Compression faible complexité et codage par régions d’intérêt d’images fixes dans les WSNs (Low complexity image compression and region of interest coding in WSN)
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Date
2016-03-10
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Abstract
Reducing the algorithmic complexity of image compression techniques is a great challenge in
wireless image sensor networks (WISNs). Many image compression standards, such as JPEG
and JPEG2000, are not suitable for implementation in WISNs because of its high energy
consumption due to their high computational complexity. To solve the problem, in this thesis
we proposed some low complexity image compression algorithms based on the discrete
cosine transform (DCT) and the Discrete Tchebichef transform (DTT). Furthermore, we
applied on them the pruned approach in order to more reduce its complexities and thence their
energy consumption. Additionally, we proposed a region-of-interest based image compression
using the DTT transform. Where, the main idea of this method is to compress certain parts of
an image which are of a higher importance in detriment of the rest of the image. Simulation
results show that the proposed compression methods require a reduced number of arithmetic
operations, energy consumption and memory. And they have at the same time competitive
compression efficiency compared with state-of-the-art image compression techniques which
make them viable options for image compression and/or communication over WISNs.