Display quality detection system and method for a display

Abstract

The present disclosure relates to a display quality detection system and method for a display, the system comprising: a data acquisition device comprising a binocular imaging luminance meter with adjustable spatial position, the binocular imaging luminance meter comprising two imaging luminance meters with adjustable distance, the binocular imaging luminance meter being configured to acquire and send to a processing device, under the condition of being adjusted to a specified spatial position and a specified distance, double-channel detection data of the display, the double-channel detection data comprising data acquired by the two imaging luminance meters respectively for detecting the display quality; and the processing device being configured to determine a display quality detection result of the display according to the double-channel detection data, the display quality detection result representing a subjective visual experience of the display quality of the display. According to the embodiments of the present disclosure, the display quality detection result with higher confidence and closer to the real subjective visual experience of the user can be output for the display in combination with the binocular imaging luminance meter.

Description

Technical Field

[0001] This disclosure relates to the field of display testing technology, and in particular to a display quality testing system and method for displays. Background Technology

[0002] With the development of display technology, the display effect of monitors is transitioning from two-dimensional (flat) to stereoscopic (three-dimensional) displays. Stereoscopic display technology has become a new trend in the field of display technology, with more and more monitors integrating stereoscopic display technology. Furthermore, to improve user experience, glasses-free stereoscopic display technology has become a research hotspot. Specifically, glasses-free stereoscopic display technology projects images with parallax information into space based on lens arrays or grating arrays. When the observer's left and right eyes receive images with parallax information, a three-dimensional perception can be formed through the brain's fusion mechanism, thus producing a stereoscopic visual effect in the brain.

[0003] Currently, there is a lack of display quality testing technologies with higher confidence and closer to the user's real visual experience to standardize and automate the display quality testing of displays using freeform stereoscopic display technology. Summary of the Invention

[0004] In view of this, this disclosure proposes a display quality testing system and method for displays, which can combine dual-channel detection data collected by a binocular imaging luminance meter to output display quality testing results with higher confidence and closer to the user's real subjective visual experience.

[0005] According to one aspect of this disclosure, a display quality testing system for a display is provided, comprising: a data acquisition device including a spatially adjustable binocular imaging luminance meter, the binocular imaging luminance meter including two imaging luminance meters with adjustable spacing, the binocular imaging luminance meter being used to acquire dual-channel detection data of the display and send it to a processing device when adjusted to a specified spatial position and a specified spacing, the dual-channel detection data including data for detecting display quality acquired by the two imaging luminance meters respectively; the processing device being used to determine a display quality testing result of the display based on the dual-channel detection data, the display quality testing result representing a subjective visual experience of the display quality of the display.

[0006] In one possible implementation, determining the display quality test result of the display based on the dual-channel detection data includes: determining objective evaluation parameters based on the dual-channel detection data, wherein the objective evaluation parameters include at least one of the following: spot distribution information, parallax information, depth information, crosstalk distribution information, brightness information, brightness uniformity information, and contrast information; inputting the objective evaluation parameters into a preset mapping model and outputting a subjective experience score, wherein the display quality test result includes the subjective experience score, and the mapping model is obtained by training based on the sample objective evaluation parameters and the corresponding sample subjective experience scores, wherein the sample objective evaluation parameters are determined based on the sample dual-channel detection data collected by the binocular imaging luminance meter at different sample spatial positions and different sample intervals, and the sample subjective experience score is obtained by observers with different interpupillary distances at the different sample spatial positions, and the sample interval corresponds to the interpupillary distance.

[0007] In one possible implementation, the display is used to display at least one of the following images: a dot matrix image, a three-dimensional image, a white image, and a black-and-white striped image, wherein the dot matrix image includes at least one lit pixel; the dual-channel detection data includes at least one of the following: dual-channel spatial distribution radiation energy acquired when the display shows the dot matrix image, a dual-channel disparity map acquired when the display shows the three-dimensional image, a dual-channel brightness map acquired when the display shows the white image, and a dual-channel black-and-white striped image acquired when the display shows the striped image; wherein, the step of using the dual-channel detection data... The objective evaluation parameters are determined, including at least one of the following: determining the spot distribution information of the dual channels based on the spatial distribution radiation energy of the dual channels; determining the disparity information based on the disparity map of the dual channels; determining the geometric depth information based on the disparity information, the specified spatial position, and the specified spacing; determining the actual depth information based on the spot similarity between the spot distribution information of the dual channels and the geometric depth information; determining the crosstalk distribution information based on the spot distribution information of the dual channels; determining the brightness information and brightness uniformity information based on the brightness map of the dual channels; and determining the contrast information based on the black and white stripe map of the dual channels.

[0008] In one possible implementation, the processing device is further configured to send a control signal to the data acquisition device according to a preset detection requirement. The control signal is used to control the binocular imaging brightness meter to move to the specified spatial position and the specified distance. The specified spatial position includes the relative distance and relative angle between the binocular imaging brightness meter and the display.

[0009] In one possible implementation, the binocular imaging luminance meter further includes: an imaging luminance meter guide rail, a transmission rod, and an electric knob; the electric knob is connected to the transmission rod, and the transmission rod is connected to the imaging luminance meter guide rail; wherein, the electric knob is used to rotate according to a control signal sent by the processing device; the transmission rod is used to transmit the rotation operation of the electric knob to a mechanical gear on the imaging luminance meter guide rail; the imaging luminance meter guide rail is used to drive the two imaging luminance meters to move in opposite directions through the mechanical gear, thereby changing the distance between the two imaging luminance meters.

[0010] In one possible implementation, the data acquisition device further includes: a guide rail and a robotic arm; the robotic arm is fixedly connected to the guide rail, the binocular imaging luminance meter is fixedly connected to the robotic arm, the guide rail is used to move according to the control signal sent by the processing device to adjust the relative distance between the binocular imaging luminance meter and the display, and the robotic arm is used to move according to the control signal sent by the processing device to adjust the relative angle between the binocular imaging luminance meter and the display.

[0011] According to another aspect of this disclosure, a method for detecting the display quality of a monitor is provided, which is applied to a processing device, comprising: acquiring dual-channel detection data collected by a data acquisition device on the monitor, the data acquisition device including a spatially adjustable binocular imaging luminance meter, the binocular imaging luminance meter including two imaging luminance meters with adjustable spacing, the binocular imaging luminance meters being used to collect dual-channel detection data on the monitor and send it to the processing device when adjusted to a specified spatial position and a specified spacing, the dual-channel detection data including data collected by the two imaging luminance meters respectively for evaluating display quality; and determining a display quality detection result of the monitor based on the dual-channel detection data, the display quality detection result representing a subjective visual experience of the display quality of the monitor.

[0012] In one possible implementation, determining the display quality test result of the display based on the dual-channel detection data includes: determining objective evaluation parameters based on the dual-channel detection data, wherein the objective evaluation parameters include at least one of the following: spot distribution information, parallax information, depth information, crosstalk distribution information, brightness information, brightness uniformity information, and contrast information; inputting the objective evaluation parameters into a preset mapping model and outputting a subjective experience score, wherein the display quality test result includes the subjective experience score, and the mapping model is obtained by training based on the sample objective evaluation parameters and the corresponding sample subjective experience scores, wherein the sample objective evaluation parameters are determined based on the sample dual-channel detection data collected by the binocular imaging luminance meter at different sample spatial positions and different sample intervals, and the sample subjective experience score is obtained by observers with different interpupillary distances at the different sample spatial positions, and the sample interval corresponds to the interpupillary distance.

