Abstract
The optimized context weighting is presented. The relationship between the weighting of context models and the weighting of the description lengths corresponding to their respective context models are discussed first and it indicates that the weighting of context models is equivalent to the weighting of the their description lengths. With these discussions, the weights optimization algorithm based on the minimum description length are presented and the least square algorithm is suggested to implement the optimization of the weights. The proposed optimization algorithm is used in the compression of genome sequences. The experiment results indicate that by using the proposed weights optimization method, our context weighting-based algorithm can achieve better results than some similar algorithms reported in the literature.
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References
Willems, F.M.J., Shtarkov, Y.M., Tjalkens, T.J.: The context-tree weighting method: basic properties. IEEE Trans. Inform. Theor. 41, 653–664 (1995)
Xiao, S., Boncelet, C.G.: On the use of context-weighting in lossless bilevel image compression. IEEE Trans. Image Process. 15(11), 3253–3260 (2006)
Pinho, A.J., Neves, A.J.R., Bastos, C.A.C., Ferreira, P.J.S.G.: DNA coding using finite-context models and arithmetic coding. In: Proceeding of ICASSP-2009, Taipei, Taiwan, April 2009
Pinho A.J., et al.: Bacteria DNA sequence compression using a mixture of finite-context models. In: IEEE Statistical Signal Processing Workshop, pp. 125–128. Portugal, (2011)
Cao, M.D., Dix, T.I., Allison, L., Mears, C.: A simple statistical algorithm for biological sequence compression. In: Proceedings of the Data Compression Conference (DCC), Snowbird, Utah, (2007)
Rissanen, J.: Strong optimality of the normalized ML models as universal codes and information in data. IEEE Trans. Inf. Theory IT-47(5), 1712–1717 (2001)
Chen, M., Chen, J., Guo, M.: Affinity propagation for the context quantization. Adv. Mater. Res. 791–793, 1533–1536 (2013)
Wu, X., Zhai, G.: Adaptive sequential prediction of multidimensional signals with applications to lossless image coding. IEEE Trans. Image Process. 20(1), 36–42 (2011)
Acknowledgment
This work was supported by Natural Science Foundation of China under Grant (61062005) and Natural Science Foundation of Yunnan Province under Grant (2013FD042) and Yunnan University Science Foundation for Graduates under Grant (ynuy201383).
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Chen, M., Chen, J., Zhang, Y., Tang, M. (2016). Optimized Context Weighting Based on the Least Square Algorithm. In: Zeng, QA. (eds) Wireless Communications, Networking and Applications. Lecture Notes in Electrical Engineering, vol 348. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2580-5_94
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DOI: https://doi.org/10.1007/978-81-322-2580-5_94
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