Dynamic Selection of Suitable Wavelet for Effective Color Image Compression using Neural Networks and Modified RLC

Mr. P. Sreenivasulu

Dynamic Selection of Suitable Wavelet for Effective Color Image Compression using Neural Networks and Modified RLC

Mr. P. Sreenivasulu * §

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Dynamic Selection of Suitable Wavelet for Effective Color Image Compression using Neural Networks and Modified RLC

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Abstract

Image Compression has become extremely important today with the continuous development of internet, remote sensing and satellite communication techniques. In general, single Wavelet is not suitable for all types of images. This paper proposes a novel approach for dynamic selection of suitable wavelet and effective Image Compression. Dynamic selection of suitable wavelet for different types of images, like natural images, synthetic images, medical images and etc, is done using Counter Propagation Neural Network which consists of two layers: Unsupervised Kohonen (SOFM) and Supervised Gross berg layers. Selection of suitable wavelet is done by measuring some of the statistical parameters of image, like Image Activity Measure (IAM) and Spatial Frequency (SF), as they are strongly correlated with each other. After selecting suitable wavelet, effective image compression is done with MLFFNN with EBP training algorithm for LL2 component. Modified run length coding is applied on LH2 and HL2components with hard threshold and discarding all other sub-bands which do not effect much the quality (both subjective and objective) (HH2, LH1, HL1 and HH1). Highest CR (191.53), PSNR (78.38 dB), and minimum MSE (0.00094) of still color images are obtained compared to SOFM, EZW and SPIHT.

References

14 Cites in Article

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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. P. Sreenivasulu. 2014. "Dynamic Selection of Suitable Wavelet for Effective Color Image Compression using Neural Networks and Modified RLC". Global Journal of Computer Science and Technology - F: Graphics & Vision GJCST-F Volume 14 (GJCST Volume 14 Issue F2).

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GJCST Volume 14 Issue F2

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Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

Keywords

counter propagation neural network dynamic selection EBP Image Compression MLFFNN wavelet

Classification

GJCST-F Classification F.1.1

Submission ReceivedDecember 8, 2013
Peer Review Double Blind
Handling Editor
Accepted December 31, 2013
Published June 24, 2014

Version of record

v1.2

Issue date

June 24, 2014

Language

English

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Article Matrices

Total Score: 107

Country: India

Subject: Global Journal of Computer Science and Technology - F: Graphics & Vision

Authors: Mr. P. Sreenivasulu, Dr. K. Anitha Sheela (PhD/Dr. count: 1)

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Publish Date: 2014 01, Wed

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