[0013] In one possible implementation, the display is used to display at least one of the following images: a dot matrix image, a three-dimensional image, a white image, and a black-and-white striped image, wherein the dot matrix image includes at least one lit pixel; the dual-channel detection data includes at least one of the following: dual-channel spatial distribution radiation energy acquired when the display shows a dot matrix image, a dual-channel disparity map acquired when the display shows a three-dimensional image, a dual-channel brightness map acquired when the display shows a white image, and a dual-channel black-and-white striped image acquired when the display shows a black-and-white striped image; wherein, based on the dual-channel detection data, The objective evaluation parameters are determined, including at least one of the following: determining the spot distribution information of the dual channels based on the spatial distribution radiation energy of the dual channels; determining the disparity information based on the disparity map of the dual channels; determining the geometric depth information based on the disparity information, the specified spatial position, and the specified spacing; determining the actual depth information based on the spot similarity between the spot distribution information of the dual channels and the geometric depth information; determining the crosstalk distribution information based on the spot distribution information of the dual channels; determining the brightness information and brightness uniformity information based on the brightness map of the dual channels; and determining the contrast information based on the black and white stripe map of the dual channels.

[0014] In one possible implementation, the method further includes: sending a control signal to the data acquisition device according to a preset detection requirement, the control signal being used to control the binocular imaging brightness meter to move to the specified spatial position and the specified spacing, the specified spatial position including the relative distance and relative angle between the binocular imaging brightness meter and the display.

[0015] According to embodiments of this disclosure, by utilizing a binocular imaging luminance meter with adjustable spatial position and spacing, it is possible to simulate the interpupillary distance of a person and the relative spatial position (including distance and angle) between the person and the display. This allows for the acquisition of dual-channel detection data at different spacings (i.e., different interpupillary distances) and different spatial positions (i.e., different distances and different angles). By then using the dual-channel detection data acquired by the binocular imaging luminance meter, a display quality detection result with higher confidence and closer to the user's actual subjective visual experience can be determined. In other words, the determined display quality detection result is more humanized and has stronger confidence.

[0016] Other features and aspects of this disclosure will become clear from the following detailed description of exemplary embodiments with reference to the accompanying drawings. Attached Figure Description

[0017] The accompanying drawings, which are included in and form part of this specification, illustrate exemplary embodiments, features, and aspects of this disclosure together with the specification and serve to explain the principles of this disclosure.

[0018] Figure 1 A schematic diagram of a display quality testing system for a display according to an embodiment of the present disclosure is shown.

[0019] Figure 2 A schematic diagram of a binocular imaging luminance meter provided according to an embodiment of the present disclosure is shown.

[0020] Figure 3 A flowchart is shown for a process of acquiring light spot distribution information according to an embodiment of the present disclosure.

[0021] Figure 4 A flowchart is shown for a depth information acquisition process provided according to an embodiment of the present disclosure.

[0022] Figure 5 A flowchart is shown to illustrate a subjective experience rating for determining display quality based on objective evaluation parameters, according to an embodiment of this disclosure.

[0023] Figure 6 A flowchart illustrating a display quality testing method for a display according to an embodiment of the present disclosure is shown.

[0024] Figure 7 A block diagram of a processing apparatus provided according to an embodiment of the present disclosure is shown. Detailed Implementation

[0025] Various exemplary embodiments, features, and aspects of this disclosure will now be described in detail with reference to the accompanying drawings. The same reference numerals in the drawings denote elements that have the same or similar functions. Although various aspects of the embodiments are shown in the drawings, they are not necessarily drawn to scale unless specifically indicated otherwise.

[0026] The term “exemplary” as used herein means “serving as an example, embodiment, or illustration.” Any embodiment illustrated herein as “exemplary” is not necessarily to be construed as superior to or better than other embodiments.

[0027] Furthermore, to better illustrate this disclosure, numerous specific details are set forth in the following detailed description. Those skilled in the art will understand that this disclosure can be practiced without certain specific details. In some instances, methods, means, components, and circuits well known to those skilled in the art have not been described in detail in order to highlight the main points of this disclosure.

[0028] It is known that the display quality of free-form stereoscopic displays is limited by many objective parameters, such as viewing distance, parallax, spot size, crosstalk, and image depth. Furthermore, different observers have different interpupillary distances and visual sensitivities, leading to varying subjective evaluations of the display quality. Current display quality testing methods for free-form stereoscopic displays typically consider only objective factors, with less emphasis on subjective factors. Even when subjective factors are considered, limitations in measuring instruments restrict the correlation between subjective factors and only a very limited number of objective factors. Therefore, there is currently a lack of display quality testing technologies with higher confidence levels and closer resemblance to real user experiences to provide standardized and automated display quality testing for displays using free-form stereoscopic technology.

[0029] In view of the above, this disclosure proposes a display quality testing system and method for displays. The display quality testing system and method of this disclosure can simulate the interpupillary distance of different observers by using a binocular imaging luminance meter with adjustable lens spacing; based on the collaborative working mechanism of the binocular imaging luminance meter and a movable robotic arm, it can acquire dual-channel detection data under different viewing distances and viewing angles, with rich and comprehensive data types; and it can use the mapping model between dual-channel detection data and display quality testing results to obtain the subjective visual experience under different dual-channel detection data, making the display quality testing results highly humanized and confident.

[0030] It should be noted that the displays mentioned in the embodiments of this disclosure may include various three-dimensional displays or various two-dimensional displays. Among them, the three-dimensional displays may include free-standing stereoscopic three-dimensional displays. The display quality detection system and method of the embodiments of this disclosure can be applied to the display quality detection of any type of display.

[0031] Figure 1 A schematic diagram of a display quality testing system for a display according to an embodiment of the present disclosure is shown. Figure 1 As shown, the display quality inspection system includes:

[0032] The data acquisition device 01 includes a binocular imaging luminance meter 11 with adjustable spatial position. The binocular imaging luminance meter 11 includes two imaging luminance meters (not shown in the figure) with adjustable spacing. The binocular imaging luminance meter 11 is used to acquire dual-channel detection data from the display 00 and send it to the processing device 02 when adjusted to a specified spatial position and a specified spacing. The dual-channel detection data includes data for detecting display quality acquired by the two imaging luminance meters respectively.

[0033] Processing device 02 is used to determine the display quality test result of display 00 based on dual-channel detection data. The display quality test result characterizes the subjective visual experience of the display quality of display 00.

[0034] In practical applications, in order to meet the display quality testing requirements under different testing needs, that is, to facilitate the testing of dual-channel detection data under different specified spatial positions and specified spacings, in one possible implementation, the processing device 02 is also used to send a control signal to the data acquisition device 01 according to the preset testing requirements. The control signal is used to control the binocular imaging brightness meter 11 to move to the specified spatial position and specified spacing. The specified spatial position includes the relative distance and relative angle between the binocular imaging brightness meter 11 and the display 00.

[0035] The preset detection requirements can indicate at least one specified spatial location and at least one specified distance. That is, the processing device 02 can control the binocular imaging luminance meter 11 in the data acquisition device 01 to acquire dual-channel detection data at at least one specified spatial location and at least one specified distance. In practical applications, users can use the keyboard, mouse, and other components provided by the processing device 02 to set the detection requirements, i.e., input the required specified spatial location and specified distance. This allows the processing device 02 to generate control signals based on the preset detection requirements and send these control signals to the data acquisition device 01.

