Context Quantization based on Minimum Description Length and Hierarchical Clustering

Context Quantization based on Minimum Description Length and Hierarchical Clustering

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 fr...

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Main Authors: Chen Hui, Chen Jianhua
Format: Article
Language:English
Published: EDP Sciences 2016-01-01
Series:MATEC Web of Conferences
Online Access:http://dx.doi.org/10.1051/matecconf/20165601001
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spelling doaj-3db710cabff14adcb0a657705617764a2021-02-02T03:07:44ZengEDP SciencesMATEC Web of Conferences2261-236X2016-01-01560100110.1051/matecconf/20165601001matecconf_iccae2016_01001Context Quantization based on Minimum Description Length and Hierarchical ClusteringChen HuiChen JianhuaThe 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.http://dx.doi.org/10.1051/matecconf/20165601001
collection DOAJ
language English
format Article
sources DOAJ
author Chen Hui
Chen Jianhua
spellingShingle Chen Hui
Chen Jianhua
Context Quantization based on Minimum Description Length and Hierarchical Clustering
MATEC Web of Conferences
author_facet Chen Hui
Chen Jianhua
author_sort Chen Hui
title Context Quantization based on Minimum Description Length and Hierarchical Clustering
title_short Context Quantization based on Minimum Description Length and Hierarchical Clustering
title_full Context Quantization based on Minimum Description Length and Hierarchical Clustering
title_fullStr Context Quantization based on Minimum Description Length and Hierarchical Clustering
title_full_unstemmed Context Quantization based on Minimum Description Length and Hierarchical Clustering
title_sort context quantization based on minimum description length and hierarchical clustering
publisher EDP Sciences
series MATEC Web of Conferences
issn 2261-236X
publishDate 2016-01-01
description 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.
url http://dx.doi.org/10.1051/matecconf/20165601001
work_keys_str_mv AT chenhui contextquantizationbasedonminimumdescriptionlengthandhierarchicalclustering
AT chenjianhua contextquantizationbasedonminimumdescriptionlengthandhierarchicalclustering
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