Kinect-Like Depth Data Compression

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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  • Jingjing Fu
  • Dan Miao
  • Weiren Yu
  • Yan Lu
  • Shipeng Li


Original languageEnglish
Article number6463455
Pages (from-to)1340-1352
Journal / PublicationIEEE Transactions on Multimedia
Issue number6
Online published15 Feb 2013
Publication statusPublished - Oct 2013
Externally publishedYes


Unlike traditional RGB video, Kinect-like depth is characterized by its large variation range and instability. As a result, traditional video compression algorithms cannot be directly applied to Kinect-like depth compression with respect to coding efficiency. In this paper, we propose a lossy Kinect-like depth compression framework based on the existing codecs, aiming to enhance the coding efficiency while preserving the depth features for further applications. In the proposed framework, the Kinect-like depth is reformed first by divisive normalized bilateral filter (DNBL) to suppress the depth noises caused by disparity normalization, and then block-level depth padding is implemented for invalid depth region compensation in collaboration with mask coding to eliminate the sharp variation caused by depth measurement failures. Before the traditional video coding, the inter-frame correlation of reformed depth is explored by proposed 2D+T prediction, in which depth volume is developed to simulate 3D volume to generate pseudo 3D prediction reference for depth uniqueness detection. The unique depth region, called active region is fed into the video encoder for traditional intra and inter prediction with residual coding, while the inactive region is skipped during depth coding. The experimental results demonstrate that our compression scheme can save 55%-85% in terms of bit cost and reduce coding complexity by 20%-65% in comparison with the traditional video compression algorithms. The visual quality of the 3D reconstruction is also improved after employing our compression scheme.

Research Area(s)

  • 2D+T prediction, denoising, depth volume, Kinect-like depth, lossy compression, padding

Citation Format(s)

Kinect-Like Depth Data Compression. / Fu, Jingjing; Miao, Dan; Yu, Weiren et al.

In: IEEE Transactions on Multimedia, Vol. 15, No. 6, 6463455, 10.2013, p. 1340-1352.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review