Context Modeling Based on Description Length

CHEN Jianhua; WANG Yong; ZHANG Hong

doi:10.11999/JEIT150562

Volume 38 Issue 3

Mar. 2016

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CHEN Jianhua, WANG Yong, ZHANG Hong. Context Modeling Based on Description Length[J]. Journal of Electronics & Information Technology, 2016, 38(3): 661-667. doi: 10.11999/JEIT150562

Citation:

CHEN Jianhua, WANG Yong, ZHANG Hong. Context Modeling Based on Description Length[J]. Journal of Electronics & Information Technology, 2016, 38(3): 661-667. doi: 10.11999/JEIT150562

CHEN Jianhua, WANG Yong, ZHANG Hong. Context Modeling Based on Description Length[J]. Journal of Electronics & Information Technology, 2016, 38(3): 661-667. doi: 10.11999/JEIT150562

Citation:

CHEN Jianhua, WANG Yong, ZHANG Hong. Context Modeling Based on Description Length[J]. Journal of Electronics & Information Technology, 2016, 38(3): 661-667. doi: 10.11999/JEIT150562

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Context Modeling Based on Description Length

doi: 10.11999/JEIT150562

Funds:

The National Natural Science Foundation of China (61062005)

Received Date: 2015-05-11
Rev Recd Date: 2015-12-04
Publish Date: 2016-03-19

Abstract

Abstract

In entropy coding systems based on the context modeling, the context dilution problem introduced by high-order context models needs to be alleviated by the context quantization to achieve the desired compression gain. Therefore, an algorithm is proposed to implement the Context Quantization by the Minimizing Description Length (MDLCQ) in this paper. With the description length as the evaluation criterion, the Context Quantization Of Single-Condition (CQOSC) is attained by the dynamic programming algorithm. Then the context quantizer of multi-conditions can be designed by the iterated application of CQOSC. This algorithm can not only design the optimized context quantizer for multi-valued sources, but also determine adaptively the importance of every condition so as to design the best order of the model. The experimental results show that the context quantizer designed by the MDLCQ algorithm can apparently improve the compression performance of the entropy coding system.
- Conditional entropy coding,
- Context quantization,
- Description length,
- Arithmetic coding

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References(15)

References

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