Distributed image coding based on integrated Markov modeling and LDPC decoding

We present in this paper a novel distributed image coding scheme by exploiting image spatial correlation via Markov modeling at the decoding end. The exploitation of image statistics at the decoding end allows us to design a simple yet efficient encoder suitable for various energy efficient imaging sensor network applications. Existing distributed coding schemes developed for imaging sensor networks mostly attempt to exploit inter-image correlation. We develop in this research an integration of LDPC decoding and Markov model estimation in order to jointly exploit both interimage and intra-image correlation. Simulations have been carried out to demonstrate that this Markov model-based approach is able to achieve significant gains over the schemes without Markov model. The simulation results also show that the 2D Markov model is able to achieve additional gains over the 1D Markov model.