Search Results for: MTF
How to Test Lenses with SFR (deprecated)
Testing lenses (Deprecated): introduction Note: This page has been largely replaced by Testing Lenses with SFRplus. It is being kept for reference only. Lens quality (especially sharpness) has always been of great interest to photographers. It’s what we spend the big bucks for— if we have them. And we look for it in a lens at any price point. Traditionally, lens testing has been highly tedious, best left to professionals and large publications. With Imatest, that has changed forever. All you need to do is photograph a simple target (with careful technique, of course), run Imatest SFR, the similar but […]
The Imatest Pass/Fail Monitor
The Pass/Fail monitor (introduced In Imatest 4.0) provides a real-time indication of whether a device has passed or failed a test. It saves the trouble of digging through results— in figures or CSV or JSON files. Key features: It can stay open while modules run, displaying results immediately after calculations are complete. It works best on systems with a high resolution screen or dual screens. It interfaces with most Imatest analysis modules. It can be extremely valuable for developing and testing Pass/Fail criteria for Imatest IT (Industrial Testing). It can call several utilities for helping with this process. It displays […]
RAW Files
RAW files Introduction – Using RAW files – Bayer RAW and RCCC files – dcraw demosaicing – DNG files – Rawview utility – Generalized Read Raw – Creating Synthetic raw images The unprocessed digital output of an image sensor is called RAW image data. In this document we sometimes refer to raw files from commercial cameras or development systems as Camera RAW to distinguish them from Bayer RAW files, which are standard monochrome image files that contain undemosaiced (Bayer) data. New in Imatest 4.5: RCCC (Red-Clear-Clear-Clear) raw files, used by the automotive industry, are now supported in the Monochrome […]
Imatest Image Acquisition with Omnivision OVTAPantherM
Imatest 5.0: All functions of Imatest Image Sensor have been incorporated into Imatest Master. Introduction This page briefly illustrates how to use the Imatest with Omnivision OVTAPantherM. Please note that Omnivision cameras are not supported in the OSX version of Imatest. However, Imatest supports other development systems to varying degrees. Contact us for details. Load images directly from Omnivision sensors. This eliminates the need to capture images separately, store them, then load them individually into Imatest, dramatically improving productivity. Download and Install Software To download Imatest software, go to store.imatest.com/software.html Download and open the self-extracting executable (Imatest-IS-3.n.exe). This will install […]
IEEE Camera Phone Image Quality (CPIQ) Support
This page describes details about the implementation of our CPIQ Solution. For additional details about the initiative, see this presentation assembled by committee chair Margaret Belska. Imatest’s CPIQ Implementation Imatest 4.4+ supports IEEE CPIQ P1858TM (Camera Phone Image Quality) 2016 measurements. As members of CPIQ, we are strongly committed to providing convenient CPIQ-compliant and equivalent image quality measurements. NOTE: You should use at least Imatest 4.4.9 for the latest corrections to visual noise calculations. This page has similar order to chapters the CPIQ Standard, “P1858TM/D1 Standard for Camera Phone Image Quality“ document. The first four chapters are 1. Overview, 2. […]
Skype video specification support
Instructions and comments We are updating this page for the latest Skype/Lync specification. An index of of the Skype/Lync specifications can be found on http://technet.microsoft.com/en-us/lync/gg278181.aspx. This document contains instructions for using Imatest with the Skype Hardware Certification Specification — For all Skype Video Devices Version 5.0. It also contains comments and suggestions (some of which we hope might be adopted in a future release of the spec). The Skype spec uses only a tiny fraction of Imatest’s powerful capabilities. To learn more, see Image Quality Factors and SFRplus (which allows many factors to be measured from a single image). In […]
Getting Started With Image Quality Testing
Overview Why Imatest? | Test Charts | Lighting | Image Capture Technique | Setting up Your Lab | Using Imatest Software Why Imatest? Introduction Imatest charts and software allow you to measure the characteristics and quality of cameras and imaging systems. To begin measuring the resolution or color of your camera, you’ll need an image of a test chart. Imatest offers all-in-one test charts that can test a wide variety of image quality factors with one image. We also offer specialized test charts for measuring system properties like dynamic range, […]
