Reduced-dimension linear transform coding of distributed correlated signals with incomplete observations

H.I. Nurdin, Ravi R. Mazumdar, Arunabha Bagchi

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    We study the problem of optimal reduced-dimension linear transform coding and reconstruction of a signal based on distributed correlated observations of the signal. In the mean square estimation context this involves finding he optimal signal representation based on multiple incomplete or only partial observations that are correlated. In particular this leads to the study of finding the optimal Karhunen-Loeve basis based on the censored observations. The problem has been considered previously by Gestpar, Dragotti and Vitterli in the context of jointly Gaussian random variables based on using conditional covariances. In this paper, we derive the estimation results in the more general setting of second-order random variables with arbitrary distributions, using entirely different techniques based on the idea of innovations. We explicitly solve the single transform coder case, give a characterization of optimality in the multiple distributed transform coders scenario and provide additional insights into the structure of the problm.
    Original languageUndefined
    Article number10.1109/TIT.2009.2018349
    Pages (from-to)2848-2858
    Number of pages11
    JournalIEEE transactions on information theory
    Issue number6
    Publication statusPublished - Jun 2009


    • EWI-16461
    • Optimal linear estimation
    • Distributed signal processing
    • IR-68307
    • Karhunen-Loeve transform
    • METIS-264126
    • Innovations

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