NDLI: Comparison of perceptual shaping techniques for digital image watermarking

Content Provider	IEEE Xplore Digital Library
Author	Belet, P. Dams, T. Bardyn, D. Dooms, A.
Copyright Year	2009
Description	Author affiliation: Artesis University College of Antwerp, Applied Engineering, Paardenmarkt 92, 2000, Belgium (Belet, P.) \|\| Vrije Universiteit Brussel, ETRO, Pleinlaan 2, 1050, Belgium (Dams, T.; Bardyn, D.; Dooms, A.)
Abstract	Digital watermarking is the process of embedding information into another signal like an image. Embedding a watermark results in modifications of the image, leading to a decrease of the perceived quality. Perceptual shaping uses perceptual information of the image to camouflage these modifications, thereby increasing the perceived quality. In this paper four perceptual models are evaluated: the empirical noise visibility function, the noise visibility function based on a stationary generalized Gaussian distribution, the noise tolerance model and the Watson model. These models are applied on two watermarking schemes: correlation-based watermarking and dither-modulated quantization index modulation. We describe how these schemes utilize the perceptual models for improving the perceived quality and experimentally compare their performance. We conclude that the noise tolerance model offers the best results regarding the perceived quality. The empirical noise visibility function was found to offer best robustness.
Starting Page	1
Ending Page	6
File Size	477789
Page Count	6
File Format	PDF
ISBN	9781424432974
DOI	10.1109/ICDSP.2009.5201253
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-07-05
Publisher Place	Greece
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Digital images Watermarking Blanking Delay Pulse compression methods Band pass filters Matched filters Receiving antennas Radar antennas Flexible printed circuits Watson model Perceptual shaping watermarking human visual system noise visibility function noise tolerance model
Content Type	Text
Resource Type	Article

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