Digital Color Imaging

This paper surveys current technology and research in the area of digital color imaging. In order to establish the background and lay down terminology, fundamental concepts of color perception and measurement are first presented us-ing vector-space notation and terminology. Present-day color recording and reproduction systems are reviewed along with the common mathematical models used for representing these devices. Algorithms for processing color images for display and communication are surveyed, and a forecast of research trends is attempted. An extensive bibliography is provided

A single-lobe photometric stereo approach for heterogeneous material

Shape from shading with multiple light sources is an active research area, and a diverse range of approaches have been proposed in recent decades. However, devising a robust reconstruction technique still remains a challenging goal, as the image acquisition process is highly nonlinear. Recent Photometric Stereo variants rely on simplifying assumptions in order to make the problem solvable: light propagation is still commonly assumed to be uniform, and the Bidirectional Reflectance Distribution Function is assumed to be diffuse, with limited interest for specular materials. In this work, we introduce a well-posed formulation based on partial differential equations (PDEs) for a unified reflectance function that can model both diffuse and specular reflections. We base our derivation on ratio of images, which makes the model independent from photometric invariants and yields a well-posed differential problem based on a system of quasi-linear PDEs with discontinuous coefficients. In addition, we directly solve a differential problem for the unknown depth, thus avoiding the intermediate step of approximating the normal field. A variational approach is presented ensuring robustness to noise and outliers (such as black shadows), and this is confirmed with a wide range of experiments on both synthetic and real data, where we compare favorably to the state of the art.Roberto Mecca is a Marie Curie fellow of the “Istituto Nazionale di Alta Matematica” (Italy) for a project shared with University of Cambridge, Department of Engineering and the Department of Mathematics, University of Bologna

Color in computing

Color in the computing environment, once considered a luxury, is becoming more available compared to being just the occasional exception. As the number of users exploring the uses of color through displayed and printed images increases, the problems associated with its use are becoming widely known. What worked in black and white is not easily translated into color. The use of color needs to begin with the basic understanding of what is color, its terminology and its utilization as an enhancement to communications tool. Only after the basic terminology and effective means of communication are understood will color flourish as a successful means of communication in the computing environment. Currently, a number of products are seen as solutions in the realm of color usage in the computing environment. Four different contributions, PostScript Level 2 (Adobe), PhotoYCC(Eastman Kodak), Pantone Matching System (Pantone), and TekHVC (Tektronix), each deliver a component of electronic color reproduction. PostScript Level 2 delivers consistent color from monitor to printer, with variations based on printer manufacture and the printing technology utilized. PhotoYCC defines a format for image capture and retrieval with a wealth of possibilities for image sources. Pantone Matching System expands the accessibility of simulated prepress work, coupled with ink formulation and quality control. Tektronix attempted to define TekHVC as an industry standard based on a more uniform color space than that which is defined by previous industry standards. Because of the lack of acceptance, Tektronix has limited this solution to their printers. Solutions are abundant, but as costs continue to fall, the expectation of consistent color will rise. The adoption of standards across operating environments and software packages is critical to continued increase of the use of color in the computing environment

The LLAB model for quantifying colour appearance

A reliable colour appearance model is desired by industry to achieve high colour fidelity between images produced using a range of different imaging devices. The aim of this study was to derive a reliable colour appearance model capable of predicting the change of perceived attributes of colour appearance under a wide range of media/viewing conditions. The research was divided into three parts: characterising imaging devices, conducting a psychophysical experiment, and developing a colour appearance model. Various imaging devices were characterised including a graphic art scanner, a Cromalin proofing system, an IRIS ink jet printer, and a Barco Calibrator. For the former three devices, each colour is described by four primaries: cyan (C), magenta (M), yellow (Y), and black (K). Three set of characterisation samples (120 and 31 black printer, and cube data sets) were produced and measured for deriving and testing the printing characterisation models. Four black printer algorithms (BPA), were derived. Each included both forward and reverse processes. A 2nd BPA printing model taking into account additivity failure, grey component replacement (GCR) algorithm gave the most accurate prediction to the characterisation data set than the other BPA models. The PLCC (Piecewise Linear interpolation assuming Constant Chromaticity coordinates) monitor model was also implemented to characterise the Barco monitor. The psychophysical experiment was conducted to compare Cromalin hardcopy images viewed in a viewing cabinet and softcopy images presented on a monitor under a wide range of illuminants (white points) including: D93, D65, D50 and A. Two scaling methods: category judgement and paired comparison, were employed by viewing a pair of images. Three classes of colour models were evaluated: uniform colour spaces, colour appearance models and chromatic adaptation transforms. Six images were selected and processed via each colour model. The results indicated that the BFD chromatic transform gave the most accurate predictions of the visual results. Finally, a colour appearance model, LLAB, was developed. It is a combination of the BFD chromatic transform and a modified version of CIELAB uniform colour space to fit the LUTCRI Colour Appearance Data previously accumulated. The form of the LLAB model is much simpler and its performance is more precise to fit experimental data than those of the other models

Study of Camera Spectral Reflectance Reconstruction Performance using CPU and GPU Artificial Neural Network Modelling

