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

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.