WO2024063287A1

WO2024063287A1 - Display device and operation method thereof

Info

Publication number: WO2024063287A1
Application number: PCT/KR2023/010203
Authority: WO
Inventors: 원강영
Original assignee: 삼성전자 주식회사
Priority date: 2022-09-21
Filing date: 2023-07-17
Publication date: 2024-03-28
Also published as: KR20240040508A

Abstract

A display device according to an embodiment may comprise: a display which includes a plurality of LEDs; a memory in which one or more instructions are stored; and at least one processor which executes the one or more instructions to: collect data for the LEDs; analyze the collected data to predict abnormality in the image quality of the display; acquire LED correction information for prevention of the abnormality in the image quality; and control driving of the LEDs on the basis of the correction information.

Description

Display device and method of operation thereof

Various embodiments relate to a display device capable of maintaining display image quality at an optimal level and a method of operating the same.

In a display device including a display, when an error in the display's picture quality occurs, a consumer requests service for the picture quality problem, and a service technician visits the device directly to resolve the problem in the picture quality of the display. In addition, the service technician checks for deteriorated parts of the display or parts where there are problems with picture quality, and improves picture quality by manually changing the display settings one by one. In this case, the service technician performs calibration by adjusting various parameters of the display, and the time spent to improve the quality or picture quality of the service may vary depending on the service technician's individual capabilities.

In addition, even when performing automatic calibration, there is the inconvenience of having to photograph the display screen with separate dedicated equipment or a camera and perform calibration by analyzing the captured image.

It is difficult to take action in advance of display quality problems before they occur, and service costs and time are required when display picture quality problems occur.

A display device according to an embodiment may include a display including a plurality of LEDs.

A display device according to an embodiment may include a memory that stores one or more instructions and at least one processor that executes the one or more instructions.

The at least one processor may collect data about the LEDs by executing the one or more instructions.

The at least one processor may analyze the collected data and predict image quality abnormalities of the display by executing the one or more instructions.

The at least one processor may analyze the collected data by executing the one or more instructions to obtain correction information for the LEDs to prevent image quality abnormalities.

The at least one processor may control driving of the LEDs based on the correction information by executing the one or more instructions.

A method of operating a display device according to an embodiment may include collecting data on a plurality of LEDs included in the display.

A method of operating a display device according to an embodiment may include predicting an abnormality in image quality of the display by analyzing the collected data.

A method of operating a display device according to an embodiment may include obtaining correction information for the LEDs to prevent image quality abnormalities.

A method of operating a display device according to an embodiment may include controlling driving of the LEDs based on the correction information.

1 is a diagram illustrating a display device according to an embodiment.

Figure 2 is a diagram showing the structure of a display according to one embodiment.

FIG. 3 is a flowchart illustrating a method by which a display device controls driving of LEDs to maintain optimal image quality, according to an embodiment.

FIG. 4 is a diagram illustrating a method by which a display device maintains display image quality in an optimized state according to an embodiment.

FIG. 5 is a diagram illustrating a method in which a display device according to an embodiment controls the operation of LEDs to maintain optimal image quality at the boundary of the LED module.

FIG. 6 is a diagram illustrating an operation of a display device to change mapping to a color of an LED according to an embodiment.

FIG. 7 is a diagram illustrating an operation of a display device to change mapping to a color of an LED, according to an embodiment.

Figure 8 is a block diagram showing the configuration of a display device according to an embodiment.

Figure 9 is a block diagram showing the configuration of a display device according to an embodiment.

The terms used in this specification will be briefly explained, and the present invention will be described in detail.

The terms used in the present invention are general terms that are currently widely used as much as possible while considering the function in the present invention, but this may vary depending on the intention or precedent of a person working in the art, the emergence of new technology, etc. In addition, in certain cases, there are terms arbitrarily selected by the applicant, and in this case, the meaning will be described in detail in the description of the relevant invention. Therefore, the terms used in the present invention should be defined based on the meaning of the term and the overall content of the present invention, rather than simply the name of the term.

When it is said that a part "includes" a certain element throughout the specification, this means that, unless specifically stated to the contrary, it does not exclude other elements but may further include other elements. In addition, terms such as "... unit" and "module" used in the specification refer to a unit that processes at least one function or operation, which may be implemented as hardware or software, or as a combination of hardware and software. .

Below, with reference to the attached drawings, embodiments will be described in detail so that those skilled in the art can easily implement the present invention. However, the present invention may be implemented in many different forms and is not limited to the embodiments described herein. In order to clearly explain the present invention in the drawings, parts that are not related to the description are omitted, and similar parts are given similar reference numerals throughout the specification.

In embodiments herein, the term “user” refers to a person who controls a system, function, or operation, and may include a developer, administrator, or installer.

Additionally, in the embodiments of the present specification, 'image' or 'picture' may refer to a still image, a moving image composed of a plurality of consecutive still images (or frames), or a video.

1 is a diagram illustrating a display device according to an embodiment.

Referring to FIG. 1, a display device 100 according to an embodiment is a device for processing image signals and displaying images and may include a display 120.

The display device 100 according to one embodiment includes a TV, a smart monitor, a mobile phone, a smartphone, a tablet PC, a digital camera, a camcorder, a laptop computer, a desktop, an e-book reader, a digital computer, etc., including a display 120. Broadcasting terminals, PDAs (Personal Digital Assistants), PMPs (Portable Multimedia Players), navigation, MP3 players, DVDs (Digital Video Disk) players, wearable devices, video walls, digital signage Signage), DID (Digital Information Display), projector display, refrigerator, washing machine, etc. Additionally, the display device 100 may be a fixed electronic device placed in a fixed location or a mobile electronic device in a portable form, and may be a digital broadcasting receiver capable of receiving digital broadcasting. However, it is not limited to this.

