Symmetric Disparity Estimation in Distributed Coding of Stereo Images

Disparity estimation has been widely studied in the literature of stereo matching and coding. However, how to shift disparity estimation from encoder to decoder to support distributed coding applications poses a new challenge. In this paper, we present a symmetric distributed coding protocol in which interlaced representations of stereo pair are finely and coarsely quantized as primary and secondary channels respectively. At the decoder, side information is generated from the primary channel by an expectation maximization (EM)-like algorithm and a novel dual exploitation of the secondary channel is proposed to simultaneously resolve intensity uncertainty and refine disparity estimation. Preliminary experimental results for synthetic images are reported to demonstrate the potential of the proposed approach.