Stereo image coder based on the MRF model for disparity compensation

Abstract

This paper presents a stereoscopic image coder based on the MRF model and MAP estimation of the disparity field. The MRF model minimizes the noise of disparity compensation, because it takes into account the residual energy, smoothness constraints on the disparity field, and the occlusion field. Disparity compensation is formulated as an MAP-MRF problem in the spatial domain, where the MRF field consists of the disparity vector and occlusion fields. The occlusion field is partitioned into three regions by an initial double-threshold setting. The MAP search is conducted in a block-based sense on one or two of the three regions, providing faster execution. The reference and residual images are decomposed by a discrete wavelet transform and the transform coefficients are encoded by employing the morphological representation of wavelet coefficients algorithm. As a result of the morphological encoding, the reference and residual images together with the disparity vector field are transmitted in partitions, lowering total entropy. The experimental evaluation of the proposed scheme on synthetic and real images shows beneficial performance over other stereoscopic coders in the literature.