The display 120 of the display device 100 according to an embodiment may use LED as a light source, use LED as a backlight, or include a micro LED or organic LED (OLED). It may be a light-emitting display. For example, the display 120 according to one embodiment may include a plurality of LED modules. This will be explained in detail with reference to FIG. 2.

Referring to FIG. 2, the display 120 according to one embodiment may include a plurality of display modules. At this time, each of the plurality of display modules may include an LED module including a plurality of LEDs. Hereinafter, for convenience of explanation, an example will be given where the plurality of display modules are a plurality of LED modules 200.

The display 120 according to one embodiment may be implemented in a form in which a plurality of LED modules 200 are physically connected. For example, a plurality of LED modules 200 may be arranged in a matrix form with rows and columns to form one display 120. For example, the display 120 of FIG. 2 may have 20 LED modules 200 arranged in a 5 x 4 matrix. However, it is not limited to this, and the number and arrangement form of the plurality of LED modules 200 constituting the display 120 may vary.

One LED module 210 may include a plurality of pixels arranged in a matrix form (eg, M x N). A plurality of pixels may be implemented as LED devices. For example, the LED module 210 may be implemented with LED, micro LED, organic LED (OLED), passive-matrix OLED (PMOLED), etc. Each pixel (P) may be implemented as an RGB LED, and the RGB LED may include red LED, green LED, and blue LED.

Additionally, the LED device may be implemented as a micro LED. Here, a micro LED is an LED with a size of about 5 to 100 micrometers, and can be an ultra-small light-emitting device that emits light on its own without a color filter.

The LED included in the display 120 according to one embodiment may emit light of any one of three colors, RGB. Additionally, the display 120 may include an LED driver that drives each LED by applying voltage to each LED.

One pixel P may include three LEDs that individually emit light of each of the three RGB colors. The color of light displayed by one pixel P may be determined by mixing the light emitted from the three color LEDs included in the pixel P. A control signal, current, or voltage corresponding to various brightnesses and colors is applied to each pixel (P), and each LED is individually driven according to the applied control signal, current, or voltage, thereby producing a specific brightness and color from the pixel (P). light can be irradiated.

Meanwhile, if the image quality characteristics such as white balance or color of each LED module or each LED are not uniform, the image quality characteristics of the display 120 connecting the LED modules or LEDs may not be uniform. Additionally, since the degree of deterioration of the LEDs included in the display 120 is different, differences may occur in the lifespan or image quality characteristics of the display 120.

Accordingly, calibration is required to improve the image quality of the display 120. The display device 100 according to an embodiment can analyze and predict an abnormality in the image quality of the display 120 and obtain driving information for LEDs to maintain optimal image quality based on the analyzed and predicted results. there is. Additionally, the display device 100 according to one embodiment can maintain optimal image quality by controlling the driving of LEDs based on the obtained driving information.

A method by which a display device according to an embodiment controls the operation of LEDs to maintain optimal image quality will be described in detail below with reference to the drawings.

Referring to FIG. 3, the display device 100 according to one embodiment may collect data on a plurality of LEDs included in the display (S310). Additionally, the display device 100 may store collected data.

The display device 100 according to one embodiment may collect device characteristic values for each of a plurality of LEDs. For example, the device characteristic value may include the wavelength value of the LED device, the luminance value, the luminance deviation value within the LED module, etc. However, it is not limited to this.

Additionally, the display device 100 may collect driving parameters for each of the plurality of LEDs. For example, driving parameters may include current values, voltage values, etc. applied to the LED device when driving the LED device. However, it is not limited to this.

Additionally, the display device 100 may collect device characteristic values and driving parameters for each of a plurality of initially set LEDs when the display device 100 is shipped. For example, when producing the display 120, calibration is performed so that the image quality characteristics of each of the plurality of LED modules included in the display 120 are uniform. Accordingly, a plurality of LEDs included in the display 120 can be set and driven to calibrated initial values. The display device 100 may store calibrated initial values for each of the plurality of LEDs.

Additionally, the display device 100 may collect temperature data for each of a plurality of LEDs or temperature data for each of a plurality of LED modules. For example, temperature data for an LED module may include an average value, deviation, etc. of temperature data for LEDs included in the LED module. However, it is not limited to this.

In addition, the display device 100 collects data on the number of on/off times, on/off frequency, and cumulative use time (on time) for each of the plurality of LEDs. You can.

Additionally, the display device 100 can identify defective LEDs. For example, the display device 100 may detect that the LED does not light up even when current or voltage is applied, or that the LED is open or shorted.

Additionally, if calibration is performed after the display device 100 is shipped, the display device 100 may store calibrated data for each of the plurality of LEDs.

The display device 100 according to one embodiment may collect data on the plurality of LEDs described above periodically or when a preset event occurs. However, it is not limited to this.

The display device 100 according to one embodiment may analyze collected data and predict image quality abnormalities (S320).

The display device 100 may analyze data collected for each of a plurality of LEDs to identify or predict an LED with an abnormality, thereby predicting an abnormal image quality of the display 120.

For example, the display device 100 may identify a defective LED by analyzing a two-dimensional distribution using two of the device characteristic values for the LED as variables. For example, the display device 100 may identify an LED corresponding to an outlier in a two-dimensional distribution. An outlier is a value that is abnormally outside the distribution of values.

