ABSTRACT Background Vision science has established precedent for examining unusual visual experiences, including hypnosis‐induced color perception, synesthetic phenomena, and meditative states. Such cases can provide insights into post‐retinal visual processing mechanisms. Methods This study presents a phenomenological analysis of visual experiences reported in the writings of Chiara Lubich (1920–2008) during mystical experiences in 1949. A taxonomy of visual events was developed to categorize 198 paragraphs of her account, distinguishing between experiences attributed to “physical eyes” versus “eyes of the soul,” metaphorical imagery, and future expectations. Temporal patterns of visual adaptation were reconstructed from her descriptions and compared with known mechanisms of biological vision. Results The account covers 9 days (16–24 July 1949) and reveals an adaptation process extending over approximately 8 days, characterized by: (1) initial perception of overwhelming brightness without accompanying discomfort; (2) gradual emergence of spatial detail and scale discrimination while maintaining predominantly monochromatic (gold/flame) perception; (3) sudden onset of full polychromatic vision on day 9; and (4) reported co‐existence of sequential perceptual states. This progression differs markedly from biological light adaptation in both timescale (days vs. minutes) and sequence (decoupled brightness and color adaptation). Conclusions The reported experiences suggest visual adaptation proceeding through more discrete stages than observed in typical oculocerebral vision, with characteristics partially resembling infant visual development. These observations generate testable hypotheses about post‐retinal adaptation mechanisms and the potential effects of direct stimulation of later stages in the visual pathway.
ABSTRACT Pseudo‐isochromatic plates (PIPs) are widely used for screening color vision deficiency (CVD), yet their fixed design limits both diagnostic sensitivity and adaptability to individual perceptual thresholds. This work presents an interactive, digitally rendered PIP system in which seven spatial and chromatic parameters can be systematically varied to modulate task difficulty. Stimuli were generated along three evenly spaced confusion lines for protanopia, deuteranopia, and tritanopia, derived from updated copunctal points following Smith–Pokorny fundamentals. Eighty observers completed an adaptive test procedure, and performance was analyzed using chi‐square tests, generalized linear modeling, and response‐time measures. Four parameters—Noise, Color Noise, Dot Size, and Relative Luminance Level—demonstrated significant influence on correct identification rates, whereas Border, Background, and Dot Shape produced negligible effects within the tested ranges. A subgroup of eight observers exhibited performance patterns consistent with CVD, showing reduced accuracy and increased response times, particularly across specific confusion‐line positions. Motif‐related errors showed substantial shape‐driven confusions, independent of chromaticity. The results demonstrate that dynamic modulation of PIP parameters provides a controlled framework for testing chromatic discrimination and offers a practical foundation for more precise, adaptive, and perceptually informed color vision assessments.
ABSTRACT Maintaining color consistency across different illuminants is a long‐standing problem in digital imaging. In this context, color consistency refers to a system's ability to map a given surface to the same output color regardless of the capture illuminant. Residual color errors can persist under spectrally complex illumination. RGB inputs provide limited spectral information to separate illuminants with similar appearance but different spectra. In this study, we propose a three‐stage white balance correction framework that combines multispectral imaging (MSI) with deep learning. The framework integrates illuminant classification, class‐specific spectral regression, and an imaging pipeline informed by the predicted spectral power distribution (SPD). To address class imbalance, class‐weighted learning is employed during classification, and both shared and class‐specific architectures are evaluated for SPD prediction. The proposed method is compared against two baselines: direct SPD regression without classification and uncorrected RGB images converted to sRGB. Experiments on real‐world data show that the classification‐guided framework significantly reduces color error relative to the spectroradiometric reference, with decreasing from 14.1 to 4.6 under LED lighting and from 4.9 to 1.5 under fluorescent conditions. The model also achieves improved stability (lower variance) and spectral prediction accuracy using class‐specific architectures. While limitations remain, such as dataset imbalance and the lack of value‐level augmentation, these results demonstrate the feasibility of MSI‐based white balance correction and underscore its potential to enhance perceptual color fidelity in consumer imaging.
