April 2020
Intermediate to advanced
210 pages
4h 47m
Chinese
Content preview from 数据压缩入门
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71
第 6 章
自适应统计编码
6.1
位置对熵的重要性
第
5
章介绍的所有统计编码算法，在编码开始之前都需要遍历一次数据，以计算出各符号
出现的概率。这样做是有缺点的，为了计算概率总需要多遍历一次数据集，而在计算出整
个数据集中各符号的出现概率后，还要继续处理这些数值。如果是相对较小的数据集，那
么这些就不是什么问题。
然而，随着要压缩的数据集变大，统计编码的结果与熵的偏差也会越来越大，这是因为数
据集的不同部分有着不同的概率特征。如果处理的是流数据，比如视频流或音频流，由于
整个数据集没有“结尾”，因此就不能“遍历两次”。
这些概念适用于所有的流数据，下面就在相对简单的示例数据集中来看一看这些概念。在
数据流中，字符
Q
可能会在前三分之一部分出现很多次，而在后三分之二部分则一次也没
有出现。统计编码算法的概率表无法处理字符
Q
分布的这种局部性。如果字符
Q
出现的
概率为
0.01
，那么通常我们会期望它在整个数据流中均匀分布，也就是说，大约每
100
个
字符中就有
1
个是
Q
。
然而实际数据的情况并非如此，数据中总会存在某种类型的
局部偏态
（
locality-dependent
skewing
）
1
，将某些符号、想法或者单词集中在数据集的某个子区间里。
结果是统计编码算法生成的编码比根据熵生成的更臃肿，这是因为其所依据的概率信息没
注
1
：
这个词完全是我们自造的。
72
｜
第
6
章
有考虑统计上的局部变化。例如，如果将数据流分为
N
块并且每块都单独压缩，那么得到
的结果可能会比将数据流整体压缩得到的结果小（如果数据流中存在很多局部偏态的情况 ...
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