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

Towards Colour Imaging with the Image Ranger

Many of the colour images captured by different types of digital camera do not provide quality colour image according to human visual perception. In this study we explore technique for colour correction of the colour images from the Waikato Image Ranger. Colour images were captured using three different illuminants with the Waikato Image Ranger. The colour image formed from the ranger data do not have good quality because illuminants used do not match usual RGB standard illuminants. The spectral power distribution values of the illuminants were measured using spectroradiometer. To calculate tristimulus values the reflectance function of the scene is required. A mechanism of calculating the reflectance functions from the ranger data using genetic algorithm was explained. The reflectance functions are approximated using variable Gaussian basis functions, and fit to the ranger colour triplets by genetic algorithms. From the estimated reflectance functions standard CRT RGB values were calculated. It was found that the genetic algorithm approach was for too slow for practical purposes and produced images with far too much colour variation

Spectrally Based Material Color Equivalency: Modeling and Manipulation

A spectrally based normalization methodology (Wpt normalization) for linearly transforming cone excitations or sensor values (sensor excitations) to a representation that preserves the perceptive concepts of lightness, chroma and hue is proposed resulting in a color space with the axes labeled W , p, t. Wpt (pronounced “Waypoint ) has been demonstrated to be an effective material color equivalency space that provides the basis for defining Material Adjustment Transforms that predict the changes in sensor excitations of material spectral reflectance colors due to variations in observer or illuminant. This is contrasted with Chromatic Adaptation Transforms that predict color appearance as defined by corresponding color experiments. Material color equivalency as provided by Wpt and Wpt normalization forms the underlying foundation of this doctoral research. A perceptually uniform material color equivalency space (“Waypoint Lab or WLab) was developed that represents a non-linear transformation of Wpt coordinates, and Euclidean WLab distances were found to not be statistically different from ∆E⋆94 and ∆E00 color differences. Sets of Wpt coordinates for variations in reflectance, illumination, or observers were used to form the basis of defining Wpt shift manifolds. WLab distances of corresponding points within or between these manifolds were utilized to define metrics for color inconstancy, metamerism, observer rendering, illuminant rendering, and differences in observing conditions. Spectral estimation and manipulation strategies are presented that preserve various aspects of “Wpt shift potential as represented by changes in Wpt shift manifolds. Two methods were explored for estimating Wpt normalization matrices based upon direct utilization of sensor excitations, and the use of a Wpt based Material Adjustment Transform to convert Cone Fundamentals to ”XYZ-like Color Matching Functions was investigated and contrasted with other methods such as direct regression and prediction of a common color matching primaries. Finally, linear relationships between Wpt and spectral reflectances were utilized to develop approaches for spectral estimation and spectral manipulation within a general spectral reflectance manipulation framework – thus providing the ability to define and achieve “spectrally preferred color rendering objectives. The presented methods of spectral estimation, spectral manipulation, and material adjustment where utilized to: define spectral reflectances for Munsell colors that minimize Wpt shift potential; manipulate spectral reflectances of actual printed characterization data sets to achieve colorimetry of reference printing conditions; and lastly to demonstrate the spectral estimation and manipulation of spectral reflectances using images and spectrally based profiles within an iccMAX color management workflow

Analysis of image noise in multispectral color acquisition

The design of a system for multispectral image capture will be influenced by the imaging application, such as image archiving, vision research, illuminant modification or improved (trichromatic) color reproduction. A key aspect of the system performance is the effect of noise, or error, when acquiring multiple color image records and processing of the data. This research provides an analysis that allows the prediction of the image-noise characteristics of systems for the capture of multispectral images. The effects of both detector noise and image processing quantization on the color information are considered, as is the correlation between the errors in the component signals. The above multivariate error-propagation analysis is then applied to an actual prototype system. Sources of image noise in both digital camera and image processing are related to colorimetric errors. Recommendations for detector characteristics and image processing for future systems are then discussed

