Improving distributed video coding by exploiting context-adaptive modeling
The statistical model of the bits to be encoded is crucial for the coding performance of distributed video coding (DVC). In this paper, a bit-level context-adaptive correlation model is proposed to exploit high-order statistical correlation for better channel coding performance, which consequently improves the video coding efficiency. In the proposed scheme, the wavelet domain DVC is considered and the coefficients are coded in a bit-plane fashion. The context for each bit to be coded is first formed. Then the probability distribution of each bit is estimated by using previously available data with the same context. For magnitude coding, the significant state of the following elements are included in the context, (1) the side information, (2) the local neighborhood, (3) the parent coefficients (if applicable). The condition of side information is considered as well. For sign coding, the context consists of the sign and the quality of the side information. The proposed model is implemented within a recently proposed DVC framework with decoderside multi-resolution motion refinement (MRMR). Experimental results show the effectiveness of the proposed scheme with significant coding gain over the original MRMR based DVC system, especially for videos with high motion intensity and for lower bit rates.