Additionally, the display device 100 may analyze the degree to which the collected data for each of the plurality of LEDs changes and identify the LED with an abnormality. For example, the display device 100 may monitor changes in current or voltage values for each of a plurality of LEDs and identify LEDs whose changes in current or voltage values are different from other LEDs. .

In addition, the display device 100 analyzes the number of times each of the plurality of LEDs is turned on and the time they are turned on (or the cumulative use time), and can identify the LED that is predicted to have an abnormality. . For example, the display device 100 obtains a two-dimensional distribution with the number of times the LEDs are turned on and the time they are turned on (On time or cumulative use time) as variables, and the ) and LEDs with relatively large on times (or cumulative use times) can be identified.

However, the present invention is not limited to this, and the display device 100 according to an embodiment may analyze the collected data in various ways to predict abnormalities in the image quality of the display.

The display device 100 according to one embodiment may analyze collected data and predict image quality abnormalities using at least one of machine learning, neural network, or deep learning algorithms as rule-based or artificial intelligence algorithms. For example, the display device 100 can analyze collected data and predict image quality abnormalities of the display using a pre-trained neural network.

The display device 100 according to one embodiment may obtain correction information for LEDs to prevent image quality abnormalities (S330).

The display device 100 identifies abnormal LEDs (e.g., LEDs expected to cause image quality abnormalities) and obtains information about characteristic values or driving parameters of the LEDs to prevent image quality abnormalities of the display. can do.

For example, the display device 100 may identify an LED whose deviation in luminance data is greater than or equal to a first threshold value among LEDs included in one LED module or LEDs included in a boundary area between adjacent LED modules. You can. At this time, the first threshold value may be determined based on the distribution or deviation of luminance data of LEDs included in one LED module or LEDs included in a boundary area between adjacent LED modules. However, it is not limited to this.

The display device 100 may obtain correction information for the identified LED. For example, the display device 100 obtains the current value or voltage value of the identified LED as correction information to ensure that the deviation between the luminance data of the identified LED and the luminance data of other LEDs is smaller than the first threshold value. can do.

Alternatively, the display device 100 may select an LED whose on/off count (frequency) and on time are equal to or greater than the second threshold value among LEDs included in one LED module or LEDs included in the boundary area between adjacent LED modules. can be identified. At this time, the second threshold may be determined based on the on/off count and on time of LEDs included in one LED module or LEDs included in the boundary area between adjacent LED modules.

Additionally, the display device 100 may change the mapping information to reduce the number of on/off times of the identified LED or replace the driving of the identified LED with the driving of other surrounding LEDs.

Additionally, the display device 100 may obtain characteristic values to change the color value corresponding to the identified LED as correction information. However, it is not limited to this.

The display device 100 according to an embodiment can obtain correction information for LEDs to prevent image quality abnormalities by using at least one of machine learning, neural network, or deep learning algorithms as rule-based or artificial intelligence algorithms. there is. For example, the display device 100 may use a pre-trained neural network to obtain correction information for LEDs to prevent image quality abnormalities.

The display device 100 according to one embodiment may control the driving of LEDs based on correction information (S340).

The display device 100 may control the driving of LEDs using the correction information obtained in step 330 (S330). For example, the display device 100 may adjust characteristic values or driving parameters of an LED identified as having an abnormality according to correction information. Alternatively, the display device 100 may replace the driving of the first LED with the driving of the second LED based on the changed mapping information. However, it is not limited to this.

Referring to FIG. 4 , the display device 100 according to one embodiment may include an LED data collection unit 410, an LED data analysis unit 420, and a display driver 430.

The LED data collection unit 410 according to one embodiment may collect data for each of a plurality of LEDs included in the display 120. The LED data collection unit 410 may collect data about LEDs periodically or when a preset event occurs.

The LED data collection unit 410 may collect device characteristic values and driving parameters for each of the plurality of LEDs. Additionally, the LED data collection unit 410 may collect initial setting values for device characteristic values and driving parameters of each of the plurality of LEDs.

Additionally, the LED data collection unit 410 may collect temperature data for each of a plurality of LEDs or temperature data for each of a plurality of LED modules. Additionally, the LED data collection unit 410 may collect data on the on/off count and on/off frequency for each of the plurality of LEDs. Additionally, the LED data collection unit 410 can identify an open or shorted LED among a plurality of LEDs. Additionally, the LED data collection unit 410 may collect calibrated data for each of the plurality of LEDs.

The LED data analysis unit 420 according to one embodiment may analyze collected data, predict image quality abnormalities, and obtain correction information to prevent image quality abnormalities.

The LED data analysis unit 420 according to one embodiment includes appropriate logic, circuits, interfaces, and/or to analyze collected data, predict image quality abnormalities, and obtain correction information to prevent image quality abnormalities. Can contain code.

The LED data analysis unit 420 analyzes data collected for each of the plurality of LEDs and identifies or predicts abnormal LEDs, thereby predicting image quality abnormalities of the display 120.

For example, the LED data analysis unit 420 may identify an LED corresponding to an outlier by analyzing a two-dimensional distribution using two values as variables among the collected data. Alternatively, the LED data analysis unit 420 may analyze the degree of change in the collected data and identify an LED whose change pattern is different from that of other LEDs. Alternatively, the LED data analysis unit 420 analyzes data related to the lifespan of the LED (e.g., on/off number (frequency), on time, etc.) to identify LEDs with a relatively large degree of deterioration. can do.

The LED data analysis unit 420 can predict image quality abnormalities by analyzing the collected data using at least one of machine learning, neural network, or deep learning algorithms as rule-based or artificial intelligence algorithms. For example, the LED data analysis unit 420 can identify abnormal LEDs by inputting the collected data into a pre-trained neural network. However, it is not limited to this.

