Correlation noise classification based on matching success for transform domain Wyner-Ziv video coding

Distributed source coding strongly depends on the knowledge of statistical dependency between source and side information. In transform domain Wyner-Ziv video coding (TDWZ) this statistical dependency (also known as correlation noise) has been usually modeled by a unique Laplacian distribution for e...

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Main Authors: Esmaili, G.R., Cosman, P.C.
Format: Conference Proceeding
Language:English
Subjects:
Cameras
Correlation noise
Decoding
Distributed source coding
Encoding
Frequency estimation
Interpolation
Laplace equations
Motion estimation
Rate-distortion
Source coding
Video coding
Wyner-Ziv coding
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Cosman, P.C.
description Distributed source coding strongly depends on the knowledge of statistical dependency between source and side information. In transform domain Wyner-Ziv video coding (TDWZ) this statistical dependency (also known as correlation noise) has been usually modeled by a unique Laplacian distribution for each frequency band. In this paper, we propose a method to define different classes of correlation noise for each frequency band based on the accuracy of the side information. With this approach the correlation between source and side information is estimated separately for each frequency band of each class. Therefore, the decoder can discriminate blocks in order to estimate the correlation noise of their frequency bands. Simulation results show that applying the proposed method improves rate-distortion performance.
doi_str_mv 10.1109/ICASSP.2009.4959705
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subjects Cameras
Correlation noise
Decoding
Distributed source coding
Encoding
Frequency estimation
Interpolation
Laplace equations
Motion estimation
Rate-distortion
Source coding
Video coding
Wyner-Ziv coding
title Correlation noise classification based on matching success for transform domain Wyner-Ziv video coding
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