Survey of data compression techniques

PM-AIM must provide to customers in a timely fashion information about Army acquisitions. This paper discusses ways that PM-AIM can reduce the volume of data that must be transmitted between sites. Although this paper primarily discusses techniques of data compression, it also briefly discusses other options for meeting the PM-AIM requirements. The options available to PM-AIM, in addition to hardware and software data compression, include less-frequent updates, distribution of partial updates, distributed data base design, and intelligent network design. Any option that enhances the performance of the PM-AIM network is worthy of consideration. The recommendations of this paper apply to the PM-AIM project in three phases: the current phase, the target phase, and the objective phase. Each recommendation will be identified as (1) appropriate for the current phase, (2) considered for implementation during the target phase, or (3) a feature that should be part of the objective phase of PM-AIM`s design. The current phase includes only those measures that can be taken with the installed leased lines. The target phase includes those measures that can be taken in transferring the traffic from the leased lines to the DSNET environment with minimal changes in the current design. The objective phase includesmore » all the things that should be done as a matter of course. The objective phase for PM-AIM appears to be a distributed data base with data for each site stored locally and all sites having access to all data.« less

[1]  Robert G. Gallager,et al.  Variations on a theme by Huffman , 1978, IEEE Trans. Inf. Theory.

[2]  Alberto Apostolico,et al.  Robust transmission of unbounded strings using Fibonacci representations , 1987, IEEE Trans. Inf. Theory.

[3]  P. Kabal,et al.  A low delay 16 kbits/sec speech coder , 1988, ICASSP-88., International Conference on Acoustics, Speech, and Signal Processing.

[4]  Andrzej Sieminski,et al.  Fast Decoding of the Huffman Codes , 1988, Inf. Process. Lett..

[5]  Lawrence L. Larmore,et al.  A fast algorithm for optimal length-limited Huffman codes , 1990, JACM.

[6]  Ian H. Witten,et al.  Modeling for text compression , 1989, CSUR.

[7]  G. David Forney,et al.  Efficient Modulation for Band-Limited Channels , 1984, IEEE J. Sel. Areas Commun..

[8]  Gottfried Ungerboeck,et al.  Channel coding with multilevel/phase signals , 1982, IEEE Trans. Inf. Theory.

[9]  K. A. Hake An examination of electronic file transfer between host and microcomputers for the AMPMODNET/AIMNET (Army Material Plan Modernization Network/Acquisition Information Management Network) classified network environment , 1990 .

[10]  Andrew J. Viterbi,et al.  Trellis Encoding of memoryless discrete-time sources with a fidelity criterion , 1974, IEEE Trans. Inf. Theory.

[11]  Daniel S. Hirschberg,et al.  Data compression , 1987, CSUR.

[12]  S. Bende VARIABLE - LENGTH ENCODING , 1970 .

[13]  J.D. Gibson,et al.  Adaptive prediction in speech differential encoding systems , 1980, Proceedings of the IEEE.

[14]  M. Hankamer A Modified Huffman Procedure with Reduced Memory Requirement , 1979, IEEE Trans. Commun..

[15]  William A. Pearlman,et al.  Source coding bounds using quantizer reproduction levels , 1984, IEEE Trans. Inf. Theory.

[16]  Hatsukazu Tanaka,et al.  Data structure of Huffman codes and its application to efficient encoding and decoding , 1987, IEEE Trans. Inf. Theory.

[17]  Robert M. Gray,et al.  Time-invariant trellis encoding of ergodic discrete-time sources with a fidelity criterion , 1977, IEEE Trans. Inf. Theory.

[18]  Michael W. Marcellin,et al.  Predictive trellis coded quantization of speech , 1990, IEEE Trans. Acoust. Speech Signal Process..

[19]  Ian H. Witten,et al.  Arithmetic coding for data compression , 1987, CACM.

[20]  Michael W. Marcellin,et al.  Trellis coded quantization of memoryless and Gauss-Markov sources , 1990, IEEE Trans. Commun..

[21]  James A. Storer,et al.  Data Compression: Methods and Theory , 1987 .

[22]  W. Pearlman Sliding-Block and Random Source Coding with Constrained Size Reproduction Alphabets , 1982, IEEE Trans. Commun..

[23]  R. Bellman Dynamic programming. , 1957, Science.

[24]  Jerry D. Gibson,et al.  Backward Adaptive Lattice and Transversal Predictors in ADPCM , 1985, IEEE Trans. Commun..

[25]  Thomas R. Fischer,et al.  Variance estimation and adaptive quantization , 1985, IEEE Trans. Inf. Theory.

[26]  Eugene S. Schwartz,et al.  Generating a canonical prefix encoding , 1964, CACM.

[27]  C. E. SHANNON,et al.  A mathematical theory of communication , 1948, MOCO.

[28]  N. Jayant Adaptive quantization with a one-word memory , 1973 .

[29]  Robert M. Gray,et al.  The Design of Trellis Waveform Coders , 1982, IEEE Trans. Commun..

[30]  D. Huffman A Method for the Construction of Minimum-Redundancy Codes , 1952 .