Study of chromatic adaptation using memory color matches, Part II: colored illuminants

Adaptation under dichromatic illumination

Over the years, many CATs (chromatic adaptation transforms) have been developed, such as CMCCAT97, CAT02 and CAT16, to predict the corresponding colors under different illuminants. These CATs were derived from uniform simple stimuli surrounded by a uniform background with a single illuminant. Although some mixed adaptation models have been proposed in literature to predict the adaptation under more than one illuminant, these models are typically limited to a certain scene and exclude the impact of spatial complexity. To investigate chromatic adaptation under more complex conditions, an achromatic matching experiment was conducted with (simultaneously) spatially dichromatic illumination for three illumination color pairs and various spatial configurations. Spatial configuration was found to have an impact on both the degree of adaptation and the equivalent illuminant chromaticity, which is the chromaticity of a single uniform adapting illumination that results in the same corresponding colors as for the dichromatic lighting condition. A preliminary CAT model is proposed that considers the spatial and colorimetric complexity of the illumination.

Effect of surrounding objects in the adapting scene on chromatic adaptation

Most of the existing chromatic adaptation transforms (CATs) were developed for flat uniform stimuli presented in a uniform background, which substantially simplifies the complexity of the real scene by excluding surrounding objects from the viewing field. The impact of the background complexity, in terms of the spatial properties of the objects surrounding the stimulus, on chromatic adaptation is ignored in most CATs. This study systematically investigated how the background complexity and color distribution affect the adaptation state. Achromatic matching experiments were conducted in an immersive lighting booth, with the illumination varying in chromaticity and the adapting scene varying in surrounding objects. Results show that compared to the uniform adapting field, increasing the scene complexity can significantly improve the degree of adaptation for the Planckian illuminations with low CCT levels. In addition, the achromatic matching points are substantially biased by the color of the surrounding object, implying the interactive effect of the illumination color and the dominant scene color on the adapting white point.

Predictive performance of the standard and the modified von Kries chromatic adaptation transforms

To investigate chromatic adaptation and develop chromatic adaptation transforms (CATs), many psychophysical experiments have been conducted to collect corresponding colors (CC) under various illumination conditions. Most modern CATs have been developed based on a database of CC sets collected in the 20^th century. More recently, several additional CC sets have been collected by Smet et al., Wei et al., and Ma et al. using memory color matching or achromatic matching methods. The analysis of these CC data indicates that for yellowish (located on or close to the Planckian locus) and greenish illuminations, the short-wave (S) sensitive cones show a lower degree of adaptation compared to the long-wave (L) and medium-wave (M) sensitive cones. This can result in a large prediction error of the standard von Kries CAT, which adopts a single degree of adaptation value for all three cone types. A modified von Kries CAT is proposed that accounts for these differences between the L-, M- and S-cone signals by applying a compression to the rescaling factor for the S-cones. It outperforms the standard von Kries CAT for the Breneman-C, Smet, Wei, and Ma data, while for other data sources the two CATs have similar performance.

Memory colors and the assessment of color quality in lighting applications

Due to their potential use as an internal reference, memory colors may provide an excellent approach for the color rendition evaluation of white light sources in terms of predicting visual appreciation. Because of certain limitations in the design of existing memory-related color quality measures, a new metric based on the outcome of a series of recently conducted memory color appearance rating experiments is proposed in this work. In order to validate its predictive performance, a meta-correlation analysis on a comprehensive set of preference rating data collected from literature is performed. Results indicate that the new metric proposal outperforms established color quality measures and is capable of correctly predicting the rank order of light sources in different lighting scenarios. The future inclusion of this new metric into a comprehensive lighting quality model may serve as a valuable tool for the lighting designer to create optimally lit environments for humans that do not only support the visual task fulfillment but also increase the users' well-being and emotional comfort by rendering the perceived space in such a way that it complies with the people's inherent memory references.

