MATEC Web of Conferences
Volume 56, 20162016 8th International Conference on Computer and Automation Engineering (ICCAE 2016)
|Number of page(s)||5|
|Section||Computer and Information technologies|
|Published online||26 April 2016|
Context Quantization based on Minimum Description Length and Hierarchical Clustering
Department of Electronic Engineering, Dianchi College, Kunming, China
The code length of a source can be reduced effectively by using conditional probability distributions in a context model. However, the larger the size of the context model, the more difficult the estimation of the conditional probability distributions in the model by using the counting statistics from the source symbols. In order to deal with this problem, a hierarchical clustering based context quantization algorithm is used to combine the conditional probability distributions in the context model to minimize the description length. The simulation results show that it is a good method for quantizing the context model. Meanwhile, the initial cluster centers and the number of classes do not need to be determined in advance any more. Thus, it can greatly simplify the quantizer design for the context quantization problem.
© Owned by the authors, published by EDP Sciences, 2016
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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