© 2017, Society for Imaging Science and Technology. This paper suggests a new quality measure of an image, pertaining to its contrast. Several contrast measures exist in the current research. However, due to the abundance of Image Processing software solutions, the perceived (or measured) image contrast can be misleading, as the contrast may be significantly enhanced by applying grayscale transformations. Therefore, the real challenge, which was not dealt with in the previous literature, is measuring the contrast of an image taking into account all possible grayscale transformations, leading to the best "potential" contrast. Hence, we suggest an alternative "Potential Contrast" measure, based on sampled populations of foreground and background pixels (e.g. scribbles or saliency-based criteria). An exact and efficient implementation of this measure is found analytically. The new methodology is tested and is shown to be invariant to invertible grayscale transformations.