Performance of a Chromatic Adaptation Transform Based on Spectral Sharpening

The Bradford chromatic adaptation transform, empirically derived by Lam, models illumination change. Specifically, it pro-vides a means of mapping XYZs under a reference light source to XYZs for a target light source such that the corresponding XYZs produce the same perceived color. One implication of the Bradford chromatic adaptation transform is that color correction for illumination takes place not in cone space but rather in a ‘narrowed’ cone space. The Bradford sensors have their sensitivity more narrowly concentrated than the cones. However, Bradford sensors are not optimally narrow. Indeed, recent work has shown that it is possible to sharpen sensors to a much greater extent than Bradford. The focus of this paper is comparing the perceptual error between actual appearance and predicted appearance of a color under different illuminants, since it is perceptual error that the Bradford transform minimizes. Lam’s original experiments are revisited and perceptual per-formance of the Bradford transform and linearized Bradford transform is compared with that of a new adaptation transform that is based on sharp sensors. Perceptual errors in CIELAB delta E, delta ECIE94, and delta ECMC(1:1) are calculated for several corresponding color data sets and analyzed for their statistical significance. The results are found to be similar for the two transforms, with Bradford performing slightly better depending on the data set and color difference metric used. The sharp transform performs equally well as the linearized Bradford transform: there is no statistically significant difference in performance for most data sets

A Matrix Based Approach for Color Transformations in Reflections

In this thesis, I demonstrate the feasibility of linear regression with 4 × 4 matrices to perform color transformations, specifically looking at the case of color transformations in reflections. I compare and analyze the power and performance linear regression models based on 3 × 3 and 4 × 4 matrices. I conclude that using 4 × 4 matrices in linear regression is more advantageous in power and performance over using 3 × 3 matrices in linear regressions, as 4 × 4 matrices allow for categorically more transformations by including the possibility of translation. This provides more general affine transformations to a color space, rather than being restricted to passing through the origin. I examine the benefits of allowing for negative elements in color transformation matrices. I also touch on the possible differences in application between filled 4 × 4 matrices and diagonal 4 × 4 matrices, and discuss the limitations inherent to linear regression used in any type of matrix operations

The CIECAM02 color appearance model

The CIE Technical Committee 8-01, color appearance models for color management applications, has recently proposed a single set of revisions to the CIECAM97s color appearance model. This new model, called CIECAM02, is based on CIECAM97s but includes many revisions1-4 and some simplifications. A partial list of revisions includes a linear chromatic adaptation transform, a new non-linear response compression function and modifications to the calculations for the perceptual attribute correlates. The format of this paper is an annotated description of the forward equations for the model

A Matrix Based Approach for Color Transformations in Reflections

In this thesis, I demonstrate the feasibility of linear regression with 4 × 4 matrices to perform color transformations, specifically looking at the case of color transformations in reflections. I compare and analyze the power and performance linear regression models based on 3 × 3 and 4 × 4 matrices. I conclude that using 4 × 4 matrices in linear regression is more advantageous in power and performance over using 3 × 3 matrices in linear regressions, as 4 × 4 matrices allow for categorically more transformations by including the possibility of translation. This provides more general affine transformations to a color space, rather than being restricted to passing through the origin. I examine the benefits of allowing for negative elements in color transformation matrices. I also touch on the possible differences in application between filled 4 × 4 matrices and diagonal 4 × 4 matrices, and discuss the limitations inherent to linear regression used in any type of matrix operations

Color Ratios and Chromatic Adaptation

In this paper, the performance of chromatic adaptation transforms based on stable color ratios is investigated.It was found that for three different sets of reflectance data, their performance was not statistically different from CMCCAT2000,when applying the chromatic adaptation transforms to Lam’s corresponding color data set and using a perceptual error metric of CIE Delta E94.The sensors with the best color ratio stability are much sharper and more de-correlated than the CMCCAT2000 sensors, corresponding better to sensor responses found in other psychovisual studies.The new sensors also closely match those used by the sharp adaptation transform

