Digital control of color television monitors—in particular, via frame buffers—has added precise control of a large subset of human colorspace to the capabilities of computer graphics. This subset is the gamut of colors spanned by the red, green, and blue (RGB) electron guns exciting their respective phosphors. It is called the
RGB monitor gamut
. Full-blown color theory is a quite complex subject involving physics, psychology, and physiology, but restriction to the RGB monitor gamut simplifies matters substantially. It is linear, for example, and admits to familiar spatial representations. This paper presents a set of alternative models of the RGB monitor gamut based on the perceptual variables hue (H), saturation (S), and value (V) or brightness (L). Algorithms for transforming between these models are derived. Particular emphasis is placed on an RGB to HSV non-trigonometric pair of transforms which have been used successfully for about four years in frame buffer painting programs. These are fast, accurate, and adequate in many applications. Computationally more difficult transform pairs are sometimes necessary, however. Guidelines for choosing among the models are provided. Psychophysical corrections are described within the context of the definitions established by the NTSC (National Television Standards Committee).
Although fractal models of natural phenomena have received much attention recently, there are other models of complex natural objects which have been around longer in Computer Imagery but are not widely known. These are procedural models of plants and trees. An interesting class of these models is presented here which handles plant growth, sports an efficient data representation, and has a high “database amplification” factor. It is based on an extension of the well-known formal languages of symbol strings to the lesser-known formal languages of labeled graphs. It is so tempting to describe these plant models as “fractal” that the similarities of this class of models with fractal models are explored in an attempt at rapprochement. The models are not fractal so the common parts of fractal theory and plant theory are abstracted to form a class of objects, the
graftals
. This class may prove to be of great interest to the future of Computer Imagery. Determinism is shown to provide adequate complexity, whereas randomness is only convenient and often inefficient. Finally, a nonfractal, nongraftal family of trees by Bill Reeves is introduced to emphasize some of the paper's nongrammatical themes.
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