CN116386498A - Quantitative evaluation data set construction method, device and equipment - Google Patents
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Abstract
The invention relates to a quantitative evaluation data set construction method, a quantitative evaluation data set construction device and quantitative evaluation data set construction equipment, which comprise the following steps: performing XYZ-to-RGB conversion on the actual screen Mura image pixel by pixel to obtain an RGB simulation image; performing quantization scoring based on the RGB analog graph and obtaining a data set; the dataset was applied to mura tests. According to the method, the device and the equipment for constructing the quantitative evaluation data set, the actual screen Mura image is subjected to the conversion from XYZ to RGB pixel by pixel, the obtained RGB analog image is described on the basis of the XYZ color space, and at the moment, even if the Mura analog image is driven to be displayed by using the RGB image, the same Mura analog image in the data set can have the same display effect on different display equipment, namely equipment independence, so that the subjective evaluation values of the same Mura analog image are not different, and the development of a Mura quantization algorithm is not easily affected.
Description
Technical Field
The invention relates to the field of Mura quantitative evaluation of display panels, in particular to a method, a device and equipment for constructing a quantitative evaluation data set.
Background
At present, display screens are penetrated into every corner of daily life of people, such as mobile phones, televisions, electronic signboards, outdoor advertisements and the like. In the manufacturing process of the display screen, a plurality of defects are inevitably introduced, so that the display effect and even the product quality are affected. Conventional practice has been to introduce AOI (automated optical inspection) based Mura defect detection and manual visual inspection to rank Mura for several important parts of the manufacturing process.
The AOI detection mainly uses a high-resolution industrial camera, and based on a large number of actual Mura photo data sets obtained from a production line and by means of the existing mature deep learning algorithm, the current Mura defect detection can obtain good results, the accuracy rate can reach 98%, and the omission factor is less than 0.2%. However, mura quantitative judgment and the like still depend on manpower, and have the problems of time and labor waste and poor repeatability. In order to solve the problem that the Mura quantitative judgment and the like are realized by relying on manpower, researchers propose to use a Mura quantitative evaluation algorithm to replace the traditional Mura manual judgment and the like. Although the Mura quantization algorithm has advanced to some extent, the effect of the Mura quantization algorithm is not acceptable in industry, and the industry still uses manual visual inspection as a main means at present. And a data set formed by the Mura simulation graph and the corresponding subjective scores is a key factor for determining the quality of the development result of the Mura quantization algorithm.
In the related art, when the quantization scoring is performed, the mainstream method generally regenerates a plurality of different Mura simulated diagrams of RGB color gamut space descriptions according to the type of the screen Mura in practice, the simulated diagrams are displayed on a display screen one by one, and then the five-level image quality subjective evaluation experiment recommended by ITU-R is used for quantization scoring.
However, the simulated images are expressed by using RGB color space, and the display effect of the color space is device-dependent, that is, the display effect of the same Mura simulated image on different screen bodies is different, and the subjective evaluation values of the same Mura simulated image are different, which ultimately affects the development of the Mura quantization algorithm.
Therefore, there is a need to devise a new quantitative evaluation dataset construction method to overcome the above-mentioned problems.
Disclosure of Invention
The embodiment of the invention provides a method, a device and equipment for constructing a quantitative evaluation data set, which are used for solving the problems that the display effects of the same Mura analog graph on different screen bodies in the related technology are different, the subjective evaluation values of the same Mura analog graph are different, and the development of a Mura quantitative algorithm is finally influenced.
In a first aspect, a method of constructing a quantitative evaluation dataset is provided, comprising the steps of: performing XYZ-to-RGB conversion on the actual screen Mura image pixel by pixel to obtain an RGB simulation image; performing quantization scoring based on the RGB analog graph and obtaining a data set; the dataset was applied to mura tests.
In some embodiments, the converting the actual screen Mura image from XYZ to RGB pixel by pixel to obtain an RGB analog image includes: obtaining an XYZ simulation diagram based on an actual screen Mura diagram; and according to the sub-pixel luminescence characteristics of the experimental ideal screen body, converting the XYZ analog diagram into the RGB analog diagram pixel by pixel.
