Name
Abstract | ||
|---|---|---|
In this note we study the under-addressed quantization stage implicit in any compressed sensing signal acquisition paradigm. We also study the problem of compressing the bit-stream resulting from the quantization. We propose using Sigma-Delta (ΣΔ) quantization followed by a compression stage comprised of a discrete Johnson-Linden Strauss embedding, and a subsequent reconstruction scheme based on convex optimization. We show that this encoding/decoding method yields near-optimal rate-distortion guarantees for sparse and compressible signals and is robust to noise. Our results hold for sub-Gaussian (including Gaussian and Bernoulli) random compressed sensing measurements, and they hold for high bit-depth quantizers as well as for coarse quantizers including 1-bit quantization. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/DCC.2015.31 | DCC |
Field | DocType | ISSN |
Computer science,Theoretical computer science,Delta-sigma modulation,Vector quantization,Gaussian,Quantization (image processing),Decoding methods,Quantization (signal processing),Convex optimization,Compressed sensing | Conference | 1068-0314 |
Citations | PageRank | References |
1 | 0.36 | 15 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rayan Saab | 1 | 149 | 14.56 |
| Rongrong Wang | 2 | 7 | 1.90 |
| Özgür Yilmaz | 3 | 685 | 51.36 |