38 research outputs found
Efficient LDPC Codes over GF(q) for Lossy Data Compression
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(.n.q.log q), where 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).Comment: 5 pages, 3 figure
Lossy Source Coding via Spatially Coupled LDGM Ensembles
We study a new encoding scheme for lossy source compression based on
spatially coupled low-density generator-matrix codes. We develop a
belief-propagation guided-decimation algorithm, and show that this algorithm
allows to approach the optimal distortion of spatially coupled ensembles.
Moreover, using the survey propagation formalism, we also observe that the
optimal distortions of the spatially coupled and individual code ensembles are
the same. Since regular low-density generator-matrix codes are known to achieve
the Shannon rate-distortion bound under optimal encoding as the degrees grow,
our results suggest that spatial coupling can be used to reach the
rate-distortion bound, under a {\it low complexity} belief-propagation
guided-decimation algorithm.
This problem is analogous to the MAX-XORSAT problem in computer science.Comment: Submitted to ISIT 201
Lossy source coding using belief propagation and soft-decimation over LDGM codes
This paper focus on the lossy compression of a binary symmetric source. We propose a new algorithm for binary quantization over low density generator matrix (LDGM) codes. The proposed algorithm is a modified version of the belief propagation (BP) algorithm used in the channel coding framework and has linear complexity in the code block length. We also provide a common framework under which the proposed algorithm and some previously proposed algorithms fit. Simulation results show that our scheme achieves close to state-of-the-art performance with reduced complexity
Binary dirty paper coding
This paper proposes a practical scheme for implementing binary dirty paper coding (DPC) using a low density generator matrix code (LDGM) concatenated with a high rate low density parity check (LDPC) code. We also propose a new algorithm, a modified version of the belief propagation algorithm (BP), for doing lossy source coding at the encoder, with linear complexity in the block length. In contrast to the superposition coding framework, where high order alphabet codes are used, we propose to implement binary DPC using only binary codes. Through application of approximate density evolution and linear programming we optimize the degree distribution of the proposed code. Simulation results show that our scheme achieves close to state-of-the-art performance with reduced complexity