ZCAM, a colour appearance model based on a high dynamic range uniform colour space

A colour appearance model based on a uniform colour space is proposed. The proposed colour appearance model, ZCAM, comprises of comparatively simple mathematical equations, and plausibly agrees with the psychophysical phenomenon of colour appearance perception. ZCAM consists of ten colour appearance attributes including brightness, lightness, colourfulness, chroma, hue angle, hue composition, saturation, vividness, blackness, and whiteness. Despite its relatively simpler mathematical structure, ZCAM performed at least similar to the CIE standard colour appearance model CIECAM02 and its revision, CAM16, in predicting a range of reliable experimental data.

Color reproduction method based on neural networks and visual matching

A new method is proposed to build up a color reproduction model based on neural networks and visual matching. Training data were obtained from a visual matching experiment and the commonly used CIECAM02 model to set up a link between the device parameters and human color perception. The process of training neural networks is presented. An experiment simulating an office environment was conducted, together with a user study, to verify the performance of the proposed method in identifying and reproducing color.

Effect of adapting field size on chromatic adaptation

The human visual system adapts to changes in white tone of the illumination to maintain approximately the same object color appearance. Chromatic adaptation transforms (CAT) were developed to predict corresponding colors, which are colors that look the same under a wide range of illuminants. However, existing CATs fail to accurately predict corresponding colors, particularly under colored illumination, because of an inaccurate estimation of the degree of adaptation. In this study, the impact of the adapting field size on the degree of adaptation was investigated. A memory color matching experiment was conducted, in a real scene, with the background adapting field varying in the field of view, luminance and chromaticity to provide data for the development of a more comprehensive CAT. Results show that a larger field of view leads to a more complete adaptation, despite a much lower background luminance.

Impact of the starting point chromaticity on memory color matching accuracy

In this study, the impact of starting point chromaticity and number of observers on memory color matching results was investigated. Matching data were obtained for 3 objects (neutral grey cube, yellow lemon and green apple) under a neutral white and a yellow background illumination. Memory color matchings were made for ten starting points of which eight chromaticities were symmetrically distributed along the hue circle and centered at the equal energy white (EEW) chromaticity of the neutral white background illumination; one starting point at the EEW chromaticity and one with the same chromaticity as the background. The matching track from starting point to the memory matched chromaticity was also recorded. It did not tend to cross over the central region towards the complementary hue, especially for experienced observers. The results also demonstrated a significant starting point bias, whereby the matched chromaticities were biased towards the chromaticity of the starting point. Starting point bias can be minimized by selecting three starting points symmetrically distributed around the expected memory color chromaticity. Furthermore, at least, ten observers are needed to achieve stable results for the grey cube and yellow lemon. For the green apple, the results are less conclusive and around 40 observers would be needed to obtain a stable average estimate for the chromaticity of the memory color. The large inter-observer variation may result from cultural differences or from natural variations in the "green" apple appearance. This study provides a well-founded guidance for future application of the memory color matching method.

Improving color constancy by selecting suitable set of training images

With very simple implementation, regression-based color constancy (CC) methods have recently obtained very competitive performance by applying a correction matrix to the results of some low level-based CC algorithms. However, most regression-based methods, e.g., Corrected Moment (CM), apply a same correction matrix to all the test images. Considering that the captured image color is usually determined by various factors (e.g., illuminant and surface reflectance), it is obviously not reasonable enough to apply a same correction to different test images without considering the intrinsic difference among images. In this work, we first mathematically analyze the key factors that may influence the performance of regression-based CC, and then we design principled rules to automatically select the suitable training images to learn an optimal correction matrix for each test image. With this strategy, the original regression-based CC (e.g., CM) is clearly improved to obtain more competitive performance on four widely used benchmark datasets. We also show that although this work focuses on improving the regression-based CM method, a noteworthy aspect of the proposed automatic training data selection strategy is its applicability to several representative regression-based approaches for the color constancy problem.

