We report the results of a comparative study of the effect of several lossy compression techniques on satellite-based meteorological imagery. Three algorithms, implemented by participants in this effort, were tested on a variety of satellite data products, at several compression, ratios ranging to as much as a factor of 20. The compression techniques included a scene-adaptive discrete-co-sine-transform technique, an adaptive differential pulse-code-modulation technique, and a vector quantization algorithm. A variety of quantitative measures were applied in the evaluation of these lossy image-compression algorithms. Included were the mean-square error, mean absolute error, and error histograms. We also evaluated the effect on interpretation by a meteorologist trained in the use of satellite imagery for synoptic forecasting. Finally, we applied an automated cloud-fraction-analysis routine to the data in an effort to determine its effect on performance. Additional efforts similar to these should provide useful measures of the operation of proposed image-compression techniques. In particular, additional assessment of automated-image-characterization algorithms and the inclusion of input data with more variation are expected to result in a more thorough understanding of the effects of these compression techniques.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.