[0036] Optionally, such as Figure 1 As shown, the data acquisition device 01 and the processing device 02 can be connected by a cable 03 to realize information transmission between the data acquisition device 01 and the processing device 02. That is, the processing device 02 can send control signals to the data acquisition device 01 through the cable 03. For example, it can send control signals to the binocular imaging brightness meter 11, the guide rail 12 and the robotic arm 13 in the data acquisition device 01 through the cable 03 to control the binocular imaging brightness meter 11, the guide rail 12 and the robotic arm 13 respectively. The data acquisition device 02 can send the acquired dual-channel detection data to the processing device 02 through the cable 03.

[0037] It should be understood that the above-described connection of the data acquisition device 01 and the processing device 02 via cable 03 is one possible implementation provided by the embodiments of this disclosure. In fact, those skilled in the art can use communication technologies known in the art to realize information transmission between the data acquisition device 01 and the processing device 02, and the embodiments of this disclosure do not limit this.

[0038] In this embodiment, the processing device 02 can be any electronic device with computing capabilities, such as a computer, laptop, or mobile terminal, and this embodiment does not impose any limitations on it. The imaging luminance meter in this embodiment can be any commercially available imaging luminance meter, and this embodiment does not impose any limitations on the manufacturer, model, etc., of the imaging luminance meter.

[0039] In the actual process of testing the display quality of the display 00, the display 00 and the data acquisition device 01 can be fixedly placed in the actual space. Based on this, in order to achieve adjustable spatial position of the binocular imaging luminance meter 11, one possible implementation is as follows: Figure 1 As shown, the data acquisition device 01 may further include a guide rail 12 and a robotic arm 13; the robotic arm 13 is fixedly connected to the guide rail 12, and the binocular imaging luminance meter 11 is fixedly connected to the robotic arm 13. The guide rail 12 is used to move according to the control signal sent by the processing device 02 to adjust the relative distance between the binocular imaging luminance meter 11 and the display 00. The robotic arm 13 is used to move according to the control signal sent by the processing device 02 to adjust the relative angle between the binocular imaging luminance meter 11 and the display 00. The robotic arm 13 can be fixed to the guide rail 12 by a fastener; the binocular imaging luminance meter 11 can be fixed to the robotic arm 03 by a connector. This embodiment does not limit the connection method between components.

[0040] The guide rail 12 can move in the z-direction, thereby changing the relative distance between the binocular imaging luminance meter 11 and the display 00. This simulates the visual experience of an observer at different viewing distances, allowing for the study of the impact of viewing distance on display quality. The robotic arm 13 can drive the binocular imaging luminance meter 11 to move in the xy-plane, thereby changing the relative angle between the binocular imaging luminance meter 11 and the display 00. This simulates the visual experience of an observer at different viewing angles, allowing for the study of the impact of parallax and crosstalk on display quality.

[0041] It should be understood that Figure 1 The mechanical structure of the data acquisition device 01 shown is one possible implementation provided by the embodiments of this disclosure. In fact, those skilled in the art can customize the mechanical structure of the data acquisition device 01, as long as the spatial position of the binocular imaging brightness meter 11 is adjustable. This disclosure does not limit this.

[0042] Figure 2 This diagram illustrates the structure of a binocular imaging luminance meter according to an embodiment of the present disclosure, as shown below. Figure 2 As shown, the binocular imaging luminance meter 11 includes two imaging luminance meters 110, each of which includes an imaging lens 1101, a visual matching function filter 1102, and an imaging sensor 1103. The imaging lens 1101 can acquire the raw dual-channel detection data of the light signal generated by the display. The visual matching function filter 1102 is used to correct the photometric characteristics of the raw dual-channel detection data acquired by the imaging lens 1101. The imaging sensor 1103 is used to perform photoelectric conversion on the photometrically corrected raw dual-channel detection data to obtain dual-channel detection data of electrical signals, which are then sent to the processing device 02 for processing to obtain the display quality detection result.

[0043] To achieve adjustable spacing between the two imaging luminance meters in a binocular imaging luminance meter, one possible implementation is as follows: Figure 2 As shown, the binocular imaging luminance meter 11 may further include: an imaging luminance meter guide rail 111, a transmission rod 112, and an electric knob 113; the electric knob 113 is connected to the transmission rod 112, and the transmission rod 112 is connected to the imaging luminance meter guide rail 111; wherein, the electric knob 113 is used to rotate according to the control signal sent by the processing device 02; the transmission rod 112 is used to transmit the rotation operation of the electric knob 113 to the mechanical gear of the imaging luminance meter guide rail 111; the imaging luminance meter guide rail is used to drive the two imaging luminance meters 110 to move in opposite directions through the mechanical gear, so as to change the distance between the two imaging luminance meters 110.

[0044] The imaging luminance meter guide rail 111 can drive two imaging luminance meters to move in opposite directions via mechanical gears, thereby changing their distance. This simulates the visual experience when the observer's interpupillary distance is different, and can be used to study the impact of interpupillary distance on display quality. The transmission rod 112 transmits the rotation of the electric knob 113 to the imaging luminance meter guide rail 111. The electric knob 113 rotates mechanically based on a control signal, and the rotation operation is transmitted to the imaging luminance meter guide rail 111 via the transmission rod 112, causing the two imaging luminance meters to move in opposite directions. In practical applications, the binocular imaging luminance meter 11 can also be encapsulated in a metal casing, which provides support and protection for the various components inside the binocular imaging luminance meter 11.

[0045] By using the aforementioned binocular imaging luminance meter, the distance between the two luminance meters can be varied to simulate the visual experience of an observer with different interpupillary distances, thereby studying the impact of interpupillary distance on display quality. Furthermore, by acquiring photometrically corrected dual-channel detection data, the different visual information perceived by the left and right eyes of an observer at a fixed observation position can be simulated, thus allowing for the study of the impact of spot size on display quality. Based on the photometrically corrected dual-channel detection data, the depth perception mechanism of the human eye and the image depth information perceived by the observer can be further simulated, thereby allowing for the study of the impact of image depth information on the display quality of a 3D display.

[0046] In practical applications, to more comprehensively test the display quality of a monitor, the monitor can be controlled to display multiple images to collect dual-channel detection data under different images to determine the display quality. In one possible implementation, the monitor is used to display at least one of the following images: a dot matrix image, a three-dimensional image, a white image, and a black and white striped image. The dot matrix image includes at least one lit pixel. When the monitor is a three-dimensional monitor, it can be controlled to display a three-dimensional image. This embodiment does not limit the content of the three-dimensional image, as long as it can provide a stereoscopic visual effect to the naked eye. The lit pixels in the dot matrix image can be user-defined pixels, meaning the user can select at least one preset pixel on the monitor panel to be lit, while other pixels remain unlit, thus generating the dot matrix image. A white image is a pure white image. The black and white areas in the black and white striped image can be distributed horizontally or vertically; this embodiment does not limit this distribution.

[0047] Based on the various images displayed on the aforementioned display, the dual-channel detection data can include at least one of the following: dual-channel spatial distribution radiation energy acquired when the display shows a dot matrix image, dual-channel parallax map acquired when the display shows a three-dimensional image, dual-channel luminance map acquired when the display shows a white image, and dual-channel black and white alternating stripe map acquired when the display shows a striped image. The dual-channel spatial distribution radiation energy refers to the spatial distribution radiation energy acquired by the two imaging luminance meters in the binocular imaging luminance meter, where each imaging luminance meter acquires a set of luminance and spectral data for each illuminated pixel in the dot matrix image.

