New Artificial Neural Network Models for Bio Medical Image Compression: Bio Medical Image Compression

G. Vimala Kumari, G. Sasibhushana Rao and B. Prabhakara Rao
Additional contact information
G. Vimala Kumari: MVGR College of Engineering, Vizianagaram, India
G. Sasibhushana Rao: Andhra University College of Engineering, Visakhapatnam, India
B. Prabhakara Rao: Jawaharlal Nehru Technological University, Kakinada, Andhra Pradesh, India

International Journal of Applied Metaheuristic Computing (IJAMC), 2019, vol. 10, issue 4, 91-111

Abstract: This article presents an image compression method using feed-forward back-propagation neural networks (NNs). Marked progress has been made in the area of image compression in the last decade. Image compression removing redundant information in image data is a solution for storage and data transmission problems for huge amounts of data. NNs offer the potential for providing a novel solution to the problem of image compression by its ability to generate an internal data representation. A comparison among various feed-forward back-propagation training algorithms was presented with different compression ratios and different block sizes. The learning methods, the Levenberg Marquardt (LM) algorithm and the Gradient Descent (GD) have been used to perform the training of the network architecture and finally, the performance is evaluated in terms of MSE and PSNR using medical images. The decompressed results obtained using these two algorithms are computed in terms of PSNR and MSE along with performance plots and regression plots from which it can be observed that the LM algorithm gives more accurate results than the GD algorithm.

Date: 2019
References: Add references at CitEc
Citations:

Downloads: (external link)
http://services.igi-global.com/resolvedoi/resolve. ... 018/IJAMC.2019100106 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:igg:jamc00:v:10:y:2019:i:4:p:91-111

Access Statistics for this article

International Journal of Applied Metaheuristic Computing (IJAMC) is currently edited by Peng-Yeng Yin

More articles in International Journal of Applied Metaheuristic Computing (IJAMC) from IGI Global
Bibliographic data for series maintained by Journal Editor ().