KEYWORDS: Modulation transfer functions, Scanners, Printing, Reflectivity, RGB color model, Signal to noise ratio, Image quality, CMYK color model, Silicon, Error analysis
Low-cost, color input and output are being increasingly adopted for desktop color applications. Many of these input devices (scanners) share common design attributes and objectives. Output devices (printers) are more diverse in their print engine technology and design intents. As would be expected, the image quality produced by the devices varies considerably from manufacturer to manufacturer. Fortunately, principles of image science and color science can be applied to quantify the image quality performance of these devices and summarize these characterizations with several simple figures of merit.
Color calibration involves the assessment of a tristimulus vector describing a surface. A color calibration device must return this tristimulus vector under various surface modes and/or illuminants. For reflective surfaces, the illuminant must be known in order to assess the surface reflectance. For luminous surfaces, a pseudo-illuminant may be used to account for the adaptation state of the observer. In each separate case, the color calibration device requires a different set of color matching functions. These several sets do not span a single three-dimensional space. Ideally, a device capable of operating under k modes should have basis functions spanning a 3k dimensional space. This paper considers the selection of fewer than 3k basis functions for operation under k modes. It discusses the selection of error criteria and a strategy to minimize the error due to the reduced dimensional approximation.
This study addresses how to generate high spatial resolution image data from a low spatial resolution imaging source. An edge-restricted spatial interpolation algorithm is developed to increase the image resolution and at the same time to enhance the
sharp edges and details from the original imaging source. The algorithm is based on a cubic spline-under-tension interpolation kernel. The weights of the interpolation kernel can be adjusted adaptively
according to the edge information in the neighborhood of the interpolated pixels. The algorithm can be applied to a relatively low spatial resolution image source, such as video, to generate highresolution
image data for high-quality printing devices.
KEYWORDS: RGB color model, Image quality, Quantization, Diffusion, Image processing, Human vision and color perception, Visual process modeling, Electronic imaging, Information technology, Copper
Algorithms are investigated for the printing or display of color images at near original image quality with a
minimum number of output colors. Each algorithm consists of a quantizer possibly used in conjunction with haiftoning.
We consider both image independent and image dependent quantizers implemented in RGB or in the uniform
color space L*u*v*. The halftoning techniques that we use are multilevel extensions of error diffusion and ordered
dither. Image quality resulting from use of these algorithms is measured by subjective evaluation.
A technique is presented for intraframe color image data compression which produces visually lossless imagery
compared to the original. This algorithm consists of a color vector quantizer operating in the Luv uniform color
space, followed by a reversible codeword assignment strategy that uses prediction to achieve conditional entropy type
bit rates. Unlike differential pulse code modulation (DPCM), predicted values are used for codebook selection instead
of the computation and coding of a residual signal.
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