[0048] As mentioned above, free-form stereoscopic display technology projects images with parallax information into space based on lens arrays or grating arrays. When the observer's left and right eyes receive images with parallax information respectively, a three-dimensional perception can be formed through the brain's fusion mechanism. Based on this, a dual-channel parallax map is a parallax map acquired by two imaging luminance meters in a binocular imaging luminance meter, and each parallax map is an image with parallax information.

[0049] As we know, an imaging luminance meter can collect the brightness of each pixel on a display screen. Based on this, a dual-channel luminance map is a luminance map collected separately by the two imaging luminance meters in a binocular imaging luminance meter when the display screen shows a white image. Each luminance map includes the brightness information of each pixel in the white image. Similarly, a dual-channel black-and-white striped pattern is a black-and-white striped pattern collected separately by the two imaging luminance meters in a binocular imaging luminance meter when the display screen shows a black-and-white striped image. Each black-and-white striped pattern includes the brightness information of each pixel in the striped image. It should be understood that the brightness of the black areas in the striped image is lower than the brightness of the white areas; therefore, the striped pattern collected by the imaging luminance meter can also present a black-and-white effect.

[0050] In one possible implementation, the processing device 02 may be equipped with a trained mapping model. This mapping model can be used to map dual-channel detection data into display quality detection results. That is, after the processing device 02 receives dual-channel detection data sent by the data acquisition device, it can input the dual-channel detection data into the mapping model to obtain display quality detection results. This embodiment of the present disclosure does not limit the model structure, model type, or training method of the mapping model. For example, the mapping model can use artificial intelligence models such as neural network models (e.g., convolutional neural networks), machine learning models, etc., and can be trained using sample dual-channel detection data and subjective display quality scores labeled for different sample dual-channel detection data, so that the mapping model can directly map dual-channel detection data into display quality detection results.

[0051] According to embodiments of this disclosure, by utilizing a binocular imaging luminance meter with adjustable spatial position and spacing, it is possible to simulate the interpupillary distance of a person and the relative spatial position (including distance and angle) between the person and the display. This allows for the acquisition of dual-channel detection data at different spacings (i.e., different interpupillary distances) and different spatial positions (i.e., different distances and different angles). By then using the dual-channel detection data acquired by the binocular imaging luminance meter, a display quality detection result with higher confidence and closer to the user's actual subjective visual experience can be determined. In other words, the determined display quality detection result is more humanized and has stronger confidence.

[0052] To improve the processing accuracy of the mapping model, the dual-channel detection data can be first converted into objective evaluation parameters, and then the display quality detection results can be determined using these objective evaluation parameters. In one possible implementation, the processing device 02 determines the display quality detection results of the monitor based on the dual-channel detection data, including:

[0053] Based on the dual-channel detection data, objective evaluation parameters are determined. These objective evaluation parameters include at least one of the following: spot distribution information, parallax information, depth information, crosstalk distribution information, brightness information, brightness uniformity information, and contrast information. The objective evaluation parameters are input into a preset mapping model, and a subjective experience score is output. The quality detection results, including the subjective experience score, are displayed.

[0054] The information includes: light spot distribution information (brightness value of the illuminated pixels in the dot matrix image); parallax information (parallax value between the two-channel parallax maps, i.e., difference value); depth information (distance between each three-dimensional pixel in the three-dimensional image and the imaging brightness meter); crosstalk distribution information (characterizing the degree of mutual interference between the light spots (i.e., the illuminated pixels) displayed on the monitor); brightness information (brightness value of each pixel in the white image); brightness uniformity information (characterizing the brightness contrast result of different display areas in the monitor when displaying high brightness (i.e., white); and contrast information (characterizing the brightness contrast result of the monitor when displaying low brightness (i.e., black) and high brightness (i.e., white).

[0055] As described above, the objective evaluation parameters include spot distribution information. In one possible implementation, the objective evaluation parameters are determined based on the dual-channel detection data, including: determining the spot distribution information of the dual channels based on the spatial distribution radiation energy of the dual channels. The spatial distribution radiation energy of the dual channels includes the spatial distribution radiation energy of the first channel and the spatial distribution radiation energy of the second channel. The spot distribution information of the first channel can be calculated based on the spatial distribution radiation energy of the first channel, and the spot distribution information of the second channel can be calculated based on the spatial distribution radiation energy of the second channel. Optionally, formula (1) can be used to determine the spot distribution information of the dual channels based on the spatial distribution radiation energy of the dual channels.

[0056] D N =D dark +kL (1)

[0057] In the formula, N represents the Nth pixel that is lit up in the dot matrix image, and D N D represents the spatial distribution radiant energy value corresponding to the Nth pixel. dark D represents the dark current noise generated in the imaging luminance meter corresponding to the Nth pixel, k represents the linear response coefficient of the imaging luminance meter, and L represents the luminance value corresponding to the Nth pixel. dark and k are inherent values ​​in the imaging luminance meter and can be considered constants. Therefore, based on the spatially distributed radiant energy value D corresponding to each pixel... NThis allows us to obtain the brightness value L corresponding to each pixel. By calculating the value of L point by point, we can obtain the dual-channel spot distribution information Φ at a specified interval (i.e., a specified interpupillary distance) and a specified spatial position (i.e., a specified viewing distance and a specified viewing angle). By repeating the above process, we can obtain the dual-channel spot distribution information at any desired interpupillary distance, viewing distance, and viewing angle.

[0058] Based on the above Figure 1 The display quality inspection system shown in this disclosure also provides, in embodiments such as Figure 3 The flowchart shown is for the process of acquiring light spot distribution information, as follows: Figure 3 As shown, the process of acquiring the light spot distribution information includes: the display 00 displays a dot matrix image according to preset requirements, that is, lighting up the selected pixels on the display panel; under the drive of the control signal sent by the processing device 02, the states of the guide rail 12, the robotic arm 13 in the data acquisition device 01 and the electric knob 113 in the binocular imaging luminance meter 11 change, and the binocular imaging luminance meter 11 reaches the specified spacing and specified spatial position; the binocular imaging luminance meter 11 acquires the dual-channel spatial distribution radiation energy at the specified spacing and specified spatial position; the dual-channel spatial distribution radiation energy is corrected for photometric characteristics and transmitted to the processing device 02 via the cable 03; the processing device 02 processes the spatial distribution radiation energy corresponding to each pixel to obtain the dual-channel light spot distribution information at the specified spacing and specified spatial position; it determines whether all the required specified spacing and specified spatial position have been traversed. If not, the processing device 02 continues to send control signals to control the binocular imaging luminance meter 11 to change the spacing and spatial position. If yes, the processing device 02 acquires all the required dual-channel light spot distribution information at the specified spacing and specified spatial position.

[0059] As described above, the aforementioned display 00 can be a three-dimensional display. Therefore, the objective evaluation parameters can include parallax information and depth information. In one possible implementation, the objective evaluation parameters are determined based on dual-channel detection data, including:

[0060] Based on the dual-channel disparity map, determine the disparity information;

[0061] Determine the geometric depth information based on the parallax information, the specified spatial location, and the specified spacing;

[0062] The actual depth information is determined based on the similarity of the light spot distribution information between the two channels and the geometric depth information.

[0063] The dual-channel disparity map includes a first-channel disparity map and a second-channel disparity map. Determining disparity information based on the dual-channel disparity map can include: projecting the first-channel disparity map onto the second-channel disparity map using triangulation to obtain a projection map; and determining the disparity value of corresponding points for each pixel based on the difference between the projection map and the second-channel disparity map. The disparity information includes the disparity value of corresponding points for each pixel. Alternatively, the second-channel disparity map can be projected onto the second-channel disparity map using triangulation to calculate the disparity value of corresponding points; this embodiment does not limit this approach.

