To overcome this issue, authors in this research have chosen Run Length Encoding (RLE) and Adaptive Huffman Encoding (AHE) data compression techniques as they can be executed on sensor nodes. Many data compression methods have been proposed but mainly for image processing and a vast majority of them are not pertinent on sensor nodes because of memory impediment, energy utilization and handling speed.
Compressing the data before transmission will help alleviate the problem. Sensor nodes have limited resources such as memory, energy and computation capabilities restricting their ability to process large volume of data that is generated. WSN comprises of sensor nodes and hubs fit for detecting, processing, and communicating remotely. The exponential growth in remote sensing, coupled with advancements in integrated circuits (IC) design and fabrication technology for communication, has prompted the progress of Wireless Sensor Networks (WSN). As such, the algorithm is very suitable for the modern heterogeneous nature of the Internet users to satisfy their different capabilities and desires in terms of image quality and resolution. The second algorithm, which is the major contribution of the work, upgrades the modified SLS to produce a bitstream that is both quality and resolution scalable (highly scalable). The first algorithm modifies SLS to reduce its complexity and improve its performance. This paper introduces two new algorithms that are based on SLS. The Single List SPIHT (SLS) algorithm resolved the high memory problem of SPIHT by using only one list of fixed size equals to just 1/4 the image size, and an average of 2.25 bits/pixel. In additions, it does not exploit the multi-resolution feature of the wavelet transform to produce a resolution scalable bitstream by which the image can be decoded at numerous resolutions (sizes). However, it suffers from the enormous computer memory consumption due to utilizing linked lists of size of about 2–3 times the image size to save the coordinates of the image pixels and the generated sets. The SPIHT algorithm is characterized by low computational complexity, good performance, and the production of an embedded bitstream that can be decoded at several bit-rates with image quality enhancement as more bits are received.
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