A distributed data compression/ decompression technique for images via conformal mapping

摘要

This works deals with a new technique for large remote-sensing data compression. Contour mapping of two dimensional objects is of fundamental importance in remote sensing and computer vision applications. We present extensive algorithms applied to polygonized, simply-connected contours and reproduce desired shapes using an innovative data compression technique based on conformal mapping. In a previous work, through a conformal mapping process, we demonstrated the ability to 1) recognize shapes, and 2) concisely represent shape boundaries using a set of polynomial coefficients derived in the mapping process. In this work we illustrate how these previous results can be applied to data compression. In particular, in the approach outlined herein, a syntactic representation is formed for polygon shapes whose representation we desire to extract and reproduce compactly. Additionally, we present a problem of concavity in shape boundaries and a proposed solution in which polygons are divided into convex subsets and reconstructed accordingly. Each convex subset is then being processed in parallel, which lends to the extension of computational platform to parallel/distributed environment to improve the processing time. We show the potential of the proposed generalized technique in its ability to handle both polygonal, non-polygonal and mixed polygonal-non-polygonal object shapes.