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Entropy Based Image Segmentation With Wavelet Compression for Energy Efficient LTE Systems
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DOI：10.1109/ICT.2016.7500430.png)
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In the era of advanced web based applications, energy consumption needs to be analyzed for mobile devices running on batteries. In this paper, we have considered image transfer application from mobile to cloud through LTE network. We took realistic energy consumption model which includes radio energy, circuit energy along with computation energy. We have proposed an algorithm to minimize the total energy consumption per information bit for a specific bit error rate requirement. This algorithm segments an image and compress some of them based on their information entropy. We have found that optimal segmentation is beneficial as opposed to fully uncompressed or fully compressed image. We have compared wavelet and JPEG compression techniques. We have observed that same number of segments to be compressed at relatively smaller distance when required BER improves. We have seen that as the distance between transmitter and receiver increases more number of segments have to be compressed to get the minimum energy consumption. With the optimized algorithm we can save energy up to 28.26% compared to fully compressed or fully uncompressed image.
Keyword:
Energy consumption
energy per bit optimization
entropy
image partitioning
LTE uplink transmission
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