James R. Sullivan

Design of minimum visual modulation halftone patterns

The visibility of binary image noise at low-to-medium dot densities of current binary printers, i.e 300-400 dots/in, is not subthreshold. Standard halftoning algorithms such as clustered-dot or dispersed-dot dither produce periodic patterns at these dot densities that are easily visible at normal viewing distances in uniform areas. A novel method of halftoning computer-generated uniform areas that reduces noise visibility by using a database of minimum visual modulation bit patterns is introduced. Applications include all paint-by-numbers prints such as those that offer tint-fill or object highlighting. >

UltraColor: a new gamut-mapping strategy

Many color calibration and enhancement strategies exist for digital systems. Typically, these approaches are optimized to work well with one class of images, but may produce unsatisfactory results for other types of images. For example, a colorimetric strategy may work well when printing photographic scenes, but may give inferior results for business graphic images because of device color gamut limitations. On the other hand, a color enhancement strategy that works well for business graphics images may distort the color reproduction of skintones and other important photographic colors. This paper describes a method for specifying different color mapping strategies in various regions of color space, while providing a mechanism for smooth transitions between the different regions. The method involves a two step process: (1) constraints are applied so some subset of the points in the input color space explicitly specifying the color mapping function; (2) the color mapping for the remainder of the color values is then determined using an interpolation algorithm that preserves continuity and smoothness. The interpolation algorithm that was developed is based on a computer graphics morphing technique. This method was used to develop the UltraColor gamut mapping strategy, which combines a colorimetric mapping for colors with low saturation levels, with a color enhancement technique for colors with high saturation levels. The result is a single color transformation that produces superior quality for all classes of imagery. UltraColor has been incorporated in several models of Kodak printers including the Kodak ColorEase PS and the Kodak XLS 8600 PS thermal dye sublimation printers.

Vector space approach to color imaging systems

This paper describes the color imaging system in terms of vector space notation. This includes the effects of the scanning filters and the response of the eye as defined by the CIE color matching functions. This formulation allows many image processing techniques to be generalized to include more accurate models. The problem ofimage restoration is used as an example for the vector space approach. The problem is presented in hierarchical steps. Color scanning and effect of the human observer is presented first. The problem is extended to spatial representation and spatial processing. Finally, the effects of image reproduction are considered. The assumptions at each step of the modelling process are made explicit and simplifications are noted. The choice of defining the most appropriate optimization function is considered with respect to mathematical tractability as well as subjective accuracy.

Secondary quantization of color images for minimum visual distortion

Many times in electronic imaging systems it is necessary to reduce the precision of the digital data for the display, storage, manipulation, or transformation of an image. For example, it may be necessary to reduce 12 bits/channel RGB data to 8 bits/channel due to storage requirements. To accomplish this reduction in the number of digital levels, a series of input levels must be grouped together for each output level. Since this process involves the quantization of previously quantized data, it is sometimes referred to as secondary quantization. The secondary quantization process necessarily results in artifacts, such as contouring in the image, where many colors have been mapped to a single color. Conventional methods, such as linear or power-law resampling, are suboptimal and do not consider intercolor effects. This paper describes a method for determining the quantization functions that will minimize the observable image artifacts generated by the secondary quantization process. The basic approach involves the use of nonlinear optimization techniques to minimize a cost function that provides a measure of the visible color error. Examples are presented that compare the optimized quantization process to conventional techniques.

A New ADPCM Image Compression Algorithm and the Effect of Fixed-Pattern Sensor Noise

High speed image compression algorithms that achieve visually lossless quality at low bit-rates are essential elements of many digital imaging systems. In examples such as remote sensing, there is often the additional requirement that the compression hardware be compact and consume minimal power. To meet these requirements a new adaptive differential pulse code modulation (ADPCM) algorithm was developed that significantly reduces edge errors by including quantizers that adapt to the local bias of the differential signal. In addition, to reduce the average bit-rate in certain applications a variable rate version of the algorithm called run adaptive differential coding (RADC) was developed that combines run-length and predictive coding and a variable number of levels in each quantizer to produce bit-rates comparable with adaptive discrete cosine transform (ADCT) at a visually lossless level of image quality. It will also be shown that this algorithm is relatively insensitive to fixed-pattern sensor noise and errors in sensor correction, making it possible to perform pixel correction on the decompressed image.

Secondary quantization of gray-level images for minimum visual distortion

In digital imaging systems it is often necessary to reduce the bit precision of an image due to limitations in transmission or storage. The 8 bit storage of a 12 bit medical image is a good example. Generally, the input images are linearly quantized, and the goal is to find the fixed, digital mapping that preserves the highest image quality at the reduced bit precision. Since the response of the visual system to brightness differences is nonlinear, the optimal mapping is nonlinear. The traditional approach is to use one of the commonly accepted models of the visual system, e.g. a logarithm or power-law, to construct a Look-Up-Table (LUT) that performs the digital mapping. This paper will demonstrate that this approach is visually suboptimal for finite input precision, even if the visual model is perfect. A better method for constructing the digital mapping or LUT will be derived by posing the problem as a combinational optimization problem of taking N bits from M bits, where N is less than M, such that a visual distortion metric is minimized. Computer generated images will be used to demonstrate the method in a 12 bit to 8 bit application, and a 6 bit to 2 bit example will be included to illustrate its convergence characteristics.

Lossless coding techniques for color graphical images

Summary form only given. Two types of images are considered: line-art images and computer generated graphics. The line art format specifies the standard representation format for digital images used in electronic pre-press systems. Colors are defined in a palette table that specifies the values of color components for each entry in the palette. The proposed techniques have been extended to full color graphics. The input data is transformed into color spaces which tend to concentrate a significant amount of the signal modulation into a single channel, thereby reducing the entropy in the other two channels. Each component channel is then processed by an independent spatial coder. A lower bound on the color entropy for the sample set of images is being derived by means of the optimal linear KL transformation to obtain the principal color components. An alternative method of compressing color graphics is described which utilizes a spatial DPCM codec operating directly on the composite color signal. Various approaches for determining the optimal values for the color correlation matrix are explored and comparisons made on the example image set.<<ETX>>

Modification of a laser recording beam for image quality improvement

The image quality of a discretely reconstructed image depends directly on the pixel exposure profile of thediscrete image recorder. By minimizing the mean- square error (MSE) of the recorded signal, optimum intensity and exposure profiles can be derived. This paper develops a method based on minimum MSE for determining the optimal exposure profile for an assumed sampling lattice and analog signal spectrum, and determines a physically realizable approximation to this profile by amplitude phase modification of the source-amplitude distribution.

Noise Restoration Of Compressed Image Data

Image noise restoration and predictive image coding are combined by implementing a maximum-a-posteriori (MAP) estimator on the differential signal in a differential pulse code modulation (DPCM) image compression scheme. For a Laplacian differential-signal probability density function (pdf) and a Gaussian noise pdf, the MAP estimator is an adaptive coring operator which is linear in the uncored region with a bias toward zero and a null operator in the cored region. The bias and the width of the coring region are functions of the noise and differential-signal variance, which are estimated from local image statistics over variable-length line segments. Independent segments are isolated by using a generalized-likelihood-ratio-test (GLRT) for Laplacian signals to determine whether or not adjacent segments have statistically equivalent differential variances. Because the MAP operator is an additive bias, it can be inserted in the transmitter of a DPCM encoder without error build-up or overhead information, and since it lowers the variance of the signal to be quantized by reducing the noise it can simplify the encoder by decreasing the number of levels that are required.