Mr. Ning Liu Profile

Ning Liu

SPIE Involvement:

Author

Publications (2)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 21 March 2005 Paper

TCQ-based quantizer design for data hiding in images

Ning Liu, Koduvayur Subbalakshmi

Proceedings Volume 5681, (2005) https://doi.org/10.1117/12.591861

KEYWORDS: Data hiding, Distortion, Quantization, Digital watermarking, Computer programming, Binary data, Signal to noise ratio, Multimedia, Telecommunications, Interference (communication)

Read 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.

Proceedings Article | 22 June 2004 Paper

Vector-quantization-based scheme for data embedding for images

Ning Liu, Koduvayur Subbalakshmi

Proceedings Volume 5306, (2004) https://doi.org/10.1117/12.527219

KEYWORDS: Data hiding, Distortion, Digital watermarking, Binary data, Quantization, Signal to noise ratio, Computer programming, Interference (communication), Multimedia, Telecommunications

Read Abstract +

Today, data hiding has become more and more important in a variety of applications including security. Since Costa's work in the context of communication, the set of quantization based schemes have been proposed as one class of data hiding schemes. Most of these schemes are based on uniform scalar quantizer, which is optimal only if the host signal is uniformly distributed. In this paper, we propose pdf -matched embedding schemes, which not only consider pdf -matched quantizers, but also extend them to multiple dimensions. Specifically, our contributions to this paper are: We propose a pdf-matched embedding (PME) scheme by generalizing the probability distribution of host image and then constructing a pdf-matched quantizer as the starting point. We show experimentally that the proposed pdf-matched quantizer provides better trade-offs between distortion caused by embedding, the robustness to attacks and the embedding capacity. We extend our algorithm to embed a vector of bits in a host signal vector. We show by experiments that our scheme can be closer to the data hiding capacity by embedding larger dimension bit vectors in larger dimension VQs. Two enhancements have been proposed to our method: by vector flipping and by using distortion compensation (DC-PME), that serve to further decrease the embedding distortion. For the 1-D case, the PME scheme shows a 1 dB improvement over the QIM method in a robustness-distortion sense, while DC-PME is 1 dB better than DC-QIM and the 4-D vector quantizer based PME scheme performs about 3 dB better than the 1-D PME.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

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

Members:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years