
We present a new method for measuring a camera’s Dynamic Range (DR) and low light performance, both of which are derived from C4 information capacity, which is measured directly from ISO 12233-standard 4:1 contrast slanted edges. The method uses a new test chart that consists of groups of squares in a compact arrangement, where each square differs in transmittance or reflectance from its neighbors by a factor of 4, so that all edges between adjacent squares in a group have 4:1 contrast ratio (a density step of 0.602). The major advantage of C4 is that it completely characterizes the performance of cameras for objects with 4:1 contrast, whereas the traditional metrics, signal amplitude, sharpness, and noise, each of which contributes to information capacity, do not individually constitute complete camera performance metrics. Because the new technique uses the difference in Digital Numbers (DNs) across an edge as the signal for calculating C4, it avoids a measurement issue with simple flat patches, where stray light can be misinterpreted as improved Signal-to-Noise Ratio (SNR), distorting the measurements. It does, however, require that the test chart be well-focused. (The old technique was tolerant of moderate misfocus.) Finally, we examine a new plot of C4 as a function of exposure, which is a superior representation of camera performance over a wide range of illumination.

We present an image sensor noise model that can be used in a complete image system simulation that includes image generation, lens degradations, and ISP (Image Signal Processing), and can produce classic measurements (SFR, noise, etc.) as well as new information metrics such as information capacity and SNRi. The noise model is derived from a classic Photon Transfer Curve (PTC) obtained from one or more raw (undemosaiced) images of a high dynamic range grayscale test chart. Image sensor noise is composed of three factors. 1. Dark noise, which includes electronic noise, dark current noise, DSNU fixed-pattern noise, and noise from several other sources. It is independent of signal amplitude, A. 2. Photon shot noise, which varies with √𝐴, and 3. PRNU fixed-pattern noise, which varies linearly with A. The coefficients for the three factors are determined using a Levenberg Marquardt optimizer that provides an extremely close fit between the measured data and the calculated PTC. The coefficients can also be derived from EMVA 1288 measurements, which are more accurate and detailed, but require the acquisition of a large number of images. We show how the model can predict performance over a wide range of conditions, and most importantly, for low light.

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.