Image Processing
Operation – Image processing blocks – Displays and analysis – Optical Character Recognition (OCR) Face and People Detection The Imatest Image Processing module, introduced in Imatest 4.4 and significantly enhanced in 4.5 and 5.0, simulates a number of image processing operations, including image degradations such as noise and blurring as well as image enhancements such as applying a Color Correction Matrix, tone mapping (used in High Dynamic Range (HDR) imaging), Unsharp Masking (USM), and bilateral filtering. You can instantly observe the visible effects of these operations by switching between unprocessed and processed images. You can see how processing affects several measurements, including SSIM, PSNR, and MTF. […]
Shannon information capacity
NOTE: The method in this page is obsolete and is deprecated in Imatest 2020.1. It does not produce accurate results. The new method uses the Siemens star test chart. There is also a new method for slanted-edges that is less accurate than the star method, but is somewhat interesting as an approximation. Photographic scientists and engineers stress the fact that no single number satisfactorily describes the ability of a photographic system to reproduce the small-scale attributes of the subject —Leslie Stroebel,John Compton, Ira Current, Richard Zakia Basic Photographic Materials and Processes, Second edition, p. 273 (Micro-image evaluation chapter), Focal Press, […]
Distortion: methods and modules
This page describes Imatest’s distortion calculations, comparing the different distortion formulas and modules. Distortion formulas – Modules – TV Distotion and Field of View – Image, Geometry, Distortion, FoV display Radial distortion plot – Compare results – Links Lens distortion has two basic forms, barrel and pincushion, as illustrated below. None Barrel Pincushion In addition to these three, there can be “mustache” or “wave” distortion, which is barrel near the center and pincushion near the edges, or vice-versa. Distortion formulas In the equations which apply to several modules, rd is the distorted (measured) radius normalized to the center-to-corner distance. ru is the undistorted radius. […]
Chromatic Aberration AKA Color fringing
Introduction Chromatic aberration (CA) is one of several aberrations that degrade lens performance. (Others include coma, astigmatism, spherical aberration, and curvature of field.) It occurs because the index of refraction of glass varies with the wavelength of light, i.e., glass bends different colors by different amounts. This phenomenon is called dispersion. It appears as color fringing, most visibly on tangential edges near the boundaries of the image. It is sometimes confused with another effect, which we call pixel shift— a color channel offset that is typically uniform over the sensor and can be caused by physical misalignment of multi-chip sensors […]
Nyquist frequency, Aliasing, and Color Moire
Although sharpness is an important image quality factor, a sharper lens is not always better. A lens can be too sharp for a sensor, resulting in disturbing visual artifacts. These artifacts, which include “stair-stepping” and moiré patterns (low frequency patterns that can be strongly colored), can appear because of a property that digital cameras share with all digitally sampled systems— a maximum spatial frequency, called the Nyquist frequency, beyond which scene information cannot be correctly reproduced. Any information above the Nyquist frequency that reaches the sensor will be aliased to a lower spatial frequency, which can result in the artifacts described below. […]
Acutance and SQF (Subjective Quality Factor)
Measurement of perceptual image sharpness Introduction to Acutance and SQF Acutance and SQF (Subjective Quality Factor) are measures of perceived print or display sharpness. SQF was used for years in the photographic industry but has remained unfamiliar to most photographers. Acutance is a relatively new measurement from the IEEE Camera Phone Image Quality (CPIQ) group. Both include the effects of MTF: the imaging system’s Modulation Transfer Function, which is synonymous with Spatial Frequency Response (SFR), CSF: the human eye’s Contrast Sensitivity Function (closely related to its MTF response, but including a low frequency rolloff), image display height, and viewing […]
Chart Quality Index (CQI) (deprecated)
