Image-adaptive hidgh-volume data hiding based

on scalar quantization

N. Jacobsen, K. Solanki, U. Madhow, B. S. Manjunath, and S. Chandrasekaran
Dept. of Electrical and Computer Engineering
University of California at Santa Barbara,
Santa Barbara, CA 93106
　

Abstract

Information-theoretic analyses for data hiding prescribe
embedding the hidden data in the choice of quantizer for
the host data. In this paper, we consider a suboptimal implementation
of this prescription, with a view to hiding
high volumes of data in images with low perceptual degradation.
The three main findings are as follows:
(i) Scalar quantization based data hiding schemes incur a
2 dB penalty from the optimal embedding strategy, which
involves vector quantization of the host.
(ii) In order to limit perceivable distortion while hiding large
 amounts of data, hiding schemes must use local perceptual
 criteria in addition to information-theoretic guidelines.
(iii) Powerful erasures and errors correcting codes provide
 a flexible framework that allows the data-hider freedom of
 choice of where to embed without requiring synchronization
 between encoder and decoder.