TITLE:
Wavelet Transform for Image Compression Using Multi-Resolution Analytics: Application to Wireless Sensors Data
AUTHORS:
Wasiu Opeyemi Oduola, Cajetan M. Akujuobi
KEYWORDS:
Wavelets, Multi-Resolution Analysis, Image Compressions, Wireless Sensor Networks, Mathematical Data Analytics
JOURNAL NAME:
Advances in Pure Mathematics,
Vol.7 No.8,
August
14,
2017
ABSTRACT: The aggregation of data in recent years has been expanding at an exponential
rate. There are various data generating sources that are responsible for such a
tremendous data growth rate. Some of the data origins include data from the
various social media, footages from video cameras, wireless and wired sensor
network measurements, data from the stock markets and other financial
transaction data, supermarket transaction data and so on. The aforementioned
data may be high dimensional and big in Volume, Value, Velocity, Variety,
and Veracity. Hence one of the crucial challenges is the storage, processing
and extraction of relevant information from the data. In the special
case of image data, the technique of image compressions may be employed in
reducing the dimension and volume of the data to ensure it is convenient for
processing and analysis. In this work, we examine a proof-of-concept multiresolution
analytics that uses wavelet transforms, that is one popular mathematical
and analytical framework employed in signal processing and representations,
and we study its applications to the area of compressing image data
in wireless sensor networks. The proposed approach consists of the applications
of wavelet transforms, threshold detections, quantization data encoding
and ultimately apply the inverse transforms. The work specifically focuses on
multi-resolution analysis with wavelet transforms by comparing 3 wavelets at
the 5 decomposition levels. Simulation results are provided to demonstrate
the effectiveness of the methodology.