Compressing 3-D human motions via keyframe-based geometry videos

Junhui Hou, Lap Pui Chau, Nadia Magnenat-Thalmann, Ying He

Research output: Journal article publicationJournal articleAcademic researchpeer-review

25 Citations (Scopus)

Abstract

This paper presents keyframe-based geometry video (KGV), a novel framework for compressing 3-D human motion data by using geometry videos. Given a motion data encoded in a geometry video (GV) format, our method extracts the keyframes and produces a reconstruction matrix. Then it applies the video compression technique (e.g., H.264/Advanced Video Coding) to the reordered keyframes, which can significantly reduce the spatial and temporal redundancy in the KGV. We develop a rate distortion-based optimization algorithm to determine the parameters (i.e., the number of keyframes and quantization parameter) leading to optimal performance. Experimental results show that the proposed KGV framework significantly outperforms the existing GV techniques in terms of both the rate distortion performance and visual quality. Besides, the computational cost of the KGV is rather low at the decoder, making it highly desirable for power-constrained devices. Last but not least, our method can be easily extended to progressive compression with heterogeneous communication network.

Original languageEnglish
Article number6826514
Pages (from-to)51-62
Number of pages12
JournalIEEE Transactions on Circuits and Systems for Video Technology
Volume25
Issue number1
DOIs
Publication statusPublished - 1 Jan 2015
Externally publishedYes

Keywords

  • 3-D motion data
  • geometry video (GV)
  • keyframe
  • rate distortion analysis

ASJC Scopus subject areas

  • Media Technology
  • Electrical and Electronic Engineering

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