Authors: Dara Molloy, Patrick Müller, Brian Deegan, Darragh Mullins, Jonathan Horgan, Enda Ward, Edward Jones, Alexander Braun, and Martin Glavin (University of Galway, Hochschule Düsseldorf, and Valeo).

Published in IEEE Access, vol. 12, pp. 3554–3569, 2024. doi:10.1109/ACCESS.2023.3348663

Summary

Camera-based object detection underpins ADAS and autonomous driving, and how sharp the camera has to be is a question with direct safety consequences. This study measures that relationship rather than assuming it. Six current object detection models were run against a purpose-built raw dataset of pedestrians and cars at distances out to 100 m, captured across a range of lens defocus levels, and the minimum spatial frequency response (SFR) needed for reliable detection was derived for each case.

The headline result is that the relationship between detection performance and lens blur is considerably more complex than earlier work suggested. Real lenses do not defocus uniformly: field curvature, chromatic aberration, and astigmatism all shape how blur varies across the field, so a single global sharpness figure does not describe what the detector actually sees. The authors also report that smaller objects are disproportionately affected by blur, and that the detection models themselves differ markedly in how robust they are to it.

Why it matters for camera testing

Two of these findings line up closely with work we have been pursuing. The first is that a single summary sharpness number is not sufficient to predict machine vision performance. That is the same conclusion we reached comparing MTF50 against detection confidence in Validating Information Metrics Correlation with Object Detection. The second is that blur has to be characterized across the field rather than at a single point, because the aberrations that dominate off-axis are exactly the ones that move the detection result.

The overlap is not only thematic. Several of the authors (Brian Deegan, Dara Molloy, Enda Ward, Edward Jones, and Martin Glavin) are part of the University of Galway group we have worked with on Information Capacity as a Predictor of Perception Performance, which approaches the same question from the information-theoretic side.

See also

• Analysis of the Impact of Lens Blur on Safety-Critical Automotive Object Detection (IEEE Access, open access)
• Information Capacity as a Predictor of Perception Performance
• Validating Information Metrics Correlation with Object Detection
• Image Information Metrics
• Sharpness: What is it and how is it measured?

The post Analysis of the Impact of Lens Blur on Safety-Critical Automotive Object Detection appeared first on Imatest.

Read the full white paper (PDF)

White paper by Norman Koren (Imatest LLC) — May 29, 2026

Overview

This white paper is a concise, practical guide to camera image quality testing — with an emphasis on machine vision — built around metrics derived from information theory, which predict real-world camera performance better than traditional metrics such as sharpness (MTF) or noise alone.

It introduces Information Capacity (C) as the key performance metric, derived from sharpness, noise, and signal amplitude, and C4, the information capacity measured directly from 4:1-contrast slanted edges (the maximum information per pixel for an object with 4:1 contrast). From C4 and object size it develops the ideal-observer signal-to-noise ratio (SNRi) and Object Detection Error Probability (ODEP), and presents the new Information-based Dynamic Range (InfoDR) test chart and its plot of C4 versus illumination for characterizing camera performance across a wide range of light levels, including low light and dynamic range.

Along the way it reviews the underlying imaging concepts (raw conversion and demosaicing, gamma and tonal response, color spaces, MTF/SFR and slanted-edge charts, noise, and lighting), walks through spatial (slanted-edge) and tonal camera characterization with the relevant Imatest charts — including the 36-patch Dynamic Range (DR36), Contrast Resolution, and InfoDR charts — and covers the Photon Transfer Curve and the Simatest camera/ISP simulator.

It closes with color, uniformity (light falloff) and defect (blemish) measurements, and a discussion of problematic images and misleading results, including obsolete test charts, oversharpening, bilateral filtering and texture loss, and tone mapping.

See also

• Image Information Metrics
• Full PDF
• Imatest InfoDR Chart
• Using InfoDR: Information-based Dynamic Range, Part 1
• Using InfoDR, Part 2: Chart analysis
• InfoDR (Information-based Dynamic Range) Part 3: Results
• Image information metrics from Slanted edges: Instructions

The post Image Quality Testing Based on Information Metrics appeared first on Imatest.