5th International Mobile Multimedia Communications Conference

Research Article

Distributed multi-view image coding with learned dictionaries

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  • @INPROCEEDINGS{10.4108/ICST.MOBIMEDIA2009.7466,
        author={ Ivana Tošic and Pascal Frossard},
        title={Distributed multi-view image coding with learned dictionaries},
        proceedings={5th International Mobile Multimedia Communications Conference},
        publisher={ICST},
        proceedings_a={MOBIMEDIA},
        year={2010},
        month={5},
        keywords={Distributed source coding sparse approximations multiview images},
        doi={10.4108/ICST.MOBIMEDIA2009.7466}
    }
    
  • Ivana Tošic
    Pascal Frossard
    Year: 2010
    Distributed multi-view image coding with learned dictionaries
    MOBIMEDIA
    ICST
    DOI: 10.4108/ICST.MOBIMEDIA2009.7466
Ivana Tošic1,*, Pascal Frossard1,*
  • 1: Ecole Polytechnique Fédérale de Lausanne, Signal Processing Laboratory (LTS4), Lausanne, Switzerland.
*Contact email: ivana.tosic@epfl.ch, pascal.frossard@epfl.ch

Abstract

This paper addresses the problem of distributed image coding in camera neworks. The correlation between multiple images of a scene captured from different viewpoints can be effiiciently modeled by local geometric transforms of prominent images features. Such features can be efficiently represented by sparse approximation algorithms using geometric dictionaries of various waveforms, called atoms. When the dictionaries are built on geometrical transformations of some generating functions, the features in different images can be paired with simple local geometrical transforms, such as scaling, rotation or translations. The construction of the dictionary however represents a trade-off between approximation performance that generally improves with the size of the dictionary, and cost for coding the atoms indexes. We propose a learning algorithm for the construction of dictionaries adapted to stereo omnidirectional images. The algorithm is based on a maximum likelihood solution that results in atoms adapted to both image approximation and stereo matching. We then use the learned dictionary in a Wyner- Ziv multi-view image coder built on a geometrical correlation model. The experimental results show that the learned dictionary improves the rate-distortion performance of the Wyner-Ziv coder at low bit rates compared to a baseline parametric dictionary.