The Chart Quality Index measurement has been deprecated because Imatest’s improved capabilities for measuring and correcting MTF degradation caused by test charts are far easier to understand and apply. Chart quality calculator Compensating camera MTF measurements for chart and sensor MTF The Chart Finder is an interactive web page that can help you select the appropriate chart size and media. It contains results from this page, but without the math. Summary In order to determine the suitability of a test chart is for measuring the performance of a given camera, i.e., to answer the question of how much a chart’s (imperfect) sharpness […]
Chart Quality Calculator
Imatest 5.1 has several new features that let you use measured chart MTF (modulation transfer function) to determine the suitability of a test chart characterized by size (height), print media, and printing technology for camera MTF measurements, where the camera is characterized by pixel size, typically the number of vertical pixels. This can help determine the appropriate test chart for a specific application. This capability has been enhanced in Imatest 5.2 with the addition of the Chart suitability display. increase the accuracy of MTF measurements by dividing the measured camera MTF by the appropriately-scaled chart MTF, which is equivalent […]
Slanted-Edge versus Siemens Star, Part 2
A comparison of sensitivity to signal processing: Results for additional cameras This page contains additional Slanted-edge, Siemens Star, and Log F-Contrast results for four cameras, in support of claims in Slanted-edge versus Siemens Star that Siemens Star MTF measurements are nearly as sensitive to sharpening as low-contrast (4:1) slanted-edge measurements. The Siemens Star’s high contrast (specified at >50:1) makes it quite sensitive to saturation and to “shoulders” (regions of reduced contrast) in camera tonal response. Slanted-edge MTF measurements are stable, reliable, and more representative of perceived image sharpness under a wide range of conditions (in addition to their many well-known […]
Slanted-Edge versus Siemens Star
A comparison of sensitivity to signal processing In this page we address concerns about the sensitivity of slanted-edge patterns to signal processing, especially sharpening, and we correct the misconception that sinusoidal patterns, such as the Siemens star, are insensitive to sharpening, and hence provide more robust and stable MTF measurements. The Siemens Star is of particular interest because, along with the slanted-edge, it is included in the ISO 12233:2014 standard. To summarize our results, we found that sinusoidal patterns are sensitive to sharpening, though often less so than low contrast (4:1) slanted-edges. The relatively high contrast of the Siemens Star […]
Sharpening
Introduction – Examples – Oversharpening and Undersharpening – Examples Unsharp masking (USM) – Links – Standardized sharpening Introduction to sharpening Sharpening is an important part of digital image processing. It restores some of the sharpness lost in the lens and image sensor. Every digital image benefits from sharpening at some point in its workflow— in the camera, the RAW conversion software, and/or image editor. Sharpening has a bad name with some photographers because it’s overdone in some cameras (mostly low-end compacts and camera phones), resulting in ugly “halo” effects near edges. But it’s entirely beneficial when done properly. Almost every digital camera sharpens images to some degree. Some models […]
Sharpness: What is it and how is it measured?
Introduction – MTF – MTF equation – Slanted-edge measurements – Why a slanted edge? – MTF measurement matrix Spatial frequency units – Summary metrics – Results – Noise reduction – Diffraction & Optimum aperture Interpreting MTF50 – Auto-focus – Calculation details – Imatest vs. ISO calculation – Links Introduction Sharpness is arguably the most important photographic image quality factor because it determines the amount of detail an imaging system can reproduce. Imatest also measures a great many others. Sharpness is defined by the boundaries between zones of different tones or colors. It is illustrated by the bar pattern of increasing spatial frequency, below. The top portion is sharp; its boundaries are crisp steps, not gradual. The bottom portion illustrates how the pattern is degraded after it […]
Image Quality Factors (Key Performance Indicators)
Overview and introduction to measurements Summary table— Image quality factors and corresponding test charts and modules Sharpness Noise Tonal response & contrast Color accuracy Lens Distortion Light Falloff & Nonuniformities Blemishes Dynamic range Lateral Chromatic Aberration Veiling Glare Color aliasing and moiré ISO Sensitivity Data compression losses Texture Detail Print Dmax & color gamut Software artifacts Overview and Imatest measurements Image quality is one of those concepts that’s greater than the sum of its parts. But you can’t ignore the parts if your goal is to produce […]