Adaptive distributed source coding

David Varodayan, Yao Chung Lin, Bernd Girod

Research output: Contribution to journalArticlepeer-review


We consider distributed source coding in the presence of hidden variables that parameterize the statistical dependence among sources. We derive the Slepian-Wolf bound and devise coding algorithms for a block-candidate model of this problem. The encoder sends, in addition to syndrome bits, a portion of the source to the decoder uncoded as doping bits. The decoder uses the sum-product algorithm to simultaneously recover the source symbols and the hidden statistical dependence variables. We also develop novel techniques based on density evolution (DE) to analyze the coding algorithms. We experimentally confirm that our DE analysis closely approximates practical performance. This result allows us to efficiently optimize parameters of the algorithms. In particular, we show that the system performs close to the Slepian-Wolf bound when an appropriate doping rate is selected. We then apply our coding and analysis techniques to a reduced-reference video quality monitoring system and show a bit rate saving of about 75% compared with fixed-length coding.

Original languageEnglish (US)
Article number6081940
Pages (from-to)2630-2640
Number of pages11
JournalIEEE Transactions on Image Processing
Issue number5
StatePublished - May 2012
Externally publishedYes


  • Source coding
  • sum product algorithm
  • video signal processing

ASJC Scopus subject areas

  • Software
  • Computer Graphics and Computer-Aided Design


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