[0064] After obtaining the disparity information, the corresponding geometric depth information can be obtained based on the disparity information and the geometric optics model. The depth information includes geometric depth information. For example, the geometric depth information v can be determined according to the disparity information, the specified spatial position, and the specified spacing through formula (2). geo .

[0065]

[0066] Where d represents the relative distance in the specified spatial location, e represents the specified spacing, and p out This represents the disparity value of points with the same name.

[0067] Since the geometric depth information obtained by formula (2) is based on ideal optical parameters, the actual system is affected by pixel size and optical aberrations, and usually cannot achieve the ideal geometric depth. Therefore, in order to obtain the actual depth, it is necessary to consider the influence of pixel size and optical aberrations on the 3D display based on the geometric depth. Therefore, the similarity between the spot distribution information of the two channels can be determined based on the above dual-channel spot distribution information, and then the actual depth information can be obtained based on the physical optical model. The depth information includes the actual depth information. For example, formula (3) can be used to determine the actual depth information v based on the similarity between the spot distribution information of the two channels and the geometric depth information. phy .

[0068]

[0069] in, This represents the light spot distribution information of the first channel. This represents the light spot distribution information of the second channel. This represents the similarity of the light spot distribution information between the two channels.

[0070] Based on the above Figure 1 The display quality inspection system shown in this disclosure also provides, in embodiments such as Figure 4 The flowchart shown is for the depth information acquisition process. Figure 4As shown, the depth information acquisition process includes: the display 00 displays a rendered 3D image on the display panel according to preset requirements; driven by the control signal sent by the processing device 02, the states of the guide rail 12, the robotic arm 13, and the electric knob 113 in the binocular imaging luminance meter 11 in the data acquisition device 01 change, and the binocular imaging luminance meter 11 reaches the specified spacing and specified spatial position; the binocular imaging luminance meter 11 acquires a dual-channel disparity map at the specified spacing and specified spatial position; the dual-channel disparity map is transmitted to the processing device 02 via the cable 03, and the disparity value of the corresponding point is obtained using the triangulation method, thereby obtaining the geometric depth information; the processing device 02 retrieves the dual-channel light spot distribution information at the specified spacing and specified spatial position to determine the light spot similarity, and then calculates the actual depth information; it determines whether all the required specified spacing and specified spatial positions have been traversed. If not, the processing device 02 continues to send control signals to control the binocular imaging luminance meter 11 to change the spacing and spatial position. If yes, the processing device 02 acquires all the depth information at the required specified spacing and specified spatial position.

[0071] As mentioned above, the objective evaluation parameters may also include crosstalk distribution information, brightness information, brightness uniformity information, and contrast information. In one possible implementation, the objective evaluation parameters are determined based on dual-channel detection data, and may further include at least one of the following:

[0072] Based on the spot distribution information of the dual channels, determine the crosstalk distribution information;

[0073] Based on the dual-channel luminance map, determine the luminance information and luminance uniformity information;

[0074] Contrast information is determined based on the dual-channel black-and-white stripe pattern.

[0075] Those skilled in the art can use known crosstalk calculation methods to determine crosstalk distribution information based on the spot distribution information of the dual channels, and this disclosure does not limit such determination.

[0076] As described above, the luminance map includes the luminance values ​​of each pixel in the white image. Therefore, luminance information can be directly obtained based on the dual-channel luminance map, meaning the luminance information includes the luminance values ​​of each pixel in the white image. Those skilled in the art can use known luminance uniformity calculation methods to determine luminance uniformity information based on the dual-channel luminance map. For example, the dual-channel luminance map can be partitioned, and the differences in luminance values ​​between pixels in different partitions can be compared to obtain luminance uniformity information. Specifically, the luminance information and luminance uniformity information corresponding to each of the two channels can be calculated separately.

[0077] As mentioned above, the black and white striped pattern includes the brightness values ​​of each pixel in the black and white striped image. The black area can be considered as the area with the weakest light intensity, that is, the area with the lowest brightness value, and the white area can be considered as the area with the strongest light intensity, that is, the area with the highest brightness value. Therefore, the contrast information can be determined by using the brightness values ​​of the pixels in the black area and the brightness values ​​of the pixels in the white area.

[0078] Those skilled in the art can use contrast calculation methods known in the art to determine contrast information based on a dual-channel black-and-white stripe pattern. For example, the global strongest point (i.e., the pixel with the strongest brightness value) and the global weakest point (i.e., the pixel with the weakest brightness value) can be found in the black-and-white stripe pattern. Then, by comparing the brightness values ​​between the global strongest point and the global weakest point, the global contrast information can be obtained. For example, the global contrast information can be equal to (global strongest point - global weakest point) / (global strongest point + global weakest point). Alternatively, local contrast information can be obtained by comparing the brightness values ​​between the local weakest point in the black area and the local strongest point in the adjacent white area. For example, the local contrast information can be equal to (local strongest point - local weakest point) / (local strongest point + local weakest point).

[0079] In this embodiment, objective evaluation parameters are input into a preset mapping model, and a subjective experience score is output. This can be understood as mapping objective evaluation parameters into a subjective experience score through the mapping model, which can characterize the human eye's subjective visual experience of display quality. This disclosure does not limit the model structure or type of the mapping model; for example, the mapping model can employ a convolutional neural network.

[0080] The mapping model can be trained based on the objective evaluation parameters of the samples and the corresponding subjective experience scores of the samples. The objective evaluation parameters of the samples are determined by the dual-channel detection data of the samples collected by the binocular imaging luminance meter at different sample spatial positions and different sample spacings. The subjective experience scores of the samples are obtained by the subjective visual experience scores of the display quality of the samples by observers with different interpupillary distances at different sample spatial positions. The sample spacing corresponds to the interpupillary distance.

[0081] It should be understood that the determination process of the objective evaluation parameters of the samples can be the same as that of the objective evaluation parameters mentioned above, and the acquisition process of the dual-channel detection data of the samples can be the same as that of the dual-channel detection data mentioned above. The dual-channel detection data of the samples can include at least one of the following: dual-channel spatial distribution radiation energy collected when the sample display shows a dot matrix image, dual-channel disparity map collected when the sample display shows a three-dimensional image, dual-channel brightness map collected when the sample display shows a white image, and dual-channel black and white alternating stripe map collected when the sample display shows a striped image. The sample display can be understood as the display used when training the mapping model, and the objective evaluation parameters of the samples can include at least one of the following: spot distribution information, disparity information, depth information, crosstalk distribution information, brightness information, brightness uniformity information, and contrast information. This disclosure embodiment does not limit the determination process of the above-mentioned dual-channel detection data of the samples.

[0082] The multiple sets of objective evaluation parameters determined under various sample spatial locations and sample intervals can constitute an objective parameter database for iterative training of the mapping model. In practical applications, a certain number of observers with different interpupillary distances can be gathered to subjectively score the display quality of sample displays with different objective evaluation parameters at different sample spatial locations. This yields the sample subjective experience score corresponding to the sample objective evaluation parameter, which is equivalent to labeling different sample objective evaluation parameters, thereby establishing a subjective experience database. The subjective experience score can be positively or negatively correlated with display quality; for example, a higher sample subjective experience score can be set to represent higher display quality. This disclosed example does not limit the specific numerical value of the sample subjective experience score.

