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This paper continues our research on high capacity prediction error expansion reversible watermarking. Three prediction error expansion (PEE) schemes are considered, namely histogram shifting (HS), location map (LM) and block map (BM) schemes. Their performances for embedding capacities greater than 1 bit per pixel are analyzed. For embedding capacity less than 1 bit per pixel, it is known that the...
In this paper we propose the optimization of the snow sub-model of MORDOR using MODIS and in situ measurements for the case study of the Serre-Ponçon reservoir (one of the largest artificial lakes in Western Europe) on the Durance River in the French Alps. We consider the problem of optimizing the snow model as an unconstrained nonlinear optimization problem.
The classical difference expansion (DE) reversible watermarking expands two times a difference in order to embed one bit of data. The upper limit of such DE schemes in a single embedding level is usually bounded by 1 bpp. In order to obtain more than 1 bpp, several embedding stages are chained. The multibit DE schemes expand n times the difference in order to embed up to log2 n bpp. This paper proposes...
This paper investigates the block implementation of median edge detector (MED) based prediction error expansion reversible watermarking. Image is divided in overlapping blocks. A block is considered for data embedding only if all its pixels can be embedded. By embedding on blocks, the size of the location map is reduced. The block map implementation provides considerably better results than the classical...
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