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PD IEC TR 62977 — Electronic Display Devices Technical Report Series (Measurements of Optical Properties Multi-Colour Test Patterns and Evaluation of Optical Characteristics Mura)
Part 2-3: Measurements of optical properties — Multi-colour test patterns; Part 3-2: Evaluation of optical characteristics — Mura
🖥️ PD IEC/TR 62977 is the UK adoption of technical reports from the IEC 62977 family, developed under the IEC committee for electronic display devices (TC 110; in this batch adopted in the UK through Committee EPL/47, Semiconductors). IEC 62977 is the international standard system for the optical property measurement and evaluation of electronic display devices. This batch contains two documents: Part 2-3 gives the multi-colour test patterns for measuring optical properties, and Part 3-2 gives the standard evaluation method for Mura. These technical reports provide unified, reproducible measurement and evaluation methods for the optical quality of displays (LCD, OLED, etc.).
💡 The challenge of display quality assessment is “reproducibility”: the same display gives different results with different measurement geometries, test patterns and evaluation algorithms. The IEC 62977 series achieves comparability and traceability across laboratories and vendors by unifying test patterns, measuring conditions and data-processing methods.
1. Scope and Series Structure 📌
The IEC 62977 family is organised as “general — specific”: Part 2 series covers the measurement of optical performance (chromaticity, luminance, contrast, viewing angle, response time, etc.), while Part 3 series covers the evaluation of optical characteristics (including defects and uniformity). This batch contains two documents:
| Document | Subject | Content | Year |
|---|---|---|---|
| PD IEC/TR 62977-2-3:2017 | Multi-colour test patterns | Electronic display devices — Measurements of optical properties — Multi-colour test patterns | 2017 |
| PD IEC/TR 62977-3-2:2016 | Mura evaluation | Electronic display devices — Evaluation of optical characteristics — Mura | 2016 |
⚠️ The two documents are technically complementary: Part 2-3 addresses “what pattern to measure”, while Part 3-2 addresses “how to evaluate non-uniformity”. Test patterns are the measurement input, and Mura evaluation is the output judgment of display uniformity quality — together they form a complete chain of display quality assessment.
2. Part 2-3 — Multi-Colour Test Patterns for Optical Property Measurement 🎨
Part 2-3 (2017) gives the method for multi-colour test patterns for measuring the optical properties of colour display devices. Core content includes:
- Test pattern design: specifying the design principles and formats of multi-colour test patterns (colour fields, mixed colours, black/white greyscale, etc.) used to measure luminance, chromaticity, contrast, viewing angle and uniformity;
- Colour gamut and multi-colour measurement: how multi-colour test patterns are used to measure a display’s colour gamut, white balance, chromaticity coordinates and chromaticity uniformity;
- Measurement configuration and conditions: the measuring environment (dark room), instruments (colorimeter/spectrometer), screen area and viewing angle conditions;
- Measurement uncertainty: discussion of sources and estimation of measurement uncertainty in multi-colour measurement from instrument, pattern and environment.
💡 Test patterns are the “ruler” of optical measurement: the same parameter (e.g. luminance uniformity) measured with different patterns (white field, greyscale, mixed colour) yields different results. Standardised test-pattern definitions are the prerequisite for comparability between laboratories and vendors.
3. Part 3-2 — Evaluation of Optical Characteristics: Mura 🕳️
Part 3-2 (2016) gives the standard evaluation method for Mura on displays. Mura refers to localised patches of luminance or chromaticity non-uniformity (e.g. greyscale clouding, bright/dark spots) — an important display quality defect. Core content includes:
- Measuring conditions: the display luminance, greyscale setting, viewing distance and display area for Mura measurement;
- Measurement data processing method: the procedure for converting raw image data into luminance data suitable for evaluation;
- Measurement data processing flow: the steps including median filter, transformation from CIE XYZ to an opponent colour space, and convolution with the contrast sensitivity function (CSF);
- Standard evaluation algorithm: applying a human visual system (HVS) model to the filtered data to produce a quantitative Mura evaluation result.
✅ The difficulty of Mura evaluation is “making the machine emulate the human eye”: directly measuring luminance difference cannot reflect the human perception of spatial frequency, contrast and colour differences. Through visual-model steps such as CSF convolution and opponent colour space transformation, the evaluation result matches subjective human perception.
4. Engineering and Application Practice 🛠️
- 🔍 Unified measurement scheme: in incoming and outgoing inspection of display devices, uniformly adopt the standard test patterns and Mura evaluation algorithm so results are comparable across batches and production lines.
- 🎯 Greyscale and luminance levels: select representative greyscale and luminance settings per the standard, covering the evaluation range from low greyscale (where Mura is prone to appear) to high luminance.
- 📊 Visual model parameter calibration: calibrate the contrast sensitivity function and colour space transformation parameters for different display technologies and pixel structures, so algorithmic evaluation matches actual perception.
- 📋 Data traceability: record measuring conditions, test patterns and algorithm versions so Mura determination is reproducible and traceable, supporting alignment with customer acceptance criteria.
5. Frequently Asked Questions ❓
❓ Q: How do Parts 2-3 and 3-2 relate?
A: Part 2-3 specifies the multi-colour test patterns (measurement input), while Part 3-2 specifies Mura evaluation (uniformity quality judgment). The former ensures a consistent “ruler” for measurement, the latter a consistent “algorithm” for evaluation.
A: Part 2-3 specifies the multi-colour test patterns (measurement input), while Part 3-2 specifies Mura evaluation (uniformity quality judgment). The former ensures a consistent “ruler” for measurement, the latter a consistent “algorithm” for evaluation.
❓ Q: What is Mura?
A: Mura is a visual defect of localised luminance or chromaticity non-uniformity on a display — patches, clouds, bands and similar, often visible in greyscale images (e.g. dark-grey clouding). It is an important display quality defect that must be quantified by measurement and visual models.
A: Mura is a visual defect of localised luminance or chromaticity non-uniformity on a display — patches, clouds, bands and similar, often visible in greyscale images (e.g. dark-grey clouding). It is an important display quality defect that must be quantified by measurement and visual models.
❓ Q: Why is the contrast sensitivity function (CSF) used in Mura evaluation?
A: The human eye has different contrast sensitivity at different spatial frequencies; CSF describes the perception of spatial contrast. Convolving the measured data with CSF brings the machine evaluation close to human subjective perception, avoiding “measured non-uniformity that the eye cannot see” or the reverse.
A: The human eye has different contrast sensitivity at different spatial frequencies; CSF describes the perception of spatial contrast. Convolving the measured data with CSF brings the machine evaluation close to human subjective perception, avoiding “measured non-uniformity that the eye cannot see” or the reverse.
🔍 Q: What roles do filtering and colour space transformation play in Mura measurement?
A: The median filter removes sensor noise and isolated defects; the opponent colour space transformation separates luminance and chromaticity information, consistent with the human colour vision mechanism; subsequent convolution with the CSF simulates visual perception. Together they achieve a human-consistent quantification of non-uniformity.
A: The median filter removes sensor noise and isolated defects; the opponent colour space transformation separates luminance and chromaticity information, consistent with the human colour vision mechanism; subsequent convolution with the CSF simulates visual perception. Together they achieve a human-consistent quantification of non-uniformity.