Distributed Coding of Quantized Random Projections

In this paper we propose a new framework for distributed source coding of structured sources, such as sparse signals. Our framework capitalizes on recent advances in the theory of linear inverse problems and signal representations using incoherent projections. Our approach acquires and quantizes inc...

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Veröffentlicht in:IEEE transactions on signal processing Jg. 68; S. 5924 - 5939
Hauptverfasser: Goukhshtein, Maxim, Boufounos, Petros T., Koike-Akino, Toshiaki, Draper, Stark C.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Coding
Compressed sensing
Decoding
Distributed source coding
Image coding
Information theory
Inverse problems
lossy compression
low complexity encoder
Measurement
quantization
Quantization (signal)
Remote sensing
side information
Source code
Source coding
sparsity
syndrome decoding
ISSN:1053-587X, 1941-0476
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Beschreibung
Zusammenfassung:In this paper we propose a new framework for distributed source coding of structured sources, such as sparse signals. Our framework capitalizes on recent advances in the theory of linear inverse problems and signal representations using incoherent projections. Our approach acquires and quantizes incoherent linear measurements of the signal, which are represented as separate bitplanes. Each bitplane is coded using a distributed source code of the appropriate rate, and transmitted. The decoder, starts from the least significant biplane and, using a prediction of the signal as side information, iteratively recovers each bitplane based on the source prediction and the signal, assuming all the previous bitplanes of lower significance have already been recovered. We provide theoretical results guiding the rate selection, relying only on the least squares prediction error of the source. This is in contrast to existing approaches which rely on difficult-to-estimate information-theoretic metrics to set the rate. We validate our approach using simulations on remote-sensing multispectral images, comparing them with existing approaches of similar complexity.
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ISSN:1053-587X
1941-0476
DOI:10.1109/TSP.2020.3029499