Artistic Colour Theory


Fig.1 An example comparing the discerning power of Manet’s palette and a typical rainbow palette. Even with three rainbow palettes, a lot of the data’s nuances are lost

Colour Theory

Artistic colour contrast theory is the basis upon which artists build affect and organization into their paintings. Artists systematically structure paintings using colour to create relationships, categories and hierarchies within the work, allowing the viewer to visually determine the relative importance of various aspects of the work.

Basic Definitions: Artistic colour contrast theory further characterizes colour contrast into seven types[1]. Below is a brief summary.

Value Contrast: the range between white and black, light over dark.

Saturation Contrast: the purity of the colour; the grayer the hue, the lower the saturation; fully saturated dominates low saturation.

Complimentary Contrast: opposites on the colour wheel. Analogous contrast: hues adjacent to one another on the colour wheel, equalizing contrast.

Cool/Warm Contrast: blues and greens are cool; reds to yellow are warm colours; warm over cool.

Contrast of Extension: the portion of area verses the visual weight of a colour. For example, to be in balance one would need only a small amount of red to balance a larger area of gray.

Simultaneity: vibration caused by two abutting saturated hues.

Color Triad: three colours equally spaced the colour wheel: red, blue and yellow (primaries); orange, green and purple (secondaries).

Affect Theory


Fig.2 Affect Colour Space

Affect is a concept used in psychology to describe experiential response: feeling, impression, mood or emotion. It is typically classified by the well-known PAD model of affect [3] that plots them in a dimensional space defined by pleasure (valence) and arousal axes. Valence covers hedonic range, from positive (happiness, pleasure, love) to negative (pain, anger, sadness, fear). Arousal reflects intensity from calm (unaroused, relaxed, sleepy, etc.) to excited (high arousal, stimulated, nervous, alert, etc.). Typical emotions such as surprise, disgust or compassion can be placed in this 2D space; extensive emotion research has defined many more nuanced affects (such as affection or boredom) in this model as well [4]. While designers and artists understand the more complex properties of palettes (organized groups of colours), there has been relatively little research specifically on the affect of palettes and visualization. Recently, Bartram et al.’s study of affective colour sets in visualization [2] showed that simple 5-colour combinations selected for categorical mappings differed significantly by affect. Figure 2 illustrates the most common colours selected for palettes for the four poles of the affect axes, where size represents frequency of use.

These colours were selected from a set of 41 possible, and by definition their importance in the palette was normalized: that is, in categorical mapping, no palette colour had more inherent weight or importance than any other. Even in this limited colour space, there are clear patterns in the different affect groups. This study also looked at other palettes mapped to different affects located in the 2D affect space and found they combined colours from the 4 polar colour groups, reinforcing the correspondence between the validated PAD space and colour space. However, this work looked only at limited palettes and categorical mapping. We turn to artistic colour theory to enrich the affective understanding of more complex palettes and visualization applications.

[1] J. Itten and F. Birren. The Elements of Color: A Treatise on the Color System of Johannes Itten Based on His Book the Art of Color. Van Nostrand Reinhold Company, New York, NY, 1st edition, 1970.

[2] M. S. P. Abisekh, L.Bartram. Affective color in visualization. In Proceedings of the 2017 SIGCHI, 2017.

[3] J. Posner, J. Russell, and B. s. Peterson. The circumplex model of affect: An integrative approach to affective neuroscience, cognitive development, and psychopathology. Development and psychopathology, 17(3):715–734, 2005.

[4] K. Reinecke, D. Flatla, and C. Brooks. Enabling designers to foresee which colors users cannot see. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems, CHI ’16, pages 2693–2704, New York, NY, USA, 2016. ACM.