ABSTRACT In this study, a spectral ink trapping evaluation approach based on ΔE ab color difference between ideal and real colors was developed and systematically compared with the conventional Preucil, Ritz, and Brunner trapping formulas. The results indicate that the calculated (ideal) reflectance spectra show excellent agreement with the measured (real) data. Statistical parameters such as the root mean square error (RMSE) and standard deviation (Std. Dev.) demonstrate that the proposed model provides accurate predictions across different ink volume ratios and printing sequences. The goodness‐of‐fit coefficient (GFC) values are extremely high (mostly > 0.99), confirming that the calculated spectral shapes closely match those measured by the spectrophotometer. The results showed strong inverse linear correlations between ΔE ab and conventional ink trapping values for most overprint systems. The obtained results demonstrate the higher sensitivity of the spectral ink trapping approach based on ΔE ab compared with the conventional Preucil, Ritz, and Brunner formulas. The proposed spectral trapping method demonstrates promising potential for quality control, process monitoring, and color reproduction in offset printing.
ABSTRACT In the ICC v4 color management architecture, the gamut mapping algorithm from the Perceptual Reference Medium Gamut (PRMG) to the output medium gamut has not been standardized, resulting in insufficient reproduction performance of the perceptual rendering intent. This paper proposes a gamut mapping algorithm based on the PRMG CIELAB grid. The algorithm constructs the source gamut boundary using the CIELAB grid method and describes the gamut surface via face/vertex encoding. Furthermore, it innovatively adopts cognate tetrahedral mapping to realize the mapping from the PRMG to the output gamut while maintaining consistency with the media‐relative colorimetric intent. In the experiments, the widely used FOGRA39 standard printing characterization data set is employed as the output medium gamut, and the proposed algorithm is compared with existing algorithms such as HPMINDE and SGCK through psychophysical experiments. The psychophysical experiment demonstrates that the proposed algorithm provides more visually pleasing reproductions. Objective evaluation based on the CIEDE2000 metric further shows that the proposed method achieves a favorable balance between color fidelity and perceptual image quality. with favorable pleasantness particularly in the highlight and shadow regions of images. This work provides a feasible solution for the implementation of the perceptual rendering intent in ICC v4 output profiles.
ABSTRACT Electric two‐wheelers are a very popular type of personal transportation solution in China due to their convenience, cost‐effectiveness, and low environmental impact. Despite the increasingly fierce market competition, most manufacturers place greater emphasis on technological improvements and design of the product shape, while giving insufficient attention to the color design of the products. This study established a comprehensive method framework integrating color analysis, eye‐tracking, and semantic differential hedonic evaluation, and systematically investigated users' preferences for the colors of electric two‐wheelers. This study focuses on the plastic body components of electric two‐wheelers and adopts a multistage progressive analytical framework. First, a large collection of product images is gathered through data mining, and a quantitative color analysis is conducted. Based on this, color space conversion and feature calculation are performed using Python and OpenCV to identify the color design patterns of existing products. Representative product colors are then extracted based on color clustering patterns to serve as stimuli for an eye‐tracking experiment. Eye‐tracking is employed to investigate participants' unconscious gaze patterns and color preferences. Meanwhile, subjective evaluations of product colors are collected through questionnaires combined with the semantic differential method. A cross‐analysis of objective and subjective data is performed to explore users' preferences for product colors and the underlying psychological factors, revealing the characteristics and intrinsic mechanisms of user color preferences for electric two‐wheelers. A pronounced visual preference is observed for high‐luminance, low‐saturation tones (white, cyan, and light yellow), whereas medium‐luminance, high‐chroma colors (red, blue, and pure black) elicit moderate visual attention. Conversely, low‐luminance achromatic shades (mid‐gray and dark‐gray) demonstrate minimal visual appeal. This study quantitatively establishes consumer color preferences for electric two‐wheelers through eye‐tracking technology, providing empirically validated guidance for future color design selection in the electric vehicle industry.
