As Machine Vision (MV) and Artificial Intelligence (AI) systems are incorporated to an ever-increasing range of imaging applications, there is a corresponding need for camera measurements that can accurately predict the performance of these systems. At the present time, the standard practice is to separately measure the two major factors, sharpness and noise (or Signal-to-Noise Ratio), along with several additional factors, then to estimate system performance based on a combination of these factors. This estimate is usually based on experience, and is often more of an art than a science. Camera information capacity (C), based on Claude Shannon's ground-breaking work on information theory, holds great promise as a figure of merit for a variety of imaging systems, but it has traditionally been difficult to measure. We describe a new method for measuring camera information capacity that uses the popular slanted-edge test pattern, specified by the ISO 12233:2014/2017 standard. Measuring information capacity requires no extra effort: it essentially comes for free with slanted-edge MTF measurements. C has units of bits per pixel or bits per image for a specified ISO speed and chart contrast, making it easy to compare very different cameras. The new measurement can be used to solve some important problems, such as finding a camera that meets information capacity requirements with a minimum number of pixels, important because fewer pixels mean faster processing as well as lower cost.