Title :
Smooth compression, Gallager bound and nonlinear sparse-graph codes
Author :
Montanari, Andrea ; Mossel, Elchanan
Author_Institution :
EE & Stat. Depts., Stanford Univ., Stanford, CA
Abstract :
A data compression scheme is defined to be smooth if its image (the codeword) depends gracefully on the source (the data). Smoothness is a desirable property in many practical contexts, and widely used source coding schemes lack of it. We introduce a family of smooth source codes based on sparse graph constructions, and prove them to achieve the (information theoretic) optimal compression rate for a dense set of iid sources. As a byproduct, we show how Gallager bound on sparsity can be overcome using non-linear function nodes.
Keywords :
graph theory; nonlinear codes; smoothing methods; source coding; Gallager bound; data compression; information theory; nonlinear sparse-graph code; optimal compression rate; smooth compression; smooth source codes; source coding; Chaos; Data compression; Error probability; Hamming distance; Image coding; Noise robustness; Random variables; Source coding; Statistics; Temperature sensors;
Conference_Titel :
Information Theory, 2008. ISIT 2008. IEEE International Symposium on
Conference_Location :
Toronto, ON
Print_ISBN :
978-1-4244-2256-2
Electronic_ISBN :
978-1-4244-2257-9
DOI :
10.1109/ISIT.2008.4595436