Computational-geometric methods for polygonal approximations of a curve
Computer Vision, Graphics, and Image Processing
Optimum polygonal approximation of digitized curves
Pattern Recognition Letters
An efficient algorithm for the optimal polygonal approximation of digitized curves
Pattern Recognition Letters
Dictionary Design Algorithms for Vector Map Compression
DCC '02 Proceedings of the Data Compression Conference
Reduced-search dynamic programming for approximation of polygonal curves
Pattern Recognition Letters
Distortion-constrained compression of vector maps
Proceedings of the 2007 ACM symposium on Applied computing
A Multi-Resolution Compression Scheme for EfficientWindow Queries over Road Network Databases
ICDMW '06 Proceedings of the Sixth IEEE International Conference on Data Mining - Workshops
Data reduction of large vector graphics
Pattern Recognition
Optimal encoding of vector data with polygonal approximation and vertex quantization
SCIA'05 Proceedings of the 14th Scandinavian conference on Image Analysis
IEEE Transactions on Information Theory
Optimum quantizers and permutation codes
IEEE Transactions on Information Theory
An optimal polygonal boundary encoding scheme in the rate distortion sense
IEEE Transactions on Image Processing
Adaptive approximation bounds for vertex based contour encoding
IEEE Transactions on Image Processing
A discrete geometry approach for dominant point detection
Pattern Recognition
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An algorithm for lossy compression of vector data (vector maps, vector graphics, contours of shapes) was developed. The algorithm is based on optimal polygonal approximation for error measure L2 and dynamic quantization of the vector data. The algorithm includes optimal distribution of the approximation line segments among the vector objects, optimal polygonal approximation of the objects with dynamic quantization and construction of the optimal variable-rate vector quantizer. The developed algorithm can be used for lossy compression of one-dimensional signals and multidimensional vector data.