Fractal image compression by improved balanced tree clustering

Chang-Hyuk An; Kelly Berry; Alan Cosby

doi:10.1117/12.279580

30 October 1997 Fractal image compression by improved balanced tree clustering

Chang-Hyuk An, Kelly Berry, Alan Cosby

Proceedings Volume 3164, Applications of Digital Image Processing XX; (1997) https://doi.org/10.1117/12.279580
Event: Optical Science, Engineering and Instrumentation '97, 1997, San Diego, CA, United States

Abstract

Recent industry efforts have focused on the Balanced Tree Clustering (BTC) approach to speed up the encoding process for fractal image compression. Since the BTC algorithm compares intensity pixel by pixel for blocks in a mother cluster, clocks are divided into different clusters when they have different orientations or when they have different amplitudes of intensity offset with the averaged values but otherwise the same intensity distribution, causing poor clustering. In this paper we present and discuss the improvement of the original BTC algorithm by devising a method to include all 8 isometric versions of a block without enlarging the size of the domain pool and without sacrificing the encoding time. The results of compression are compared using the BTC algorithm based on the three different pixel values, intensity, intensity offset by the average value, and intensity variance, with and without considering 8 isometries of each block. For a sample image of 256 X 256 X 8 with 4 X 4 block size and with about 10 blocks in each cluster, the BTC using intensity variance and considering the 8 isometries the encoding speed about 200 times while decreases PSNR less than 8 percent over the ordered quadrant intensity classification into three classes. The BTC based on intensity variance also improves the encoding time and the fidelity of compression over the BTC based on the other two pixel values. The BTC algorithm based on the three different pixel values also generate different fractal code books which may affect the utility of the code books for image classification and image matching.

Citation Download Citation

Chang-Hyuk An, Kelly Berry, and Alan Cosby "Fractal image compression by improved balanced tree clustering", Proc. SPIE 3164, Applications of Digital Image Processing XX, (30 October 1997); https://doi.org/10.1117/12.279580

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