Novel Approaches to the Spectral and Colorimetric Color Reproduction

All the different approaches taken for spectral data acquisition can be narrowed down to two main methods; the first one is using spectrophotometer, spectroradiometer, hyper- and multi- spectral camera through which the spectra can be most probably attained with a high level of accuracy in a direct manner. Nonetheless, the price at which the spectra are acquired is very high. However, there is also a second approached in which the spectra are estimated from the colorimetric information. The second approach, even though it is very cost efficient, is of limited level of accuracy, which could be due to the methods or the dissmiliarity of learning and testing samples used. In this work, through looking upon the spectral estimation in a different way, it is attempted to enhance the accuracy of the spectral estimation procedures which is fulfilled by associating the spectral recovery process with spectral sensitivity variability present in both different human observers and RGB cameras. The work is split into two main sections, namely, theory and practice. In the first section, theory, the main idea of the thesis is examined through simulation, using different observers’ color matching functions (CMFs) obtained from Asano’s vision model and also different cameras’ spectral sensitivities obtained from an open database. The second part of the work is concerned with putting the major idea of the thesis into use and is comprised of three subsections itself. In the first subsection, real cameras and cellphones are used. In the second subsection, using weighted regression, the idea presented in this work, is extended to a series of studies in which spectra are estimated from their corresponding CIEXYZ tristimulus values. In the last subsection, obserevers’ colorimetric responses are simulated using color matching. Finally, it is shown that the methods presented in this work have a great potential to even rival multi-spectral cameras, whose equipment could be as expensive as a spectrophotometer

Evaluation and optimal design of spectral sensitivities for digital color imaging

The quality of an image captured by color imaging system primarily depends on three factors: sensor spectral sensitivity, illumination and scene. While illumination is very important to be known, the sensitivity characteristics is critical to the success of imaging applications, and is necessary to be optimally designed under practical constraints. The ultimate image quality is judged subjectively by human visual system. This dissertation addresses the evaluation and optimal design of spectral sensitivity functions for digital color imaging devices. Color imaging fundamentals and device characterization are discussed in the first place. For the evaluation of spectral sensitivity functions, this dissertation concentrates on the consideration of imaging noise characteristics. Both signal-independent and signal-dependent noises form an imaging noise model and noises will be propagated while signal is processed. A new colorimetric quality metric, unified measure of goodness (UMG), which addresses color accuracy and noise performance simultaneously, is introduced and compared with other available quality metrics. Through comparison, UMG is designated as a primary evaluation metric. On the optimal design of spectral sensitivity functions, three generic approaches, optimization through enumeration evaluation, optimization of parameterized functions, and optimization of additional channel, are analyzed in the case of the filter fabrication process is unknown. Otherwise a hierarchical design approach is introduced, which emphasizes the use of the primary metric but the initial optimization results are refined through the application of multiple secondary metrics. Finally the validity of UMG as a primary metric and the hierarchical approach are experimentally tested and verified

Parameric Decomposition for Evaluating Metamerism

In those industries in which materials are colored to close specifications, a means of evaluating the degree of metamerism of colored objects is of considerable importance. Based on Wyszecki\u27s hypothesis and its application to quantifying metamerism as described by Fairman, parameric decomposition is a technique to adjust one spectrum of a parameric match in order to achieve a perfect (metameric) match under a specific illumination and observer condition. This method can be viewed as batch correction using three colorants where the color-mixing model is linear in reflectance. The research in this thesis presented these methods using the basis functions from the CIE color-matching functions (CMFs) as well as alternative basis functions derived from dimensionality reduction techniques such as principal component analysis (PCA) and independent component analysis (ICA) for a pre-defined DuPont spectral dataset and Munsell dataset. 1,152 parameric pairs surrounding 24 color centers were synthesized using an automotive finish paint system and two-constant Kubelka-Munk turbid-media theory. Each parameric pair was corrected to a metameric pair using these various methods. The corrected spectra were compared with the formulated spectra using Kubelka-Munk theory to evaluate the parameric decomposition accuracy in terms of special and general metameric indices. The results showed that the estimated metameric indices from the CMFs-based process primaries presented relatively poor correlation to those from Kubelka-Munk theory. The process primaries from ICA for the Munsell IV dataset showed almost indentical performance in estimation of metameric indices to the process primaries from the PCA for Munsell dataset as well as those from ICA for the DuPont dataset. These three sets of process primaries showed slightly better performance in estimation of metameric indices than the process primaries from PCA for the DuPont dataset