GaussianSource Coding Based on P-LDPC Code

A lossy source coding system based on the protograph low-density parity-check (P-LDPC) code is proposed for Gaussian source compression. In the proposed system, the conventional belief propagation (BP) algorithm is modified to be a concatenated BP-inverse BP (BP-[Formula Omitted]BP) for encoding and...

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Published in	IEEE transactions on communications Vol. 71; no. 4; pp. 1970 - 1981
Main Authors	Song, Dan, Ren, Jinkai, Wang, Lin, Chen, Guanrong
Format	Journal Article
Language	English
Published	New York The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 01.04.2023
Subjects	Algorithms Codes Coding Complexity Decoding Distortion Error correcting codes Iterative methods Low density parity check codes Message passing Neural networks Optimization Splicing
Online Access	Get full text
ISSN	0090-6778 1558-0857
DOI	10.1109/TCOMM.2023.3245660

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Abstract	A lossy source coding system based on the protograph low-density parity-check (P-LDPC) code is proposed for Gaussian source compression. In the proposed system, the conventional belief propagation (BP) algorithm is modified to be a concatenated BP-inverse BP (BP-[Formula Omitted]BP) for encoding and decoding, where the [Formula Omitted]BP is constructed by a fully-connected layer of a neural network. Compared to the existing approximate message passing algorithm, the proposed BP-[Formula Omitted]BP realizes a float-to-bit compression with low complexity for arbitrary Gaussian sources. The BP-[Formula Omitted]BP is implemented based on the linking relation of the protograph; therefore, it is necessary to optimally design the protograph to obtain better rate-distortion function (RDF) performance. Regarding the coding optimal procedure, a mutual information iteration convergence (MIIC) algorithm is designed as the optimal criterion to determine the source P-LDPC code with minimum distortion. Inspired by the plane construction of quantum stabilizer code, a lattice topological splicing (LTS) algorithm is proposed for regularly building the protograph to reduce the code searching complexity. By using the MIIC and the LTS algorithms, the BP-[Formula Omitted]BP based on the designed P-LDPC code maintains good distortion performance close to the RDF limit.
AbstractList	A lossy source coding system based on the protograph low-density parity-check (P-LDPC) code is proposed for Gaussian source compression. In the proposed system, the conventional belief propagation (BP) algorithm is modified to be a concatenated BP-inverse BP (BP-[Formula Omitted]BP) for encoding and decoding, where the [Formula Omitted]BP is constructed by a fully-connected layer of a neural network. Compared to the existing approximate message passing algorithm, the proposed BP-[Formula Omitted]BP realizes a float-to-bit compression with low complexity for arbitrary Gaussian sources. The BP-[Formula Omitted]BP is implemented based on the linking relation of the protograph; therefore, it is necessary to optimally design the protograph to obtain better rate-distortion function (RDF) performance. Regarding the coding optimal procedure, a mutual information iteration convergence (MIIC) algorithm is designed as the optimal criterion to determine the source P-LDPC code with minimum distortion. Inspired by the plane construction of quantum stabilizer code, a lattice topological splicing (LTS) algorithm is proposed for regularly building the protograph to reduce the code searching complexity. By using the MIIC and the LTS algorithms, the BP-[Formula Omitted]BP based on the designed P-LDPC code maintains good distortion performance close to the RDF limit.
Author	Wang, Lin Ren, Jinkai Song, Dan Chen, Guanrong
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SubjectTerms	Algorithms Codes Coding Complexity Decoding Distortion Error correcting codes Iterative methods Low density parity check codes Message passing Neural networks Optimization Splicing
Title	GaussianSource Coding Based on P-LDPC Code
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