On Predictive Coding for Erasure Channels Using a Kalman Framework

Thomas Arildsen, Manohar Murthi, Søren Vang Andersen, Søren Holdt Jensen

Research output: Contribution to journalJournal articleResearchpeer-review

7 Citations (Scopus)
374 Downloads (Pure)

Abstract

We present a new design method for robust low-delay coding of autoregressive sources for transmission across erasure channels. It is a fundamental rethinking of existing concepts. It considers the encoder a mechanism that produces signal measurements from which the decoder estimates the original signal. The method is based on linear predictive coding and Kalman estimation at the decoder. We employ a novel encoder state-space representation with a linear quantization noise model. The encoder is represented by the Kalman measurement at the decoder. The presented method designs the encoder and decoder offline through an iterative algorithm based on closed-form minimization of the trace of the decoder state error covariance. The design method is shown to provide considerable performance gains, when the transmitted quantized prediction errors are subject to loss, in terms of signal-to-noise ratio (SNR) compared to the same coding framework optimized for no loss. The design method applies to stationary auto-regressive sources of any order. We demonstrate the method in a framework based on a generalized differential pulse code modulation encoder. The presented principles can be applied to more complicated coding systems that incorporate predictive coding as well.
Original languageEnglish
JournalIEEE Transactions on Signal Processing
Volume57
Issue number11
Pages (from-to)4456-4466
Number of pages11
ISSN1053-587X
DOIs
Publication statusPublished - 2009

Fingerprint

Differential pulse code modulation
Signal to noise ratio

Keywords

  • differential pulse code modulation
  • erasure channels
  • joint source-channel coding
  • Kalman filtering
  • linear predictive coding
  • quantization

Cite this

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title = "On Predictive Coding for Erasure Channels Using a Kalman Framework",
abstract = "We present a new design method for robust low-delay coding of autoregressive sources for transmission across erasure channels. It is a fundamental rethinking of existing concepts. It considers the encoder a mechanism that produces signal measurements from which the decoder estimates the original signal. The method is based on linear predictive coding and Kalman estimation at the decoder. We employ a novel encoder state-space representation with a linear quantization noise model. The encoder is represented by the Kalman measurement at the decoder. The presented method designs the encoder and decoder offline through an iterative algorithm based on closed-form minimization of the trace of the decoder state error covariance. The design method is shown to provide considerable performance gains, when the transmitted quantized prediction errors are subject to loss, in terms of signal-to-noise ratio (SNR) compared to the same coding framework optimized for no loss. The design method applies to stationary auto-regressive sources of any order. We demonstrate the method in a framework based on a generalized differential pulse code modulation encoder. The presented principles can be applied to more complicated coding systems that incorporate predictive coding as well.",
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On Predictive Coding for Erasure Channels Using a Kalman Framework. / Arildsen, Thomas; Murthi, Manohar; Andersen, Søren Vang; Jensen, Søren Holdt.

In: IEEE Transactions on Signal Processing, Vol. 57, No. 11, 2009, p. 4456-4466.

Research output: Contribution to journalJournal articleResearchpeer-review

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AU - Murthi, Manohar

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AU - Jensen, Søren Holdt

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AB - We present a new design method for robust low-delay coding of autoregressive sources for transmission across erasure channels. It is a fundamental rethinking of existing concepts. It considers the encoder a mechanism that produces signal measurements from which the decoder estimates the original signal. The method is based on linear predictive coding and Kalman estimation at the decoder. We employ a novel encoder state-space representation with a linear quantization noise model. The encoder is represented by the Kalman measurement at the decoder. The presented method designs the encoder and decoder offline through an iterative algorithm based on closed-form minimization of the trace of the decoder state error covariance. The design method is shown to provide considerable performance gains, when the transmitted quantized prediction errors are subject to loss, in terms of signal-to-noise ratio (SNR) compared to the same coding framework optimized for no loss. The design method applies to stationary auto-regressive sources of any order. We demonstrate the method in a framework based on a generalized differential pulse code modulation encoder. The presented principles can be applied to more complicated coding systems that incorporate predictive coding as well.

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