Name
Abstract | ||
|---|---|---|
Spread spectrum (SS) or known-host-statistics technique has shown the best performance in terms of both rate of reliable communications and bit error probability at the low watermark-to-noise ratio (WNR) regime. These results were obtained assuming that the host data follows an in- dependent and identically distributed (i.i.d.) Gaussian dis- tribution. However, in some widely used in practical data- hiding transform domains (like wavelet or discrete cosine transform domains) the host statistics have strong non- Gaussian character. Motivated by this stochastic modeling mismatch between the used assumption and the real case, a new set-up of the SS-based data-hiding with Laplacian host is presented for performance enhancement in terms of both bit error probability and achievable rates in additive white Gaussian noise (AWGN) channels based on the par- allel splitting of Laplacian source. Motivated by the mismatch in the stochastic modeling of the host data in the performance analysis of the SS-based data-hiding, we formulate the main goal of this paper as fol- lows: to analyze the performance of known-host-statistics data-hiding methods in terms of both achievable rates and probability of error for a realistic host data model. In par- ticular, we select an i.i.d. stationary Laplacian pdf to model the host statistics, as well as an independent non-stationary Gaussian representation. We consider a novel formulation of the data-hiding set-up as communications with side in- formation available at the decoder. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1109/ICIP.2005.1529726 | ICIP |
Keywords | Field | DocType |
AWGN channels,Gaussian distribution,Laplace transforms,data encapsulation,error statistics,spread spectrum communication,AWGN channels,Laplacian host data,additive white Gaussian noise,asymmetric spread spectrum data-hiding,bit error probability,discrete cosine transform domains,identically distributed Gaussian distribution,known-host-statistics technique,spread spectrum,watermark-to-noise ratio,wavelet transform domains | Discrete cosine transform,Theoretical computer science,Artificial intelligence,Wavelet,Spread spectrum,Pattern recognition,Laplace transform,Algorithm,Gaussian,Independent and identically distributed random variables,Additive white Gaussian noise,Mathematics,Laplace operator | Conference |
Volume | ISSN | Citations |
1 | 1522-4880 | 1 |
PageRank | References | Authors |
0.35 | 6 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| José-emilio Vila-forcén | 1 | 19 | 2.29 |
| Oleksiy J. Koval | 2 | 118 | 17.75 |
| Sviatoslav Voloshynovskiy | 3 | 773 | 80.94 |
| Thierry Pun | 4 | 3553 | 290.95 |