Advances in Pure Mathematics, 2017, 7, 430-440 http://www.scirp.org/journal/apm ISSN Online: 2160-0384 ISSN Print: 2160-0368 DOI: 10.4236/apm.2017.78028 Aug. 14, 2017 430 Advances in Pure Mathematics Wavelet Transform for Image Compression Using Multi-Resolution Analytics: Application to Wireless Sensors Data Wasiu Opeyemi Oduola, Cajetan M. Akujuobi Electrical and Computer Engineering Department, Prairie View A & M University, Member of Texas A & M University System, Prairie View, USA 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, super-market transaction data and so on. The aforemen- tioned data may be high dimensional and big in Volume, Value, Velocity, Va- riety, and Veracity. Hence one of the crucial challenges is the storage, pro- cessing 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 multi- resolution analytics that uses wavelet transforms, that is one popular mathe- matical and analytical framework employed in signal processing and repre- sentations, and we study its applications to the area of compressing image da- ta in wireless sensor networks. The proposed approach consists of the applica- tions 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. Keywords Wavelets, Multi-Resolution Analysis, Image Compressions, Wireless Sensor Networks, Mathematical Data Analytics How to cite this paper: Oduola, W.O. and Akujuobi, C.M. (2017) Wavelet Transform for Image Compression Using Multi-Reso- lution Analytics: Application to Wireless Sensors Data. Advances in Pure Mathe- matics, 7, 430-440. https://doi.org/10.4236/apm.2017.78028 Received: July 20, 2017 Accepted: August 11, 2017 Published: August 14, 2017 Copyright © 2017 by authors and Scientific Research Publishing Inc. This work is licensed under the Creative Commons Attribution International License (CC BY 4.0). http://creativecommons.org/licenses/by/4.0/ Open Access