Contrast detection probability (CDP) is proposed as an IEEE P2020 metric to predict camera performance intended for computer vision tasks for autonomous vehicles. Its calculation involves comparing combinations of pixel values between imaged patches. Computation of CDP for all meaningful combinations of m patches involves approximately 3/2(m2-m).n4 operations, where n is the length of one side of the patch in pixels. This work presents a method to estimate Weber contrast based CDP based on individual patch statistics and thus reduces to computation to approximately 4n2m calculations. For 180 patches of 10×10 pixels this is a reduction of approximately 6500 times and for 180 25×25 pixel patches, approximately 41000. The absolute error in the estimated CDP is less than 0.04 or 5% where the noise is well described by Gaussian statistics. Results are compared for simulated patches between the full calculation and the fast estimate. Basing the estimate of CDP on individual patch statistics, rather than by a pixel-to-pixel comparison facilitates the prediction of CDP values from a physical model of exposure and camera conditions. This allows Weber CDP behavior to be investigated for a wide variety of conditions and leads to the discovery that, for the case where contrast is increased by decreasing the tone value of one patch and therefore increasing noise as contrast increases, there exists a maxima which yields identical Weber CDP values for patches of different nominal contrast. This means Weber CDP is predicting the same detection performance for patches of different contrast.