Natural language color (NLC) was initially developed as a web-based application and then deployed in one
Xerox print driver. NLC changes the image-editing paradigm from the use of curves, sliders, and knobs, to the
use of verbal text-based commands such as "make light green much less yellowish". The technology appeals
to a common user who has no expert knowledge in color science, and this naturally leads one to think about
its use in mobile devices. A prototype GUI design for a language-based color editing on iPhone platform will
be presented that uses several of its haptic interfaces (e.g. "slot-machine", shaking, swiping, etc.). A textual
interface is provided to select a color to be modified within the image and a direction of change for the
modification. A swipe interface is provided to select a magnitude and polarity for the modification. Actions on
the textual and swipe interface are converted to natural language commands that are in turn used to derive a
color transformation that is applied to relevant portions of the image to yield a modified image. The
modifications are displayed in real time to the user.
KEYWORDS: Digital watermarking, RGB color model, Printing, CMYK color model, Image processing, Calibration, Scanners, Image resolution, Data hiding, Error analysis
We present a method to include watermarks in printed images. With accurate printer calibration, in theory, the same color under different gray component replacement (GCR) strategies should look the same, under specific viewing conditions. We spatially vary the GCR along the image in a manner that is not perceptible, and we employ an estimation method to detect such changes. The choice of GCR for a given pixel (or region) comprises an additional information channel that embeds a watermark or hidden information. The challenge is how to detect which GCR was used and that is our focus. For that, we estimate the RGB value of each pixel and the CMYK values intended to be put onto the paper by scanning the printed page. With that information, we can estimate which GCR strategy was used in a given region and retrieve the watermark message. Instead of focusing on a particular watermarking scheme, we are concerned only with the practical aspects of producing a spatially varying GCR and of robustly estimating which GCR strategy was used at a region. Promising GCR detection results are shown to illustrate the method's potential to watermark printed images.
KEYWORDS: Visualization, Color difference, Printing, RGB color model, Raster graphics, Data modeling, Colorimetry, Associative arrays, LCDs, Color reproduction
Monochrome devices that receive color imagery must perform a conversion from color to grayscale. The most common approach is to calculate the luminance signal from the three color signals. The problem with this approach is that the distinction between two colors of similar luminance (but different hue) is lost. This can be a significant problem when rendering colors within graphical objects such as pie charts and bar charts, which are often chosen for maximum discriminability.
This paper proposes a method of converting color business graphics to grayscale in a manner that preserves discriminability. Colors are first sorted according to their original lightness values. They are then spaced equally in gray, or spaced according to their 3-D color difference from colors adjacent to them along the lightness dimension. This is most useful when maximum differentiability is desired in images containing a small number of colors, such as pie charts and bar graphs. Subjective experiments indicate that the proposed algorithms outperform standard color-to-grayscale conversions.
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