<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Braunstein, A.</style></author><author><style face="normal" font="default" size="100%">Farbod Kayhan</style></author><author><style face="normal" font="default" size="100%">Zecchina, Riccardo</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Efficient LDPC Codes over GF(q) for Lossy Data Compression</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE International Symposium on Information Theory, 2009. ISIT 2009</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">biocomp</style></keyword><keyword><style  face="normal" font="default" size="100%">coding</style></keyword><keyword><style  face="normal" font="default" size="100%">optimization</style></keyword><keyword><style  face="normal" font="default" size="100%">statphys</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2009</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2009</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://arxiv.org/abs/0901.4467</style></url></web-urls><related-urls><url><style face="normal" font="default" size="100%">http://areeweb.polito.it/ricerca/cmp/sites/default/files/ISIT2009.pdf</style></url></related-urls></urls><pub-location><style face="normal" font="default" size="100%">Seul, Korea</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">In this paper we consider the lossy compression of a binary symmetric source. We present a scheme that provides a low complexity lossy compressor with near optimal empirical performance. The proposed scheme is based on b-reduced ultra-sparse LDPC codes over GF(q). Encoding is performed by the Reinforced Belief Propagation algorithm, a variant of Belief Propagation. The computational complexity at the encoder is O(d.n.q.log q), where d is the average degree of the check nodes. For our code ensemble, decoding can be performed iteratively following the inverse steps of the leaf removal algorithm. For a sparse parity-check matrix the number of needed operations is O(n). </style></abstract></record></records></xml>