Abstract
Short Text Compression is a great concern for data engineering and management. The rapid use of small devices especially, mobile phones and wireless sensors have turned short text compression into a demand-of-thetime. In this paper, we propose an approach of compressing short English text for smart devices. The prime objective of this proposed technique is to establish a low-complexity lossless compression scheme suitable for smart devices like cellular phones and PDAs (Personal Digital Assistants) having small memory and relatively low processing speed. The main target is to compress short messages up to an optimal level, which requires optimal space, consumes less time and low overhead. Here a new static-statistical context model has been proposed to obtain the compression. We use character masking with space integration, syllable based dictionary matching and static coding in hierarchical steps to achieve low complexity lossless compression of short English text for low-powered electronic devices. We also propose an efficient probabilistic distribution based content-ranking scheme for training the statistical model. We analyze the performance of the proposed scheme as well as the other similar existing schemes with respect to compression ratio, computational complexity and compression-decompression time. The analysis shows that, the required number of operations for the proposed scheme is less than that of other existing systems. The experimental results of the implemented model give better compression for small text files using optimum resources. The obtained compression ratio indicates a satisfactory performance in terms of compression parameters including better compression ratio, lower compression and decompression time with reduced memory requirements and lower complexity. The compression time is also lower because of computational simplicity. In overall analysis, the simplicity of computational requirement encompasses the compression effective and efficient.
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