Adaptive image compression based on backpropagation neural networks

Defu Cai; Ming Zhou

doi:10.1117/12.130873

16 December 1992 Adaptive image compression based on backpropagation neural networks

Defu Cai, Ming Zhou

Proceedings Volume 1766, Neural and Stochastic Methods in Image and Signal Processing; (1992) https://doi.org/10.1117/12.130873
Event: San Diego '92, 1992, San Diego, CA, United States

Abstract

Increasingly huge amounts of digital data from a wide range of sources such as B-ISDN services, satellite transmission of photographs, and police database of human face images are being transmitted and stored. Therefore, both transmission channel capacity and disk space are limited. For some advanced techniques, such as multi-media terminal and HDTV etc., the problems are even more apparent. Based on this it is important that efficient image compression algorithms are used in order to reduce the transmission capacity and storage space. In this paper, a scheme of image data compression with an adaptive BP neural network is presented. The data compression property of mapping original image to a feature space of reduced dimensionality is utilized. Images are divided as a set of 8 X 8 sub-image blocks which apply to a three layer BP neural network as inputs. It is shown from computer simulation that the results are better than Sonehara, et al.

Citation Download Citation

Defu Cai and Ming Zhou "Adaptive image compression based on backpropagation neural networks", Proc. SPIE 1766, Neural and Stochastic Methods in Image and Signal Processing, (16 December 1992); https://doi.org/10.1117/12.130873

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