In some embodiments, the obtaining the XYZ simulation map based on the actual screen Mura map includes: extracting pixel-by-pixel brightness of an actual screen Mura graph, and normalizing to obtain a normalized graph; multiplying the normalized graph with the set white point to obtain an XYZ simulated graph.
In some embodiments, the converting from the XYZ analog graph to the RGB analog graph according to the sub-pixel luminescence characteristic of the experimental ideal screen body includes: measuring the XYZ value and gamma value of RGB sub-pixels of an ideal experimental screen under G0 gray scale, wherein the G0 gray scale is the gray scale with the brightness closest to the preset brightness of the screen actually measured; and converting the XYZ analog image into the RGB analog image pixel by utilizing the XYZ values and gamma values of the RGB sub-pixels of the experimental ideal screen body under the G0 gray scale.
In some embodiments, the performing quantization scoring and obtaining the data set based on the RGB analog graph includes: and (5) performing double-stimulus scoring on the RGB analog graph based on the limit sample to obtain RGB analog graph scoring.
In some embodiments, the performing the dual stimulus scoring on the RGB simulated graph based on the limit sample to obtain the RGB simulated graph score includes: displaying the RGB analog diagram and the limit sample analog diagram on a screen; and finding out a limit sample closest to the visual parameters of the RGB analog map, and scoring the RGB analog map by taking the quantized value of the limit sample as a reference.
In some embodiments, before the converting the actual screen Mura image from XYZ to RGB pixel by pixel, the method further includes: and performing Mura identification on the actual screen Mura graph by using the AOI to obtain a Mura type and a binarized mask graph.
In some embodiments, the dataset includes XYZ simulated drawings obtained based on actual screen Mura drawings, scores corresponding to the simulated drawings, binarized mask drawings for describing the positions of the simulated drawings, and simulated drawing information description text files.
In a second aspect, there is provided a quantitative evaluation dataset construction apparatus, comprising: the conversion module is used for carrying out the conversion from XYZ to RGB on the actual screen Mura image pixel by pixel to obtain an RGB analog image; a data construction module for performing quantization scoring based on the RGB analog map and obtaining a data set; a test module for applying the data set to mura tests.
In a third aspect, a quantized evaluation dataset construction apparatus is provided, the quantized evaluation dataset construction apparatus comprising a processor, a memory, and a quantized evaluation dataset construction program stored on the memory and executable by the processor, wherein the quantized evaluation dataset construction program, when executed by the processor, implements the steps of the quantized evaluation dataset construction method described above.
The technical scheme provided by the invention has the beneficial effects that:
the embodiment of the invention provides a method, a device and equipment for constructing a quantitative evaluation data set, wherein an actual screen Mura image is subjected to XYZ-to-RGB conversion pixel by pixel, an obtained RGB analog image is described based on an XYZ color space, and at the moment, even if the RGB image is used for driving and displaying the Mura analog image, the same Mura analog image in the data set can have the same display effect on different display equipment, namely equipment independence, so that the difference of subjective evaluation values aiming at the same Mura analog image is not easy to occur, and the development of a Mura quantization algorithm is not easy to be influenced.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method for constructing a quantitative evaluation dataset according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a Mura simulation diagram provided by an embodiment of the present invention;
fig. 3 is a schematic diagram of a binarized mask chart according to an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The embodiment of the invention provides a method for constructing a quantitative evaluation data set, which can solve the problems that the display effects of the same Mura analog graph on different screens in the related technology are different, subjective evaluation values aiming at the same Mura analog graph are different, and the development of a Mura quantitative algorithm is finally influenced.
Referring to fig. 1, a method for constructing a quantitative evaluation dataset according to an embodiment of the present invention may include the following steps:
s1: and carrying out XYZ-to-RGB conversion on the actual screen Mura graph pixel by pixel to obtain an RGB simulation graph. In this embodiment, an actual screen Mura image may be captured using an imaging device.
