Prof. Nicholas Tran Profile

Prof. Nicholas Tran

at Santa Clara Univ

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 | 18 February 2010 Paper

Nicholas Tran

Proceedings Volume 7527, 75270M (2010) https://doi.org/10.1117/12.845400

KEYWORDS: Image processing, Digital watermarking, Image compression, Binary data, Digital filtering, Lithium, Image filtering, Optical character recognition, Composites, Raster graphics

Read Abstract +

We present an empirical study of properties of the remarkable Normalized Compression Distance (NCD), a mathematical formulation by M. Li et al. that quantifies similarity between two binary strings as the additional amount of algorithmic information required to transform the description of one to the other. In particular, we are interested in the NCD values between an image and its modified versions by common image processing techniques. Experimental data obtained indicate that the NCD is symmetric and transitive, and that it can be used as a reasonable perceptual measure of similarity, but only in the spatial domain. Further, the NCD clusters the common image processing techniques into three groups in a manner consistent with the human perception of similarity. We also introduce two independent modifications to the NCD and study their properties. The first modification calls for applying a median filter and then thresholding to the input images before the NCD is computed, which results in a more uniform distribution of the NCD values. The second modification modifies the NCD formula to reflect the running time as well as the size of the shortest program that transforms one input string to the other. Obtained data show that using this modified NCD, it is possible to classify subtle changes (e.g., watermarking) to a font character image as similar to the original and drastic changes (e.g., rotation by 90 degrees) as different.

Proceedings Article | 12 February 2007 Paper

The normalized compression distance and image distinguishability

Nicholas Tran

Proceedings Volume 6492, 64921D (2007) https://doi.org/10.1117/12.704334

KEYWORDS: Visualization, Image compression, Digital watermarking, Binary data, Lithium, Distortion, Visual process modeling, Image processing, Distance measurement, Retina

Read Abstract +

We use an information-theoretic distortion measure called the Normalized Compression Distance (NCD), first proposed by M. Li et al., to determine whether two rectangular gray-scale images are visually distinguishable to a human observer. Image distinguishability is a fundamental constraint on operations carried out by all players in an image watermarking system. The NCD between two binary strings is defined in terms of compressed sizes of the two strings and of their concatenation; it is designed to be an effective approximation of the noncomputable but universal Kolmogorov distance between two strings. We compare the effectiveness of different types of compression algorithms? in predicting image distinguishability when they are used to compute the NCD between a sample of images and their watermarked counterparts. Our experiment shows that, as predicted by Li's theory, the NCD is largely independent of the underlying compression algorithm. However, in some cases the NCD fails as a predictor of image distinguishability, since it is designed to measure the more general notion of similarity. We propose and study a modified version of the NCD to model the latter, which requires that not only the change be small but also in some sense random with respect to the original image.

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