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Lossy Compression of Elevation Data (1996)
| Citations: | 11 - 3 self |
BibTeX
@MISC{Franklin96lossycompression,
author = {Wm. Randolph Franklin and Amir Said},
title = {Lossy Compression of Elevation Data},
year = {1996}
}
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Abstract
Generic lossy image compression algorithms, such as Said & Pearlman's, perform excellently on gridded terrain elevation data. For example, an average DEM compressed down to 0.1 bits per point has an RMS elevation error of 3 meters, although di#erent DEMs ranged from 0.25 to 15 meters. This performance compares favorably to compressing with Triangulated Irregular Networks. Preliminary tests also suggest that the visibility indices of the points are robust with respect to our lossy compression. 1 Franklin&Said Lossy Compression of Elevation Data 2 Contents 1 Introduction 3 2 Review 3 3 Lossy Compression Experiments 6 4 E#ect of Lossy Compression on Visibility Analysis 16 5 Implementation Details 17 6 Conclusion 17 References 17 List of Figures 1 The 24 Sample USGS DEMs ............................... 5 2 Lossy Compression of First 12 DEMs . . . . . . ..................... 7 3 Lossy Compression of Second 12 DEMs ......................... 8 4 Hailey E Cell . . . . . ..............
Keyphrases
lossy compression elevation data visibility index franklin said lossy compression average dem triangulated irregular network preliminary test hailey cell di erent dems visibility analysis generic lossy image compression lossy compression experiment gridded terrain elevation data said pearlman sample usgs dems implementation detail rms elevation error