[0083] Considering that the pupillary distance data of different observers are different, the pupillary distance data can also be included in the objective parameter database. That is, the sample spacing of the binocular imaging luminance meter when constructing the objective parameter database should be the same as the pupillary distance of the observer, so as to simulate the pupillary distance of different observers and accurately label the subjective experience score of the sample corresponding to the objective evaluation parameters of different samples.

[0084] Figure 5This diagram illustrates a flowchart of a method for determining a subjective experience score for display quality based on objective evaluation parameters, according to an embodiment of this disclosure. An objective parameter database is established, containing multiple sets of objective evaluation parameters for samples with different sample spacings and spatial locations. Simultaneously, a subjective experience database is established, containing subjective experience scores for master samples with different sample spacings and spatial locations. A convolutional neural network (CNN) is constructed, where the input to the CNN is the sample objective evaluation parameters, and the output is the predicted subjective experience score. The network parameters of the CNN are trained using the difference between the predicted subjective experience score and the labeled sample subjective experience score, thereby obtaining a mapping model that maps objective evaluation parameters to subjective experience scores. The trained mapping model can be deployed in the aforementioned processing device 02. Based on this model, subjective experience scores corresponding to various objective evaluation parameters can be obtained. That is, even if the objective evaluation parameters change and are not present in the pre-constructed objective parameter database, the trained mapping model can still output the corresponding subjective experience score.

[0085] According to embodiments of this disclosure, by converting dual-channel detection data into objective evaluation parameters and then using a mapping model to map the objective evaluation parameters into subjective experience scores, the accuracy and precision of the subjective experience scores output by the mapping model can be improved, which is equivalent to improving the accuracy and precision of the display quality detection results.

[0086] For example, this disclosure provides a workflow for performing display quality testing on a free-form stereoscopic 3D display using the above-described display quality testing system as follows:

[0087] Step 1: The free-standing stereoscopic 3D display 00 illuminates selected pixels on the panel according to preset requirements, i.e., displays a dot matrix image. Driven by control signals from the processing device 02, the states of the guide rail 12, robotic arm 13, and electric knob 113 change. The binocular imaging luminance meter 11 collects dual-channel spatial distribution radiation energy at specified intervals, viewing distances, and viewing angles. After photometric characteristic correction, the dual-channel spatial distribution radiation energy is transmitted to the processing device 02 via cable, and the dual-channel spot distribution information is calculated. The above process is repeated to obtain dual-channel spot distribution information at all required interpupillary distances, viewing distances, and viewing angles. The dual-channel spot distribution information at different interpupillary distances, viewing distances, and viewing angles is processed to calculate the corresponding crosstalk distribution information.

[0088] Step 2: The free-form stereoscopic 3D display 00 displays a rendered 3D image on the panel according to preset requirements. Driven by control signals from the processing device 02, the states of the guide rail 12, robotic arm 13, and electric knob 113 change. The binocular imaging luminance meter 11 reaches the specified interpupillary distance, viewing distance, and viewing angle, and acquires a dual-channel disparity map at that spatial location to simulate the left and right views of the human eye. The dual-channel disparity map is transmitted to the processing device 02 via cable and processed by triangulation to obtain the disparity value of the corresponding point. Based on the disparity value of the corresponding point and the geometric optics model, the corresponding geometric depth information is obtained. The spot distribution information of the dual channels at the specified interpupillary distance, viewing distance, and viewing angle in Step 1 is retrieved to obtain the spot similarity. Then, based on the physical optics model, the actual depth information is determined. The above process is repeated to obtain the actual depth information at all required interpupillary distances, viewing distances, and viewing angles.

[0089] Step 3: The free-standing stereoscopic 3D display 00 displays a uniform white image on the panel according to preset requirements. Driven by the control signal from the processing device 02, the states of the guide rail 12, robotic arm 13, and electric knob 113 change. The binocular imaging luminance meter 11 reaches the specified interpupillary distance, viewing distance, and viewing angle, and acquires a dual-channel luminance map at that spatial location to obtain luminance information. The dual-channel luminance map is divided into zones, and the luminance differences within different zones are compared to obtain luminance uniformity information at the specified interpupillary distance, viewing distance, and viewing angle. The above process is repeated to obtain luminance information and luminance uniformity information at all required interpupillary distances, viewing distances, and viewing angles.

[0090] Step 4: The free-standing stereoscopic 3D display 00 displays a black and white striped pattern on the panel according to preset requirements. Driven by the control signal from the processing device 02, the states of the guide rail 12, robotic arm 13, and electric knob 113 change. The binocular imaging luminance meter 11 reaches the specified interpupillary distance, viewing distance, and viewing angle, and acquires the dual-channel black and white striped pattern at that spatial position. Based on the dual-channel black and white striped pattern, the contrast information at the specified interpupillary distance, viewing distance, and viewing angle is calculated. The above process is repeated to obtain the contrast information at all required interpupillary distances, viewing distances, and viewing angles.

[0091] Step 5: Gather a certain number of observers with different interpupillary distances to view the free stereoscopic 3D display from different viewing distances and angles, and score the display quality based on their subjective visual experience to obtain the sample subjective experience score.

[0092] Step 6: Based on the objective evaluation parameters of the samples obtained in Steps 1 to 5, such as interpupillary distance, viewing distance, viewing angle, parallax information, spot distribution information, depth information, crosstalk distribution information, brightness information, brightness uniformity information, and contrast information, an objective parameter database is established; based on the subjective experience scores of the samples obtained in Step 5, a subjective experience database is established. A convolutional neural network is constructed, using the objective evaluation parameters of the samples as input and the subjective experience scores of the samples as labels, to train the parameters of the convolutional neural network and obtain a mapping model that maps the objective evaluation parameters to the subjective experience scores.

[0093] Step 7: Based on the trained mapping model, when the input objective evaluation parameters change, the trained hardware device can automatically output the corresponding subjective experience score, and the display quality detection results of the display include the subjective experience score.

[0094] According to embodiments of this disclosure, a display quality testing system for free-standing stereoscopic displays can be constructed based on a binocular imaging luminance meter and a movable robotic arm. This system enables the measurement of three-dimensional objective evaluation parameters such as interpupillary distance, viewing distance, parallax, spot size, crosstalk, and image depth. Based on convolutional neural networks and human subjective experience scoring data, a mapping model from objective evaluation parameters to subjective experience scores is constructed, enabling subjective evaluation of display quality based on these objective parameters. Embodiments of this disclosure provide a high-confidence three-dimensional display quality testing system for free-standing stereoscopic 3D displays.

[0095] According to embodiments of this disclosure, a binocular imaging luminance meter with adjustable lens spacing can simulate the interpupillary distance of different observers; based on the collaborative working mechanism of the binocular imaging luminance meter and a movable robotic arm, parallax, spot size, and crosstalk at different viewing distances can be measured; based on geometric optics and physical optics models, depth information of the 3D display can be calculated and obtained; and based on convolutional neural networks, a mapping model between objective evaluation parameters such as interpupillary distance, parallax, spot size, crosstalk, and image depth, and the observer's subjective experience score can be constructed. The display quality testing system of this disclosure can be used in the field of high-confidence 3D display quality testing.

[0096] The display quality detection system proposed in this disclosure can obtain objective evaluation parameters such as interpupillary distance, viewing distance, parallax, spot size, crosstalk, and depth, encompassing almost all types of data related to 3D display quality detection, providing a rich and comprehensive range of data. Based on the proposed convolutional neural network, a mapping model from objective evaluation parameters to subjective visual experience scores can be constructed to obtain display quality detection results under different objective evaluation parameters, resulting in highly user-friendly data. In other words, this disclosure has the technical advantages of rich data types and highly user-friendly data.

