Lossy subset source coding

Ebrahim Molavianjazi, Aylin Yener

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    This paper studies the lossy version of a problem recently proposed by the authors termed subset source coding, where the focus is on the fundamental limits of compression for subsets of the possible realizations of a discrete memoryless source. An upper bound is derived on the subset rate-distortion function in terms of the subset mutual information optimized over the set of conditional distributions that satisfy the expected distortion constraint with respect to the subset-typical distribution and over the set of certain auxiliary subsets. By proving a strong converse result, this upper bound is shown to be tight for a class of symmetric subsets. As illustrated in our numerical examples, more often than not, one achieves a gain in the fundamental limit, in that the optimal lossy compression rate for the subset can be strictly smaller than the rate-distortion function of the source, although exceptions can also be constructed.

    Original languageEnglish (US)
    Title of host publication2016 Information Theory and Applications Workshop, ITA 2016
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    ISBN (Electronic)9781509025299
    DOIs
    StatePublished - Mar 27 2017
    Event2016 Information Theory and Applications Workshop, ITA 2016 - La Jolla, United States
    Duration: Jan 31 2016Feb 5 2016

    Publication series

    Name2016 Information Theory and Applications Workshop, ITA 2016

    Other

    Other2016 Information Theory and Applications Workshop, ITA 2016
    CountryUnited States
    CityLa Jolla
    Period1/31/162/5/16

    All Science Journal Classification (ASJC) codes

    • Computer Networks and Communications
    • Computer Science Applications
    • Artificial Intelligence
    • Information Systems
    • Signal Processing

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  • Cite this

    Molavianjazi, E., & Yener, A. (2017). Lossy subset source coding. In 2016 Information Theory and Applications Workshop, ITA 2016 [7888152] (2016 Information Theory and Applications Workshop, ITA 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ITA.2016.7888152