Cameras record three color responses (RGB) which are device dependent. Camera coordinates are mapped to a standard color space, such as XYZ-useful for color measurement-by a mapping function, e.g., the simple 3×3 linear transform (usually derived through regression). This mapping, which we will refer to as linear color correction (LCC), has been demonstrated to work well in the number of studies. However, it can map RGBs to XYZs with high error. The advantage of the LCC is that it is independent of camera exposure. An alternative and potentially more powerful method for color correction is polynomial color correction (PCC). Here, the R, G, and B values at a pixel are extended by the polynomial terms. For a given calibration training set PCC can significantly reduce the colorimetric error. However, the PCC fit depends on exposure, i.e., as exposure changes the vector of polynomial components is altered in a nonlinear way which results in hue and saturation shifts. This paper proposes a new polynomial-type regression loosely related to the idea of fractional polynomials which we call root-PCC (RPCC). Our idea is to take each term in a polynomial expansion and take its kth root of each k-degree term. It is easy to show terms defined in this way scale with exposure. RPCC is a simple (low complexity) extension of LCC. The experiments presented in this paper demonstrate that RPCC enhances color correction performance on real and synthetic data.

The computational problem underlying color vision is to recover the invariant surface-spectral-reflectance properties of an object. Lightness algorithms, which recover an approximation to surface reflectance in independent wavelength channels, have been proposed as one method to compute color. This paper clarifies and formalizes the lightness problem by proposing a new formulation of the intensity equation on which lightness algorithms are based and by identifying and discussing two basic subproblems of lightness and color computation: spatial decomposition and spectral normalization of the intensity signal. Several lightness algorithms are reviewed, and a new extension (the multiple-scales algorithm) of one of them is proposed. The main computational result is that each of the lightness algorithms may be derived from a single mathematical formula, under different conditions, which, in turn, imply limitations for the implementation of lightness algorithms by man or machine. In particular, the algorithms share certain limitations on their implementation that follow from the physical constraints imposed on the statement of the problem and the boundary conditions applied in its solution.

The deficits in texture, motion, and depth perception incurred in monkeys at isoluminance were compared with the responses of neurons of the color-opponent and broad-band systems in the lateral geniculate nucleus. Texture perception, assumed to be carried by the color-opponent system, and motion and depth perception, ascribed to the broad-band pathway, were all found to be compromised but not abolished at isoluminance. Correspondingly, both the color-opponent and the broad-band systems were affected at isoluminance, but the activity of the neurons in neither system was abolished. These results suggest that impairment of visual capacities at isoluminance cannot be uniquely attributed to either of these systems and that isoluminant stimuli are inappropriate for the psychophysical isolation of these pathways.

The phenomenon of colour constancy in human visual perception keeps surface colours constant, despite changes in their reflected light due to changing illumination. Although colour constancy has evolved under a constrained subset of illuminations, it is unknown whether its underlying mechanisms, thought to involve multiple components from retina to cortex, are optimised for particular environmental variations. Here we demonstrate a new method for investigating colour constancy using illumination matching in real scenes which, unlike previous methods using surface matching and simulated scenes, allows testing of multiple, real illuminations. We use real scenes consisting of solid familiar or unfamiliar objects against uniform or variegated backgrounds and compare discrimination performance for typical illuminations from the daylight chromaticity locus (approximately blue-yellow) and atypical spectra from an orthogonal locus (approximately red-green, at correlated colour temperature 6700 K), all produced in real time by a 10-channel LED illuminator. We find that discrimination of illumination changes is poorer along the daylight locus than the atypical locus, and is poorest particularly for bluer illumination changes, demonstrating conversely that surface colour constancy is best for blue daylight illuminations. Illumination discrimination is also enhanced, and therefore colour constancy diminished, for uniform backgrounds, irrespective of the object type. These results are not explained by statistical properties of the scene signal changes at the retinal level. We conclude that high-level mechanisms of colour constancy are biased for the blue daylight illuminations and variegated backgrounds to which the human visual system has typically been exposed.