A Scheme for Attentional Video Compression

  • Rupesh Gupta
  • Santanu Chaudhury
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6744)


In this paper an improved, macroblock (MB) level, visual saliency algorithm, aimed at video compression, is presented. A Relevance Vector Machine (RVM) is trained over 3 dimensional feature vectors, pertaining to global, local and rarity measures of conspicuity, to yield probabalistic values which form the saliency map. These saliency values are used for non-uniform bit-allocation over video frames. A video compression architecture for propagation of saliency values, saving tremendous amount of computation, is also proposed.


Salient Object Video Compression Visual Saliency Dimensional Feature Vector Saliency Propagation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Rupesh Gupta
    • 1
  • Santanu Chaudhury
    • 1
  1. 1.Dept. of EEIndian Institute of Technology DelhiIndia

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