[0097] Based on the above-described display quality detection system, embodiments of this disclosure also provide, for example... Figure 6 The method shown is for testing the display quality of a monitor. This method is applied to processing devices, such as... Figure 6 As shown, the display quality inspection method includes:

[0098] Step S601: Acquire dual-channel detection data collected by the data acquisition device on the display. The data acquisition device includes a binocular imaging luminance meter with adjustable spatial position. The binocular imaging luminance meter includes two imaging luminance meters with adjustable spacing. The binocular imaging luminance meter is used to collect dual-channel detection data on the display and send it to the processing device when adjusted to a specified spatial position and a specified spacing. The dual-channel detection data includes data collected by the two imaging luminance meters respectively for evaluating display quality.

[0099] Step S602: Based on the dual-channel detection data, determine the display quality detection result of the display, wherein the display quality detection result characterizes the subjective visual experience of the display quality of the display.

[0100] In one possible implementation, determining the display quality test result of the display based on the dual-channel detection data includes: determining objective evaluation parameters based on the dual-channel detection data, wherein the objective evaluation parameters include at least one of the following: spot distribution information, parallax information, depth information, crosstalk distribution information, brightness information, brightness uniformity information, and contrast information; inputting the objective evaluation parameters into a preset mapping model for subjective experience scoring, wherein the display quality test result includes the subjective experience score, and the mapping model is obtained by training based on sample objective evaluation parameters and corresponding subjective experience scores, wherein the sample objective evaluation parameters are determined based on sample dual-channel detection data collected by the binocular imaging luminance meter at different sample spatial positions and at different sample intervals, and the subjective experience score is obtained by observers with different interpupillary distances giving subjective visual experience scores to the display quality of the sample display at the different sample spatial positions, wherein the sample interval corresponds to the interpupillary distance.

[0101] In one possible implementation, the display is used to display at least one of the following images: a dot matrix image, a three-dimensional image, a white image, and a black-and-white striped image, wherein the dot matrix image includes at least one lit pixel; the dual-channel detection data includes at least one of the following: dual-channel spatial distribution radiation energy acquired when the display shows a dot matrix image, a dual-channel disparity map acquired when the display shows a three-dimensional image, a dual-channel brightness map acquired when the display shows a white image, and a dual-channel black-and-white striped image acquired when the display shows a black-and-white striped image; wherein, based on the dual-channel detection data, The objective evaluation parameters are determined, including at least one of the following: determining the spot distribution information of the dual channels based on the spatial distribution radiation energy of the dual channels; determining the disparity information based on the disparity map of the dual channels; determining the geometric depth information based on the disparity information, the specified spatial position, and the specified spacing; determining the actual depth information based on the spot similarity between the spot distribution information of the dual channels and the geometric depth information; determining the crosstalk distribution information based on the spot distribution information of the dual channels; determining the brightness information and brightness uniformity information based on the brightness map of the dual channels; and determining the contrast information based on the black and white stripe map of the dual channels.

[0102] In one possible implementation, the method further includes: sending a control signal to the data acquisition device according to a preset detection requirement, the control signal being used to control the binocular imaging brightness meter to move to the specified spatial position and the specified spacing, the specified spatial position including the relative distance and relative angle between the binocular imaging brightness meter and the display.

[0103] It should be understood that the various steps in the display quality testing method of this disclosure embodiment can be implemented by referring to the specific implementation method in the above display quality testing system, and will not be elaborated here.

[0104] According to embodiments of this disclosure, by utilizing a binocular imaging luminance meter with adjustable spatial position and spacing, it is possible to simulate the interpupillary distance of a person and the relative spatial position (including distance and angle) between the person and the display. This allows for the acquisition of dual-channel detection data at different spacings (i.e., different interpupillary distances) and different spatial positions (i.e., different distances and different angles). By then using the dual-channel detection data acquired by the binocular imaging luminance meter, a display quality detection result with higher confidence and closer to the user's actual subjective visual experience can be determined. In other words, the determined display quality detection result is more humanized and has stronger confidence.

[0105] Figure 7 This is a block diagram of a processing device provided in an embodiment of this disclosure. For example, the processing device 1900 may be provided as a server or a terminal device. (Refer to...) Figure 7The processing device 1900 includes a processing component 1922, which further includes one or more processors, and memory resources represented by memory 1932 for storing instructions, such as application programs, that can be executed by the processing component 1922. The application programs stored in memory 1932 may include one or more modules, each corresponding to a set of instructions. Furthermore, the processing component 1922 is configured to execute instructions to perform the methods described above.

[0106] Device 1900 may also include a power supply component 1926 configured to perform power management of device 1900, a wired or wireless network interface 1950 configured to connect device 1900 to a network, and an input / output interface 1958 (I / O interface). Device 1900 can operate on an operating system, such as Windows Server, stored in memory 1932. TM macOS X TM Unix TM Linux TM FreeBSD TM Or similar.

[0107] In an exemplary embodiment, a non-volatile computer-readable storage medium is also provided, such as a memory 1932 including computer program instructions that can be executed by a processing component 1922 of the device 1900 to perform the above-described method.

[0108] This disclosure can be a system, method, and / or computer program product. A computer program product may include a computer-readable storage medium having computer-readable program instructions loaded thereon for causing a processor to implement various aspects of this disclosure.

[0109] Computer-readable storage media can be tangible devices capable of holding and storing instructions for use by an instruction execution device. Computer-readable storage media can be, for example—but not limited to—electrical storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, or any suitable combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disc read-only memory (CD-ROM), digital multifunction disc (DVD), memory sticks, floppy disks, mechanical encoding devices, such as punch cards or recessed protrusions storing instructions thereon, and any suitable combination thereof. The computer-readable storage media used herein are not to be construed as transient signals themselves, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., light pulses through fiber optic cables), or electrical signals transmitted through wires.

[0110] The computer-readable program instructions described herein can be downloaded from computer-readable storage media to various computing / processing devices, or downloaded via a network, such as the Internet, local area network, wide area network, and / or wireless network, to an external computer or external storage device. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards them to the computer-readable storage media in the respective computing / processing device.

[0111] Computer program instructions used to perform the operations of this disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, status setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, etc., and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, electronic circuitry, such as programmable logic circuitry, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), is personalized by utilizing the status information of the computer-readable program instructions to implement various aspects of this disclosure.

[0112] Various aspects of this disclosure are described herein with reference to flowchart illustrations and / or block diagrams of methods, systems, and computer program products according to embodiments of this disclosure. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer-readable program instructions.

[0113] These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that, when executed by the processor of the computer or other programmable data processing apparatus, they create means for implementing the functions / actions specified in one or more blocks of the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium that causes a computer, programmable data processing apparatus, and / or other device to operate in a particular manner; thus, the computer-readable medium storing the instructions comprises an article of manufacture that includes instructions for implementing aspects of the functions / actions specified in one or more blocks of the flowchart and / or block diagram.

[0114] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to perform the functions / actions specified in one or more boxes of a flowchart and / or block diagram.

[0115] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of an instruction containing one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than those shown in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0116] The various embodiments of this disclosure have been described above. These descriptions are exemplary and not exhaustive, nor are they limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen to best explain the principles, practical application, or technical improvements to the embodiments in the market, or to enable others skilled in the art to understand the embodiments disclosed herein.

