What is the meaning of black color?

Answer from top 10 papers

Black color is a hue that can be attributed to various causes and has different implications depending on the context. In dermatology, black coloration in skin lesions is often due to melanin, but can also result from blood, necrotic tissue, or exogenous pigment, with the pattern and distribution providing diagnostic clues (Logvinenko & Beattie, 2011). In art, black has been used symbolically, as seen in Francis Newton Souza's monochromatic paintings, where it conveys political and spiritual meanings, particularly in the context of decolonization and civil rights movements (Emery et al., 2017).
Interestingly, black is not only a significant color in biological and artistic contexts but also in materials science and technology. MnTiO3 powder, for example, exhibits a deep black hue due to specific crystal field transitions, and it has practical applications in ceramics and solar energy utilization due to its color properties (Gupta, 2021). Moreover, in the field of color theory, black is considered one of the component colors, essential for describing the composition of hues (Qiu et al., 2019).
In summary, black color is a complex phenomenon with multiple origins and significances. It can be a natural biological pigment, a symbolic element in art, a result of specific chemical transitions in materials, and a fundamental component in color theory. Its perception and categorization can vary depending on the context and the observer, reflecting the multifaceted nature of color interpretation (Emery et al., 2017; Gupta, 2021; Logvinenko & Beattie, 2011; Qiu et al., 2019).

Source Papers

Partial hue-matching

It is widely believed that color can be decomposed into a small number of component colors. Particularly, each hue can be described as a combination of a restricted set of component hues. Methods, such as color naming and hue scaling, aim at describing color in terms of the relative amount of the component hues. However, there is no consensus on the nomenclature of component hues. Moreover, the very notion of hue (not to mention component hue) is usually defined verbally rather than perceptually. In this paper, we make an attempt to operationalize such a fundamental attribute of color as hue without the use of verbal terms. Specifically, we put forth a new method--partial hue-matching--that is based on judgments of whether two colors have some hue in common. It allows a set of component hues to be established objectively, without resorting to verbal definitions. Specifically, the largest sets of color stimuli, all of which partially match each other (referred to as chromaticity classes), can be derived from the observer's partial hue-matches. A chromaticity class proves to consist of all color stimuli that contain a particular component hue. Thus, the chromaticity classes fully define the set of component hues. Using samples of Munsell papers, a few experiments on partial hue-matching were carried out with twelve inexperienced normal trichromatic observers. The results reinforce the classical notion of four component hues (yellow, blue, red, and green). Black and white (but not gray) were also found to be component colors.

Open Access
Variations in normal color vision. VII. Relationships between color naming and hue scaling

A longstanding and unresolved question is how observers construct a discrete set of color categories to partition and label the continuous variations in light spectra, and how these categories might reflect the neural representation of color. We explored the properties of color naming and its relationship to color appearance by analyzing individual differences in color-naming and hue-scaling patterns, using factor analysis of individual differences to identify separate and shared processes underlying hue naming (labeling) and hue scaling (color appearance). Observers labeled the hues of 36 stimuli spanning different angles in cone-opponent space, using a set of eight terms corresponding to primary (red, green, blue, yellow) or binary (orange, purple, blue-green, yellow-green) hues. The boundaries defining different terms varied mostly independently, reflecting the influence of at least seven to eight factors. This finding is inconsistent with conventional color-opponent models in which all colors derive from the relative responses of underlying red-green and blue-yellow dimensions. Instead, color categories may reflect qualitatively distinct attributes that are free to vary with the specific spectral stimuli they label. Inter-observer differences in color naming were large and systematic, and we examined whether these differences were associated with differences in color appearance by comparing the hue naming to color percepts assessed by hue scaling measured in the same observers (from Emery et al., 2017). Variability in both tasks again depended on multiple (7 or 8) factors, with some Varimax-rotated factors specific to hue naming or hue scaling, but others common to corresponding stimuli for both judgments. The latter suggests that at least some of the differences in how individuals name or categorize color are related to differences in how the stimuli are perceived.

Open Access
Improved visualisation of brain arteriovenous malformations using color intensity projections with hue cycling

Color intensity projections (CIP) have been shown to improve the visualisation of greyscale angiography images by combining greyscale images into a single color image. A key property of the combined CIP image is the encoding of the arrival time information from greyscale images into the hue of the color in the CIP image. A few minor improvements to the calculation of the CIP image are introduced that substantially improve the quality of the visualisation. One improvement is interpolating of the greyscale images in time before calculation of the CIP image. A second is the use of hue cycling - where the hue of the color is cycled through more than once in an image. The hue cycling allows the variation of the hue to be concentrated in structures of interest. If there is a zero time point hue cycling can be applied after zero time and before zero time can be indicated by greyscale. If there is an end time point hue cycling can be applied before the end time and pixels can be set to black after the end time. An angiogram of a brain is used to demonstrate the substantial improvements hue cycling brings to CIP images. A third improvement is the use of maximum intensity projection for 2D rendering of a 3D CIP image volume. A fourth improvement allowing interpreters to interactively adjust the phase of the hue via standard contrast - brightness controls using lookup tables. Other potential applications of CIP are also mentioned.