3D hierarchical optimization for Multi-view depth map coding

TL;DR

This paper introduces a novel hierarchical joint encoding method for multi-view depth maps that improves compression efficiency while maintaining quality, leveraging a unified segmentation and rate-distortion optimization across views.

Contribution

It proposes a new hierarchical encoding framework for multi-view depth maps that combines independent segmentation with a global hierarchy for optimized compression.

Findings

Achieves competitive results with HEVC standards.

Provides robust multi-view depth map encoding.

Introduces a unified segmentation approach.

Abstract

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