TY - GEN

T1 - Lossy subset source coding

AU - Molavianjazi, Ebrahim

AU - Yener, Aylin

N1 - Funding Information:
This research is sponsored in part by the Army Research Laboratory under the Network Science Collaborative Technology Alliance, Agreement Number W911NF-09-2-0053.
Publisher Copyright:
© 2016 IEEE.

PY - 2017/3/27

Y1 - 2017/3/27

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85018283735&partnerID=8YFLogxK

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U2 - 10.1109/ITA.2016.7888152

DO - 10.1109/ITA.2016.7888152

M3 - Conference contribution

AN - SCOPUS:85018283735

T3 - 2016 Information Theory and Applications Workshop, ITA 2016

BT - 2016 Information Theory and Applications Workshop, ITA 2016

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2016 Information Theory and Applications Workshop, ITA 2016

Y2 - 31 January 2016 through 5 February 2016

ER -