Optimization for Hue Constant RGB Sensors

We present an optimization technique to find hue constant RGB sensors. The hue representation is based on a log RGB opponent color space that is invariant to brightness and gamma. While modeling the visual response did not derive the opponent space, the hue definition is similar to the ones found in CIE Lab and IPT. Finding hue constant RGB sensors through this optimization might be applicable in color engineering applications such as finding RGB sensors for color image encodings

Spherical Sampling and Color Transformations

In this paper, we present a spherical sampling technique that can be employed to find optimal sensors for trichromatic color applications. The advantage over other optimization techniques is that it assures a global minimum is found, and that not only one, but a set of solutions is retained if so desired. The sampling technique is used to find all possible RGB sensors that exhibit favorable chromatic adaptation transform (CAT) behavior when tested on Lam’s corresponding color data set, subject to a CIE Delta E94 error criterion. We found that there are a number of sensors that meet the criterion, and that the Bradford, Sharp, and CMCCAT2000 sensors are not unique

On Adaptive Non-Linarity for Color Discrimination and Chromatic Adaptation

Assuming that the photoreceptor response of the human visual system is adaptive and non-linear, we can derive mathematical properties that can account for both color discrimination and chromatic adaptation. This could be due to the photoreceptors’ response to illumination, which is non-linear and varies according to the adaptation state. Assuming the Naka-Rushton nonlinear function and an automatic gain control function, we can derive color discrimination and chromatic adaptation data. We extend the discussion to a three layer model of retinal color processing, and show how we could predict corresponding color data

Chromatic adaptation performance of different RGB sensors

Chromatic adaptation transforms are used in imaging system to map image appearance to colorimetry under different illumination sources. In this paper, the performance of different chromatic adaptation transforms (CAT) is compared with the performance of transforms based on RGB primaries that have been investigated in relation to standard color spaces for digital still camera characterization and image interchange. The chromatic adaptation transforms studied are von Kries, Bradford, Sharp, and CMCCAT2000. The RGB primaries investigated are ROMM, ITU-R BT.709, and 'prime wavelength' RGB. The chromatic adaptation model used is a von Kries model that linearly scales post-adaptation cone response with illuminant dependent coefficients. The transforms were evaluated using 16 sets of corresponding color dat. The actual and predicted tristimulus values were converted to CIELAB, and three different error prediction metrics, (Delta) ELab, (Delta) ECIE94, and (Delta) ECMC(1:1) were applied to the results. One-tail Student-t tests for matched pairs were calculated to compare if the variations in errors are statistically significant. For the given corresponding color data sets, the traditional chromatic adaptation transforms, Sharp CAT and CMCCAT2000, performed best. However, some transforms based on RGB primaries also exhibit good chromatic adaptation behavior, leading to the conclusion that white-point independent RGB spaces for image encoding can be defined. This conclusion holds only if the linear von Kries model is considered adequate to predict chromatic adaptation behavior

Extending minkowski norm illuminant estimation

The ability to obtain colour images invariant to changes of illumination is called colour constancy. An algorithm for colour constancy takes sensor responses - digital images - as input, estimates the ambient light and returns a corrected image in which the illuminant influence over the colours has been removed. In this thesis we investigate the step of illuminant estimation for colour constancy and aim to extend the state of the art in this field. We first revisit the Minkowski Family Norm framework for illuminant estimation. Because, of all the simple statistical approaches, it is the most general formulation and, crucially, delivers the best results. This thesis makes four technical contributions. First, we reformulate the Minkowski approach to provide better estimation when a constraint on illumination is employed. Second, we show how the method can (by orders of magnitude) be implemented to run much faster than previous algorithms. Third, we show how a simple edge based variant delivers improved estimation compared with the state of the art across many datasets. In contradistinction to the prior state of the art our definition of edges is fixed (a simple combination of first and second derivatives) i.e. we do not tune our algorithm to particular image datasets. This performance is further improved by incorporating a gamut constraint on surface colour -our 4th contribution. The thesis finishes by considering our approach in the context of a recent OSA competition run to benchmark computational algorithms operating on physiologically relevant cone based input data. Here we find that Constrained Minkowski Norms operi ii ating on spectrally sharpened cone sensors (linear combinations of the cones that behave more like camera sensors) supports competition leading illuminant estimation