Variants of Huffman codes where words are taken as the source symbols are currently the most attractive choices to compress natural language text databases. In particular, Tagged Huffman Code by Moura et al. offers fast direct searching on the compressed text and random access capabilities, in exchange for producing around 11 % larger compressed files. This work describes End-Tagged Dense Code and (s, c)-Dense Code, two new semistatic statistical methods for compressing natural language texts. These techniques permit simpler and faster encoding and obtain better compression ratios than Tagged Huffman Code, while maintaining its fast direct search and random access capabilities. We show that Dense Codes improve Tagged Huffman Code compression ratio by about 10%, reaching only 0.6% overhead over the optimal Huffman compression ratio. Being simpler, Dense Codes are generated 45% to 60 % faster than Huffman codes. This makes Dense Codes a very attractive alternative to Huffman code variants for various reasons: they are simpler to program, faster to build, of almost optimal size, and as fast and easy to search as the best Huffman variants, which are not so close to the optimal size.

We address the problem of adaptive compression of natural language text, considering the case where the receiver is much less powerful than the sender, as in mobile applications. Our techniques achieve compression ratios around 32 % and require very little effort from the receiver. Furthermore, the receiver is not only lighter, but it can also search the compressed text with less work than the necessary to uncompress it. This is a novelty in two senses: it breaks the usual compressor/decompressor symmetry typical of adaptive schemes, and it contradicts the long-standing assumption that only semistatic codes could be searched more efficiently than the uncompressed text. Our novel compression methods are in several aspects preferable over the existing adaptive and semistatic compressors for natural language texts.

Recent publications advocate the use of various variable length codes for which each codeword consists of an integral number of bytes in compression applications using large alphabets. This paper shows that another tradeoff with similar properties can be obtained by Fibonacci codes. These are fixed codeword sets, using binary representations of integers based on Fibonacci numbers of order m ≥ 2. Fibonacci codes have been used before, and this paper extends previous work presenting several novel features. In particular, the compression efficiency is analyzed and compared to that of dense codes, and various table-driven decoding routines are suggested.

Semistatic byte-oriented word-based compression codes have been shown to be an attractive alternative to compress natural language text databases, because of the combination of speed, effectiveness, and direct searchability they offer. In particular, our recently proposed family of dense compression codes has been shown to be superior to the more traditional byte-oriented word-based Huffman codes in most aspects. In this paper, we focus on the problem of transmitting texts among peers that do not share the vocabulary. This is the typical scenario for adaptive compression methods. We design adaptive variants of our semistatic dense codes, showing that they are much simpler and faster than dynamic Huffman codes and reach almost the same compression effectiveness. We show that our variants have a very compelling trade-off between compression/decompression speed, compression ratio and search speed compared with most of the state-of-the-art general compressors.