RGB-guided depth map compression via Compressed Sensing and Sparse Coding
Emmy-Charlotte Förster, Thomas Löwe, Stephan Wenger, Marcus A. Magnor · 2015
We present a novel approach for compression of depth maps based on Compressed Sensing and Sparse Coding. Our proposed scheme compresses and stores the depth map, and then-during the decompression step-makes use of the readily available additional RGB information to guide the reconstruction. We introduce additional constraints to the underlying optimization problem enforcing the correctness of the RGB image in the decompression step. A comparison with established compression schemes shows that our proposed method leads to a lower error rate at high compression ratios.