Title :
Nonlinear Sparse-Graph Codes for Lossy Compression
Author :
Gupta, Ankit ; Verdú, Sergio
Author_Institution :
Dept. of Electr. Eng., Princeton Univ., Princeton, NJ
fDate :
5/1/2009 12:00:00 AM
Abstract :
We propose a scheme for lossy compression of discrete memoryless sources: The compressor is the decoder of a nonlinear channel code, constructed from a sparse graph. We prove asymptotic optimality of the scheme for any separable (letter-by-letter) bounded distortion criterion. We also present a suboptimal compression algorithm, which exhibits near-optimal performance for moderate block lengths.
Keywords :
channel coding; decoding; graph theory; nonlinear codes; decoder; discrete memoryless sources; nonlinear channel code; nonlinear sparse-graph codes; sparse graph; suboptimal compression algorithm; Additive noise; Channel capacity; Compression algorithms; Data compression; Error correction codes; Information theory; Linear code; Maximum likelihood decoding; Nonlinear distortion; Parity check codes; Discrete memoryless sources; lossy data compression; rate–distortion theory; source–channel coding duality; sparse-graph codes;
Journal_Title :
Information Theory, IEEE Transactions on
DOI :
10.1109/TIT.2009.2016040
