SCIA 2005: Image Analysis pp 312-321 | Cite as
Lossless Compression of Map Contours by Context Tree Modeling of Chain Codes
Conference paper
Abstract
We consider lossless compression of digital contours in map images. The problem is attacked by the use of context-based statistical modeling and entropy coding of chain codes. We propose to generate an optimal context tree by first constructing a complete tree up to a predefined depth, and then create the optimal tree by pruning out nodes that do not provide improvement in compression. Experiments show that the proposed method gives lower bit rates than the existing methods for the set of test images.
Keywords
Child Node Lossless Compression Chain Code Context Tree Digital Contour
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
Download
to read the full conference paper text
References
- 1.Bossen, F., Ebrahimi, T.: Region shape coding, Technical Report M0318, ISO/IEC JTC1/SC29/WG11 (November 1995)Google Scholar
- 2.Cleary, J., Witten, I.: Data compression using adaptive coding and partial string matching. IEEE Trans. on Communications 32(4), 396–402 (1984)CrossRefGoogle Scholar
- 3.Eden, M., Kocher, M.: On performance of a contour coding algorithm in the context of image Coding Part 1: Contour Segment Coding. Signal Processing 8, 381–386 (1985)CrossRefGoogle Scholar
- 4.Estes, R., Algazi, R.: Efficient error free encoding of binary documents. In: Proc. of IEEE Data Compression Conference, March 1995, pp. 122–131 (1995)Google Scholar
- 5.Freeman, H.: Computer processing of line drawing images. ACM Computing Surveys 6, 57–59 (1974)MATHCrossRefGoogle Scholar
- 6.Freeman, H.: Application of the generalized chain coding scheme to map data processing. In: Proc. of IEEE Pattern Recognition and Image Processing, May 1978, pp. 220–226 (1978)Google Scholar
- 7.Helfgott, H., Cohn, M.: Linear-time construction of optimal context trees. In: Proc. of the IEEE Data Compression Conference, April 1998, pp. 369–377 (1998)Google Scholar
- 8.Howard, P., Vitter, J.: Analyses of arithmetic coding for data compression. In: Proc. of the IEEE Data Compression Conference, pp. 3–12 (1991)Google Scholar
- 9.JBIG: Progressive bi-level image compression, ISO/IEC International Standard 11544 (1993)Google Scholar
- 10.Kaneko, T., Okudara, M.: Encoding of arbitrary curves based on chain code representation. IEEE Trans. on Communications 33, 697–707 (1985)CrossRefGoogle Scholar
- 11.Liu, Y.K., Zalik, B.: An efficient chain code with Huffman coding. Pattern Recognition 38(4), 553–557 (2005)CrossRefGoogle Scholar
- 12.Lu, C.C., Dunham, G.: Highly efficient coding schemes for contour lines based on chain code representations. IEEE Trans. on Communications 39(10), 1511–1514 (1991)CrossRefGoogle Scholar
- 13.Martins, B., Forchhammer, S.: Tree coding of bi-level images. IEEE Trans. on Image Processing 7(4), 517–528 (1998)MATHCrossRefMathSciNetGoogle Scholar
- 14.Martin, G.: An algorithm for removing redundancy from a digitized message. Presented at: Video and Data Recording Conference (July 1979)Google Scholar
- 15.Norhe, R.: Topics in descriptive complexity, PhD Thesis, University of Lingköping, Sweden (1994)Google Scholar
- 16.Rissanen, J.: A universal data compression system. IEEE Transactions on Information Theory 29(5), 656–664 (1983)MATHCrossRefMathSciNetGoogle Scholar
- 17.Shkarin, D.: PPM: one step to practicality. In: Proc. of the IEEE Data Compression Conference, April 2002, pp. 202–211 (2002)Google Scholar
- 18.Weinberger, M., Rissanen, J.: A universal finite memory source. IEEE Trans on Information Theory 41(3), 643–652 (1995)MATHCrossRefGoogle Scholar
- 19.Weinberger, M., Rissanen, J., Arps, R.: Application of universal context modeling to lossless compression of grey-scale images. IEEE Transactions on Image Processing 5, 575–586 (1996)CrossRefGoogle Scholar
Copyright information
© Springer-Verlag Berlin Heidelberg 2005