Claims

1. A display quality testing system for a monitor, characterized in that, include: A data acquisition device includes a spatially adjustable binocular imaging luminance meter, comprising two imaging luminance meters with adjustable spacing. The binocular imaging luminance meter is used to acquire dual-channel detection data from the display and send it to a processing device when adjusted to a specified spatial position and spacing. The dual-channel detection data includes data collected by the two imaging luminance meters respectively for detecting display quality. The display is used to display at least one of the following images: a dot matrix image, a three-dimensional image, a white image, and a black-and-white striped image. The dot matrix image includes at least one lit pixel. The dual-channel detection data includes at least one of the following: dual-channel spatial distribution radiation energy acquired when the display shows the dot matrix image; a dual-channel parallax map acquired when the display shows the three-dimensional image; a dual-channel luminance map acquired when the display shows the white image; and a dual-channel black-and-white striped image acquired when the display shows the striped image. The processing device is used to determine the display quality test result of the display based on the dual-channel detection data. The display quality test result characterizes the subjective visual experience of the three-dimensional display quality of the display. The display includes a three-dimensional display. The step of determining the display quality test result of the display based on the dual-channel detection data includes: Based on the dual-channel detection data, objective evaluation parameters are determined. These objective evaluation parameters include at least one of the following: spot distribution information, depth information, crosstalk distribution information, brightness uniformity information, and contrast information. Specifically, the spot distribution information determined based on the dual-channel spatial distribution radiation energy includes the brightness value of the illuminated pixels on the display panel; the crosstalk distribution information determined based on the spot distribution information characterizes the degree of mutual interference between the illuminated pixels on the display panel; the depth information determined based on the dual-channel parallax map, the specified spatial position, the specified spacing, and the spot distribution information characterizes the distance between each three-dimensional pixel in the displayed three-dimensional image and the imaging brightness meter; the brightness uniformity information determined based on the dual-channel brightness map characterizes the brightness contrast result of different display areas in the display when displaying high brightness; and the contrast information determined based on the dual-channel black and white stripe map characterizes the brightness contrast result of the display when displaying low brightness and high brightness. The objective evaluation parameters are input into a preset mapping model, and a subjective experience score is output. The display quality detection result includes the subjective experience score, and the mapping model is used to map the objective evaluation parameters into a subjective experience score.

2. The system according to claim 1, characterized in that, The objective evaluation parameters determined based on the dual-channel detection data include at least one of the following: Based on the spatial distribution radiation energy of the dual channels, the spot distribution information of the dual channels is determined; Based on the dual-channel disparity map, determine the disparity information; Determine the geometric depth information based on the parallax information, the specified spatial position, and the specified spacing; The actual depth information is determined based on the similarity of the light spots between the light spot distribution information of the two channels and the geometric depth information; Based on the spot distribution information of the dual channels, determine the crosstalk distribution information; Based on the dual-channel luminance map, determine the luminance information and luminance uniformity information; Contrast information is determined based on the dual-channel black-and-white striped pattern.

3. The system according to claim 1, characterized in that, The processing device is also used to send a control signal to the data acquisition device according to preset detection requirements. The control signal is used to control the binocular imaging brightness meter to move to the specified spatial position and the specified distance. The specified spatial position includes the relative distance and relative angle between the binocular imaging brightness meter and the display.

4. The system according to claim 1, characterized in that, The binocular imaging luminance meter also includes: an imaging luminance meter guide rail, a transmission rod, and an electric knob; the electric knob is connected to the transmission rod, and the transmission rod is connected to the imaging luminance meter guide rail; The electric knob is used to rotate according to the control signal sent by the processing device; the transmission rod is used to transmit the rotation operation of the electric knob to the mechanical gear of the imaging luminance meter guide rail; the imaging luminance meter guide rail is used to drive the two imaging luminance meters to move in opposite directions through the mechanical gear, so as to change the distance between the two imaging luminance meters.

5. The system according to any one of claims 1 to 4, characterized in that, The data acquisition device further includes: a guide rail and a robotic arm; the robotic arm is fixedly connected to the guide rail, and the binocular imaging luminance meter is fixedly connected to the robotic arm. The guide rail is used to move according to the control signal sent by the processing device to adjust the relative distance between the binocular imaging luminance meter and the display. The robotic arm is used to move according to the control signal sent by the processing device to adjust the relative angle between the binocular imaging luminance meter and the display.

6. A method for testing the display quality of a monitor, characterized in that, Applied to processing equipment, including: The system acquires dual-channel detection data collected by a data acquisition device on the display. The data acquisition device includes a binocular imaging luminance meter with adjustable spatial position. The binocular imaging luminance meter comprises two imaging luminance meters with adjustable spacing. The binocular imaging luminance meter is used to acquire dual-channel detection data from the display and send it to the processing device when adjusted to a specified spatial position and spacing. The dual-channel detection data includes data collected by the two imaging luminance meters respectively for evaluating display quality. The display is used to display at least one of the following images: a dot matrix image, a three-dimensional image, a white image, and a black-and-white striped image. The dot matrix image includes at least one lit pixel. The dual-channel detection data includes at least one of the following: dual-channel spatial distribution radiation energy collected when the display shows the dot matrix image; a dual-channel parallax map collected when the display shows the three-dimensional image; a dual-channel luminance map collected when the display shows the white image; and a dual-channel black-and-white striped image collected when the display shows the striped image. Based on the dual-channel detection data, the display quality detection result of the display is determined. The display quality detection result characterizes the subjective visual experience of the three-dimensional display quality of the display. The display includes a three-dimensional display. The step of determining the display quality test result of the display based on the dual-channel detection data includes: Based on the dual-channel detection data, objective evaluation parameters are determined. These objective evaluation parameters include at least one of the following: spot distribution information, depth information, crosstalk distribution information, brightness uniformity information, and contrast information. Specifically, the spot distribution information determined based on the dual-channel spatial distribution radiation energy includes the brightness value of the illuminated pixels on the display panel; the crosstalk distribution information determined based on the spot distribution information characterizes the degree of mutual interference between the illuminated pixels on the display panel; the depth information determined based on the dual-channel parallax map, the specified spatial position, the specified spacing, and the spot distribution information characterizes the distance between each three-dimensional pixel in the displayed three-dimensional image and the imaging brightness meter; the brightness uniformity information determined based on the dual-channel brightness map characterizes the brightness contrast result of different display areas in the display when displaying high brightness; and the contrast information determined based on the dual-channel black and white stripe map characterizes the brightness contrast result of the display when displaying low brightness and high brightness. The objective evaluation parameters are input into a preset mapping model, and a subjective experience score is output. The display quality detection result includes the subjective experience score, and the mapping model is used to map the objective evaluation parameters into a subjective experience score.

7. The method according to claim 6, characterized in that, The objective evaluation parameters determined based on the dual-channel detection data include at least one of the following: Based on the spatial distribution radiation energy of the dual channels, the spot distribution information of the dual channels is determined; Based on the dual-channel disparity map, determine the disparity information; Determine the geometric depth information based on the parallax information, the specified spatial position, and the specified spacing; The actual depth information is determined based on the similarity of the light spots between the light spot distribution information of the two channels and the geometric depth information; Based on the spot distribution information of the dual channels, determine the crosstalk distribution information; Based on the dual-channel luminance map, determine the luminance information and luminance uniformity information; Contrast information is determined based on the dual-channel black-and-white striped pattern.

8. The method according to claim 6, characterized in that, The method further includes: According to the preset detection requirements, a control signal is sent to the data acquisition device. The control signal is used to control the binocular imaging brightness meter to move to the specified spatial position and the specified distance. The specified spatial position includes the relative distance and relative angle between the binocular imaging brightness meter and the display.

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