Karen M. Braun

Color to gray and back: color embedding into textured gray images

We have developed a reversible method to convert color graphics and pictures to gray images. The method is based on mapping colors to low-visibility high-frequency textures that are applied onto the gray image. After receiving a monochrome textured image, the decoder can identify the textures and recover the color information. More specifically, the image is textured by carrying a subband (wavelet) transform and replacing bandpass subbands by the chrominance signals. The low-pass subband is the same as that of the luminance signal. The decoder performs a wavelet transform on the received gray image and recovers the chrominance channels. The intent is to print color images with black and white printers and to be able to recover the color information afterwards. Registration problems are discussed and examples are presented.

Color-to-grayscale conversion to maintain discriminability

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.

Testing five color‐appearance models for changes in viewing conditions

Five color-appearance transformations were compared in a series of psychophysical experiments under a variety of viewing conditions. These transformations included the Hunt model, the Nayatani model, RLAB, CIELAB color space, and von Kries chromatic adaptation. Observers compared original printed images viewed under one set of viewing conditions to reproductions viewed on a monitor with differing conditions. These conditions varied in white point, luminance level, background reflectance, and surround. Reproductions were produced using the various color-appearance models and observers decided which reproduction was the closest match to the original. RLAB was found to produce the most acceptable reproductions, except for changes in surround. CIELAB and von Kries adaptation also performed well.

Psychophysical generation of matching images for cross-media color reproduction

A technique was developed and tested for generating CRT pictorial image reproductions that match the color appearance of print originals. Five observers, experienced in using Adobe Photoshop to adjust images, matched two scenes using a memory-matching technique Observers were able to produce accurate matches when originals and reproductions were viewed at the same white point. Observers then matched the reproductions at 6500 K to originals viewed at 9300 and 3000 K. These matches were compared to the predictions of various color-appearance models, using a paired-comparison technique in a psychophysical experiment using different observers. The observer-matched images were found to be equal or superior to matches produced using any of the models.

LANGUAGE-BASED COLOR EDITING FOR MOBILE DEVICES

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.

Detecting spatially varying gray component replacement with application in watermarking printed images

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 methods potential to watermark printed images.

Color embedding into gray images

We present a reversible method to convert color graphics and pictures to gray images based on mapping colors to low-visibility high-frequency textures. From a monochrome textured image, the decoder can identify the textures and recover the color information. An image is textured by carrying a wavelet transform and replacing band-pass sub-bands by the chrominance images. The low-pass sub-band is the same as that of the luminance signal. The decoder performs a wavelet transform on the received gray image and recovers the chrominance channels. Registration problems are discussed and examples are presented.

Spatially varying gray component replacement for image watermarking

We present a method to include watermarks in printed images. 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. For that, we estimate the RGB values of each pixel and the CMYK values on 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. We are only concerned with robustly estimating the GCR strategy. Promising results illustrate the methods potential to watermark printed images.