Name
Abstract | ||
|---|---|---|
In this paper, we endeavor for predicting the performance of quantized compressive sensing under the use of sparse reconstruction estimators. We assume that a high rate vector quantizer is used to encode the noisy compressive sensing measurement vector. Exploiting a block sparse source model, we use Gaussian mixture density for modeling the distribution of the source. This allows us to formulate an optimal rate allocation problem for the vector quantizer. Considering noisy CS quantized measurements, we analyze upper- and lower-bounds on reconstruction error performance guarantee of two estimators - convex relaxation based basis pursuit de-noising estimator and an oracle-assisted least-squares estimator. |
Year | Venue | Keywords |
|---|---|---|
2014 | Information Theory and its Applications | Gaussian processes,compressed sensing,estimation theory,least squares approximations,relaxation theory,signal denoising,signal reconstruction,vector quantisation,CS,Gaussian mixture density,basis pursuit denoising estimator,block sparse source model,convex relaxation,encoding,lower-bound analysis,measurement vector,optimal rate allocation problem,oracle-assisted least-square estimator,reconstruction error performance,sparse reconstruction estimator,upper-bound analysis,vector quantized compressive sensing |
Field | DocType | Volume |
Computer science,Algorithm,Basis pursuit,Theoretical computer science,Gaussian,Gaussian process,Estimation theory,Quantization (signal processing),Compressed sensing,Signal reconstruction,Estimator | Journal | abs/1404.7643 |
ISBN | Citations | PageRank |
978-1-4673-2521-9 | 4 | 0.40 |
References | Authors | |
16 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Amirpasha Shirazinia | 1 | 62 | 6.90 |
| Saikat Chatterjee | 2 | 320 | 40.34 |
| Mikael Skoglund | 3 | 1397 | 175.71 |