Fractal image compression based on intrablock variance distribution and vector quantization

Shin-Si Chen; Chang-Biau Yang; Kuo-Si Huang

doi:10.1117/1.1510743

1 November 2002 Fractal image compression based on intrablock variance distribution and vector quantization

Shin-Si Chen, Chang-Biau Yang, Kuo-Si Huang

Author Affiliations +

Optical Engineering, Vol. 41, Issue 11, (November 2002). https://doi.org/10.1117/1.1510743

In the encoding phase of fractal image compression, most of the time is taken in finding the closest match between each range block and a large pool of domain blocks. We use the intrablock variance distributions of domain blocks to reduce the search space. For finding a close match, we need search only the domain blocks whose maximal intrablock variance quadrants are at the same corner as the range block. Thus, we reduce the number of transforms applied on each domain block from eight to two. We also adopt the longest-distance-first vector quantization scheme to divide the large pool of domain blocks into clusters. Thus, the number of domain blocks to be searched is also reduced. The experimental results show that our algorithm can reduce encoding time with only slight loss of quality.

©(2002) Society of Photo-Optical Instrumentation Engineers (SPIE)

Citation Download Citation

Shin-Si Chen, Chang-Biau Yang, and Kuo-Si Huang "Fractal image compression based on intrablock variance distribution and vector quantization," Optical Engineering 41(11), (1 November 2002). https://doi.org/10.1117/1.1510743

Published: 1 November 2002

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