S2: quantization scoring is performed based on the RGB analog map and a dataset is obtained. Wherein the data set is formed by a piece of data, each piece of data may include: the score corresponding to each simulated graph is used for describing a binarized mask graph of the position of the simulated graph of Mura and the information of the simulated graph of Mura is used for describing text files and the like based on the simulated graph of Mura of XYZ. The Mura simulated drawing information description text file may contain the following: the type, number, size, location, etc. of Mura.
S3: the dataset was applied to mura tests. The data set can be applied to Mura quantization algorithm development, testing and the like, and the purposes of development, testing and the like are to serve Mura testing.
In this embodiment, since the actual screen Mura image is subjected to XYZ-to-RGB conversion pixel by pixel, and the obtained RGB analog image is described based on the XYZ color space, even if the Mura analog image is driven and displayed by using the RGB image, the same Mura analog image in the data set can have the same display effect on different display devices, that is, the device independence, so that the subjective evaluation values of the same Mura analog image are not easy to be different, and the development of the Mura quantization algorithm is not easy to be affected.
Further, fig. 2 and 3 show Mura simulation diagrams and binarization mask diagrams. The subjective score is a floating point number between 0 and 5, where 5 indicates the strongest visual perception and 0 indicates an ideal uniform picture without Mura. As can be seen from fig. 3, the Mura region in the binarized mask map of fig. 3 is identical to the Mura position in the Mura simulation map of fig. 2 to mark the exact position and extent of Mura.
Further, the present embodiment preferably describes a Mura simulation diagram in CIE1931XYZ color space. The CIE uses monochromatic light with wavelengths of 700nm, 546.1nm and 435.8nm to carry out a color matching experiment for three primary colors of RGB, and coordinate transformation is carried out on the matching result to eliminate negative values in the matching result, so that the CIE1931XYZ color space is obtained. According to the trichromatic additive model, if any one color and another color of three primary colors mixed with different components all make humans look identical, then the components of the three primary colors are called the trichromatic stimulus values of the color, i.e., X, Y and Z of the CIE1931 color space. The CIE XYZ color space is a direct measurement based on human color vision, serving as the basis for the definition of many other color spaces. Thus, the Mura described in the CIE1931XYZ color space has device independence, and the Mura simulation diagram of the color space description can theoretically completely reproduce the visual effects of actual Mura.
In the related art, a Mura simulation diagram of several different RGB color gamut space descriptions is regenerated according to the type of the screen Mura in practice, the Mura simulation diagram is too ideal, the generated simulation diagram generally uses gaussian distribution to describe the brightness transition of the Mura from the center to the background, but the brightness transition is not the same in practice, and the outline of the simulation diagram is an ideal shape, such as an ideal geometric shape of a circle, an ellipse, a horizontal straight line, and the like. In this embodiment, before performing XYZ to RGB conversion on the actual screen Mura image pixel by pixel to obtain the RGB analog image, the method may further include: the actual screen Mura pattern is subjected to Mura recognition by using AOI (Automated Optical Inspection, automatic optical inspection) to obtain a Mura type and a binarized mask pattern. In this embodiment, the Mura type, brightness distribution, shape, size, and the like of the Mura analog diagram may be generated by using the real Mura obtained in the AOI test as a sample, thereby improving the authenticity of the Mura analog diagram.
In some embodiments, in step S1, the performing XYZ to RGB conversion on the actual screen Mura image pixel by pixel to obtain an RGB analog image may include: obtaining an XYZ simulation diagram based on an actual screen Mura diagram; and according to the sub-pixel luminescence characteristics of the experimental ideal screen body, converting the XYZ analog diagram into the RGB analog diagram pixel by pixel. Since the display panel generally has RGB three primary color sub-pixels, each sub-pixel has its brightness controlled by 8bit gray scale data, and the relationship between brightness and gray scale is generally a power function relationship, where the exponent value is called gamma value. The brightness and color gamut of different display panels generally differ to some extent. The qualified display panel has consistent luminous characteristics of the sub-pixels at different positions, and can ensure that human eyes are difficult to distinguish. According to the characteristic, the Mura simulation diagram of the CIE1931XYZ color space can be converted into the RGB space of each screen body and displayed, so that the same Mura simulation display effect on different screen bodies can be realized.
