
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 discuss several common image quality measurements that are often misinterpreted, so that bad images are falsely interpreted as good, and we describe how to obtain valid measurements. Sharpness, which is measured by MTF (Modulation Transfer Function) curves, is frequently summarized by MTF50 (the spatial frequency where MTF falls to half its low frequency value) But because MTF50 strongly rewards excessive sharpening, we recommend other summary metrics, especially MTF50P (the spatial frequency where MTF falls to half its peak value), that provide a more stable indication of system performance. Camera dynamic range (DR), defined as the range of exposure (scene brightness) where the image has good contrast and Signalto- Noise Ratio (SNR), Is usually measured with grayscale step charts. We have recently seen several cases where flare light radiating out from bright areas of the image fogs dense patches, causing unreasonably high DR measurements. This situation is difficult to handle with linear test charts, where the flare light is aligned with the patches, but can be handled well in charts with circular patch patterns, where the patch where pixel level ceases to decrease defines the upper DR limit.

The automotive industry formed the initiative IEEE-P2020 to jointly work on key performance indicators (KPIs) that can be used to predict how well a camera system suits the use cases. A very fundamental application of cameras is to detect object contrasts for object recognition or stereo vision object matching. The most important KPI the group is working on is the contrast detection probability (CDP), a metric that describes the performance of components and systems and is independent from any assumptions about the camera model or other properties. While the theory behind CDP is already well established, we present actual measurement results and the implementation for camera tests. We also show how CDP can be used to improve low light sensitivity and dynamic range measurements.

Nonlinear complementary metal-oxide semiconductor (CMOS) image sensors (CISs), such as logarithmic (log) and linearâ–”logarithmic (linlog) sensors, achieve high/wide dynamic ranges in single exposures at video frame rates. As with linear CISs, fixed pattern noise (FPN) correction and salt-and-pepper noise (SPN) filtering are required to achieve high image quality. This paper presents a method to generate digital integrated circuits, suitable for any monotonic nonlinear CIS, to correct FPN in hard real time. It also presents a method to generate digital integrated circuits, suitable for any monochromatic nonlinear CIS, to filter SPN in hard real time. The methods are validated by implementing and testing generated circuits using field-programmable gate array (FPGA) tools from both Xilinx and Altera. Generated circuits are shown to be efficient, in terms of logic elements, memory bits, and power consumption. Scalability of the methods to full high-definition (FHD) video processing is also demonstrated. In particular, FPN correction and SPN filtering of over 140 megapixels per second are feasible, in hard real time, irrespective of the degree of nonlinearity. c 2018 Society for Imaging Science and Technology.