An N-Dimensional Pseudo-Hilbert Scan for Arbitrarily-Sized Hypercuboids
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
A pseudo-hilbert scan algorithm for arbitrarily-sized rectangle region
IWICPAS'06 Proceedings of the 2006 Advances in Machine Vision, Image Processing, and Pattern Analysis international conference on Intelligent Computing in Pattern Analysis/Synthesis
A generalized 3-D Hilbert scan using look-up tables
Journal of Visual Communication and Image Representation
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The use of Hilbert scan is relatively new in image compression. This scan is guided by the Hilbert space-filling curve. A Hilbert image, thus produced by this scan of a graylevel image provides better compression rate than that of a raster scanned image. Since a Hilbert image is a 1-d image, an efficient 1-d algorithm based on Bezier-Berntein polynomial has been developed to simultaneously separate out and approximate homogeneous segments of pixels depending on some absolute error based criteria. Huffman coding scheme then encodes the approximation parameters. Investigation shows that better performance on image compression can be achieved using Hilbert scan. Comparison with an existing algorithm shows also better performance of the proposed algorithm.