Compressive sensing depth video coding via gaussian mixture models and object edges

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Abstract

In this paper, we propose a novel compressive sensing depth video (CSDV) coding scheme based on Gaussian mixture models (GMM) and object edges. We first compress several depth videos to get CSDV frames in the temporal direction. A whole CSDV frame is divided into a set of non-overlap patches in which object edges is detected by Canny operator to reduce the computational complexity of quantization. Then, we allocate variable bits for different patches based on the percentages of non-zero pixels in every patch. The GMM is used to model the CSDV frame patches and design product vector quantizers to quantize CSDV frames. The experimental results show that our compression scheme achieves a significant Bjontegaard Delta (BD)-PSNR improvement about 2–10 dB when compared to the standard video coding schemes, e.g. Uniform Scalar Quantization-Differential Pulse Code Modulation (USQ-DPCM) and H.265/HEVC.

Original languageEnglish
Title of host publicationAdvances in Multimedia Information Processing – PCM 2017 - 18th Pacific-Rim Conference on Multimedia, Revised Selected Papers
EditorsBing Zeng, Hongliang Li, Abdulmotaleb El Saddik, Xiaopeng Fan, Shuqiang Jiang, Qingming Huang
PublisherSpringer Verlag
Pages96-104
Number of pages9
ISBN (Print)9783319773797
DOIs
StatePublished - 2018
Event18th Pacific-Rim Conference on Multimedia, PCM 2017 - Harbin, China
Duration: 28 Sep 201729 Sep 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10735 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference18th Pacific-Rim Conference on Multimedia, PCM 2017
Country/TerritoryChina
CityHarbin
Period28/09/1729/09/17

Keywords

  • Compressive sensing
  • Depth video coding
  • Gaussian mixture models
  • View synthesis

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