الفهرس 
Chapter 1: IntroductionOnly 14 pages are availabe for public view
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Abstract
Unlike classical wired networks and wireless sensor networks (WSN), wireless multimedia sensor network (WMSN) differs from their predecessor’s scalar network basically in the following points; nature and size of data being transmitted, important memory resources, as well as, power consumed per each node for processing and transmission. The most effective solution to overcome those problems is image compression. As the image contains massive amount of redundancies resulting from high correlation between pixels; many compression algorithms have been developed. Set Partitioning In Hierarchical Tree (SPIHT) is considered one of the most important algorithms for reducing the size of the vision data collected by the sensor node within wireless multimedia sensor network. The Traditional SPIHT algorithm suffers from image coders complexity due to large memory requirement because it needs to maintain three lists to store significance information. This is an essential problem for the implementation on limited resource environments such as WMSN. This thesis provides a study and analyze relevant research directions and the most recent algorithms of image compression over WMSN. This thesis characterizes the benefits and shortcomings of recent efforts of such algorithms. Moreover, it provides an open research issue for each compression method; and its potentials to WMSN.
The main objective of this thesis is to introduce a listless pipelined strip based SPIHT for WMSN to reduce system complexity and minimize processing time and memory usage. The proposed algorithm is implemented using discrete wavelet transform (DWT) lifting-based (Cohen-Daubechies-Feauveau wavelet CDF 9/7) instead of DWT convolution-based filter. Besides, structure of LZC (listless zero tree coding) was adapted and combined with strip based architecture. Reducing consumed power thus granting long life time is considered the main performance metric and will be the main target in the investigated solution. The Experimental results show the superiority of the proposed algorithm in terms of peak signal-to-noise ratio (PSNR) which reaches 1 dB for all bit rates. In addition, the memory requirement is reduced to 71% with 27% of energy saving as well as, minimizing processing time with 65 %.