Algorithms

838 CHAPTER 5 ■ Strings
One reason for the popularity of Huffman compression is that it is effective for
various types of files, not just natural language text. We have been careful to code the
method so that it can work properly for any 8-bit value in each 8-bit character. In
other words, we can apply it to any bytestream whatsoever. Several examples, for file
types that we have considered earlier in this section, are shown in the figure at the bottom
of this page. These examples show that Huffman compression is competitive with
both fixed-length encoding and run-length encoding, even though those methods are
designed to perform well for certain types of files. Understanding the reason Huffman
encoding performs well in these domains is instructive. In the case of genomic
data, Huffman compression essentially discovers a 2-bit code, as the four letters appear
with approximately equal frequency so that the Huffman trie is balanced, with each
character assigned a 2-bit code. In the case of run-length encoding, 00000000 and
11111111 are likely to be the most frequently occurring characters, so they are likely
to be encoded with 2 or 3 bits, leading to substantial compression.
virus (50000 bits)
% java Genome - < genomeVirus.txt | java PictureDump 512 25
12536 bits
% java Huffman - < genomeVirus.txt | java PictureDump 512 25
12576 bits Huffman compression needs just 40 more bits than custom 2-bit code
bitmap (1536 bits)
% java RunLength - < q32x48.bin | java BinaryDump 0
1144 bits
% java Huffman - < q32x48.bin | java BinaryDump 0
816 bits Huffman compression uses 29% fewer bits than customized method
higher-resolution bitmap (6144 bits)
% java RunLength - < q64x96.bin | java BinaryDump 0
2296 bits
% java Huffman - < q64x96.bin | java BinaryDump 0
2032 bits gap narrows to 11% for higher resolution
Compressing and expanding genomic data and bitmaps with Huffman encoding