Distributed image coding based on integrated Markov random field modeling and LDPC decoding

We present in this paper a novel distributed image coding scheme by exploiting image spatial correlation via Markov random field modeling at the decoding end. This allows us to design a simple yet efficient encoder suitable for various energy efficient imaging sensor network applications. The novelty is the integration of LDPC decoding and Markov random field modeling in order to jointly exploit both interimage and intra-image correlation. The current research aims at improving our previous work in which the Markov model was defined by a state transition probability matrix. In this research, we model the image via a Markov random field described by Gibbs distribution. Both analysis and simulations have been carried out to demonstrate that this Markov model-based approach is able to achieve significant gains over the schemes without Markov modeling. Furthermore, this new Gibbs-based Markov model is less sensitive to correlated noise. Our approach also outperforms a JPEG codec by up to 4 dB even if the interimage correlation is not very high.