Predictive vector quantization using neural networks

Mahmoud Reza Hashemi; Tet H. Yeap; Sethuraman Panchanathan

doi:10.1117/12.269776

1 April 1997 Predictive vector quantization using neural networks

Mahmoud Reza Hashemi, Tet H. Yeap, Sethuraman Panchanathan

Proceedings Volume 3030, Applications of Artificial Neural Networks in Image Processing II; (1997) https://doi.org/10.1117/12.269776
Event: Electronic Imaging '97, 1997, San Jose, CA, United States

Abstract

In this paper we propose a new scalable predictive vector quantization (PVQ) technique for image and video compression. This technique has been implemented using neural networks. A Kohonen self-organized feature map is used to implement the vector quantizer, while a multilayer perceptron implements the predictor. Simulation results demonstrate that the proposed technique provides a 5 - 10% improvement in coding performance over the existing neural networks based PVQ techniques.

Citation Download Citation

Mahmoud Reza Hashemi, Tet H. Yeap, and Sethuraman Panchanathan "Predictive vector quantization using neural networks", Proc. SPIE 3030, Applications of Artificial Neural Networks in Image Processing II, (1 April 1997); https://doi.org/10.1117/12.269776

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