TCQ-based quantizer design for data hiding in images

Ning Liu; Koduvayur P. Subbalakshmi

doi:10.1117/12.591861

21 March 2005 TCQ-based quantizer design for data hiding in images

Ning Liu, Koduvayur P. Subbalakshmi

Proceedings Volume 5681, Security, Steganography, and Watermarking of Multimedia Contents VII; (2005) https://doi.org/10.1117/12.591861
Event: Electronic Imaging 2005, 2005, San Jose, California, United States

Abstract

Most of quantization based data hiding schemes are based on uniform scalar quantizer, which is optimal only if the host signal is uniformly distributed. In our recent work, we proposed two pdf-matched quantizer based data hiding schemes, both of which have better embedding efficiency than conventional uniform quantizer based schemes. But these schemes require a lot of location information for qualified pixels, which increases the side information needed by the decoder. Moreover, these schemes are inconvenient when high embedding rates are desired. In this paper, we propose a trellis coded quantizer (TCQ) based data hiding scheme, which is proved to be more embedding efficient than traditional quantizer based schemes. Experimental results show that the proposed scheme can be up to 1.2 dB closer to the capacity upper bound than the QIM scheme over the whole watermark noise rate (WNR) range. Comparing with our previous work, the proposed TCQ based scheme has less side information and can easily realize a high embedding rate.

Citation Download Citation

Ning Liu and Koduvayur P. Subbalakshmi "TCQ-based quantizer design for data hiding in images", Proc. SPIE 5681, Security, Steganography, and Watermarking of Multimedia Contents VII, (21 March 2005); https://doi.org/10.1117/12.591861

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available