Distributed source coding schemes are typically based on the use of channels codes as source codes. In this paper we propose a new paradigm, named “distributed arithmetic coding,” which extends arithmetic codes to the distributed case employing sequential decoding aided by the side information. In particular, we introduce a distributed binary arithmetic coder for the Slepian–Wolf coding problem, along with a joint decoder. The proposed scheme can be applied to two sources in both the asymmetric mode, wherein one source acts as side information, and the symmetric mode, wherein both sources are coded with ambiguity, at any combination of achievable rates. Distributed arithmetic coding provides several advantages over existing Slepian–Wolf coders, especially good performance at small block lengths, and the ability to incorporate arbitrary source models in the encoding process, e.g., context-based statistical models, in much the same way as a classical arithmetic coder. We have compared the performance of distributed arithmetic coding with turbo codes and low-density parity-check codes, and found that the proposed approach is very competitive.

Distributed arithmetic coding for the Slepian-Wolf problem

GRANGETTO, Marco;
2009

Abstract

Distributed source coding schemes are typically based on the use of channels codes as source codes. In this paper we propose a new paradigm, named “distributed arithmetic coding,” which extends arithmetic codes to the distributed case employing sequential decoding aided by the side information. In particular, we introduce a distributed binary arithmetic coder for the Slepian–Wolf coding problem, along with a joint decoder. The proposed scheme can be applied to two sources in both the asymmetric mode, wherein one source acts as side information, and the symmetric mode, wherein both sources are coded with ambiguity, at any combination of achievable rates. Distributed arithmetic coding provides several advantages over existing Slepian–Wolf coders, especially good performance at small block lengths, and the ability to incorporate arbitrary source models in the encoding process, e.g., context-based statistical models, in much the same way as a classical arithmetic coder. We have compared the performance of distributed arithmetic coding with turbo codes and low-density parity-check codes, and found that the proposed approach is very competitive.
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2257
Marco Grangetto; Enrico Magli; Gabriella Olmo
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/58609
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