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Information rates for data compression.

Upper and lower bounds on required information rate for data compression fidelity criterion, comparing bounds with equivalent mean-square-error result

Davisson, L. D.↗

Data compression of stereopairs

This paper addresses the problem of stereopair data compression. Two fundamentally different techniques for compressing stereopairs are discussed. The first technique, called disparity-compensated transform-domain predictive coding, attempts to minimize the mean-square error between the original stereopair and the compressed stereopair. The second technique, called mixed-resolution coding, is a psychophysically justified technique that exploits known facts about human stereovision to code stereopairs in a subjectively acceptable manner. A method for assessing the quality of compressed stereopairs is also presented. It involves measuring the ability of an observer to perceive depth in coded stereopairs. It is found that observers generally perceived objects to be further away in compressed stereopairs than they did in originals. Finally, the problem of coding stereopairs is considered from a rate-distortion perspective. It is proved that the rate distortion limit for coding stereopairs cannot in general be achieved by a coder that first codes and decodes the right picture sequence independently of the left picture sequence, and then codes and decodes the left picture sequence given the decoded right picture sequence.

Perkins, Michael G.↗

Electromagnetic Scattered Field Evaluation and Data Compression Using Imaging Techniques

This is the final report on Project #727625 between The Ohio State University and NASA, Lewis Research Center, Cleveland, Ohio. Under this project, a data compression technique for scattered field data of electrically large targets is developed. The technique was applied to the scattered fields of two targets of interest. The backscattered fields of the scale models of these targets were measured in a ra compact range. For one of the targets, the backscattered fields were also calculated using XPATCH computer code. Using the technique all scattered field data sets were compressed successfully. A compression ratio of the order 40 was achieved. In this report, the technique is described briefly and some sample results are included.

Gupta, I. J.↗

An investigative study of multispectral data compression for remotely-sensed images using vector quantization and difference-mapped shift-coding

A study is conducted to investigate the effects and advantages of data compression techniques on multispectral imagery data acquired by NASA's airborne scanners at the Stennis Space Center. The first technique used was vector quantization. The vector is defined in the multispectral imagery context as an array of pixels from the same location from each channel. The error obtained in substituting the reconstructed images for the original set is compared for different compression ratios. Also, the eigenvalues of the covariance matrix obtained from the reconstructed data set are compared with the eigenvalues of the original set. The effects of varying the size of the vector codebook on the quality of the compression and on subsequent classification are also presented. The output data from the Vector Quantization algorithm was further compressed by a lossless technique called Difference-mapped Shift-extended Huffman coding. The overall compression for 7 channels of data acquired by the Calibrated Airborne Multispectral Scanner (CAMS), with an RMS error of 15.8 pixels was 195:1 (0.41 bpp) and with an RMS error of 3.6 pixels was 18:1 (.447 bpp). The algorithms were implemented in software and interfaced with the help of dedicated image processing boards to an 80386 PC compatible computer. Modules were developed for the task of image compression and image analysis. Also, supporting software to perform image processing for visual display and interpretation of the compressed/classified images was developed.

Jaggi, S.↗

Proposed data compression schemes for the Galileo S-band contingency mission

The Galileo spacecraft is currently on its way to Jupiter and its moons. In April 1991, the high gain antenna (HGA) failed to deploy as commanded. In case the current efforts to deploy the HGA fails, communications during the Jupiter encounters will be through one of two low gain antenna (LGA) on an S-band (2.3 GHz) carrier. A lot of effort has been and will be conducted to attempt to open the HGA. Also various options for improving Galileo's telemetry downlink performance are being evaluated in the event that the HGA will not open at Jupiter arrival. Among all viable options the most promising and powerful one is to perform image and non-image data compression in software onboard the spacecraft. This involves in-flight re-programming of the existing flight software of Galileo's Command and Data Subsystem processors and Attitude and Articulation Control System (AACS) processor, which have very limited computational and memory resources. In this article we describe the proposed data compression algorithms and give their respective compression performance. The planned image compression algorithm is a 4 x 4 or an 8 x 8 multiplication-free integer cosine transform (ICT) scheme, which can be viewed as an integer approximation of the popular discrete cosine transform (DCT) scheme. The implementation complexity of the ICT schemes is much lower than the DCT-based schemes, yet the performances of the two algorithms are indistinguishable. The proposed non-image compression algorith is a Lempel-Ziv-Welch (LZW) variant, which is a lossless universal compression algorithm based on a dynamic dictionary lookup table. We developed a simple and efficient hashing function to perform the string search.

