By Topic

On the performance of fractal compression with clustering

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)
Wein, C.J. ; Dept. of Electr. & Comput. Eng., Waterloo Univ., Ont., Canada ; Blake, I.F.

The paper investigates a technique to reduce the computational complexity of fractal image compression on gray-scale images. The technique uses a clustering process on image domain blocks with the clusters formed with the use of k-d trees and the fast pairwise nearest neighbor algorithm of Equitz (1984). Results indicate the method is effective for smaller domain block sizes and generally shows improvement in terms of picture peak signal-to-noise ratio (SNR) over the quadrant variance classification method

Published in:

Image Processing, IEEE Transactions on  (Volume:5 ,  Issue: 3 )