Of course, in other embodiments, the manufacturer of the experimental ideal screen may also provide a light emitting characteristic index document, and the light emitting characteristic index document is determined according to a direct lookup table of document parameters, and the conversion from the XYZ analog diagram to the RGB analog diagram may also be performed pixel by pixel.
In some alternative embodiments, the obtaining the XYZ simulation map based on the actual screen Mura map may include: extracting pixel-by-pixel brightness of an actual screen Mura graph, and normalizing to obtain a normalized graph; multiplying the normalized graph with the set white point to obtain an XYZ simulated graph. In this embodiment, a numerical value of a white point is required to be specified, the white point may be a common value such as D65, D50, etc., and for the purpose of the specification and standard of the database, the white point is generally not changed after the determination; the white point in this embodiment is preferably D65 (x=95.047, y=100, z= 108.883).
Preferably, after the XYZ analog diagram is obtained, the size of the analog diagram may be unified to a specific pixel by filling and clipping, for example 2400×1080 selected in this embodiment, or other values, where XYZ values of the filled pixel are equal to XYZ values of the D65 white point.
Of course, in other embodiments, an area array colorimeter may be used to obtain an XYZ simulation of the actual screen Mura, and conventional computer graphics processing methods (such as rotation, scaling, contrast stretching, shape transformation, etc.) may be used to process the image, and let the background area value equal to the set white point value, to obtain the XYZ simulation.
In some embodiments, the conversion from the XYZ analog map to the RGB analog map according to the sub-pixel luminescence characteristic of the experimental ideal screen may include: measuring the XYZ value and gamma value of an RGB sub-pixel of an ideal screen under G0 gray scale, wherein the G0 gray scale is firstly determined, the G0 gray scale can be the gray scale with the brightness closest to the preset brightness of the screen, which is actually measured, for example, the G0 gray scale can be the gray scale with the brightness closest to 100nit of a W picture of 32, 64, 96, 128, 160, 192, 224 and 255 (of course, the gray scale with the brightness closest to 100nit of other values can be selected by the 100 nit), and the XYZ value and gamma value of the RGB sub-pixel under the G0 gray scale can be measured by using a colorimeter, and meanwhile, the pixel pitch P can be measured or extracted; then, the XYZ analog image can be converted into the RGB analog image pixel by utilizing the XYZ values and gamma values of the RGB sub-pixels of the experimental ideal screen body under the G0 gray scale.
Of course, in other embodiments, conversion may be performed using other gray scales, but the conversion error may be relatively large, and the root cause is that the conversion process depends on the brightness of the screen and the gray scale to satisfy the ideal exponential model, but the actual screen may not be ideal, and there will always be a deviation, and the actual XYZ value of the G0 gray scale is close to the target value, and the error may be relatively small.
In this embodiment, the conversion from XYZ to RGB is because the experimental ideal screen displays RGB, and the XYZ is measured by a colorimeter, and the measured XYZ is consistent with the XYZ simulated image.
Wherein, the conversion formula is as follows:
wherein X is R 、Y R And Z R R sub-pixels are at G 0 XYZ values, X at gray scale G 、Y G And Z G The sub-pixels of G are at G 0 XYZ values, X at gray scale B 、Y B And Z B The sub-pixels B are at G 0 The XYZ values gammaR, gammaG and gamma b at gray scale are gamma values of RGB sub-pixels, respectively.
The conversion from XYZ analog to RGB analog can be performed using the conversion formula described above.
The subjective scoring experiments in the related art mostly use single stimulus or double stimulus image quality subjective quantitative evaluation experiments compared with the picture without Mura, and the method has good effect on the video image with texture characteristics, but has limited resolution capability on the Mura pattern without detail picture.
