Video Player is loading.
This is a modal window.
The media could not be loaded, either because the server or network failed or because the format is not supported.
Slanted Edge Based Measurements: Information Capacity & More
Presented by Norman Koren, Imatest Founder & CTO
Learn more about Information Capacity
More Resources
The following text is from Image Information Metrics, which will be kept up to date. (This page might fall behind.)
Introduction – Video – White papers – Documentation – Electronic Imaging 2024 – ISO 23654 – Call for participation
Introduction
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.
- How best to select (or qualify) cameras for MV/AI applications?
- 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
Video Player is loading.
This is a modal window.
The media could not be loaded, either because the server or network failed or because the format is not supported.
White papers
Three November 2023 white papers (probably too many; it may be reduced to two) contain the latest information capacity measurements and results, including Edge SNRi, filter design, and many other significant enhancements over the 23.2 release. They are available in the Imatest 24.1 Pilot Program until Imatest 24.1 is released in spring, 2024.
|
The three white papers linked below contain the same essential material.
|
Camera Information Capacity from Siemens Stars (2020) describes a method that is slower and less versatile than the slanted edge, but better for observing the effects of image processing artifacts such as demosaicing, data compression, etc.
Imatest Software Documentation
- Information capacity measurements from Slanted edges: Equations and Algorithms (2023) – figures of merit that combine sharpness and noise, conveniently measured from any slanted-edge, including NPS, NEQ, and SNRi. Similar to the white papers, but not updated as frequently.
- Information capacity measurements from Slanted edges: Instructions (2023) – instructions on the new calculations.
-
Improved slanted-edge MTF calculation – We found errors in calculations that depended on MTF2(f)/NPS(f). They were reduced by an improvement in the ISO 12233-based MTF calculation.
- Shannon information capacity from Siemens stars
Electronic Imaging 2024
Norman Koren will present an keynote talk (one hour time slot) on Image Information Metrics (covering much of the material in the white papers) at Electronic Imaging 2024, January 21-25, 2024 at the Hyatt Regency hotel in Burlingame, California (near SFO). The talk is scheduled for 8:45AM, Monday, January 22.
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 infocap@imatest.com if you are interested.