高效二维压缩匹配

A. Amir, Gary Benson
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引用次数: 176

摘要

众所周知,数字化图像非常消耗空间。然而,通常可以利用图像中的规律来减少必要的存储面积。因此,许多系统以压缩形式存储图像。作者建议将压缩作为一种节省时间的工具,除了其传统的节省空间的作用。他们引入了一种新的模式匹配范式——压缩匹配。文本数组T和模式数组P以压缩形式c(T)和c(P)给出。它们在T中寻找P的所有表现形式,而不解压缩T。这使得搜索时间在未压缩文本的大小上是次线性的。他们表明,对于二维运行长度压缩,存在O(mod c(T) mod log mod P mod + mod P mod),或者几乎是最优算法。该算法采用了一种新颖的多维模式匹配技术——二维周期性分析。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Efficient two-dimensional compressed matching
Digitized images are known to be extremely space consuming. However, regularities in the images can often be exploited to reduce the necessary storage area. Thus, many systems store images in a compressed form. The authors propose that compression be used as a time saving tool, in addition to its traditional role of space saving. They introduce a new pattern matching paradigm, compressed matching. A text array T and pattern array P are given in compressed forms c(T) and c(P). They seek all appearances of P in T, without decompressing T. This achieves a search time that is sublinear in the size of the uncompressed text mod T mod . They show that for the two-dimensional run-length compression there is a O( mod c(T) mod log mod P mod + mod P mod ), or almost optimal algorithm. The algorithm uses a novel multidimensional pattern matching technique, two-dimensional periodicity analysis.<>
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