In some embodiments, in step 2, the performing quantization scoring based on the RGB simulation map and obtaining the data set may include: and (5) performing double-stimulus scoring on the RGB analog graph based on the limit sample to obtain RGB analog graph scoring. In this embodiment, the quantitative value of the Mura simulated graph is obtained by a dual-stimulus method, that is, the Mura simulated graph to be evaluated is compared with the Mura limit sample with a given score, and the limit sample score with the highest matching degree can be used as the subjective evaluation value, so that subjective deviation of an observer can be greatly reduced, and objectivity and stability of the subjective score are improved.
The limiting sample is a common Mura simulation graph, the subjective scoring steps are the same as the subjective scoring workflow of the Mura simulation graph, but the scoring is determined according to the transmittance of the ND filter, and the corresponding relation is shown in the following table.
Table 1 subjective scores based on ND filters
| Subjective scoring | Conditions (conditions) |
| 0 | No ND filters are visible Mura |
| 1 | No ND filter is visible in Mura, and ND8 is not visible in Mura |
| 2 | Mura is visible through ND8 filter and Mura is not visible through ND16 |
| 3 | Mura is visible through ND16 filter, mura is not visible through ND32 |
| 4 | Mura is visible through ND32 filter and Mura is not visible through ND64 |
| 5 | Mura is visible through ND64 |
The limit sample of the embodiment is obtained by using ND filters with different transmittance, and is in line with the actual use condition of the production line, and an observer can realize the experimental process by only carrying out binary judgment of whether Mura can be seen or not, so that the experimental process is simple and the experimental result is reliable.
In some alternative embodiments, in addition to the scores obtained by the method described above for the "double stimulus experiment + limit sample", the Mura simulated graph may be directly quantitatively evaluated by the method described above in Table 1 (corresponding to the method described above for producing the limit sample score) to obtain the subjective score value. That is, the present embodiment may be a method of obtaining subjective scores of Mura simulated images directly using ND filter without using "dual stimulus experiment + limitation sample".
Further, the performing the dual stimulus scoring on the RGB simulation map based on the limit sample to obtain the RGB simulation map score may include: displaying the RGB analog diagram and the limit sample analog diagram on a screen, wherein the RGB analog diagram and the limit sample analog diagram can be displayed in a split screen or a combined screen; and finding out a limit sample closest to the visual parameters of the RGB analog map, and scoring the RGB analog map by taking the quantized value of the limit sample as a reference. In this embodiment, in actual operation, the experimenter may observe the RGB simulated image and the limited sample simulated image at a set distance, where the set distance is preferably a distance d=2750×p, where P is the measured or extracted pixel pitch P, and at this distance, the spatial frequency of one pixel of the screen body in the human eye is just 48cpd; the experimenter can then switch the limit sample simulation graph and find the limit sample closest to the visual perception of the RGB simulation graph, and score the RGB simulation graph with the quantized value of the limit sample as a reference, wherein the RGB simulation graph is described as a Mura simulation graph, so the RGB simulation graph score is also described as a Mura simulation graph score. Of course, in actual operation, the limit sample closest to the visual parameter of the RGB analog diagram can be automatically found by other machines or devices, and the RGB analog diagram is scored by taking the quantized value of the limit sample as a reference.
In other embodiments, when obtaining RGB analog graph scores based on the limit samples, the limit samples closest to the RGB analog graph may be found according to a common image similarity evaluation function (an algorithm that can be developed for source maturation), instead of manual viewing.
In this embodiment, the sensitivity of the human eye vision is related to the distance, size, contrast, etc. of the target. Since the focal length of the human eye lens is constant, the number and density of visual cells on the retina is also constant, and thus the perception of the size of the human eye to the target is based on spatial angles. Numerous studies on CSF have shown that the limit of viewing at spatial angles by the human eye is about 48cpd, which is visually imperceptible to detail people above this spatial frequency. Therefore, the spatial frequency represented by one pixel of the Mura analog diagram is 48cpd, so that all information of a picture visually perceived by human eyes can be completely described.
