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Guidelines for the Use, Maintenance, and Storage of Test Charts

Test charts are indispensable reference tools in image quality evaluation, and their integrity directly determines the accuracy of test results. Improper use and storage environments can cause charts to fade, scratch, or deform, thereby rendering them useless for calibration.</description>
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        <description>How to Choose the Right Test Chart?

In image quality evaluation (e.g., for cameras, LiDAR, etc.), test charts are standardized and reproducible core tools—they translate “subjective visual perception” into “objective data metrics.” This article starts from basic concepts, breaking down the purposes, parameters, and selection logic of test charts, as well as chart materials, mounting, usage, and storage, to help you precisely match testing requirements and use test charts correctly.$tan\frac{DFO…</description>
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Dynamic Range

I. What is Dynamic Range


Dynamic Range (DR)
For image signals, it refers to the ratio of the maximum to the minimum value of the variable signal; it is used to describe the maximum luminance range from the darkest to the brightest areas of a real scene that an imaging device can simultaneously record. It is commonly measured in decibe…</description>
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        <description>Test Tools User Guide

Test Charts


	*  How to Choose the Right Test Chart
	*  Test Chart Material Selection Guide
	*  Test Chart Usage, Maintenance and Storage Guide
	*  18% Gray

Light Sources


	*  Multispectral Light Box
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 Collimator


	*  Collimator Structure
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 RIQA Image Quality Analysis Software

RIQA is…</description>
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Language Switch Map

维护说明：

	*  第一列是中文页面ID
	*  第二列是英文页面ID
	*  只改表格，不要改格式
	*  新增映射时一行一条
 中文页ID  英文页ID  yanding:成像基础知识:光学:几何光学:主光线角</description>
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Test Chart Material Selection Guide

Test charts are core tools in fields such as optical testing and imaging system evaluation. Their materials directly affect testing accuracy, service life, and applicable scenarios. Selecting the appropriate test chart material requires a comprehensive analysis of the pros and cons of various materials, taking into…</description>
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Texture Loss

1. Concept of Texture


In the field of digital imaging, texture generally refers to low-contrast details. For example, the leaves on a tree or each blade of grass in a prairie are details, also known as texture. In principle, a camera should be able to reproduce these details. For a camera, distinguishing texture from unwanted noise in …</description>
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RIQA-Camera Distortion Module

Distortion is a geometric aberration in optical systems (such as lenses) where the magnification varies across different positions in the field of view, causing the true geometric shape of the subject to be distorted in the image (e.g., straight lines becoming curved). This module supports automatic distortion analysis f…</description>
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RIQA-Camera Grayscale Analysis Module

Grayscale refers to the continuous luminance levels in an image ranging from the darkest (black) to the brightest (white). This module supports the analysis of multi-specification grayscale test charts (both transmissive and reflective), such as 12-patch, 20-patch, and 36-patch charts. It also allows you to invok…</description>
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        <title>en:yanding:image_quality_evaluation:standardized_testing:iso_16505_cms_color_reproduction</title>
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ISO 16505: CMS Color Reproduction Test

Color reproduction (Colour rendering) refers to the ability of a Camera Monitor System ($(u'_r, v'_r)$$(u'_{it}, v'_{it})$$(\Delta u' = u'_{it} - u'_r, \Delta v' = v'_{it} - v'_r)$$\theta_{icolor}$$\left( (u'_{it} - u'_r) \geq 0 \text{ and } (v'_{it} - v'_r) \geq 0 \right): \theta_{icolor} = \arctan\left( \frac{…</description>
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        <dc:creator>Anonymous (anonymous@undisclosed.example.com)</dc:creator>
        <title>en:yanding:image_quality_evaluation:standardized_testing:qc_t_1128_2019</title>
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Image Solutions for &quot;QC/T 1128-2019: Automotive Cameras&quot;

Overview

“QC/T 1128-2019: Automotive Cameras$\text{SNR (dB)} = 20\log_{10}\left(\frac{\text{Si}}{\text{Ni}}\right)$${Si}$\( i \)${Ni}$\( i \)$$\text{DR} = 20 \left( D_{\text{SNR}=1} - D_{\text{SNR}=\text{MAX}} \right)$$$D_{\text{SNR}=1}$$D_{\text{SNR}=\text{MAX}}$$t_{on}$$t_{SL}$$$\theta_{\tex…</description>
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        <title>en:yanding:image_quality_evaluation:test_cases:adas_test_cases:contrast_performance_index_cpi</title>
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Contrast Performance Indicators (CPI)