Additionally, when an image quality abnormality is predicted, the LED data analysis unit 420 may obtain correction information to prevent the predicted image quality abnormality.

For example, the LED data analysis unit 420 may obtain information about characteristic values or driving parameters of a defective LED.

In addition, the LED data analysis unit 420 identifies an LED whose deviation in luminance data is greater than or equal to a first threshold value among LEDs included in one LED module or LEDs included in the boundary area between adjacent LED modules. , correction information for the identified LED can be obtained. For example, the LED data analysis unit 420 provides correction information for the current value or voltage value of the identified LED so that the deviation between the luminance data of the identified LED and the luminance data of other LEDs is smaller than the first threshold value. It can be obtained with

Alternatively, the LED data analysis unit 420 sets the on/off count (frequency) and on time among LEDs included in one LED module or LEDs included in the boundary area between adjacent LED modules to a second threshold value. Abnormal LEDs can be identified. The LED data analysis unit 420 may change the mapping information to reduce the number of on/off times of the identified LED or replace the driving of the identified LED with the driving of other surrounding LEDs.

The LED data analysis unit 420 may obtain characteristic values to change the color value corresponding to the identified LED as correction information. However, it is not limited to this.

The LED data analysis unit 420 according to one embodiment acquires correction information for LEDs to prevent image quality abnormalities by using at least one of machine learning, neural network, or deep learning algorithms as a rule-based or artificial intelligence algorithm. can do. For example, the LED data analysis unit 420 inputs data collected about LEDs or data about LEDs with abnormalities into a pre-trained neural network to provide correction information for LEDs to prevent image quality abnormalities. It can be obtained. However, it is not limited to this.

The display driver 430 according to one embodiment may control the driving of LEDs included in the display 120 based on the acquired correction information. The display driver 430 can adjust the characteristic values or driving parameters of the LED identified as having an abnormality according to the correction information. Alternatively, the display driver 430 may replace driving of the first LED with driving of the second LED based on the changed mapping information. However, it is not limited to this.

Referring to FIG. 5, the display device 100 according to an embodiment can optimize image quality in the boundary area of a plurality of LED modules. For example, the display device 100 includes first LEDs 515 and second LED module 520 adjacent to the second LED module 520 among the plurality of LEDs included in the first LED module 510. The characteristic values of the second LEDs 525 adjacent to the first LED module among the plurality of LEDs included in can be similarly corrected.

The numbers displayed on each of the LEDs in FIG. 5 may represent the luminance value of each LED. The display device 100 according to one embodiment displays the first LEDs 515 and the second LEDs 525 so that the difference between the luminance values of the first LEDs 515 and the luminance values of the second LEDs 525 is less than or equal to a preset value. The driving current or driving voltage of the second LEDs 525 can be adjusted.

For example, the display device 100 drives the LEDs so that the luminance values of the first LED 531 and the second LED 532 among the first LEDs increase and the luminance value of the third LED 533 decreases. Parameters can be adjusted. Alternatively, the display device 100 may be configured to increase the luminance value of the fourth LED 541 among the second LEDs 525 and decrease the luminance values of the fifth LED 542 and the sixth LED 543. Driving parameters can be adjusted. At this time, if the difference in luminance value from the surrounding LEDs is large even after reducing the luminance value of the third LED 533 to the maximum, the display device 100 may change the driving of the third LED 533 to that of the surrounding LEDs. there is. For example, when it is necessary to drive the third LED 533, the display device 100 may drive the seventh LED 551, which has a luminance value smaller than the third LED 533, instead of the third LED 533. You can. Accordingly, the user may perceive that the luminance value of the third LED 533 has decreased.

In addition, if the difference in luminance values of the fifth LED 542 and the sixth LED 543 is large even after reducing the luminance values of the fifth LED 542 and the sixth LED 543 to the maximum, the display device 100 displays the fifth LED 542 and the sixth LED 543. The driving of the LED 543 can be replaced with the driving of the surrounding LEDs. For example, when the display device 100 needs to drive the fifth LED 542, it is located around the fifth LED 542 instead of the fifth LED 542, and has a luminance value smaller than that of the fifth LED 542. The eighth LED 552 having can be driven. In addition, when the sixth LED 543 needs to be driven, the display device 100 is located around the sixth LED 543 instead of the sixth LED 543 and has a luminance value smaller than that of the sixth LED 543. The ninth LED 553 can be driven.

Accordingly, the user may perceive that the luminance values of the fifth LED 542 and the sixth LED 543 have decreased.

Meanwhile, in Figure 5, a method of controlling the driving of LEDs between adjacent LED modules is explained using the luminance value of the LED element as an example, but the method is not limited thereto. The display device 100 according to one embodiment may control the driving of the LED so that the wavelength value, on/off number, etc. of the LED element have similar values between adjacent LED modules.

Referring to FIG. 6, the display device 100 according to an embodiment may identify at least one LED with an abnormality among LEDs that display a green color. For example, as a result of analyzing the collected data of the first LED 610 showing green color, the display device 100 may identify that the degree of deterioration of the first LED 610 is greater than that of other LEDs. . Alternatively, the display device 100 may identify that the change in the characteristic value of the first LED 610 shows a different aspect from the change in the characteristic value of the other LEDs. However, it is not limited to this.

