| 引用本文: | 李京旭,张雄明.基于最优因果化提升结构及子带叠混的低内存 DWT 实现.[J].国防科技大学学报,2011,33(5):156-160,174.[点击复制] |
| LI Jingxu,ZHANG Xiongming.Low-memory Discrete Wavelet Transform with Optimization of Causal Lifting Scheme and Subband Interleaving[J].Journal of National University of Defense Technology,2011,33(5):156-160,174[点击复制] |
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| 基于最优因果化提升结构及子带叠混的低内存 DWT 实现 |
| 李京旭1,2, 张雄明3,4 |
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(1.中国人民解放军 66081部队，河北 张家口 075000;2.国防科技大学 计算机学院，湖南 长沙 410073;3.中国人民解放军 61195部队，北京 100091;4.国防科技大学 ATR重点实验室，湖南 长沙 410073)
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| 摘要: |
| 基于提升结构的因果化实现及优化在将两带滤波器组转化为单进单出系统时子带系数的叠混模式, 提出了一种改进的低内存需求的离散小波变换 (Enhanced Low-memory Discrete Wavelet Transform, ELDWT) 实现方法。相对于DWT的常规全局实现法, 基于ELDWT实现的正、逆离散小波变换均具有与图像高度无关的低内存 需求且不同的分解层可以使用不同的小波滤波器。相对于著名的基于行的离散小波变换实现(Line-Based Wavelet Transform, LBWT)，当小波滤波器组中的滤波器长度差大于 2 时, ELDWT 具有比 LBWT 更低的全局内存开销及系统时延。当采用MPEG-4标准中静态纹理压缩所采用的9/3滤波器组及典型的5层分解, 相对于 LBWT, 基于ELDWT的2维DWT实现的内存需求降低了22.7%。 |
| 关键词: 离散小波变换 因果化提升结构 低内存 最优子带叠混 |
| DOI： |
| 投稿日期：2011-03-08 |
| 基金项目:国家自然科学基金资助项目(60473080,60573103) |
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| Low-memory Discrete Wavelet Transform with Optimization of Causal Lifting Scheme and Subband Interleaving |
| LI Jingxu1,2, ZHANG Xiongming3,4 |
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(1.The Chinese People’s Liberation Army 66081 Troops, Zhangjiakou 075000, China;2.
2. College of Computer, National Univ. of Defense Technology, Changsha 410073, China;3.The Chinese People’s Liberation Army 61195 Troops, Beijing 100091, China;4.
4.National Key Laboratory of ATR, National Univ. of Defense Technology, Changsha 410073, China)
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| Abstract: |
| Based on the optimization of causal implementation for the lifting schemes and the interleaving mode for sub-band coefficients, this research presents an enhanced low-memory implementation of discrete wavelet transform called the ELDWT (Enhanced Low-Memory Discrete Wavelet Transform). In comparison with the conventional global implementation of DWT, the ELDWT has the advantages that its memory budget is independent of the image height and different DWT filter banks cad be utilized in different decomposition/reconstruction levels. When the difference between the filters’ lengths is greater than two, the ELDWT has lower memory requirement and less system latency than those of the line-based DWT (LBWT). When a 5-level decomposition with the MPEG Default 9/3 filter bank is adopted, the overall memory is reduced by 22.7% in comparison with the LBWT. |
| Keywords: discrete wavelet transform causal lifting scheme low memory optimal subband interleaving |
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