Abstract
To improve utilization of text storage resources and efficiency of data transmission, we proposed two syllable-based Uyghur text compression coding schemes. First, according to the statistics of syllable coverage of the corpus text, we constructed a 12-bit and 16-bit syllable code tables and added commonly used symbols—such as punctuation marks and ASCII characters—to the code tables. To enable the coding scheme to process Uyghur texts mixed with other language symbols, we introduced a flag code in the compression process to distinguish the Unicode encodings that were not in the code table. The experiments showed that the 12-bit coding scheme had an average compression ratio of 0.3 on Uyghur text less than 4 KB in size and that the 16-bit coding scheme had an average compression ratio of 0.5 on text less than 2 KB in size. Our compression schemes outperformed GZip, BZip2, and the LZW algorithm on short text and could be effectively applied to the compression of Uyghur short text for storage and applications.
Highlights
Network data on the internet continues to increase significantly each year
We found that the number of occurrences for the six inherent syllabic structures accounted for the majority of the syllables
According to Equation (1), the average length of a Uyghur syllable was 2.4 characters; theoretically, no matter how large the text size was, the compression ratio was stable at CRB12 = 12/(2.4 × 16) = 0.31 and CRB16 = 16/(2.4 × 16) = 0.42 or so
Summary
Network data on the internet continues to increase significantly each year. In 2018, for the mobile internet only, access traffic reached 71.1 billion GB in China. Text compression technology mainly employs statistics-based and dictionary-based methods. These methods have distinct advantages and disadvantages, depending on the specific application, and they operate differently. Shannon–Fano coding uses a top-down building tree, which has low coding efficiency and long average coding length. It is rarely used in practical applications. Huffman coding encodes the sequence according to the probability of character occurrence, so that the average code length is the shortest This method has average compression efficiency for those characters with average probability of occurrence. The LZ78 algorithm uses a dynamic dictionary to store information, extracts character strings from the character stream, and represents them by numbers and encodes the repeated character strings
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