When the first LED 610 with an abnormality (e.g., an LED expected to cause an image quality abnormality) displays a green color, the display device 100 maps the color of the first LED 610 to another LED. It can be replaced with

Since green color is more sensitive to human vision than red or blue color, the display device 100 according to one embodiment changes the color mapping of the corresponding LED to another color when there is a problem with the LED to which the green color is mapped. By changing the color, you can prevent image quality from deteriorating.

For example, the display device 100 may change the color mapped to the line 620 including the first LED 610 from green to red. Alternatively, the display device 100 may change the color mapped to the line 620 including the first LED 610 from green to blue.

Alternatively, the display device 100 may change only the mapping for the LED module including the first LED 610. For example, in the first LED module 630 including the first LED 610, the mapping color of the line 635 including the first LED 610 is changed from green to red, and the mapping color of the line 635 including the first LED 610 is changed from green to red. ) The second LED module 645 that does not include ) can maintain color mapping as is. However, it is not limited to this.

Referring to FIG. 7, the display device 100 according to an embodiment may identify a defective LED among a plurality of LEDs included in the display. For example, the display device 100 may identify a dead pixel that does not light up or has a problem among a plurality of pixels.

Since green color is more sensitive to human vision than red or blue color, when the LED corresponding to a defective pixel displays a green color, the display device 100 selects the color mapped to the line containing the corresponding LED. By changing this, deterioration in image quality can be prevented.

As shown in FIG. 7, the display device 100 may identify the first LED 710 and the second LED 720, which display green color, as defective pixels.

The display device 100 may change the color mapped to the line including the first LED 710 or the line including the second LED 720 from green to blue, or from green to red.

Alternatively, when the LED corresponding to a defective pixel displays a green color, the display device 100 can prevent image quality deterioration by changing the color mapped to the LED adjacent to the corresponding LED to green color.

For example, as shown in FIG. 7, the color mapped to the third LED 730 adjacent to the right of the first LED 710 can be changed from blue to green. Additionally, the color mapped to the fourth LED 740 adjacent to the right of the second LED 720 can be changed from blue to green. However, it is not limited to this, and the display device 100 may change the color mapped to the LED adjacent to the left of the first LED to green, or change the color mapped to the LED adjacent to the left of the second LED to green. there is.

Accordingly, even if a defective pixel occurs, it is possible to change the color mapping of the surrounding LEDs and correct it in a direction that is not easily recognized by the user, thereby reducing the recognition of defects in the display device 100 and saving service costs.

Referring to FIG. 8, the display device 100 according to one embodiment may include a processor 110, a memory 130, and a display 120.

The display 120 according to one embodiment may generate a driving signal by converting an image signal, a data signal, an OSD signal, a control signal, etc. processed by the processor 110, and display an image according to the driving signal.

The display 120 according to one embodiment may include a plurality of LEDs. For example, the display 120 may use LED as a backlight, or may be a self-luminous display including micro LED or OLED.

Additionally, the display 120 according to one embodiment may include a plurality of LED modules, and the plurality of LED modules may be arranged in a matrix form with rows and columns to form one display 120.

The processor 110 according to one embodiment controls the overall operation of the display device 100 and signal flow between durable components of the display device 100, and performs a function of processing data.

The processor 110 may include single core, dual core, triple core, quad core, and multiple cores thereof. Additionally, the processor 110 may include a plurality of processors. For example, the processor 110 may be implemented as a main processor (not shown) and a sub processor (not shown) operating in a sleep mode.

Additionally, the processor 110 It may include at least one of a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), and a Video Processing Unit (VPU). Alternatively, depending on the embodiment, it may be implemented in the form of a SoC (System On Chip) integrating at least one of CPU, GPU, and VPU. Alternatively, the processor 110 may further include a Neural Processing Unit (NPU).

The memory 130 according to one embodiment may store various data, programs, or applications for driving and controlling the display device 100.

Additionally, a program stored in memory 130 may include one or more instructions. A program (one or more instructions) or application stored in the memory 130 may be executed by the processor 110.

The processor 110 according to one embodiment may collect data on a plurality of LEDs included in the display 120 by executing one or more instructions stored in the memory 130.

Data on a plurality of LEDs according to an embodiment may include device characteristic values for each of the plurality of LEDs. For example, the device characteristic value may include the wavelength value of the LED device, the luminance value, the luminance deviation value within the LED module, etc. However, it is not limited to this.

Additionally, data on a plurality of LEDs may collect driving parameters for each of the plurality of LEDs. For example, driving parameters may include current values, voltage values, etc. applied to the LED device when driving the LED device. However, it is not limited to this.

Additionally, the processor 110 may collect device characteristic values and driving parameters for each of a plurality of initially set LEDs when the display device 100 is shipped.

Additionally, the data for the plurality of LEDs may include temperature data for each of the plurality of LEDs or temperature data for each of the plurality of LED modules. For example, temperature data for an LED module may include an average value, deviation, etc. of temperature data for LEDs included in the LED module.

Additionally, data on the plurality of LEDs may include data on the on/off count and on/off frequency for each of the plurality of LEDs.

Additionally, processor 110 may identify defective LEDs. For example, the processor 110 may detect that the LED is open or shorted.

Additionally, if calibration is performed after the display device 100 is shipped, the processor 110 may store calibrated data for each of the plurality of LEDs.

The processor 110 according to one embodiment may collect data on the plurality of LEDs described above periodically or when a preset event occurs.

The processor 110 according to one embodiment may store data collected for a plurality of LEDs in the memory 130.

The processor 110 according to one embodiment may predict image quality abnormalities of the display 120 by analyzing collected data.

The processor 110 may analyze data collected for each of the plurality of LEDs to identify or predict an LED with an abnormality, thereby predicting an abnormal image quality of the display 120.