Reconstruction of reflectance spectra from camera RGB values is possible, if characteristics of the illumination source, optics and sensors are known. If not, additional information about these has to be somehow acquired. If alongside with pictures taken, RGB values of some colour patches with known reflectance spectra are obtained under the same illumination conditions, the reflectance reconstruction models can be created based on artificial neural networks (ANN). In Matlab, multilayer feedforward networks can be trained using different algorithms. In our study we hypothesized that the scaled conjugate gradient back propagation (BP) algorithm when executed on Graphics Processing Unit, is very fast, but in terms of convergence and performance, it does not match Levenberg-Marquardt algorithm (LM), which, on the other hand, executes only on CPU and is therefore much more time-consuming. We also presumed that there exists a correlation between the two algorithms and is manifested through a dependency of MSE to the number of hidden layer neurons, and therefore the faster BP algorithm could be used to narrow the search span with the LM algorithm to find the best ANN for reflectance reconstruction. The conducted experiment confirmed speed superiority of the BP algorithm but also confirmed better convergence and accuracy of reflectance reconstruction with the LM algorithm. The correlation of reflectance recovery results with ANNs modelled by both training algorithms was confirmed, and a strong correlation was found between the 3rd order polynomial approximation of the LM and BP algorithm\u27s test performances for both mean and best performance

A Black-Point Adaption model for color reproduction

Based on the current state of CIECAM97s, there is a missing adjustment associated with a black-point unlike a white-point. As an attempt to improve the performance of CIECAM97s for color reproduction, six algorithms focusing on black-point adaptation were generated based on previous work on white-point adaptation methods and gamut mapping methods. The six algorithms were used to reproduce four original images targeted to four simulated hard-copy viewing environments that were only differentiated by their black-point settings. Then, the six algorithms were tested in a psychophysical experiment with 32 observers. As a result, linear lightness rescaling under the luminances of white and black of a specific setting was demonstrated to be the best color reproduction method across different black-point settings. The adapted black-point was defined as having the lowest lightness value with its default chromatic appearance correlates predicted by the current state of CIECAM97s under the input viewing environment and was reproduced accordingly with the same appearance correlates

Preferences and tolerances in color image reproduction

Observer preferences in the color reproduction of pictorial images have been a topic of debate for many years. Through a series of three psychophysical experiments we are trying to better understand the differences and trends in observer preferences for pictorial images, determine if cultural biases on preference exist, and finally generate a set of preferred color reproduced images for future experimentation and evaluation. The first experiment was a survey of observers rating the importance of commonly used image characteristics terms in correlation to color image quality. The data collected demonstrated that observer preferences remain relatively constant while judging color attributes between different media and for various image content. Experiment I also aided in the decision to utilize five dimensions of manipulation to generate preferred color reproductions, for Experiments II and in. The dimensions were, lightness (gamma adjustment to L*), contrast (sigmoid adjustment to L*), chroma (multiplicative factor on Cab* at a given hab), hue rotation, and color balance (additive adjustments to a* and b*). The second experiment was a rank order of image preference conducted at several research facilities around the world. The results yielded that statistical difference between peak preferences of image quality between cultures may exist but that the cultural difference is most likely not of practical significance for most applications. Furthermore, the shape of the preference curves across cultures is very similar so any cultural bias present is small. The final experiment was an adjustment experiment, in which observers were asked to generate the most preferred image possible. The observer variability (inter-observers) and repeatability (intra-observer) in generating preferred images were analyzed. The analysis of Experiment HI yielded that the intra-observer repeatability of an observer is about half of the variation between observers. Furthermore the analysis demonstrated that preferences on images with faces have a much tighter range of preference in comparison to images without faces. Finally, a cross analysis of Experiment II and HI was completed by the generation of preferred image sets from the results of the two experiments. The resultant images proved to be a good visualization of the range of variability in making preferred images from the color dimensions provided, and also visually demonstrated that the two techniques, (making one color adjustment at a time verses compounding color adjustments) of generating preferred images result in similar solutions

Driver Response to Simulated Intersections: An Analysis of Workload-Related Variables

A roadway intersection driving simulation was created to investigate driver information processing at intersections. Research participants were provided a visual simulation of approaching intersections using a video display with a 120 degree visual field. Six groups, each containing 12 subjects, were formed according to age and gender, with age ranging from 18 to 74 years. All participants viewed 14 separate intersections, which varied according to types of traffic control signs and signals. Individual workload was assessed in three categories of response: performance, subjective, and physiological. A MANOVA was performed on six dependent variables in the 3 (age) by 2 (gender) design. Results indicate significant main effects for both age and gender. The three significant dependent variables were pedal response errors, speed of response, and heart rate reactivity to each intersection. The responses suggest greater workloads for older drivers and female drivers. In addition to age and gender, a number of driver information processing characteristics were measured. Stepwise regressions indicated that performance decrements to the simulated driving situations could best be predicted by subjects\u27 scores for field dependency, visual acuity, and depth perception. However, age alone, accounted for more variance in performance than any single information processing variable

Colorimetric tolerances of digital images

An environment to derive colorimetric tolerances of images was established and an experiment using this new environment was performed. This environment allows for images to be digitally captured, colorimetrically manipulated, displayed, observed, and statistically evaluated. The visual experiment measured perceptibility and acceptability colorimetric tolerances for images using paired comparison techniques. Thirty-two observers judged six typical photographic scenes displayed on a high resolution color monitor. These scenes were manipulated using ten linear and nonlinear functions in the CIELAB dimensions of lightness, chroma, and hue angle. The tolerances were determined using probit analysis. It was found that scene content did not significantly affect the tolerances. The CIELAB, CMC, and MCSL color difference equations were shown to be inadequate for accurately modeling image tolerances. Finally, possible applications of this work are described