ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., II-5, 313-319, 2014
https://doi.org/10.5194/isprsannals-II-5-313-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
 
28 May 2014
Lossless data compression of grid-based digital elevation models: A png image format evaluation
G. Scarmana University of Southern Queensland, Australia
Keywords: Image processing, DEM compression, Terrain modelling, PNG (Portable Networks Graphics) Abstract. At present, computers, lasers, radars, planes and satellite technologies make possible very fast and accurate topographic data acquisition for the production of maps. However, the problem of managing and manipulating this data efficiently remains. One particular type of map is the elevation map. When stored on a computer, it is often referred to as a Digital Elevation Model (DEM). A DEM is usually a square matrix of elevations. It is like an image, except that it contains a single channel of information (that is, elevation) and can be compressed in a lossy or lossless manner by way of existing image compression protocols. Compression has the effect of reducing memory requirements and speed of transmission over digital links, while maintaining the integrity of data as required.

In this context, this paper investigates the effects of the PNG (Portable Network Graphics) lossless image compression protocol on floating-point elevation values for 16-bit DEMs of dissimilar terrain characteristics. The PNG is a robust, universally supported, extensible, lossless, general-purpose and patent-free image format. Tests demonstrate that the compression ratios and run decompression times achieved with the PNG lossless compression protocol can be comparable to, or better than, proprietary lossless JPEG variants, other image formats and available lossless compression algorithms.

Conference paper (PDF, 579 KB)


Citation: Scarmana, G.: Lossless data compression of grid-based digital elevation models: A png image format evaluation, ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., II-5, 313-319, https://doi.org/10.5194/isprsannals-II-5-313-2014, 2014.

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