For example, the processor 110 may identify a defective LED by analyzing a two-dimensional distribution using two values as variables among the device characteristic values for the LED. For example, the processor 110 may identify an LED corresponding to an outlier in a two-dimensional distribution. An outlier is a value that is abnormally outside the distribution of values.

Additionally, the processor 110 may analyze the degree to which the collected data for each of the plurality of LEDs changes and identify the LED with an abnormality. For example, the processor 110 may monitor changes in current or voltage values for each of a plurality of LEDs and identify LEDs whose changes in current or voltage values are different from other LEDs.

Additionally, the processor 110 may analyze the number of times each LED is turned on and the time it is turned on, and identify an LED that is predicted to have an abnormality. For example, the processor 110 may obtain a two-dimensional distribution with the turn-on count and on-time as variables for a plurality of LEDs, and identify LEDs with relatively large turn-on times and on-time.

However, the present invention is not limited to this, and the processor 110 according to one embodiment may predict image quality abnormalities of the display by analyzing the collected data in various ways.

The processor 110 according to one embodiment may analyze collected data and predict image quality abnormalities using at least one of machine learning, neural network, or deep learning algorithms as rule-based or artificial intelligence algorithms. For example, the processor 110 may use a pre-trained neural network to analyze collected data and predict image quality abnormalities of the display.

The neural network according to one embodiment may be a network that has learned various standards or conditions that can determine abnormal image quality of a display based on various training data. Here, 'training' refers to the method of analyzing the input data when various collected data are input to the neural network network and the method of determining abnormal display quality based on the input data. This may mean training a neural network so that it can discover or learn on its own. Accordingly, the trained neural network can predict abnormalities in the display quality.

At this time, the learned neural network may be stored in advance in the display device 100 and may be periodically updated through communication with an external server.

The processor 110 according to one embodiment may obtain correction information that allows the display 120 to maintain optimal image quality and control the driving of a plurality of LEDs based on the correction information.

The processor 110 identifies abnormal LEDs (e.g., LEDs expected to cause image quality abnormalities) and obtains information about characteristic values or driving parameters of the LEDs to prevent image quality abnormalities of the display. You can.

For example, the processor 110 may identify an LED whose deviation in luminance data is greater than or equal to a first threshold value among LEDs included in one LED module or LEDs included in a boundary area between adjacent LED modules. there is. At this time, the first threshold value may be determined based on the distribution or deviation of luminance data of LEDs included in one LED module or LEDs included in a boundary area between adjacent LED modules.

Processor 110 may obtain correction information for the identified LED. For example, the display device 100 obtains the current value or voltage value of the identified LED as correction information to ensure that the deviation between the luminance data of the identified LED and the luminance data of other LEDs is smaller than the first threshold value. can do.

Alternatively, the processor 110 identifies an LED whose on/off count (frequency) and on time are equal to or greater than the second threshold among LEDs included in one LED module or LEDs included in the boundary area between adjacent LED modules. can do. At this time, the second threshold may be determined based on the on/off count and on time of LEDs included in one LED module or LEDs included in the boundary area between adjacent LED modules.

The processor 110 may change the mapping information to reduce the number of on/off times of the identified LED or replace the driving of the identified LED with the driving of other surrounding LEDs.

The processor 110 may obtain characteristic values to change the color value corresponding to the identified LED as correction information. However, it is not limited to this.

The processor 110 according to one embodiment may use at least one of machine learning, neural network, or deep learning algorithms as rule-based or artificial intelligence algorithms to obtain correction information for LEDs to prevent image quality abnormalities. .

The neural network according to one embodiment may be a network in which correction information for LEDs to prevent image quality abnormalities is learned based on various training data. Here, 'training' refers to the process by which the neural network network discovers or learns correction information for LEDs to prevent image quality abnormalities based on the input data when various collected data are input to the neural network network. This may mean training a neural network to be able to Accordingly, the trained neural network can obtain correction information for LEDs to prevent image quality abnormalities.

Referring to FIG. 9 , the display device 900 of FIG. 9 may be an example of the display device 100 described with reference to FIGS. 1 to 8 .

Referring to FIG. 9, the display device 900 according to an embodiment includes a tuner unit 940, a processor 910, a display unit 920, a communication unit 950, a detection unit 930, and an input/output unit. It may include (970), a video processing unit (980), an audio processing unit (985), an audio output unit (960), a memory (990), and a power supply unit (995).

The processor 910 of FIG. 9 is the processor 110 of FIG. 8, the memory 990 of FIG. 9 is the memory 130 of FIG. 8, and the display unit 920 of FIG. 9 is the display 120 of FIG. 8. It is a configuration corresponding to . Therefore, the same content as previously described will be omitted.

The communication unit 950 according to one embodiment can transmit and receive data or signals with an external device or server. For example, the communication unit 950 may include a Wi-Fi module, a Bluetooth module, an infrared communication module, a wireless communication module, a LAN module, an Ethernet module, a wired communication module, etc. At this time, each communication module may be implemented in the form of at least one hardware chip.

The Wi-Fi module and Bluetooth module communicate using Wi-Fi and Bluetooth methods, respectively. When using a Wi-Fi module or a Bluetooth module, various connection information such as SSID and session key are first transmitted and received, and various information can be transmitted and received after establishing a communication connection using this. Wireless communication modules include zigbee, 3G ( ^3rd Generation), 3GPP ( ^3rd Generation Partnership Project), LTE (Long Term Evolution), LTE-A (LTE Advanced), 4G ( ^4th Generation), and 5G (5G). It may include at least one communication chip that performs communication according to various wireless communication standards such as ^th Generation).

