Evolutionary Computing and Second generation Wavelet Transform optimization: Current State of the Art

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Dr. S.Nagaraja Rao
Dr. S.Nagaraja Rao
σ
Dr. M.N.Giriprasad
Dr. M.N.Giriprasad
α Jawaharlal Nehru Technological University Anantapur Jawaharlal Nehru Technological University Anantapur

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Evolutionary Computing and Second generation Wavelet Transform optimization: Current State of the Art

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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.

References

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Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

Dr. S.Nagaraja Rao. 1970. \u201cEvolutionary Computing and Second generation Wavelet Transform optimization: Current State of the Art\u201d. Unknown Journal GJCST Volume 11 (GJCST Volume 11 Issue 8): .

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v1.2

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May 7, 2011

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en
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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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Evolutionary Computing and Second generation Wavelet Transform optimization: Current State of the Art

Dr. S.Nagaraja Rao
Dr. S.Nagaraja Rao Jawaharlal Nehru Technological University Anantapur
Dr. M.N.Giriprasad
Dr. M.N.Giriprasad

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