Cheung, Kar-Ming↗

An adaptive, lossless data compression algorithm and VLSI implementations

This paper first provides an overview of an adaptive, lossless, data compression algorithm originally devised by Rice in the early '70s. It then reports the development of a VLSI encoder/decoder chip set developed which implements this algorithm. A recent effort in making a space qualified version of the encoder is described along with several enhancements to the algorithm. The performance of the enhanced algorithm is compared with those from other currently available lossless compression techniques on multiple sets of test data. The results favor our implemented technique in many applications.

Venbrux, Jack↗

A CAM-based LZ data compression IC

A custom CMOS processor is introduced that implements the Data Compression Lempel-Ziv (DCLZ) standard, a variation of the LZ2 Algorithm. This component presently achieves a sustained compression and decompression rate of 10 megabytes/second by employing an on-chip content-addressable memory for string table storage.

Winters, K.↗

A seismic data compression system using subband coding

This article presents a study of seismic data compression techniques and a compression algorithm based on subband coding. The algorithm includes three stages: a decorrelation stage, a quantization stage that introduces a controlled amount of distortion to allow for high compression ratios, and a lossless entropy coding stage based on a simple but efficient arithmetic coding method. Subband coding methods are particularly suited to the decorrelation of nonstationary processes such as seismic events. Adaptivity to the nonstationary behavior of the waveform is achieved by dividing the data into separate blocks that are encoded separately with an adaptive arithmetic encoder. This is done with high efficiency due to the low overhead introduced by the arithmetic encoder in specifying its parameters. The technique could be used as a progressive transmission system, where successive refinements of the data can be requested by the user. This allows seismologists to first examine a coarse version of waveforms with minimal usage of the channel and then decide where refinements are required. Rate-distortion performance results are presented and comparisons are made with two block transform methods.

Kiely, A. B.↗

Data compression for the microgravity experiments

Researchers present the environment and conditions under which data compression is to be performed for the microgravity experiment. Also presented are some coding techniques that would be useful for coding in this environment. It should be emphasized that researchers are currently at the beginning of this program and the toolkit mentioned is far from complete.

Sayood, Khalid↗

Pipeline synthetic aperture radar data compression utilizing systolic binary tree-searched architecture for vector quantization

A system for data compression utilizing systolic array architecture for Vector Quantization (VQ) is disclosed for both full-searched and tree-searched. For a tree-searched VQ, the special case of a Binary Tree-Search VQ (BTSVQ) is disclosed with identical Processing Elements (PE) in the array for both a Raw-Codebook VQ (RCVQ) and a Difference-Codebook VQ (DCVQ) algorithm. A fault tolerant system is disclosed which allows a PE that has developed a fault to be bypassed in the array and replaced by a spare at the end of the array, with codebook memory assignment shifted one PE past the faulty PE of the array.

Chang, Chi-Yung↗

ERTS image data compression technique evaluation

The background and results of an investigation concerning the use of multispectral data compression in the ERTS program are presented. An average compression of greater than 2:1 has been achieved under the constraint of zero distortion in the reconstructed image, and four MSS tapes can be compressed to fill less than one reel of magnetic tape. A preliminary study of the hardware implementation of this processor proves the feasability of compression at input bit rates of over 100Mbs.

May, C. L.↗