number of SSME ground test-firings. This yields a large number of training vectors. Large training sets can cause the time required to train the network to be very large. Also, the network may not be able to generalize for large training sets. To reduce the size of the training sets, the SSME test-firing data is reduced using the learning vector quantization (LVQ) based technique. Different compression ratios were used to obtain compressed data in training the neural network model. The performance of the neural model trained using reduced sets of training patterns is presented and compared with the performance of the model trained using complete data. The LVQ can also be used as a function approximator. The performance of the LVQ as a function approximator using reduced training sets is presented and compared with the performance of the backpropagation network.

Doniere, Timothy F.↗
NASA NTRS1995

Associative Pattern Recognition In Analog VLSI Circuits

Winner-take-all circuit selects best-match stored pattern. Prototype cascadable very-large-scale integrated (VLSI) circuit chips built and tested to demonstrate concept of electronic associative pattern recognition. Based on low-power, sub-threshold analog complementary oxide/semiconductor (CMOS) VLSI circuitry, each chip can store 128 sets (vectors) of 16 analog values (vector components), vectors representing known patterns as diverse as spectra, histograms, graphs, or brightnesses of pixels in images. Chips exploit parallel nature of vector quantization architecture to implement highly parallel processing in relatively simple computational cells. Through collective action, cells classify input pattern in fraction of microsecond while consuming power of few microwatts.

Tawel, Raoul↗