In January 2014, Digimarc announced Digimarc® Barcode for the packaging industry to improve the check-out efficiency and customer experience for retailers. Digimarc Barcode is a machine readable code that carries the same information as a traditional Universal Product Code (UPC) and is introduced by adding a robust digital watermark to the package design. It is imperceptible to the human eye but can be read by a modern barcode scanner at the Point of Sale (POS) station. Compared to a traditional linear barcode, Digimarc Barcode covers the whole package with minimal impact on the graphic design. This significantly improves the Items per Minute (IPM) metric, which retailers use to track the checkout efficiency since it closely relates to their profitability. Increasing IPM by a few percent could lead to potential savings of millions of dollars for retailers, giving them a strong incentive to add the Digimarc Barcode to their packages. Testing performed by Digimarc showed increases in IPM of at least 33% using the Digimarc Barcode, compared to using a traditional barcode.
A method of watermarking print ready image data used in the commercial packaging industry is described. A significant proportion of packages are printed using spot colors, therefore spot colors needs to be supported by an embedder for Digimarc Barcode. Digimarc Barcode supports the PANTONE spot color system, which is commonly used in the packaging industry. The Digimarc Barcode embedder allows a user to insert the UPC code in an image while minimizing perceptibility to the Human Visual System (HVS). The Digimarc Barcode is inserted in the printing ink domain, using an Adobe Photoshop plug-in as the last step before printing. Since Photoshop is an industry standard widely used by pre-press shops in the packaging industry, a Digimarc Barcode can be easily inserted and proofed.
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.
To watermark spot color packaging images one modulates available spot color inks to create a watermark signal. By perturbing different combinations of inks one can change the color direction of the watermark signal. In this paper we describe how to calculate the optimal color direction that embeds the maximum signal while keeping the visibility below some specified acceptable value. The optimal color direction depends on the starting color for the image region, the ink density constraints and the definition of the watermark signal. After a description of the general problem of N spot color inks we shall describe two-ink embedding methods and try to find the optimal direction that will maximize robustness at a given visibility. The optimal color direction is usually in a chrominance direction and the resulting ink perturbations change the luminosity very little. We compare the optimal color embedder to a single-color embedder.
Most packaging is printed using spot colors to reduce cost, produce consistent colors, and achieve a wide color gamut on the package. Most watermarking techniques are designed to embed a watermark in cyan, magenta, yellow, and black for printed images or red, green, and blue for displayed digital images. Our method addresses the problem of watermarking spot color images. An image containing two or more spot colors is embedded with a watermark in two of the colors with the maximum signal strength within a user-selectable visibility constraint. The user can embed the maximum watermark signal while meeting the required visibility constraint. The method has been applied to the case of two spot colors and images have been produced that are more than twice as robust to Gaussian noise as a single color image embedded with a luminance-only watermark with the same visibility constraint.
A watermark embed scheme has been developed to insert a watermark with the maximum signal
strength for a user selectable visibility constraint. By altering the watermark strength and direction to
meet a visibility constraint, the maximum watermark signal for a particular image is inserted. The
method consists of iterative embed software and a full color human visibility model plus a watermark
signal strength metric.
The iterative approach is based on the intersections between hyper-planes, which represent visibility and
signal models, and the edges of a hyper-volume, which represent output device visibility and gamut
constraints. The signal metric is based on the specific watermark modulation and detection methods and
can be adapted to other modulation approaches. The visibility model takes into account the different
contrast sensitivity functions of the human eye to L, a and b, and masking due to image content.
The paper presents a watermark robustness model based on the mobile phone camera's spatial frequency response and
watermark embedding parameters such as density and strength. A new watermark robustness metric based on spatial
frequency response is defined. The robustness metric is computed by measuring the area under the spatial frequency
response for the range of frequencies covered by the watermark synchronization signal while excluding the interference
due to aliasing. By measuring the distortion introduced by a particular camera, the impact on watermark detection can be
understood and quantified without having to conduct large-scale experiments. This in turn can provide feedback on
adjusting the watermark embedding parameters and finding the right trade-off between watermark visibility and
robustness to distortion. In addition, new devices can be quickly qualified for their use in smart image applications. The
iPhone 3G, iPhone 3GS, and iPhone 4 camera phones are used as examples in this paper to verify the behavior of the
watermark robustness model.
Creating an imperceptible watermark which can be read by a broad range of cell phone cameras is a difficult problem.
The problems are caused by the inherently low resolution and noise levels of typical cell phone cameras. The quality
limitations of these devices compared to a typical digital camera are caused by the small size of the cell phone and cost
trade-offs made by the manufacturer.
In order to achieve this, a low resolution watermark is required which can be resolved by a typical cell phone camera.
The visibility of a traditional luminance watermark was too great at this lower resolution, so a chrominance watermark
was developed. The chrominance watermark takes advantage of the relatively low sensitivity of the human visual system
to chrominance changes. This enables a chrominance watermark to be inserted into an image which is imperceptible to
the human eye but can be read using a typical cell phone camera.
Sample images will be presented showing images with a very low visibility which can be easily read by a typical cell
Watermarking of printed materials has usually focused on process inks of cyan, magenta, yellow and black (CMYK). In packaging, almost three out of four printed materials include spot colors. Spot colors are special premixed inks, which can be produced in a vibrant range of colors, often outside the CMYK color gamut. In embedding a watermark into printed material, a common approach is to modify the luminance value of each pixel in the image. In the case of process color work pieces, the luminance change can be scaled to the C, M, Y and K channels using a weighting function, to produce the desired change in luminance. In the case of spot color art designs, there is only one channel available and the luminance change is applied to this channel. In this paper we develop a weighting function to embed the watermark signal across the range of different spot colors. This weighting function normalizes visibility effect and signal robustness across a wide range of different spot colors. It normalizes the signal robustness level over the range of an individual spot color’s intensity levels. Further, it takes into account the sensitivity of the capturing device to the different spot colors.
In digital watermarking, a major aim is to insert the maximum possible watermark signal while minimizing visibility. Many watermarking systems embed data in the luminance channel to ensure watermark survival through operations such as grayscale conversion. For these systems, one method of reducing visibility is for the luminance changes due to the watermark signal to be inserted into the colors least visible to the human visual system, while minimizing the changes in the image hue. In this paper, we develop a system that takes advantage of the low sensitivity of the human visual system to high frequency changes along the yellow-blue axis, to place most of the watermark in the yellow component of the image. We also describe how watermark detection can potentially be enhanced, by using a priori knowledge of this embedding system to intelligently examine possible watermarked images.
In digital watermarking, one aim is to insert the maximum possible watermark signal without significantly affecting image quality. Advantage can be taken of the masking effect of the eye to increase the signal strength in busy or high contrast image areas. The application of such a human visual system model to watermarking has been proposed by several authors. However if a simple contrast measurement is used, an objectionable ringing effect may become visible on connected directional edges. In this paper we describe a method which distinguishes between connected directional edges and high frequency textured areas, which have no preferred edge direction. The watermark gain on connected directional edges is suppressed, while the gain in high contrast textures is increased. Overall, such a procedure accommodates a more robust watermark for the same level of visual degradation because the watermark is attenuated where it is truly objectionable, and enhanced where it is not. Furthermore, some authors propose that the magnitude of a signal which can be imperceptibly placed in the presence of a reference signal can be described by a non-linear mapping of magnitude to local contrast. In this paper we derive a mapping function experimentally by determining the point of just noticeable difference between a reference image and a reference image with watermark.