IntroductionVideo – Documentation – White papersDocumentation web pagesElectronic Imaging 2024 paper
ISO 23654Call for participation


The market for cameras that produce images for Machine vision (MV) and Artificial Intelligence (AI), in contrast to pictorial images for human vision, is steadily growing. Applications include automotive (driver assistance and autonomous vehicles), robotics, security, and medical imaging systems.

Two questions arise when designing camera systems for such applications.

  1. How best to select (or qualify) cameras for MV/AI applications?
  2. What image processing (ISP or filtering) is optimal?

To answer these questions, we must go beyond standard measurements of sharpness (MTF) and noise and apply metrics derived from information theory, including information capacity and related metrics for object and edge detection.

These metrics are important because Object Recognition (OR), MV, and AI algorithms operate on information, not pixels, making them far better predictors of system performance than MTF or noise.

Imatest has developed highly convenient methods for measuring information capacity and related metrics. The white papers (with varying degrees of detail) describe how the new metrics can be used to select (or qualify) cameras and determine the optimum Image Signal Processing (ISP) for Object Recognition, which is likely to improve the performance of MV and AI algorithms.

Video: Image Information Metrics in Imatest

Documentation, white papers, and instructions

White papers

The white papers linked below contain similar material.
They range from concise and technically lightweight for managers and non-technical readers
to detailed and comprehensive (but challenging to read).

Introduction to Image Information Metrics  is a concise introduction to image information capacity and related metrics, with a minimum number of equations and technical detail. We may shrink it further for use in a brochure for marketing.

Image Information Metrics and Applications: Reference  (41 pages) has all the equations and technical detail, but it may be challenging to read. It’s the primary reference for the metrics.

Image Information Metrics in Imatest  is an intermediate-level introduction to image information capacity and related metrics with moderate technical detail. We are deprecating it to reduce the amount of redundant documentation.

Camera Information Capacity from Siemens Stars (2020) describes a method that is slower and less convenient than the slanted edge, but better for observing the effects of image processing artifacts such as demosaicing, data compression, etc.

Imatest software documentation web pages

Electronic Imaging 2024

Norman Koren presented a one-hour keynote talk on Image Information Metrics (covering much of the material in the white papers) at Electronic Imaging 2024 in Burlingame, California.

The slides from the talk (PDF format) are available for download here

The EI2024 paper, “Image information metrics from slanted edges,” is an excellent introduction to the new metrics. Recommended. It is available for download here.  I will change the link to the final paper when it is available on

Try the Latest Metrics in the Imatest Pilot Program

Imatest 24.1 Alpha includes Edge SNRi, filter design, and many other significant enhancements over the 23.2 release.

Join the Imatest Pilot Program to try out these metrics in Imatest 24.1 Alpha.

Imatest 24.1 Beta will be available in early Spring. The full release of Imatest 24.1 will be in late Spring 2024.


Interpolated slanted-edge MTF (SFR) algorithm

In the process of developing the information metric calculations, many of which depend on MTF2(f)/NPS(f) (NEQ, etc.), we observed an inconsistency in the Noise Equivalent Quanta calculations, which should be unaffected by image processing as long as it’s linear and uniform. We also noticed artifacts at high spatial frequencies that we thought might have been related to the inconsistency. The artifacts were greatly reduced when we interpolated the scan lines prior to binning, as described in.

Interpolated slanted-edge SFR (MTF) calculation  

Because we found that the interpolation attenuates the high frequency SFR response by a factor of sinc(fT) — and more importantly — because it didn’t fix the inconsistency, we do not recommend the interpolation method for most calculations (though we have found some cases where it is advantageous). I (NLK) had to curb my initial enthusiasm.

International Standard (in development)

ISO 23654 began development in early 2023. It is based on ISO 12233 and defines how to calculate information capacity from a slanted edge. It is in the PWD (preliminary working draft) phase.

Call for Participation

We are interested in working with machine vision and computer vision experts who are experienced with studying how image quality metrics correlate with object detection performance in computer vision systems.  Please contact if you are interested.