Publication

Comments on "Iterative channel decoding of FEC-based multiple-description codes"

Nikolaos Thomos
2013
Journal Articles

Abstract

In their recent paper, Chang et al. presented a method for iterative decoding of FEC-based multiple description codes in image transmission. In this correspondence we clarify that an outer interleaver used in the above work was previously proposed for the iterative decoding and optimization of product codes in image transmission.

Official source

https://infoscience.epfl.ch/entities/publication/45c12923-4fbb-4d34-8773-a92075ce1873

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Nikolaos Thomos
2013
Journal Articles

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/45c12923-4fbb-4d34-8773-a92075ce1873

About this result

Related concepts (9)

Low-density parity-check code

In information theory, a low-density parity-check (LDPC) code is a linear error correcting code, a method of transmitting a message over a noisy transmission channel. An LDPC code is constructed using a sparse Tanner graph (subclass of the bipartite graph). LDPC codes are , which means that practical constructions exist that allow the noise threshold to be set very close to the theoretical maximum (the Shannon limit) for a symmetric memoryless channel.

Error correction code

In computing, telecommunication, information theory, and coding theory, forward error correction (FEC) or channel coding is a technique used for controlling errors in data transmission over unreliable or noisy communication channels. The central idea is that the sender encodes the message in a redundant way, most often by using an error correction code or error correcting code (ECC). The redundancy allows the receiver not only to detect errors that may occur anywhere in the message, but often to correct a limited number of errors.

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A Viterbi decoder uses the Viterbi algorithm for decoding a bitstream that has been encoded using a convolutional code or trellis code. There are other algorithms for decoding a convolutionally encoded stream (for example, the Fano algorithm). The Viterbi algorithm is the most resource-consuming, but it does the maximum likelihood decoding. It is most often used for decoding convolutional codes with constraint lengths k≤3, but values up to k=15 are used in practice. Viterbi decoding was developed by Andrew J.

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