Two-stage filtering of compressed depth images with Markov Random Field

Virtual view synthesis and image comprehension have become easier with the aid of depth information. However, when a depth image is compressed, severe distortions along boundaries may occur, thus leading to performance degradation. To solve this problem, we propose in this paper a two-stage filtering that consists of binary segmentation-based depth filtering and the reconstruction using a Markov Random Field (MRF) model. The MRF model adopted in our work consists of a data term and a smoothness term so as to preserve the boundary and maintain the smoothness simultaneously. We notice that directly applying the MRF model to a distorted depth image is usually unable to produce a satisfactory performance. Then, we propose that binary segmentation based depth filtering is used to remove artifacts over discontinuous regions in the distorted depth image. Experimental results show that, through our processing, the compressed depth image can render better quality for the synthesized images than many existing depth filtering methods.