When dealing with hue-saturation-value (HSV) components, one must be very careful about nonlinearity and singularities of the HSV space, which can amplify considerably the sampling errors of the original components, namely, red-green-blue (RGB) channels. Contradictorily, almost all research in computerized image analysis or processing based on HSV (or other nonlinear color space) neglect to account for such inhomogeneous variability. In this article, we present two approaches for estimating the degree of variability of hue and saturation in the HSV space, intended to assist any color image processing or analysis based on such components. Both approaches end up with new estimators of the hue and saturation standard deviation for a pixel distribution of any color sample. The first approach consists in applying error-propagation analysis on the RGB-to-HSV formulation and the second one is based on heuristic rules inferred from geometrical relations between HSV values of pixel distributions. Experiments show that introduced estimators provide very accurate predictions of true H-S deviations obtained from a broad subset of 183 NCS (R) color samples. (c) 2011 Wiley Periodicals, Inc. Col Res Appl, 2011