Dynamic stopping criteria of turbo codes for clustered set partitioning in hierarchical trees encoded image transmission

Jiunn-Tsair Fang; Cheng-Shong Wu

doi:10.1117/1.3651512

1 October 2011 Dynamic stopping criteria of turbo codes for clustered set partitioning in hierarchical trees encoded image transmission

Jiunn-Tsair Fang, Cheng-Shong Wu

Author Affiliations +

Funded by: National Science Council (NSC), Taiwan for financial support

Journal of Electronic Imaging, Vol. 20, Issue 4, 043003 (October 2011). https://doi.org/10.1117/1.3651512

Abstract

Turbo codes adopt iterative decoding to increase the ability of error correction. However, the iterative method increases the decoding delay and power consumption. An effective approach is to decrease the number of iterations while tolerating slight performance degradation. We apply the clustered set partitioning in hierarchical trees for image coding. Different from other early stop criteria, we use the bit-error sensitivities from the image data. Then, the stop criterion is directly determined by the importance of image data. Simulation results show that our scheme can reduce more number of iterations with less degradation for peak-signal-to-noise ratio or structure similar performance.

©(2011) Society of Photo-Optical Instrumentation Engineers (SPIE)

Citation Download Citation

Jiunn-Tsair Fang and Cheng-Shong Wu "Dynamic stopping criteria of turbo codes for clustered set partitioning in hierarchical trees encoded image transmission," Journal of Electronic Imaging 20(4), 043003 (1 October 2011). https://doi.org/10.1117/1.3651512

Published: 1 October 2011

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