1. Concept of Contrast Performance Indicators (CPI)

Contrast is a crucial fundamental condition for distinguishing objects from their surroundings. Contrast Performance Indicators (CPI) are used to evaluate the contrast reproduction capability of an imaging system and are mainly divided into two types: Contrast …</description>
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        <dc:date>2026-07-23T12:51:18+00:00</dc:date>
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        <title>en:yanding:image_quality_evaluation:test_cases:camera_test_cases:distortion</title>
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Distortion

1. What is Distortion?

Distortion refers to the geometric distortion of the image formed by an optical system relative to the object itself. It is caused by the inconsistency of lateral magnification between the edge and the center of the lens. Distortion only alters the spatial geometry of the object and does not affect the sharpness (re…</description>
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        <title>en:yanding:image_quality_evaluation:test_cases:camera_test_cases:white_balance</title>
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White Balance

The environment we live in is vibrant and colorful, featuring a red umbrella, a yellow beach chair, or a clear blue sky. White balance is utilized in cameras to ensure accurate color reproduction of subjects during capture. In addition to automatic color adjustment, semi-automatic and manual adjustments are also available.</description>
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RIQA—Camera Color Analysis Module

The RIQA—Camera Color Analysis Module is used to evaluate the color metrics of imaging systems, including data on color difference, luminance, saturation, and other aspects. This module supports testing with standard 24-color, 140-color, and 14-color charts.$ CIE$$L_{ab}$</description>
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Auto Exposure (AE)

Proper exposure makes images look more realistic and comfortable, and it is the first and crucial step affecting subsequent image processing. The three common parameters for controlling exposure are exposure time, sensitivity (ISO), and aperture. As one of the three common means to control exposure, aperture is rarely mentioned in …</description>
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        <dc:date>2026-07-15T12:13:13+00:00</dc:date>
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        <title>en:keywords</title>
        <link>https://wiki.yanding.com/doku.php?id=en:keywords&amp;rev=1784117593&amp;do=diff</link>
        <description>dynamic range/color reproduction/spatial frequency response/uniformity/distortion/flare/flicker/CMS/test charts</description>
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    <item rdf:about="https://wiki.yanding.com/doku.php?id=en:start&amp;rev=1787323327&amp;do=diff">
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        <dc:date>2026-08-21T14:42:07+00:00</dc:date>
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        <title>en:start</title>
        <link>https://wiki.yanding.com/doku.php?id=en:start&amp;rev=1787323327&amp;do=diff</link>
        <description>Welcome to Yanding Knowledge Base!

This knowledge base is exclusively created by YANDING, free and publicly accessible, focusing on two core areas: imaging fundamentals and image quality evaluation, aiming to provide systematic knowledge support for users with different needs.</description>
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    <item rdf:about="https://wiki.yanding.com/doku.php?id=en:yanding:image_quality_evaluation:standardized_testing:ieee_2020_2024_cpi&amp;rev=1783670612&amp;do=diff">
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        <dc:date>2026-07-10T08:03:32+00:00</dc:date>
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        <title>en:yanding:image_quality_evaluation:standardized_testing:ieee_2020_2024_cpi</title>
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IEEE 2020-2024 CPI Test

Definition of CPI

Contrast is a crucial fundamental condition for distinguishing objects from their surroundings. $(L_{in})$$(L_{in})=(L_{max}+L_{min})/2$$L_{in}$$L_{max}$$L_{min}$$L_{max}$$L_{min}$$C_{W}$$C_{M}$$C_{W}=\frac{L_{max}}{L_{min}}-1$$C_{W}$$L_{max}$$L_{min}$$C_{W}$$L_{min}$$C_{M}=\frac{L_{max}-L_{min}}{L_{max}+L_{…</description>
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    <item rdf:about="https://wiki.yanding.com/doku.php?id=en:yanding:image_quality_evaluation:standardized_testing:ieee_2020_2024_flare&amp;rev=1783670613&amp;do=diff">
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IEEE 2020-2024 Standard Flare Test