Further, after the score is obtained, statistical data screening of the score result can be performed, and an average value is used as a subjective score value. Finally, the XYZ simulation map (preferably, a simulation map using uniform pixels), the subjective score, the binary mask map for describing the position of the RGB simulation map (i.e., mura simulation map), the Mura simulation map information description text file (which may refer to information of Mura type, number, size, position, etc.), etc. may be combined into one piece of data, and added to the data set.
The data set constructed by the embodiment of the invention can solve the problems of lack of unified standard and poor practicability in the traditional data set construction process, and can be used as a standard data set for developing and testing a Mura quantization algorithm.
Compared with the traditional method, the Mura simulation graph constructed by the method is closer to the actual Mura, supports parallel data set expansion, and is convenient for constructing a large-scale data set. The data set constructed based on the method can be used for developing, improving and testing the Mura quantization algorithm, and provides support for the Mura quantization algorithm to replace production line artificial judgment and the like.
The embodiment of the invention also provides a quantitative evaluation data set construction device, which can comprise: the conversion module is used for carrying out the conversion from XYZ to RGB on the actual screen Mura image pixel by pixel to obtain an RGB analog image; the system comprises a data construction module for quantitatively scoring and obtaining a data set based on an RGB analog diagram, and a test module for applying the data set to mura tests. The conversion module and the data construction module may implement the steps of the corresponding embodiments in the above method, which are not described herein.
An embodiment of the present invention further provides a quantized evaluation dataset construction apparatus, which is characterized in that the quantized evaluation dataset construction apparatus includes a processor, a memory, and a quantized evaluation dataset construction program stored on the memory and executable by the processor, wherein the quantized evaluation dataset construction program, when executed by the processor, implements the steps of the quantized evaluation dataset construction method provided in any of the above embodiments.
The descriptions of the processes corresponding to the drawings have emphasis, and the descriptions of other processes may be referred to for the parts of a certain process that are not described in detail.
In the above embodiments, it may be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product described above includes one or more computer instructions. When the above-described computer program instructions are loaded and executed on a computer, the processes or functions described in accordance with the embodiments of the present application are produced in whole or in part. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, from one website, computer, server, or data center to another website, computer, server, or data center via a wired (e.g., coaxial cable, fiber optic, digital subscriber line), or wireless (e.g., infrared, wireless, microwave, etc.). The computer readable storage media may be any available media that can be accessed by a computer or a data storage device such as a server, data center, or the like that contains an integration of one or more available media. The usable medium may be a magnetic medium (e.g., a floppy disk, a memory disk, a magnetic tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., an SSD), etc. In the foregoing embodiments, the descriptions of the embodiments are emphasized, and for parts of one embodiment that are not described in detail, reference may be made to related descriptions of other embodiments.
In the several embodiments provided in the present application, it should be understood that the disclosed apparatus may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, such as the division of the units, merely a logical function division, and there may be additional divisions of actual implementation, such as multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the indirect coupling or direct coupling or communication connection between the illustrated or discussed devices and units may be through some interfaces, devices or units, and may be in electrical or other forms.
The units described above as separate components may or may not be physically separate, and components shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purposes of the solution of the embodiment of the present application.
In addition, each functional unit in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated units described above, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be embodied in essence or a part contributing to the prior art or all or part of the technical solution, in the form of a software product stored in a storage medium, including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in the embodiments of the present application. The aforementioned storage medium may include, for example: a usb disk, a removable hard disk, a read-only memory, a random access memory, a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The foregoing is only a specific embodiment of the invention to enable those skilled in the art to understand or practice the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. A method of constructing a quantitative evaluation dataset, comprising the steps of:
performing XYZ-to-RGB conversion on the actual screen Mura image pixel by pixel to obtain an RGB simulation image;
performing quantization scoring based on the RGB analog graph and obtaining a data set;
the dataset was applied to mura tests.