ABSTRACT Research on Estonian, Turkish, and Swedish color idioms suggests a prevalence of similarities over differences in the figurative usage of colors in these languages. Previous studies have looked at data collected from dictionaries and web corpora. Grounded in conceptual metaphor theory, the current study extends these findings through an empirical approach, employing a free‐listing task where native speakers of Estonian, Swedish, and Turkish were asked to list color idioms in their language. This empirical approach provides nuanced insights into speakers' mental representations of the world. Directly engaging native speakers helps capture idioms that might be absent from dictionaries, revealing their cognitive salience. The results showed that Estonian and Swedish color idioms share significantly more similarities with each other than either does with Turkish. The finding reflects that while the human experience is largely universal, the cultural experience can be distinct and varied.
ABSTRACT Emotions are internal states that can be difficult to describe verbally. Worldwide, emotions are frequently conveyed, either metonymically or metaphorically, by color terms, although differences in color‐emotion associations are observed cross‐ linguistically and cross‐culturally. In this paper, we adopted a corpus‐based approach to this issue and searched for collocations in Spanish versus Chinese of 13 color terms (the basic color terms in Spanish) and the 20 basic emotion terms (from the Geneve Emotion Wheel). For Spanish, we extracted 1609 collocations from the CREA corpus; for Chinese, we extracted 107 765 collocations from the CCL corpus. Both individual and global cross‐linguistic differences in color‐emotion collocation patterns were uncovered using Generalized Linear Mixed Model (GLMM) analyses and Pearson correlation analyses (PCA), respectively. Analyses revealed significant differences, more quantitative than qualitative, in the distributional frequencies of color‐emotion collocations between the two languages, with only black and pink exhibiting similar patterns according to PCA. The color‐emotion associations identified by this corpus approach resemble findings by research conducted with human participants and Large Language Models. Overall, cross‐linguistic similarities seem to reflect common bodily experiences and cognitive processes involved in color‐emotion association, whereas dissimilarities appear to be caused by differences in the cultural contexts, linguistic conventions, and social norms.
ABSTRACT From Paleolithic cave art to modern abstraction, artists have used black not merely as a neutral tone, but as a powerful perceptual tool. Among the earliest paintings, simple black outlines on cave walls prefigured a long tradition in which black provided structure, contrast, and expressive force. Impressionists and Post‐Impressionists took divergent approaches: Renoir championed black as the “Queen of Colors” for its ability to intensify adjacent hues, while Divisionists such as Pissarro avoided it to encourage optical mixing. Van Gogh used black contours to define and energize forms by enhancing hue and saturation, and Malevich's Black Square made achromatic contrast itself the subject of painting. These artistic choices can be understood through contemporary vision science. Black is not the absence of light, but the result of active neural contrast mechanisms that require preceding or surrounding illumination. Black and white form a unique achromatic axis, behave differently from chromatic colors, and are processed through parallel channels in the visual system. Dark borders—whether painted contours or physical frames surrounding an entire canvas—shape color appearance by preventing color spreading and isolating the artwork from its environment. By integrating art history with vision science, we show that Impressionist and Post‐Impressionist artists have harnessed the same principles that vision research has described psychophysically.
ABSTRACT This study quantitatively investigates color as a visual language of power in the Joseon royal procession by integrating data‐driven analysis with traditional East Asian color philosophy. Focusing on the Yin–Yang and Five‐Element system, we analyzed Munsell data from 1299 participants aggregated into 241 group units. We examined chromatic distributions, radial structures, and transition sequences to reveal how metaphysical symbolism manifested in ceremonial space. Results indicate a structured chromatic hierarchy: red, symbolizing supreme authority, is concentrated at the center, while cool hues increase toward the periphery, reflecting symbolic principles of the Five‐Element system. Transition analyses reveal rhythmic warm‐cool alternations, while participant‐weighted sequences highlight red dominance preceding the king, serving as visual foreshadowing. These findings demonstrate that color systematically mediates spatial hierarchy and narrative progression as a strategic visual device. This study introduces an integrative methodological framework for heritage color analysis, bridging the gap between conceptual color theory and quantitative interpretation to uncover the structural flow of cultural symbolism.
