A Temporal Subsampling Approach for Multiview Depth Map Compression

In this letter, a new method is proposed for multiview depth map compression. It is intended to skip some parts of certain depth map viewpoints without encoding and to just predict those skipped parts by exploiting the multiview correspondences and some flags transmitted. It is targeted to save the bit rate allocated for depth map sequences to a great extent. Multiview correspondences are exploited for each skipped depth map frame by making use of the depth map frames belonging to neighboring views and captured at the same time instant. A prediction depth map frame is constructed block by block on a free viewpoint qualitywise selective basis from a couple of candidate predictors generated through the implicit and explicit usage of the 3-D scene geometry. Especially at lower bit rates, dropping higher temporal layers of certain depth map viewpoints and replacing them with corresponding predictors generated using the proposed multiview aided approach save a great amount of bit rate for those depth map viewpoints. At the same time, the perceived quality of the reconstructed stereoscopic videos is maintained, which is proved through a set of subjective tests.

I. Introduction

Multiview coding with depth map included has become a very attractive research area, following the rapid developments in 3-D display technologies and image processing, where the depth map represents the relative distance of each video object to the recording camera. These developments make possible applications such as 3-D-TV [1] and free viewpoint TV (FTV) [2]. However, due to the large amount of visual and nonvisual (depth information) data included in such systems, simulcast coding using existing video coding standards does not provide enough compression for cost-effective distribution of multiview content. Therefore, it is a necessity to perform compression using more advanced techniques in order to realize such systems. Research has previously been carried out to compress both color and depth information exploiting particular multiview correlations that exist between different viewpoints of a multiview set [3]–[9].