一些通用平面树的熵和最优压缩

Z. Golebiewski, A. Magner, W. Szpankowski
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引用次数: 1

摘要

我们继续发展结构化数据的信息理论。本文研究了d阶树(d≥2)和无限制阶树的生成模型。我们首先计算熵,它给出了这些树压缩的基本下界。然后,我们提出了基于算术编码的有效压缩算法,在恒定的比特数内实现熵。这些算法的naïve实现具有令人望而却步的O(nd)个基本算术运算(每个运算对应f(n, d)个位运算)的时间复杂度,但我们的高效算法运行O(n2)个这些运算,其中n是节点数。事实证明,将源编码(即压缩)从序列扩展到高级数据结构(如度无约束树)在数学上是相当具有挑战性的,并且会导致递归,在一般结构的信息论中找到大量应用(例如,分析度无约束非平面树的信息内容)。
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Entropy and Optimal Compression of Some General Plane Trees
We continue developing the information theory of structured data. In this article, we study models generating d-ary trees (d ≥ 2) and trees with unrestricted degree. We first compute the entropy which gives us the fundamental lower bound on compression of such trees. Then we present efficient compression algorithms based on arithmetic encoding that achieve the entropy within a constant number of bits. A naïve implementation of these algorithms has a prohibitive time complexity of O(nd) elementary arithmetic operations (each corresponding to a number f(n, d) of bit operations), but our efficient algorithms run in O(n2) of these operations, where n is the number of nodes. It turns out that extending source coding (i.e., compression) from sequences to advanced data structures such as degree-unconstrained trees is mathematically quite challenging and leads to recurrences that find ample applications in the information theory of general structures (e.g., to analyze the information content of degree-unconstrained non-plane trees).
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