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| 基于小波和偶合特征的多数据流压缩算法 |
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陈安龙1,2, 唐常杰1, 元昌安1,3, 朱明放1, 段磊1
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1.四川大学,计算机学院,四川,成都,610065;2.电子科技大学,计算机科学与工程学院,四川,成都,610054;3.广西师范学院,信息技术系,广西,南宁,530001
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| 摘要: |
| 提出了基于Haar小波技术和偶合特征的多数据流压缩方法.主要研究成果包括:(1) 证明了Haar小波变换服从能量守恒规律,并用于压缩数据流;(2) 揭示了数据流的偶合度与变化趋势的相关性、偶合度的平移不变性及等价规律,采用特征流序列的小波系数和流能量近似表示流的趋势,达到压缩的目的;(3) 提出了多尺度能量分解模型,提高了表示精度;(4) 设计了多尺度能量分解压缩算法以及多尺度重构算法;(5) 在真实数据集上的实验表明,新方法的压缩比是传统小波方法的2～4倍. |
| 关键词: 数据流 Haar小波 偶合特征 数据压缩 层次分解 |
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| 基金项目:Supported by the National Natural Science Foundation of China under Grant Nos.60473071, 10476006 (国家自然科学基金); the National Research Foundation for the Doctoral Program of Higher Education of China under Grant No.20020610007 (国家教育部博士点专项基金) |
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| A Compression Algorithm for Multi-Streams Based on Wavelets and Coincidence |
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CHEN An-Long,TANG Chang-Jie,YUAN Chang-An,ZHU Ming-Fang,DUAN Lei
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| Abstract: |
| Methods based on Haar wavelets and coincidence characteristics are proposed to compress multi-streams. The main contributions include: (1) Energy conservation law of Haar wavelets transform is proved to compress data streams. (2) The relation between the coincidence measure and trend of streams is revealed as along with the invariability under parallel shift and the equivalence law over coincidence measure to approximately express data-streams by the wavelet coefficient of the characteristic stream and its energy. (3) Multi-Scales energy decomposition model is proposed to improve the compression precision. (4) The multi-scales compression algorithm and the energy conservation reconstruction algorithm are designed. (5) Extended experiments show that the compression ratio of the new methods is 2~4 times as the traditional method. |
| Key words: data stream Haar wavelet coincidence characteristic data compression hierarchy decompose |