2. The method for constructing a quantitative evaluation dataset according to claim 1, wherein said converting the actual screen Mura map pixel by pixel into XYZ to RGB to obtain an RGB analog map comprises:
obtaining an XYZ simulation diagram based on an actual screen Mura diagram;
and according to the sub-pixel luminescence characteristics of the experimental ideal screen body, converting the XYZ analog diagram into the RGB analog diagram pixel by pixel.
3. The quantitative evaluation dataset construction method as claimed in claim 2, wherein the obtaining an XYZ simulation map based on an actual screen Mura map includes:
extracting pixel-by-pixel brightness of an actual screen Mura graph, and normalizing to obtain a normalized graph;
multiplying the normalized graph with the set white point to obtain an XYZ simulated graph.
4. A method of constructing a quantitative evaluation dataset according to claim 2 or 3 wherein said pixel-by-pixel conversion of an XYZ analog map to an RGB analog map in accordance with sub-pixel luminescence characteristics of an experimental ideal screen comprises:
measuring the XYZ value and gamma value of RGB sub-pixels of an ideal experimental screen under G0 gray scale, wherein the G0 gray scale is the gray scale with the brightness closest to the preset brightness of the screen actually measured;
and converting the XYZ analog image into the RGB analog image pixel by utilizing the XYZ values and gamma values of the RGB sub-pixels of the experimental ideal screen body under the G0 gray scale.
5. The quantitative evaluation dataset construction method as claimed in claim 1, wherein the performing quantitative scoring based on the RGB simulation map and obtaining a dataset includes:
and (5) performing double-stimulus scoring on the RGB analog graph based on the limit sample to obtain RGB analog graph scoring.
6. The quantitative assessment dataset construction method as claimed in claim 5, wherein said subjecting the RGB simulation map to a limit sample based dual stimulus scoring, obtaining an RGB simulation map score, comprises:
displaying the RGB analog diagram and the limit sample analog diagram on a screen;
and finding out a limit sample closest to the visual parameters of the RGB analog map, and scoring the RGB analog map by taking the quantized value of the limit sample as a reference.
7. The method of constructing a quantitative evaluation dataset of claim 1, further comprising, prior to said pixel-by-pixel XYZ to RGB conversion of the actual screen Mura map to obtain an RGB analog map:
and performing Mura identification on the actual screen Mura graph by using the AOI to obtain a Mura type and a binarized mask graph.
8. A method of constructing a quantitative assessment dataset according to claim 1 or 7, wherein,
the data set comprises an XYZ simulation diagram obtained based on an actual screen Mura diagram, scores corresponding to the simulation diagram, a binarized mask diagram for describing the position of the simulation diagram and a simulation diagram information description text file.
9. A quantitative evaluation dataset construction apparatus, comprising:
the conversion module is used for carrying out the conversion from XYZ to RGB on the actual screen Mura image pixel by pixel to obtain an RGB analog image;
a data construction module for performing quantization scoring based on the RGB analog map and obtaining a data set;
a test module for applying the data set to mura tests.
10. A quantized evaluation dataset construction apparatus, characterized in that it comprises a processor, a memory, and a quantized evaluation dataset construction program stored on the memory and executable by the processor, wherein the quantized evaluation dataset construction program, when executed by the processor, implements the steps of the quantized evaluation dataset construction method according to any of claims 1 to 8.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117727273A (en) * | 2023-12-29 | 2024-03-19 | 上海傲显科技有限公司 | Demura compensation method, device and terminal |
| CN117893514A (en) * | 2024-01-22 | 2024-04-16 | 湖北经济学院 | Mura quantitative evaluation method based on deep convolution self-encoder neural network |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117727273A (en) * | 2023-12-29 | 2024-03-19 | 上海傲显科技有限公司 | Demura compensation method, device and terminal |
| CN117893514A (en) * | 2024-01-22 | 2024-04-16 | 湖北经济学院 | Mura quantitative evaluation method based on deep convolution self-encoder neural network |
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