Colour Quality Evaluation of Bluish-Green Serpentinite Based on the CIECAM16 Model

The CIECAM16 colour appearance model is currently a model with high prediction accuracy. It can solve the problem of predicting the influence of different observation conditions on the colour of gemstones. In this study, a computer vision system (CVS) was used to measure the colour of 59 bluish-green serpentinite samples, and the tristimulus values were input into the CIECAM16 forward model to calculate the colour appearance parameters of serpentinite under different surrounds, illuminances, and light sources. It was found that the darkening of the surround causes the lightness and brightness to increase. Pearson’s r of brightness and colourfulness with illuminance is 0.885 and 0.332, respectively, which predicts the Stevens and Hunt effects. When the light source changes from D65 to A, the calculated hue angle shifts to the complementary area of the A light source, which is contrary to the CVS measurement result. The D65 light source is more suitable for the colour presentation and classification of bluish-green serpentinite.

DEGREE OF EQUIVALENCE OF TRISTIMULUS VALUES OF LEDS UNDER CONSIDERATION OF MEASUREMENT UNCERTAINTY AND CORRELATION

There is only little guidance available for the evaluation of the degree of equivalence for higher dimensional measurands, especially in respect to tristimulus values. Currently, the tristimulus values are usually treated as one-dimensional measurand in order to simplify the degree of equivalence evaluation and be able to use the well-known procedures and statistics known from one-dimensional measurands. As the tristimulus values are clearly a high-dimensional measurand, they should be treated in that way. In the following, a procedure for the degree of equivalence evaluation for high-dimensional measurands is presented where the Monte Carlo method is used to support a clear understanding of the procedure. In addition to the procedure, the limitations of this method are discussed in regard to tristimulus values.

Designing a Color Filter with High Overall Transmittance for Improving the Color Accuracy of Digital Cameras

Previously improved color accuracy of a given digital camera was achieved by carefully designing the spectral transmittance of a color filter to be placed in front of the camera. Specifically, the filter is designed in a way that the spectral sensitivities of the camera after filtering are approximately linearly related to the color matching functions (or tristimulus values) of the human visual system. To avoid filters that absorbed too much light, the optimization could incorporate a minimum per wavelength transmittance constraint. In this paper, we change the optimization so that the overall filter transmittance is bounded, i.e. we solve for the filter that (for a uniform white light) transmits (say) 50% of the light. Experiments demonstrate that these filters continue to solve the color correction problem (they make cameras much more colorimetric). Significantly, the optimal filters by restraining the average transmittance can deliver a further 10% improvement in terms of color accuracy compared to the prior art of bounding the low transmittance.

Methods to improve colour mismatch between displays

With the rapid development of display technology, the colour mismatch of the colours having same tristimulus values between devices is an urgent problem to be solved. This is related to the wellknown problem of observer metamerism, caused by the spectral power distribution (SPD) of primaries and the difference between individual observers' and the standard CIE colour matching functions. An experiment was carried out for 20 observers to perform colour matching of colour stimuli with a field-of-view of 4° between 5 displays, including two LCD and two OLED, against a reference LCD display. The results were used to derive a matrixbased colour correction method. The method was derived from colorimetric visually matched colorimetric data. Furthermore, different colour matching functions were evaluated to predict the degree of observer metamerism. The results showed that the correction method gave satisfactory results. Finally, it was found that the use of 2006 2° colour matching function outperformed 1931 2° CMFs with a large margin, most marked between an OLED and an LCD display.

A study of neural network-based LCD display characterization

In this paper we study up to what extent neural networks can be used to accurately characterize LCD displays. Using a programmable colorimeter we have taken extensive measures for a DELL Ultrasharp UP2516D to define training and testing data sets that are used, in turn, to train and validate two neural networks: one of them using tristimulus values, XYZ, as inputs and the other one color coordinates, xyY . Both networks have the same layer structure which has been experimentally determined. The errors from both models, in terms of ΔE00 color difference, are analysed from a colorimetric point of view and interpreted in order to understand how both networks have learned and how is their performance in comparison with other classical models. As we will see, the comparison is in average in favor of the proposed models but it is not better in all cases and regions of the color space.

