Modified Distributive Arithmetic based 2D-DWT for Hybrid (Neural Network-DWT) Image Compression

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Mr. Murali Mohan.S
Mr. Murali Mohan.S
σ
Dr. P.Satyanarayana
Dr. P.Satyanarayana
α Jawaharlal Nehru Technological University Anantapur Jawaharlal Nehru Technological University Anantapur

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Modified Distributive Arithmetic based 2D-DWT for Hybrid (Neural Network-DWT) Image Compression

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Abstract

Artificial Neural Networks (ANN) is significantly used in signal and image processing techniques for pattern recognition and template matching. Discrete Wavelet Transform (DWT) is combined with neural network to achieve higher compression if 2D data such as image. Image compression using neural network and DWT have shown superior results over classical techniques, with 70% higher compression and 20% improvement in Mean Square Error (MSE). Hardware complexity and power dissipation are the major challenges that have been addressed in this work for VLSI implementation. In this work, modified distributive arithmetic DWT and multiplexer based DWT architecture are designed to reduce the computation complexity of hybrid architecture for image compression. A 2D DWT architecture is designed with 1D DWT architecture and is implemented on FPGA that operates at 268 MHz consuming power less than 1W.

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

Mr. Murali Mohan.S. 2014. \u201cModified Distributive Arithmetic based 2D-DWT for Hybrid (Neural Network-DWT) Image Compression\u201d. Global Journal of Computer Science and Technology - F: Graphics & Vision GJCST-F Volume 14 (GJCST Volume 14 Issue F2): .

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Issue Cover
GJCST Volume 14 Issue F2
Pg. 37- 48
Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

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v1.2

Issue date

June 24, 2014

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en
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Artificial Neural Networks (ANN) is significantly used in signal and image processing techniques for pattern recognition and template matching. Discrete Wavelet Transform (DWT) is combined with neural network to achieve higher compression if 2D data such as image. Image compression using neural network and DWT have shown superior results over classical techniques, with 70% higher compression and 20% improvement in Mean Square Error (MSE). Hardware complexity and power dissipation are the major challenges that have been addressed in this work for VLSI implementation. In this work, modified distributive arithmetic DWT and multiplexer based DWT architecture are designed to reduce the computation complexity of hybrid architecture for image compression. A 2D DWT architecture is designed with 1D DWT architecture and is implemented on FPGA that operates at 268 MHz consuming power less than 1W.

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Modified Distributive Arithmetic based 2D-DWT for Hybrid (Neural Network-DWT) Image Compression

Mr. Murali Mohan.S
Mr. Murali Mohan.S Jawaharlal Nehru Technological University Anantapur
Dr. P.Satyanarayana
Dr. P.Satyanarayana

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