Spectral Predictors

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Many scientific, imaging, and geospatial applications produce large high-precision scalar fields sampled on a regular grid. Lossless compression of such data is commonly done using predictive coding, in which weighted combinations of previously coded samples known to both encoder and decoder are used to predict subsequent nearby samples. In hierarchical, incremental, or selective transmission, the spatial pattern of the known neighbors is often irregular and varies from one sample to the next, which precludes prediction based on a single stencil and fixed set of weights. To handle such situations and make the best use of available neighboring samples, we propose ... continued below

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PDF-file: 12 pages; size: 1.9 Mbytes

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Ibarria, L; Lindstrom, P & Rossignac, J November 17, 2006.

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Many scientific, imaging, and geospatial applications produce large high-precision scalar fields sampled on a regular grid. Lossless compression of such data is commonly done using predictive coding, in which weighted combinations of previously coded samples known to both encoder and decoder are used to predict subsequent nearby samples. In hierarchical, incremental, or selective transmission, the spatial pattern of the known neighbors is often irregular and varies from one sample to the next, which precludes prediction based on a single stencil and fixed set of weights. To handle such situations and make the best use of available neighboring samples, we propose a local spectral predictor that offers optimal prediction by tailoring the weights to each configuration of known nearby samples. These weights may be precomputed and stored in a small lookup table. We show that predictive coding using our spectral predictor improves compression for various sources of high-precision data.

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PDF-file: 12 pages; size: 1.9 Mbytes

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  • Presented at: Data Compression Conference, Snowbird, UT, United States, Mar 27 - Mar 29, 2007

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  • Report No.: UCRL-CONF-226261
  • Grant Number: W-7405-ENG-48
  • Office of Scientific & Technical Information Report Number: 907839
  • Archival Resource Key: ark:/67531/metadc891267

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Office of Scientific & Technical Information Technical Reports

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  • November 17, 2006

Added to The UNT Digital Library

  • Sept. 22, 2016, 2:13 a.m.

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  • Dec. 2, 2016, 12:28 p.m.

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Ibarria, L; Lindstrom, P & Rossignac, J. Spectral Predictors, article, November 17, 2006; Livermore, California. (digital.library.unt.edu/ark:/67531/metadc891267/: accessed December 11, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.