By Topic

Improved block truncation coding using Hopfield neural network

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $31
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Qiu, G. ; Dept. of Comput. & Electron., Lancashire Polytech., Preston, UK ; Varley, M.R. ; Terrell, T.J.

Block truncation coding (BTC), a recent technique used in the coding of image data, is based on the classification of pixels within a small image block into two classes. A new technique is introduced which uses a Hopfield neural network to define the pixel classes. Results are presented for four monochrome still images. The new algorithm is shown to provide improved performance when compared to the two previous BT algorithms.

Published in:

Electronics Letters  (Volume:27 ,  Issue: 21 )