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Author:

Ha, Rui (Ha, Rui.) | Liu, Pengyu (Liu, Pengyu.) | Jia, Kebin (Jia, Kebin.) (Scholars:贾克斌)

Indexed by:

EI Scopus

Abstract:

The depth map sequence collected by Kinect can be transmitted through the existing video compression coding platform, which saves a lot of computing resources and saves a lot of computing resources than the conversion of the point cloud data. However, the depth map sequence collected by the Kinect has an unstable characteristic in the time domain, so the prediction of the residual error is not accurate after entering the video encoder, which will consume unnecessary coding resources. In this paper, through the supplementary analysis of the work in the literature [10], an approximate three-dimensional volume algorithm is introduced to generate a stable reference frame for the Kinect depth map sequence. In addition, this article also distinguishes between dynamic macroblocks and static macroblocks in the depth map to determine whether to skip the search steps in the HEVC (efficient video coding) interframe prediction; finally, compared with the unprocessed Kinect depth map sequence in the bit mice. The performance of E, run time, PSNR (peak signal-to-noise ratio) and 3D reconstruction are better than those of the latter. © 2018 IEEE.

Keyword:

Image coding Image quality Signal to noise ratio Image reconstruction Video signal processing Image compression Mammals

Author Community:

  • [ 1 ] [Ha, Rui]Faculty of Information Technology, Beijing University of Technology, Beijing; 100124, China
  • [ 2 ] [Ha, Rui]Beijing Laboratory of Advanced Information Networks, Beijing; 100124, China
  • [ 3 ] [Ha, Rui]Beijing Advanced Innovation Center for Future Internet Technology, Beijing University of Technology, Beijing; 100124, China
  • [ 4 ] [Liu, Pengyu]Faculty of Information Technology, Beijing University of Technology, Beijing; 100124, China
  • [ 5 ] [Liu, Pengyu]Beijing Laboratory of Advanced Information Networks, Beijing; 100124, China
  • [ 6 ] [Liu, Pengyu]Beijing Advanced Innovation Center for Future Internet Technology, Beijing University of Technology, Beijing; 100124, China
  • [ 7 ] [Liu, Pengyu]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing; 100124, China
  • [ 8 ] [Jia, Kebin]Faculty of Information Technology, Beijing University of Technology, Beijing; 100124, China
  • [ 9 ] [Jia, Kebin]Beijing Laboratory of Advanced Information Networks, Beijing; 100124, China
  • [ 10 ] [Jia, Kebin]Beijing Advanced Innovation Center for Future Internet Technology, Beijing University of Technology, Beijing; 100124, China
  • [ 11 ] [Jia, Kebin]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing; 100124, China

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Year: 2018

Page: 463-467

Language: English

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count: 1

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 12

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