We consider a resource-constrained scenario where a compressed sensing- (CS)
based sensor has a low number of measurements which are quantized at a low rate
followed by transmission or storage. Applying this scenario, we develop a new
quantizer design which aims to attain a high-quality reconstruction performance
of a sparse source signal based on analysis-by-synthesis framework. Through
simulations, we compare the performance of the proposed quantization algorithm
vis-a-vis existing quantization methods.Comment: 5 pages, Published in ICASSP 201

Chatterjee, Saikat

Shirazinia, Amirpasha

Skoglund, Mikael

English

arXiv

We consider a resource-constrained scenario where a compressed sensing- (CS) based sensor has a low number of measurements which are quantized at a low rate followed by transmission or storage. Applying this scenario, we develop a new quantizer design which aims to attain a high-quality reconstruction performance of a sparse source signal based on analysis-by-synthesis framework. Through simulations, we compare the performance of the proposed quantization algorithm vis-a-vis existing quantization methods.QC 20130902</p

Publikationer från KTH

Analysis-by-synthesis-based Quantization of Compressed Sensing Measurements

Academic Archive On-line (KTH, Sweden)

We consider a resource-limited scenario where a sensor that uses compressed sensing (CS) collects a low number of measurements in order to observe a sparse signal, and the measurements are subsequently quantized at a low bit-rate followed by transmission or storage. For such a scenario, we design new algorithms for source coding with the objective of achieving good reconstruction performance of the sparse signal. Our approach is based on an analysis-by-synthesis principle at the encoder, consisting of two main steps: 1) the synthesis step uses a sparse signal reconstruction technique for measuring the direct effect of quantization of CS measurements on the final sparse signal reconstruction quality, and 2) the analysis step decides appropriate quantized values to maximize the final sparse signal reconstruction quality. Through simulations, we compare the performance of the proposed quantization algorithms vis-a-vis existing quantization schemes.QC 20131115</p

Analysis-by-Synthesis-based Quantization of Compressed Sensing Measurements

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Publikationer från KTH