Depth map compression via compressed sensing

We propose in this paper a new scheme based on compressed sensing to compress a depth map. We first subsample the entity in the frequency domain to take advantage of its compressibility. We then derive a reconstruction scheme to recover the original map from the subsamples using a non-linear conjugate gradient minimization scheme. We preserve the discontinuities of the depth map at the edges and ensure its smoothness elsewhere by incorporating the Total Variation constraint in the minimization. The results we obtained on various test depth maps show that the proposed method leads to lower error rate at high compression ratio when compared to standard image compression techniques like JPEG and JPEG 2000.