Predefined Redundant Dictionary for Effective Depth Maps Representation

Abstract

The multi-view video plus depth (MVD) video format consists of two components: texture and depth map, where a combination of these components enables a receiver to generate arbitrary virtual views. However, MVD presents a very voluminous video format that requires a compression process for storage and especially for transmission. Conventional codecs are perfectly efficient for texture images compression but not for intrinsic depth maps properties. Depth images indeed are characterized by areas of smoothly varying grey levels separated by sharp discontinuities at the position of object boundaries. Preserving these characteristics is important to enable high quality view synthesis at the receiver side. In this paper, sparse representation of depth maps is discussed. It is shown that a significant gain in sparsity is achieved when particular mixed dictionaries are used for approximating these types of images with greedy selection strategies. Experiments are conducted to confirm the effectiveness at producing sparse representations, and competitiveness, with respect to candidate state-of-art dictionaries. Finally, the resulting method is shown to be effective for depth maps compression and represents an advantage over the ongoing 3D high efficiency video coding compression standard, particularly at medium and high bitrates.