Adaptive constructive neural networks using Hermite polynomials for compression of still and moving images

Liying Ma; Khashayar Khorasani; Mahmood R. Azimi-Sadjadi

doi:10.1117/12.458703

11 March 2002 Adaptive constructive neural networks using Hermite polynomials for compression of still and moving images

Liying Ma, Khashayar Khorasani, Mahmood R. Azimi-Sadjadi

Author Affiliations +

Proceedings Volume 4739, Applications and Science of Computational Intelligence V; (2002) https://doi.org/10.1117/12.458703
Event: AeroSense 2002, 2002, Orlando, FL, United States

Abstract

Compression of digital images has been a very important subject of research for several decades, and a vast number of techniques have been proposed. In particular, the possibility of image compression using Neural Networks (Nns) has been considered by many researchers in recent years, and several Feed-forward Neural Networks (FNNs) have been proposed with reported promising experimental results. Constructive One-Hidden-Layer Feedforward Neural Network (OHL-FNN) is one such architecture. At previous SPIE conferences, we have proposed a new constructive OHL-FNN using Hermite polynomials for regression and recognition problems, and good experimental results were demonstrated. In this paper, we first modify and then apply our proposed OHL-FNN to compress still and moving images and investigated its performance in terms of both training and generalization capabilities. Extensive experimental results for still images (Lena, Lake, and Girl) and moving images (football game) are presented. It is revealed that the performance of the constructive OHL-FNN using Hermite polynomials is quite good for both still and moving image compression.

Citation Download Citation

Liying Ma, Khashayar Khorasani, and Mahmood R. Azimi-Sadjadi "Adaptive constructive neural networks using Hermite polynomials for compression of still and moving images", Proc. SPIE 4739, Applications and Science of Computational Intelligence V, (11 March 2002); https://doi.org/10.1117/12.458703

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available