The communication unit 950 according to one embodiment may be installed outside the display device 900 and communicate with an external camera capable of taking pictures of the display unit 920. For example, the communication unit 950 may receive an image of a screen displayed on the display unit 920 from an external camera. At this time, the display unit 920 can display a test pattern, etc., and the communication unit 950 can receive an image in which the test pattern is captured. The processor 910 according to one embodiment may detect an abnormality in the image quality of the display unit 920 based on an image in which a test pattern is captured.

The tuner unit 940 according to an embodiment amplifies, mixes, resonates, etc. broadcast signals received by wire or wirelessly to determine which of the many radio wave components the display device 1100 wants to receive. You can select only the frequency of the channel by tuning it. Broadcast signals include audio, video, and additional information (eg, Electronic Program Guide (EPG)).

The tuner unit 940 can receive broadcast signals from various sources, such as terrestrial broadcasting, cable broadcasting, satellite broadcasting, and Internet broadcasting. The tuner unit 940 may receive broadcast signals from sources such as analog broadcasting or digital broadcasting.

The detection unit 930 detects the user's voice, the user's image, or the user's interaction, and may include a microphone 931, a camera unit 932, and a light receiver 933.

The microphone 931 receives the user's uttered voice. The microphone 931 may convert the received voice into an electrical signal and output it to the processor 910. The user voice may include, for example, a voice corresponding to a menu or function of the display device 900.

The camera unit 932 may receive images (eg, consecutive frames) corresponding to the user's motion, including gestures, within the camera recognition range. The processor 910 can use the received motion recognition result to select a menu displayed on the display device 900 or perform control corresponding to the motion recognition result.

The light receiver 933 receives an optical signal (including a control signal) received from an external control device through a light window (not shown) of the bezel of the display unit 1320. The light receiver 933 may receive an optical signal corresponding to a user input (eg, touch, press, touch gesture, voice, or motion) from the control device. A control signal may be extracted from the received optical signal under the control of the processor 910.

The input/output unit 970 provides video (e.g., video, etc.), audio (e.g., voice, music, etc.), and additional information (e.g., EPG, etc.) from the outside of the display device 900. receives. Input/output interfaces include HDMI (High-Definition Multimedia Interface), MHL (Mobile High-Definition Link), USB (Universal Serial Bus), DP (Display Port), Thunderbolt, VGA (Video Graphics Array) port, and RGB port. , D-SUB (D-subminiature), DVI (Digital Visual Interface), component jack, or PC port.

The processor 910 controls the overall operation of the display device 900 and signal flow between internal components of the display device 900, and performs the function of processing data. The processor 910 may execute an operating system (OS) and various applications stored in the memory 990 when there is a user input or a preset and stored condition is satisfied.

The processor 910 stores signals or data input from the outside of the display device 900, or uses RAM, which is used as a storage area corresponding to various tasks performed on the display device 900. It may include a ROM and a processor storing a control program for control.

The video processing unit 980 performs processing on video data received by the display device 900. The video processing unit 980 can perform various image processing such as decoding, scaling, noise filtering, frame rate conversion, and resolution conversion on video data.

The audio processing unit 985 performs processing on audio data. In the audio processing unit 985, various processing such as decoding, amplification, noise filtering, etc. can be performed on audio data. Meanwhile, the audio processing unit 985 may be equipped with a plurality of audio processing modules to process audio corresponding to a plurality of contents.

The audio output unit 960 outputs audio included in the broadcast signal received through the tuner unit 940 under the control of the processor 910. The audio output unit 960 may output audio (eg, voice, sound) input through the communication unit 950 or the input/output unit 970. Additionally, the audio output unit 960 may output audio stored in the memory 990 under the control of the processor 910. The audio output unit 960 may include at least one of a speaker, a headphone output terminal, or a Sony/Philips Digital Interface (S/PDIF) output terminal.

The power unit 995 supplies power input from an external power source to the components inside the display device 900 under the control of the processor 910. Additionally, the power unit 995 may supply power output from one or more batteries (not shown) located inside the display device 900 to internal components under the control of the processor 910.

The memory 990 may store various data, programs, or applications for driving and controlling the display device 900 under the control of the processor 910. The memory 990 includes a broadcast reception module (not shown), a channel control module, a volume control module, a communication control module, a voice recognition module, a motion recognition module, an optical reception module, a display control module, an audio control module, an external input control module, and a power supply. It may include a control module, a power control module of an external device connected wirelessly (eg, Bluetooth), a voice database (DB), or a motion database (DB). Not shown modules and database of the memory 990 include broadcast reception control function, channel control function, volume control function, communication control function, voice recognition function, motion recognition function, and light reception control function in the display device 900. , may be implemented in software form to perform a display control function, an audio control function, an external input control function, a power control function, or a power control function of an external device connected wirelessly (eg, Bluetooth). The processor 910 can perform each function using these software stored in the memory 990.

Meanwhile, the block diagrams of the

display devices

100 and 900 shown in FIGS. 8 and 9 are block diagrams for one embodiment. Each component of the block diagram may be integrated, added, or omitted depending on the specifications of the

display device

100 or 900 that is actually implemented. That is, as needed, two or more components may be combined into one component, or one component may be subdivided into two or more components. In addition, the functions performed by each block are for explaining the embodiments, and the specific operations or devices do not limit the scope of the present invention.

A display device according to an embodiment may include a memory that stores one or more instructions, and at least one processor that executes the one or more instructions.

