By Topic

Image categorization and coding using neural networks and adaptive wavelet filters

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 $13
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

2 Author(s)
Saha, S. ; California Univ., Davis, CA, USA ; Vemuri, R.

Wavelet based compression schemes are the natural choice for the multi-resolution representation of images because of their successive approximation and better decorrelation property. Experiments conducted by compressing images through wavelet filters and integer wavelet transforms suggest that the filter performance indeed is image dependent. It is observed that no wavelet filter outperforms others uniformly while compressing sample images drawn from a large selection. In fact, a detailed analysis of the results reveals that certain wavelets perform better on certain classes of images. A neural network can therefore, be used to categorize the input image into one of these classes. A wavelet-based lossy or lossless coder is then used to compress the image using the most "appropriate" wavelet filter or integer-transform suitable for that class.

Published in:

Industrial Technology 2000. Proceedings of IEEE International Conference on  (Volume:1 )

Date of Conference:

19-22 Jan. 2000