Geometric enumerated chrominance watermark embed for spot colors

Lyons, Robert; Reed, Alastair; Stach, John

doi:10.1117/12.2009488

Cited by 1 publication

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“…This can be described by the following shift invariant convolution: (1) where is an input image, is the spatial domain CSF, and is the frequency normalized output image.…”

Section: Pyramid Processing Methodsmentioning

confidence: 99%

“…Image watermarking is a technique that covertly embeds additional information in a cover image, such that the ownership, copyright and other details about the cover image can be communicated. Watermarks used for packaging are inserted in the chrominance domain to obtain the best robustness per unit visibility [1]. The chrominance image watermark is embedded in a way that the color component in the cover image is minimally altered and is hardly noticeable, due to human vision system's low sensitivity to color changes.…”

Section: Introductionmentioning

confidence: 99%

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Full-color visibility model using CSF which varies spatially with local luminance

Reed¹,

Berfanger²,

Bai³

et al. 2014

SPIE Proceedings

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A full color visibility model has been developed that uses separate contrast sensitivity functions (CSFs) for contrast variations in luminance and chrominance (red-green and blue-yellow) channels. The width of the CSF in each channel is varied spatially depending on the luminance of the local image content. The CSF is adjusted so that more blurring occurs as the luminance of the local region decreases. The difference between the contrast of the blurred original and marked image is measured using a color difference metric.This spatially varying CSF performed better than a fixed CSF in the visibility model, approximating subjective measurements of a set of test color patches ranked by human observers for watermark visibility. The effect of using the CIEDE2000 color difference metric compared to CIEDE1976 (i.e., a Euclidean distance in CIELAB) was also compared.

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“…This can be described by the following shift invariant convolution: (1) where is an input image, is the spatial domain CSF, and is the frequency normalized output image.…”

Section: Pyramid Processing Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%