ABSTRACT Chromatic adaptation transform (CAT) plays an important role in uniform color spaces and color appearance models, with great efforts made to collect corresponding color datasets for developing CATs. These datasets, however, were generally collected using surface color samples (i.e., Munsell samples) and conventional displays having relatively small color gamut. The development of display technologies has allowed a larger color gamut and stimuli with higher chroma levels. In this study, the calculations using the NCS color samples suggested the poor performance of the six CATs (i.e., von Kries, Sharp, CIECAT97, CMCCAT2000, CIECAT02, and CIECAT16) for stimuli with high chroma levels. A short‐term memory matching experiment was then carried out, asking observers to adjust the color appearance of a stimulus under a 6500 K condition to match the appearance of 28 stimuli under a 2700 K condition. Also, the observers performed achromatic matching under the 2700 K condition. The results clearly suggested the poor performance of all the six CATs. Though using the adjusted white points, instead of the chromaticities of the 2700 K condition, in the CAT calculations can significantly improve the average performance of all the six CATs, the worse performance of all the six CATs for the stimuli having high chroma levels remained same, suggesting the necessity to carry out further investigations.
Picture books are a central medium through which children learn to interpret stories, emotions, and symbols, yet the role of color in these narratives remains underexplored and theoretically fragmented. This systematic review investigates how color in picture book illustrations has been reported to influence or appear to contribute to children's narrative comprehension and cognitive development, addressing persistent gaps in methodology and theory. Following PRISMA guidelines, four databases were systematically searched (2000–October 2024), identifying 78 eligible studies from an initial pool of 717 records. Studies were thematically categorized into four domains: the impact of color on emotional perception, cognitive processing and recognition, narrative function in storytelling, and experimental applications. Across these domains, findings demonstrate that color functions simultaneously as a representational code, a cognitive scaffold, and an affective cue—shaping emotional engagement, guiding attention and memory, structuring narrative coherence, and enabling transfer of learning in experimental contexts. This review demonstrates that color in picture book illustration is not a decorative feature but a multimodal narrative device that modulates emotional tone, scaffolds cognitive processing, and structures story coherence, supporting theories such as Dual Coding and multimodal discourse. While evidence confirms systematic links between color choices, comprehension, and engagement, significant gaps remain, particularly the lack of longitudinal and cross‐cultural studies, the absence of a unified terminology, and limited integration across disciplines. The review concludes with a call for interdisciplinary, technologically informed, and developmentally sensitive research to advance a comprehensive theoretical model of color in visual storytelling.
Color is a crucial factor in the textile industry, directly affecting product quality and customer satisfaction. Traditionally, fabric color measurement has primarily relied on spectroscopic methods and colorimeters, which require direct contact with the fabric samples and often involve high costs. With the innovations of image processing technology, image‐based color measurement (IBCM) methods have provided new possibilities thanks to their flexibility and automation potential. This paper provides an overview of IBCM approaches, including multispectral imaging systems, hyperspectral imaging, digital cameras, and scanners. Recent studies have shown that the application of artificial intelligence (AI) and computer vision in color measurement can enhance measurement accuracy and stability. However, this method still faces many challenges, such as the influence of ambient lighting, camera angles, fabric materials, and discrepancies among measuring devices. In addition, the work discusses color correction techniques aimed at improving the IBCM's accuracy, including spectral reflectance reconstruction, color balancing, and machine learning algorithms. Furthermore, the paper analyzes the applicability of IBCM technology in industrial textile quality control, and proposes future research directions, such as developing advanced AI algorithms, integrating Internet of Things (IoT) technology, and establishing standards for IBCM. The findings suggest that, despite remaining challenges, IBCM is emerging as a promising solution to replace or complement traditional color measurement methods in the textile industry.
