Improving Semistatic Compression via Phrase-based Modeling

Nieves Brisaboa, Antonio Fariña, Gonzalo Navarro, and José Paramá

In the last years, new semistatic word-based byte-oriented text compressors, such as Tagged Huffman and those based on Dense Codes, have shown that it is possible to perform fast direct search over compressed text and decompression of arbitrary text passages over collections reduced to around 30-35% of their original size.

Much of their success is due to the use of words as source symbols and a byte-oriented target alphabet. This approach represented a break with traditional statistical compressors, which use characters as source symbols and a bit-oriented target alphabet.

In this work, we go one step beyond by using phrases as source symbols. We present two new semistatic modelers we combined with a dense coding scheme to obtain two new compressors: Pair-Based End-Tagged Dense Code (PETDC), where source symbols can be either words or pairs of words and Phrase-Based End-Tagged Dense Code (PhETDC), which considers words and sequences of words (phrases). PETDC compresses English texts to 28-29% and PhETDC to around 23%, outperforming the optimal byte-oriented zero-order prefix-free word-based semistatic compressor by up to 8 percentage points. Moreover, PETDC and PhETDC still permit random access and efficient direct searches using fast Boyer-Moore algorithms.