Methods for Assessing Quantity and Quality of Illumination

Human vision provides useful information about the shape and color of the objects around us. It works well in many, but not all, lighting conditions. Since the advent of human-made light sources, it has been important to understand how illumination affects vision quality, but this has been surprisingly difficult. The widespread introduction of solid-state light emitters has increased the urgency of this problem. Experts still debate how lighting can best enable high-quality vision-a key issue since about one-fifth of global electrical power production is used to make light. Photometry, the measurement of the visual quantity of light, is well established, yet significant uncertainties remain. Colorimetry, the measurement of color, has achieved good reproducibility, but researchers still struggle to understand how illumination can best enable high-quality color vision. Fortunately, in recent years, considerable progress has been made. Here, we summarize the current understanding and discuss key areas for future study.

Exploring the applicability of the CAM18sl brightness prediction

Recently, a color appearance model, CAM18sl, has been published. The model can predict the color perception (hue, colorfulness, brightness) of self-luminous stimuli seen against neutral backgrounds varying in luminance. In this paper, the applicability of CAM18sl to situations different from the one for which it was originally developed is explored. The brightness perception predicted by this model (expressed in "bright") is compared with the outcome of the CIE Unified Glare Rating (UGR) for luminaires, the limiting luminance values for Variable Message Signs as indicated in the European standard for outdoor situations, the visual gloss (VG) formula to characterize the gloss of objects, the CIE grey-scale calculation for self-luminous devices, and the predicted brightness value of illuminated objects according to CIECAM02. Although the application domains mentioned above are very distinct, it appears that the predicted brightness perception of the CAM18sl model correlates well with the outcome of the particular and dedicated metrics. It seems that, for brightness perception, CAM18sl can be considered as a general model applicable for a wide range of applications. This could lead to a reduction of a number of application-specific metrics.

Color appearance model for self-luminous stimuli

A model for brightness and hue perception of self-luminous stimuli surrounded by a self-luminous achromatic background has been developed based on a series of visual experiments. In the model, only the absolute spectral radiance values of the stimulus and background are used as input. Normalized cone excitations are calculated using the 10° Commission Internationale de l'Éclairage (CIE) 2006 cone fundamentals. A von Kries chromatic adaptation transform applied in the CIE 2006 cone space is adopted, and luminance compression and adaptation due to the self-luminous background are included by using a Michaelis-Menten function. Model parameters are determined by fitting the model to the experimental visual data obtained for brightness, hue, and the amount of color versus neutral. The model is validated with additional experimental data. An absolute brightness scale expressed in "bright" is proposed.

Study of chromatic adaptation via neutral white matches on different viewing media

Two experiments were carried out to study the neutral white and the chromatic adaptation in human vision and color science. After matching neutral whites under different illuminants using both surface and self-luminous colors, the result were used to verify the previous study about the chromatic adaptation. Not all the white illuminants were found neutral even the adaptation time is long. The baseline illuminant of the two-step chromatic adaptation transform was found as the illuminant with the same chromaticity of the neutral white under it and depended on viewing medium in the present study. The results were also used as corresponding colors to derive models of the effective degree of chromatic adaptation, which were found highly associated with the chromaticity of the adapting illuminant.

White appearance of a tablet display under different ambient lighting conditions

In comparison to the great efforts made on the enhancement of image quality for tablet displays, little attention has been paid on the concept of white point. Given the increasing popularity of the light sources with chromaticities off the Planckian locus and color-tunable LED lighting, it is important to investigate human's white perception of tablet display under different ambient lighting conditions. This study investigated the white appearance of a tablet display under 17 ambient lighting conditions, including a dark condition, seven conditions with chromaticities on the Planckian locus, and nine conditions with chromaticities off the Planckian locus, (i.e., D_uv = + 0.02, -0.02, and -0.04). It was found that both the white appearance boundary defined by the fitted one-standard-deviation error ellipse and the whitest stimulus rated by the observers or identified by the bivariate Gaussian distribution were different under the various ambient lighting conditions. The optimization based on the whitest stimulus under each ambient lighting condition suggested a lower degree of chromatic adaptation under the conditions with a lower Correlated Color Temperature (CCT). For the conditions with a same CCT, a D_uv of -0.02 was found to provide a higher degree of chromatic adaptation than D_uv values of + 0.02 and -0.04.