Incremental low-rank and sparse decomposition for compressing videos captured by fixed cameras

Chongyu Chen, Jianfei Cai, Weisi Lin, Guangming Shi

Research output: Contribution to journalArticleResearchpeer-review

17 Citations (Scopus)


Videos captured by stationary cameras are usually with a static or gradually changed background. Existing schemes are not able to globally exploit the strong background temporal redundancy. In this paper, motivated by the recent advance on low-rank and sparse decomposition (LRSD), we propose to apply it for the compression of videos captured by fixed cameras. In particular, the LRSD is employed to decompose the input video into the low-rank component, representing the background, and the sparse component, representing the moving objects, which are encoded by different methods. Moreover, we further propose an incremental LRSD (ILRSD) algorithm to reduce the large memory requirement and high computational complexity of the existing LRSD algorithm, which facilitates the process of large-scale video sequences without much performance loss. Experimental results show that the proposed coding scheme can significantly improve the existing standard codecs, H.264/AVC and HEVC, and outperform the state-of-the-art background modeling based coding schemes.

Original languageEnglish
Pages (from-to)338-348
Number of pages11
JournalJournal of Visual Communication and Image Representation
Publication statusPublished - Jan 2015
Externally publishedYes


  • Background prediction based video coding
  • Background subtraction
  • Background subtraction based video coding
  • Cur decomposition
  • Decomposition
  • Incremental low-rank and sparse
  • Low-rank and sparse decomposition
  • Stationary camera
  • Video coding

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