Color Research and Application最新文献

Color palette is a critical component of art, design, and lots of applications, providing the basis for organizing and utilizing colors to achieve specific objectives. However, generating color palettes from digital images presents unique challenges due to the complexity of colors in images. This review comprehensively investigates various techniques for generating color palettes from digital images and provides a thorough classification and discussion of these techniques from multiple perspectives. A color space must be selected to generate a color palette, and a generation method must be employed. This paper offers a concise overview of color spaces, an introduction to current palette generation methods, and an analysis of the metrics used to evaluate color differences between palettes. The review encompasses traditional manual methods and computer-aided automation methods, further categorized as histogram-based, clustering-based, and neural network-based methods. Discussion on the strengths, weaknesses, and applicability of existing methods are presented, and also opportunities for future research to enhance color palette generation from digital images are identified.

Individual differences are a prominent feature of normal color vision and range from variations in sensitivity to perception and color naming. Corrections for differences in spectral sensitivity are common, and there is growing interest in calibrating displays for the sensitivity of an individual observer. In contrast, few studies have explored calibrations for aspects of color appearance. We developed a technique for adjusting images based directly on an individual's hue percepts and illustrate the principle of the approach using a set of hue scaling functions measured previously for a large sample of color-normal observers (Emery et al. PNAS 2023). Colors in an image are mapped onto the average scaling function to define the hue perceived by the average “standard observer.” This hue is then mapped back to the chromaticity that would elicit the same response in any individual observer. With this correction, different observers – each looking at physically different images calibrated for their own hue percepts – should in principle agree on the perceived colors. Adjustments of this kind could be easily implemented on standard displays, because they require only measures of hue percepts and not spectral sensitivity, and could lead to greater consistency in the perception and communication about color across individuals with potentially widely different perceptual experiences of color.

Garden color plays a key role in creating a spatial atmosphere, artistic conception and regional landscape characteristics. The Chinese classical garden has a profound traditional Chinese culture at its core, and its color collocation is a microcosm of Chinese culture. An in-depth exploration of the color matching of classical gardens is helpful for promoting the organic combination of modern urban construction needs and traditional culture. Therefore, this paper takes Tang-style gardens in Xi'an as an example and selects five typical classical gardens to study the color characteristics of garden elements, uses natural color system (NCS) to record color data from classical garden elements, establishes a color map, and quantitatively evaluates the color harmony of classical garden elements on the basis of Moon Spencer (M-S) color harmony theory. Research has revealed that the color system of Tang-style gardens in Xi'an is unique and includes 244 NCS standard colors with a wide distribution of hues. The study distinguishes between humanistic environment colors and natural environment colors that vary greatly with season, and provides a wide color range that reflects the beauty of nature. The color range of the humanistic environment appears calm and solemn, with colors having low blackness and low chromaticness in general. There is a certain difference in color harmony between natural and cultural environment colors, but the overall color harmony is good. This paper not only enriches the color theory system of Tang-style gardens but also provides a reference for promoting the scientific application of traditional urban colors in modern urban landscape construction.

Just-noticeable color difference (JNCD) is important in color specification and characterization. The commonly referenced specification of JNCD (i.e., 0.004 or 0.0033 u′v′ unit) is thought to originate from the MacAdam ellipses, which were derived using 2° color stimuli and characterized using the CIE 1931 2° color matching functions (CMFs). However, there is no universally agreed or clear definition of JNCD. Also, such a specification is widely used in various ways based on an assumption that it is applicable regardless of the actual size of the stimuli and CMFs. In this study, an experiment using a constant stimuli method was carried out. The human observers evaluated a series pairs of test and reference stimuli, with a field of view (FOV) of 2° or 10°. The chromaticities of the test stimuli were carefully calibrated using four standard CIE CMFs (i.e., CIE 1931 2°, 1964 10°, 2006 2°, and 10° CMFs). The results suggested that the widely used specification of JNCD seems to be derived based on the one standard deviation ellipses, the use of these four CMFs has little effect on the specification, and the JNCD value for stimuli with an FOV of 10° is 0.0025–0.0027 u′v′ unit depending on the CMFs.

In 2022, CIE recommend the CIECAM16 color appearance model to replace the current CIECAM02 model for color management systems. In this paper we will report on how CIECAM16 was developed, describe the differences between CIECAM16 and CIECAM02, describe the phenomena CIECAM16 can predict, and show the performance of CIECAM16 in the prediction of perceptual color attributes. We will then discuss the numerical determination of the domain and range of the CIECAM16 forward transformation. Finally, the domain and ranges in various conditions are visualized. CIECAM16 is capable of the accurate prediction of color appearance under a wide range of viewing conditions and with the domain and range problems now solved, it can be effectively applied to cross-media color image reproduction. It can also be used to estimate the color rendering properties of light sources and for the establishment of a uniform color space for color difference evaluation.

Human color vision exhibits substantial variability, challenging the accurate measurement and reproduction of individual color appearances. This paper introduces a novel cross-media color-matching experiment that combines physical pigments and digital displays, providing a practical and efficient solution to measure individual color matching functions (CMFs). By using the Kubelka–Munk theory to synthesize broadband spectral pigments, optimized metameric pairs can be created to significantly differentiate categorical observers. Psychophysical experiment results reveal the superiority of specific CMFs for individual observers, surpassing standard models like the CIE 1931 2-degree CMFs. This study also underscores the significance of employing CMFs for personalized color reproduction and explores the potential benefits of using higher bit-depth displays, narrower-band displays, and increasing repetition counts to enhance the accuracy of the color matching process.

The goal of this study is to process digital images to facilitate color discrimination in dichromatic vision without changing their appearances in trichromatic vision as much as possible. Therefore, we propose a simple and effective image enhancement method using the geometric properties of the RGB color space. Concretely, a novel image representation that clarifies the relationship between dichromatic and trichromatic visions in the RGB color space, and an image enhancement method based on this representation are presented. The effectiveness of the proposed method is verified by experiments using several digital images.