 I. Definition of Flare 

Flare refers to non-imaging light that propagates to the image plane along unintended optical paths, resulting in reduced image contrast and undesirable imaging artifacts. In this standard, $$F=\frac{S_{Black}}{S_{White}}\times 100\%$$$S_{Black}$$S_{White}$$L_{ref}$$R_{ref}$$R_{flare}$$E_{f…</description>
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    <item rdf:about="https://wiki.yanding.com/doku.php?id=en:yanding:image_quality_evaluation:standardized_testing:iso_15739_noise&amp;rev=1783670614&amp;do=diff">
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        <dc:date>2026-07-10T08:03:34+00:00</dc:date>
        <dc:creator>Anonymous (anonymous@undisclosed.example.com)</dc:creator>
        <title>en:yanding:image_quality_evaluation:standardized_testing:iso_15739_noise</title>
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ISO 15739: Noise Measurements

Overview

The ISO 15739 standard specifies the test methods for noise and dynamic range of digital cameras.${L_{j}}$$g(L_{j})$$$          g(L_{j})       =          \frac{1}{2}        [     \frac{  I(L_{j})-I(L_{i}) }  {L_{j}-L_{i} }   +    \frac   {   I(L_{k}) - I(L_{j})   }    {L_{k}-L_{j}}   ]           $$$I(L_{j})$$L_…</description>
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    <item rdf:about="https://wiki.yanding.com/doku.php?id=en:yanding:image_quality_evaluation:test_cases:camera_test_cases:chromatic_aberration&amp;rev=1784807986&amp;do=diff">
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        <title>en:yanding:image_quality_evaluation:test_cases:camera_test_cases:chromatic_aberration</title>
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Chromatic Aberration

1. What is Chromatic Aberration


Chromatic aberration, also known as “color fringing</description>
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Image Quality Assessment

Overview

Image Quality Assessment (IQA) refers to the subjective and objective analysis and evaluation of the imaging performance (such as resolution, noise, distortion, color, dynamic range, etc.) of an imaging system under given conditions (such as the captured scene, lighting environment, and display device input of a cam…</description>
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    <item rdf:about="https://wiki.yanding.com/doku.php?id=en:yanding:image_quality_evaluation:test_cases:camera_test_cases:sfr&amp;rev=1784807986&amp;do=diff">
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        <title>en:yanding:image_quality_evaluation:test_cases:camera_test_cases:sfr</title>
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Spatial Frequency Response (SFR)

1. Concept of Spatial Frequency Response

Spatial Frequency Response (</description>
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    <item rdf:about="https://wiki.yanding.com/doku.php?id=en:yanding:image_quality_evaluation:test_cases:cms_test_cases:cms_gray_level_reproduction&amp;rev=1783670607&amp;do=diff">
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CMS Gray Scale Reproduction

Test Method:

Display the gray scale test chart on the monitor, measure the luminance of each gray patch displayed on the monitor, calculate the lightness of each gray patch according to Formula (1), and then calculate the lightness difference between adjacent gray patches according to Formula (2). The requirement is that …</description>
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    <item rdf:about="https://wiki.yanding.com/doku.php?id=en:yanding:image_quality_evaluation:tools:light_sources:traffic_light_simulation_platform&amp;rev=1783670586&amp;do=diff">
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        <title>en:yanding:image_quality_evaluation:tools:light_sources:traffic_light_simulation_platform</title>
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Yanding Road Traffic Signal Light Simulation and Testing Platform

With the continuous development of autonomous driving technology, corresponding testing methods must also be upgraded synchronously. Current testing mostly focuses on lane detection, object tracking, and scene understanding. However, road traffic signal light recognition, as a critical…</description>
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    <item rdf:about="https://wiki.yanding.com/doku.php?id=en:yanding:image_quality_evaluation:tools:riqa_software:riqa_camera_resolution_module&amp;rev=1783670582&amp;do=diff">
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RIQA-Camera Resolution Module

Resolution is used to evaluate the ability of an imaging system (such as cameras, lenses, etc.) to reproduce details at different spatial frequencies. This module supports the analysis of various mainstream test targets, including slanted-edge targets, line pair targets, Siemens star targets, and dead leaves targets, mee…</description>
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