Recent trends in telecommuting have increased the need for workspaces that support multiple task types within a single physical environment. Lighting design plays an important role not only in ensuring visual performance but also in supporting occupants' comfort and well-being. This study conducted a subjective experiment to investigate the potential of luminous color, in interaction with interior surface conditions, as a means of gently differentiating zones within a multifunctional workspace. Fourteen lighting conditions were evaluated by combining four luminous colors with two wall-surface finishes. Task performance, subjective evaluations, and physiological responses were measured. Although the results should be regarded as exploratory rather than conclusive, subjective evaluations indicated that certain lighting and wall-surface combinations influenced perceived workspace suitability. In particular, white light combined with low-reflectance woodgrain-pattern textiles was consistently rated as suitable for both individual tasks and group discussions. These findings suggest that luminous color may influence perceived workspace quality through interaction with interior surface properties, even though its effects on objective performance measures were limited under the present experimental conditions. Further research in larger and more realistic office environments is required.
Accurate color reproduction in metal can printing is critical for brand consistency and production efficiency, yet this field remains underrepresented in research, with most studies focusing on less complex printing substrates and processes. In beverage can decorating, high-speed cylindrical printing and reflective surfaces make color management particularly challenging. This study evaluates the colorimetric reliability of conventional and digital proofing technologies used in dry offset can printing, with the aim of assessing whether digital proofing can function as a credible and operationally sustainable alternative to traditional methods. A widely used red spot color, representative of common industrial applications, served as the reference. Both systems were assessed using spectrophotometric measurements and visual evaluation. Results show that conventional proofing provides the most reliable prediction of final print outcomes, while digital proofing offers consistent color reproduction, faster turnaround, and reduced material use. Based on these insights, a hybrid industrial model is recommended-digital proofing as the primary tool for initial evaluation and collaboration, with conventional proofing reserved for final validation when necessary. Future research should expand toward a broader range of brand colors with complex visual designs, where limitations of digital proofing extend beyond color accuracy to full visual fidelity, as well as the influence of coatings.
Accurate estimation of the absorption (K) and scattering (S) coefficients in the two-constant Kubelka-Munk (KM) model is essential for spectral reflectance prediction and color matching in precolored fiber blends. However, conventional least squares approaches often suffer from numerical instability and require large amounts of calibration data, limiting their practical use in real-world manufacturing. This study presents an improved least squares method that incorporates a simple constraint by fixing the scattering coefficient of the undyed white fiber. This modification reduces the number of unknowns and improves the conditioning of the coefficient matrix, enabling accurate KM coefficient estimation even with limited calibration data. The method is evaluated on 48 blending samples of five pre-colored cotton fibers using cross-validation over a range of training sample sizes. Prediction performance is assessed using CIEDE2000 color difference (Delta E 00), spectral root mean square error (RMSE), and the condition number of the system matrix. Results show that the proposed method achieves lower prediction errors and improved numerical stability compared to the original formulation, even with fewer training samples. These advantages make the method readily applicable in industrial color spinning and sustainable textile coloration.
To determine the preferred memory color ranges in printed images and to provide guidance for ink separation and ink adjustment in the printing process, this study conducted preference evaluation experiments based on eight types of memory-color images, including sky, fruit, and skin tones, selected from the ISO 400 standard image set. Two ink adjustment methods were applied respectively to the original images: total ink limit settings ranging from 260% to 330% and channel-wise ink adjustments for cyan (C), magenta (M), yellow (Y), and black (K). A total of 1056 printed images (8 image types & times; 132 ink adjustments) were produced, and the preference evaluations were conducted by 15 observers with normal color vision. The results show that, for different image contents, the highest average preference scores were generally observed under the 300% total ink limitation; the observers preferred the skin color with CIELAB hue angles between 39.7 degrees and 47.9 degrees and exhibited stronger preference for highly saturated fruit colors and sky blues. Overall, this study proposes an ink adjustment strategy for achieving preferred memory colors in printed images, which can reduce ink consumption, improving perceived color quality and production efficiency in the printing industry.
