Wavelet subband coding of computer simulation output using the A++ array class library

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The goal of the project is to produce utility software for off-line compression of existing data and library code that can be called from a simulation program for on-line compression of data dumps as the simulation proceeds. Naturally, we would like the amount of CPU time required by the compression algorithm to be small in comparison to the requirements of typical simulation codes. We also want the algorithm to accomodate a wide variety of smooth, multidimensional data types. For these reasons, the subband vector quantization (VQ) approach employed in has been replaced by a scalar quantization (SQ) strategy using a ... continued below

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16 p.

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Bradley, J.N.; Brislawn, C.M.; Quinlan, D.J.; Zhang, H.D. & Nuri, V. July 1, 1995.

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Description

The goal of the project is to produce utility software for off-line compression of existing data and library code that can be called from a simulation program for on-line compression of data dumps as the simulation proceeds. Naturally, we would like the amount of CPU time required by the compression algorithm to be small in comparison to the requirements of typical simulation codes. We also want the algorithm to accomodate a wide variety of smooth, multidimensional data types. For these reasons, the subband vector quantization (VQ) approach employed in has been replaced by a scalar quantization (SQ) strategy using a bank of almost-uniform scalar subband quantizers in a scheme similar to that used in the FBI fingerprint image compression standard. This eliminates the considerable computational burdens of training VQ codebooks for each new type of data and performing nearest-vector searches to encode the data. The comparison of subband VQ and SQ algorithms in indicated that, in practice, there is relatively little additional gain from using vector as opposed to scalar quantization on DWT subbands, even when the source imagery is from a very homogeneous population, and our subjective experience with synthetic computer-generated data supports this stance. It appears that a careful study is needed of the tradeoffs involved in selecting scalar vs. vector subband quantization, but such an analysis is beyond the scope of this paper. Our present work is focused on the problem of generating wavelet transform/scalar quantization (WSQ) implementations that can be ported easily between different hardware environments. This is an extremely important consideration given the great profusion of different high-performance computing architectures available, the high cost associated with learning how to map algorithms effectively onto a new architecture, and the rapid rate of evolution in the world of high-performance computing.

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16 p.

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OSTI as DE95015332

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  • NASA space and earth science data compression workshop, Salt Lake City, UT (United States), 27 Mar 1995

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  • Other: DE95015332
  • Report No.: LA-UR--95-1659
  • Report No.: CONF-9503181--1
  • Grant Number: W-7405-ENG-36
  • Office of Scientific & Technical Information Report Number: 105846
  • Archival Resource Key: ark:/67531/metadc619114

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  • July 1, 1995

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  • June 16, 2015, 7:43 a.m.

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  • Feb. 29, 2016, 8:17 p.m.

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Bradley, J.N.; Brislawn, C.M.; Quinlan, D.J.; Zhang, H.D. & Nuri, V. Wavelet subband coding of computer simulation output using the A++ array class library, article, July 1, 1995; New Mexico. (digital.library.unt.edu/ark:/67531/metadc619114/: accessed December 16, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.