Low complexity text and image compression for wireless devices and sensors

The primary intention in data compression has been for decades to improve the compression performance, while more computational requirements were accepted due to the evolving computer hardware. In the recent past, however, the attributes to data compression techniques have changed. Emerging mobile devices and wireless sensors require algorithms that get along with very limited computational power and memory. The first part of this thesis introduces a low-complexity compression technique for short messages in the range of 10 to 400 characters. It combines the principles of statistical context modeling with a novel scalable data model. The proposed scheme can cut the size of such a message in half while it only requires 32 kByte of RAM. Furthermore it is evaluated to account for battery savings on mobile phones. The second part of this thesis concerns a low-complexity wavelet compression technique for pictures. The technique consists of a novel computational scheme for the picture wavelet transform, i.e., the fractional wavelet filter, and the introduced wavelet image two-line (Wi2l) coder, both having extremely little memory requirements: For compression of a 256x256x8 picture only 1.5 kBytes of RAM are needed, while the algorithms get along with 16 bit integer calculations. The technique is evaluated on a small microchip with a total RAM size of 2 kBytes, but is yet competitive to current JPEG2000 implementations that run on personal computers. Typical low-cost sensor networks can thus employ state-of-the-art image compression by a software update.

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