RGB-guided depth map compression via Compressed Sensing and Sparse Coding

Abstract

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 optimiza tion 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 com pression ratios.

A General Framework for Depth Compression and Multi-Sensor Fusion in Asymmetric View-Plus-Depth 3D Representation

“…In another test, we calculate the BAD metric as plotted in Figure 12 (g). The curves show that for a wide range of tested stereo-matching datasets of Middlebury [8], the proposed technique robustly fades the BAD percentage to about 3−5% for bitrates below < 0.2 bpp [60]. Such performance is in par with the performance of the highest-ranked stereomatching estimation algorithms [8].…”

Depth map coding with elastic contours and 3D surface prediction

“…Color data can also been used to assist the compression of depth information as in [14], [15], [16] and [17]. The coding of depth videos has also been considered, e.g., in [18] the idea that pixels with similar depth have similar motion is exploited.…”