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|a 10.1109/83.766862
|2 doi
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|a eng
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| 100 |
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|a Park, M
|e verfasserin
|4 aut
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|a Improved image decoding over noisy channels using minimum mean-squared estimation and a Markov mesh
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|c 1999
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
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|2 rdamedia
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|a ƒa Online-Ressource
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|2 rdacarrier
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| 500 |
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|a Date Completed 28.06.2010
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|a Date Revised 12.02.2008
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|a published: Print
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|a Citation Status PubMed-not-MEDLINE
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|a Joint source-channel (JSC) decoding based on residual source redundancy is a technique for providing channel robustness to quantized data. Previous work assumed a model equivalent to viewing the encoder/noisy channel tandem as a discrete hidden Markov model (HMM) with transmitted indices the hidden states. We generalize this HMM-based (1-D) approach for images, using the more powerful hidden Markov mesh random field (HMMRF) model. While previous state estimation methods for HMMRFs base estimates on only a causal subset of the observed data, our new method uses both causal and anticausal subsets. For JSC-based image decoding, the new method provides significant benefits over several competing techniques
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|a Letter
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|a Miller, D J
|e verfasserin
|4 aut
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|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g 8(1999), 6 vom: 28., Seite 863-7
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|x 1941-0042
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|g year:1999
|g number:6
|g day:28
|g pages:863-7
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|u http://dx.doi.org/10.1109/83.766862
|3 Volltext
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