2000 Fiscal Year Final Research Report Summary
Study of Sort-Based Data Compression Algorithms
| Project/Area Number |
11680339
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
計算機科学
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| Research Institution | Gunma University |
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Principal Investigator |
HIDETOSHI Yokoo Gunma University, Department of Computer Science, Professor, 工学部, 教授 (70134153)
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| Project Period (FY) |
1999 – 2000
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| Keywords | Data compression / universal coding / sorting / block sorting / context sorting / statistical model / lossless compression / adaptive alphabet decomposition |
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| Research Abstract |
We have analyzed several sort-based algorithms for lossless data compression and developed new data structures and algorithms which supersede existing ones. Sort-based lossless compression methods studied in this project include the block-sorting data compression method and the context-sorting method. They all define a total order on the set of symbol candidates based on the similarity between the current context and previous contexts. The context similarity essentially produces a partial order, which is in turn converted into a total order by the help of an artificial transformation. We have proposed a new adaptive model which can avoid such an artificial transformation. In the proposed model, we keep a partial order on the set of symbol candidates. When predicting an upcoming symbol, we can group symbol candidates into several equivalence classes according to their contexts. The proposed scheme adaptively decomposes the source alphabet into such equivalence classes. These sub-alphabets are sequentially traversed to encode an upcoming symbol. We use the PPM-like escape mechanism to indicate an alphabet which includes an upcoming symbol. The symbol is then encoded using a probability distribution estimated over that sub-alphabet. Simulation results show that the proposed method outperforms PPM* and the block-sorting algorithm.
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Research Products
(2 results)
