Evolutionary Computing and Second generation Wavelet Transform optimization: Current State of the Art
Keywords:
volutionary Computation (E C ), Genetic Algorithms (GA), Image Compression, E Lifting Scheme (LS), E v o lutionary Algorithms (A), E v o lu t i o nary
Abstract
The Evolutionary Computation techniques are exposed to number of domains to achieve optimization. One of those domains is second generation wavelet transformations for image compression. Various types of Lifting Schemes are being introduced in recent literature. Since the growth in Lifting Schemes is in an incremental way and new types of Lifting Schemes are appearing continually. In this context, developing flexible and adaptive optimization approaches is a severe challenge. Evolutionary Computing based lifting scheme optimization techniques are a valuable technology to achieve better results in image compression. However, despite the variety of such methods described in the literature in recent years, security tools incorporating anomaly detection functionalities are just starting to appear, and several important problems remain to be solved. In this paper, we present a review of the most well-known EC approaches for optimizing Secondary level Wavelet transformations.
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Published
2011-03-15
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Copyright (c) 2011 Authors and Global Journals Private Limited
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