A STUDY ON IMAGE COMPRESSION WITH NEURAL NETWORKS USING MODIFIED LEVENBERG METHOD

Dr. K.PREMA; N.SREEKUMAR

A STUDY ON IMAGE COMPRESSION WITH NEURAL NETWORKS USING MODIFIED LEVENBERG METHOD

Dr. K.PREMA * §

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N.SREEKUMAR

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Bharathiar University

A STUDY ON IMAGE COMPRESSION WITH NEURAL NETWORKS USING MODIFIED LEVENBERG METHOD

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Abstract

In this paper, an adaptive method for image compression that is subjective on neural networks based on complexity level of the image. The multilayer perceptron artificial neural network uses the different Back-Propagation artificial neural networks in processing of the image. The original images taken, for instance 256*256 pixels of bitmap image, each block of image into one network selection, according to each block the value of pixels in image complexity value is calculated. To estimate each value of the images in a block can be evaluated and trained. Best PSNR in selecting images to be compressed with a modification Levenberg-Marquart for MLP neural network is taken. The algorithm taken a good research of result to each block of image. The taken time reduces the learning procedure for running each block of images. Finally, a neural network taken for the Back Propagation artificial neural network.

References

4 Cites in Article

Reference Format

R Gonzales,R Woods (2002). Digital Image Processing and Analysis.
H Veisi,M Jamzad (2005). Image Compression Using and Machine Vision Conference (MVIP).
Sonehara,Kawato,Miyake,Nakane (1989). Image data compression using a neural network model.
G Sicuranza (1992). Multidimensional Processing of Video Signals.

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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. K.PREMA, N.SREEKUMAR. 1970. "A STUDY ON IMAGE COMPRESSION WITH NEURAL NETWORKS USING MODIFIED LEVENBERG METHOD". Global Journal of Computer Science and Technology GJCST Volume 11 (GJCST Volume 11 Issue 3).

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GJCST Volume 11 Issue 3

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Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

Keywords

Not Found

Classification

GJCST Classification I.2.6

I.4.m

Submission ReceivedDecember 27, 2010
Peer Review Double Blind
Handling Editor
Accepted January 26, 2011
Published February 6, 2011

Version of record

v1.2

Issue date

March 12, 2011

Language

English

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Article Matrices

Total Score: 147

Country: India

Subject: Global Journal of Computer Science and Technology

Authors: Dr. K.PREMA,N.SREEKUMAR (PhD/Dr. count: 1)

View Count (all-time): 227

Total Views (Real + Logic): 6452

Total Downloads (simulated): 481

Publish Date: 2011 02, Sun

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Total Views: 6.5K

Total Downloads: 481

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