3D hierarchical optimization for Multi-view depth map coding
This work addresses the need for efficient depth data compression in 3D sensing applications, representing an incremental improvement over existing methods.
The paper tackles the problem of efficiently encoding multiple depth maps in multi-view sequences by proposing a joint encoding method that builds a coherent hierarchy across views and applies Rate-Distortion optimization, achieving competitive results with HEVC coding standards.
Depth data has a widespread use since the popularity of high-resolution 3D sensors. In multi-view sequences, depth information is used to supplement the color data of each view. This article proposes a joint encoding of multiple depth maps with a unique representation. Color and depth images of each view are segmented independently and combined in an optimal Rate-Distortion fashion. The resulting partitions are projected to a reference view where a coherent hierarchy for the multiple views is built. A Rate-Distortionoptimization is applied to obtain the final segmentation choosing nodes of the hierarchy. The consistent segmentation is used to robustly encode depth maps of multiple views obtaining competitive results with HEVC coding standards. Available at: http://link.springer.com/article/10.1007/s11042-017-5409-z