Methods for improving the accuracy of CIE tristimulus values of object color by calculation Part II: Improvement on measurement wavelength ranges

The previous paper (part I) analyzed test errors of the spectrophotometer and their reasons, then systematically investigated the algorithms to reduce measuring bandpass error and intervals error. This paper (part II) focuses on the influence of measurement wavelength ranges and their truncation errors, and some algorithms to overcome the truncation errors. CIE recommends that tristimulus values are calculated over a range of 360–830 nm. However, most spectrophotometers do not meet it. The reduction of measurement range will result in a measurement range error or a truncation error. In this study, five ranges commonly employed in practice are selected for investigating the truncation errors, and three extrapolation methods are used to extend the data to compensate for the measurement range loss. Results are obtained by employing 1301 Munsell color chips under illuminant D65 and CIE 1964 standard observer. For the standard 1-nm intervals, the narrower the range, the larger the truncation error. For the usual-measured 10-nm intervals, bandpass error and intervals error should be handled at the same time, 380–780 nm Table LWL gives the highest accurate outcomes, which even improve the accuracy of the range 360–750 nm to an acceptable level. Whereas, ranges of 360–700 nm and 400–700 nm still need extrapolation to reduce their truncation errors even with Table LWL. Three extrapolation methods of nearest, linear and second-order all reduce the truncation error, but for different ranges, algorithms and illuminants, the optimal method of extrapolation varies.

BRDF rendering by interpolation of optimised model parameters

In this paper, we discuss an interpolation method which can be used to create a look up table to map tristimulus values to BRDF parameters. For a given tristimulus value, we interpolate the XYZ lattice formed by eight primaries and secondaries that were printed and measured, and their corresponding optimised BRDF parameters. The BRDF parameters are obtained by careful optimisation of the Ward model and Cook Torrance model with the BRDF measurements of these primaries. The interpolated BRDF parameters of nine test samples from the same printed samples were then evaluated against the optimised BRDF parameters and their reference BRDF measurements. The results show that, this simple and efficient interpolation method produces consistent BRDF parameters that preserves the diffuse colour of the input sample.

Perfect appearance match between self-luminous and surface colors can be performed with isomeric spectra

Surface color results from a reflected light bounced off a material, such as a paper. By contrast, self-luminous color results directly from an emitting light, such as a Liquid Crystal (LC) display. These are completely different mechanisms, and thus, surface color and self-luminous color cannot be matched even though both have identical tristimulus values. In fact, previous research has reported that metameric color matching fails among diverse media. However, the reason for this failure remains unclear. In the present study, we created isomeric color-matching pairs between self-luminous and surface colors by modulating the spectral distribution of the light for surface colors. Then, we experimentally verified whether such color matching can be performed. The results show that isomeric color matching between self-luminous and surface colors can be performed for all participants. However, metameric color matching fails for most participants, indicating that differences in the spectral distributions rather than the different color-generating mechanisms themselves are the reason for the color matching failure between different devices. We experimentally demonstrated that there is no essential problem in cross-media color matching by generating isomeric pairs. Our results can be considered to be of great significance not only for color science, but also for the color industry.

The Ever-Changing Work That is Digital Preservation

Conventional color imaging has three channels—R, G, and B. In multispectral imaging within the visible spectrum, the number of channels increases in order to improve color accuracy and estimate spectral reflectance factor. Image quality criteria important in multispectral imaging include colorimetric accuracy, sharpness, registration, and low noise. The color transformation matrix, connecting camera signals with CIE tristimulus values, affects color accuracy and the visibility of image noise and misregistration when the multiple channels are combined to a colormanaged image. When the final goal is a color-accurate image for one set of illuminating and viewing conditions, the color transformation is often derived directly using nonlinear optimization minimizing the average color difference between spectrophotometer- and camera-based colorimetric coordinates. Optimization requires starting values and least squares minimizing spectral or tristimulus RMS error is typically used. Although it is effective for achieving convergence, the optimized matrix can result in a large reduction in image quality caused by noise propagation via the color transformation matrix. These concepts are reviewed.