Correlation estimation for distributed source coding under information exchange constraints

Distributed source coding (DSC) depends strongly on accurate knowledge of correlation between sources. Previous works have reported capacity-approaching code constructions when exact knowledge of correlation is available at the encoder. However, in many applications exact correlation information may...

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Veröffentlicht in:IEEE International Conference on Image Processing 2005 Jg. 2; S. II - 682
Hauptverfasser: Ngai-Man Cheung, Huisheng Wang, Ortega, A.
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 2005
Schlagworte:
Channel coding
Decoding
Entropy
Estimation error
Feedback
Hyperspectral imaging
Hyperspectral sensors
Parity check codes
Source coding
Turbo codes
ISBN:9780780391345, 0780391349
ISSN:1522-4880
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Zusammenfassung:Distributed source coding (DSC) depends strongly on accurate knowledge of correlation between sources. Previous works have reported capacity-approaching code constructions when exact knowledge of correlation is available at the encoder. However, in many applications exact correlation information may not be available, and correlation estimation is necessary. While error in estimation is inevitable, the impact of estimation error on compression efficiency has not been sufficiently studied for the DSC problem. In this paper we study correlation estimation subject to complexity constraints, and its impact on coding efficiency in a DSC framework. In particular, we consider the case where estimation entails information exchange between spatially separate sources and thus correlation estimation is subject to rate constraints. We first derive optimal strategies for information exchange that minimize the rate penalty due to inaccurate estimation, under constraints on the number of bits that can be exchanged between sources. Experimental results show that significant gain is possible by optimally exchanging information. We then derive analytical expressions to quantify the rate penalty, and analyze how rate penalty changes with a priori knowledge of correlation. In addition, we present a model-based estimation method which can achieve more accurate estimation results compared to directly inspecting the data.
ISBN:9780780391345
0780391349
ISSN:1522-4880
DOI:10.1109/ICIP.2005.1530147