Distributed Lossy Source Coding Using Real-Number Codes

We show how real-number codes can be used to compress correlated sources, and establish a new framework for distributed lossy source coding, in which we quantize compressed sources instead of compressing quantized sources. This change in the order of binning and quantization blocks makes it possible...

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Published in:2012 IEEE Vehicular Technology Conference (VTC Fall) pp. 1 - 5
Main Authors: Vaezi, M., Labeau, F.
Format: Conference Proceeding
Language:English
Published: IEEE 01.09.2012
Subjects:
Correlation
Decoding
Discrete Fourier transforms
Quantization
Source coding
Vectors
ISBN:1467318809, 9781467318808
ISSN:1090-3038
Online Access:Get full text
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Abstract We show how real-number codes can be used to compress correlated sources, and establish a new framework for distributed lossy source coding, in which we quantize compressed sources instead of compressing quantized sources. This change in the order of binning and quantization blocks makes it possible to model correlation between continuous-valued sources more realistically and correct quantization error when the sources are completely correlated. The encoding and decoding procedures are described in detail, for discrete Fourier transform (DFT) codes. Reconstructed signal, in the mean-squared error sense, is seen to be better than or close to quantization error level in the conventional approach.
AbstractList We show how real-number codes can be used to compress correlated sources, and establish a new framework for distributed lossy source coding, in which we quantize compressed sources instead of compressing quantized sources. This change in the order of binning and quantization blocks makes it possible to model correlation between continuous-valued sources more realistically and correct quantization error when the sources are completely correlated. The encoding and decoding procedures are described in detail, for discrete Fourier transform (DFT) codes. Reconstructed signal, in the mean-squared error sense, is seen to be better than or close to quantization error level in the conventional approach.
Author Vaezi, M.
Labeau, F.
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  givenname: F.
  surname: Labeau
  fullname: Labeau, F.
  email: fabrice.labeau@mcgill.ca
  organization: Dept. of Electr. & Comput. Eng., McGill Univ., Montreal, QC, Canada
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Snippet We show how real-number codes can be used to compress correlated sources, and establish a new framework for distributed lossy source coding, in which we...
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SubjectTerms Correlation
Decoding
Discrete Fourier transforms
Quantization
Source coding
Vectors
Title Distributed Lossy Source Coding Using Real-Number Codes
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