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http://dx.doi.org/10.7472/jksii.2020.21.4.35

Improvement of AMR Data Compression Using the Context Tree Weighting Method  

Lee, Eun-su (Department of Computer Science, Sangmyung University)
Oh, Eun-ju (Department of Computer Science, Sangmyung University)
Yoo, Hoon (Department of Electronics, Sangmyung University)
Publication Information
Journal of Internet Computing and Services / v.21, no.4, 2020 , pp. 35-41 More about this Journal
Abstract
This paper proposes an algorithm to improve the compression performance of the adaptive multi-rate (AMR) speech coding using the context tree weighting (CTW) method. AMR is the voice encoding standard adopted by IMT-2000, and supports 8 transmission rates from 4.75 kbit/s to 12.2 kbit/s to cope with changes in the channel condition. CTW as a kind of the arithmetic coding, uses a variable-order Markov model. Considering that CTW operates bit by bit, we propose an algorithm that re-orders AMR data and compresses them with CTW. To verify the validity of the proposed algorithm, an experiment is conducted to compare the proposed algorithm with existing compression methods including ZIP in terms of compression ratio. Experimental results indicate that the average additional compression rate in AMR data is about 3.21% with ZIP and about 9.10% with the proposed algorithm. Thus our algorithm improves the compression performance of AMR data by about 5.89%.
Keywords
AMR coding; Context Tree Weighting; Arithmetic coding; Data compression;
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