The at least one processor may obtain correction information for the LEDs to prevent image quality abnormalities by executing the one or more instructions.

The data for the LEDs may include at least one of characteristic values, driving parameters, calibration data, temperature data, on/off frequency data, and defect status data for each of the LEDs. .

The at least one processor may collect data about the LEDs periodically or based on the occurrence of a preset event by executing the one or more instructions.

The at least one processor may store the collected data in the memory by executing the one or more instructions.

The at least one processor may analyze the collected data and predict image quality abnormalities of the display using an artificial intelligence algorithm by executing the one or more instructions.

The at least one processor may obtain correction information for the LEDs to prevent image quality abnormalities using an artificial intelligence algorithm by executing the one or more instructions.

The display may include a plurality of LED modules including the plurality of LEDs.

By executing the one or more instructions, the at least one processor controls first LEDs included in a first LED module among the plurality of LED modules and second LEDs included in a second LED module adjacent to the first LED module. Based on data about LEDs, the difference in image quality between the first LED module and the second LED module can be analyzed.

The at least one processor may obtain correction information for the first LEDs and the second LEDs to minimize the image quality difference by executing the one or more instructions.

The at least one processor, by executing the one or more instructions, determines characteristic values of a third LED adjacent to the second LED module among the first LEDs and a fourth LED adjacent to the first LED module among the second LEDs. Correction information for characteristic values of LEDs can be obtained.

The at least one processor may correct characteristic values of the third and fourth LEDs based on the correction information by executing the one or more instructions.

When driving the first LEDs by executing the one or more instructions, the at least one processor changes the driving of any one of the third LEDs to one of the remaining LEDs other than the third LEDs. It can be replaced.

By executing the one or more instructions, the at least one processor may identify a first LED among the LEDs that generates the image quality abnormality as a result of analyzing the collected data.

The at least one processor may correct characteristic values of the first LED by executing the one or more instructions.

The at least one processor may change the color corresponding to the first LED from a first color to a second color by executing the one or more instructions.

The at least one processor may change the color corresponding to the LEDs included in the line including the first LED from the first color to the second color by executing the one or more instructions.

The first LED may be a deteriorated LED in which at least one of usage time and usage frequency exceeds a preset threshold, or may include an LED that does not light up.

Collecting data about the plurality of LEDs may include collecting data about the LEDs periodically or based on the occurrence of a preset event.

A method of operating a display device according to an embodiment may include storing the collected data.

Predicting an image quality abnormality of the display may include predicting an image quality abnormality of the display by analyzing the collected data using an artificial intelligence algorithm.

The step of obtaining correction information for the LEDs to prevent image quality abnormalities. It may include obtaining correction information for the LEDs to prevent image quality abnormalities using an artificial intelligence algorithm.

The step of predicting an image quality abnormality of the display includes first LEDs included in a first LED module among the plurality of LED modules and second LEDs included in a second LED module adjacent to the first LED module. Based on the data, it may include analyzing the difference in image quality between the first LED module and the second LED module.

Obtaining correction information may include obtaining correction information for the first LEDs and the second LEDs to minimize the image quality difference.

The step of acquiring the correction information includes characteristic values of a third LED adjacent to the second LED module among the first LEDs and characteristic values of a fourth LED adjacent to the first LED module among the second LEDs. It may include obtaining correction information.

Controlling the driving of the LEDs based on the correction information may include correcting characteristic values of the third and fourth LEDs based on the correction information.

The step of controlling the driving of the LEDs based on the correction information includes driving any one of the third LEDs when driving the first LEDs and controlling the driving of any one of the remaining LEDs other than the third LEDs. It may include a step of replacing with driving.

The step of predicting an image quality abnormality of the display by analyzing the collected data may include identifying a first LED among the LEDs that causes the image quality abnormality as a result of analyzing the collected data. .

Controlling the driving of the LEDs may include correcting characteristic values of the first LED.

Controlling the driving of the LEDs may include changing the color corresponding to the first LED from a first color to a second color.

Controlling the driving of the LEDs may include changing the color corresponding to the LEDs included in the line including the first LED from a first color to a second color.

A display device and a method of operating the same according to an embodiment find areas of image quality deterioration through artificial intelligence-based analysis of characteristic data of LEDs included in the display, find the optimal combination of various parameters, and automatically distribute and display image quality. Lifespan can be optimized.

In addition, by predicting the image quality deterioration and lifespan of the display based on accumulated data about LEDs and maintaining the image quality in an optimized state in advance, service costs can be reduced and user inconvenience can be minimized.

A method of operating a display device according to an embodiment may be implemented in the form of program instructions that can be executed through various computer means and recorded on a computer-readable medium. The computer-readable medium may include program instructions, data files, data structures, etc., singly or in combination. Program instructions recorded on the medium may be specially designed and constructed for the present invention or may be known and usable by those skilled in the art of computer software. Examples of computer-readable recording media include magnetic media such as hard disks, floppy disks, and magnetic tapes, optical media such as CD-ROMs and DVDs, and magnetic media such as floptical disks. -Includes optical media (magneto-optical media) and hardware devices specifically configured to store and execute program instructions, such as ROM, RAM, flash memory, etc. Examples of program instructions include machine language code, such as that produced by a compiler, as well as high-level language code that can be executed by a computer using an interpreter, etc.

Additionally, at least one of the method of operating a display device and the method of operating an electronic device according to the disclosed embodiments may be included and provided in a computer program product. Computer program products are commodities and can be traded between sellers and buyers.

A computer program product may include a S/W program and a computer-readable storage medium in which the S/W program is stored. For example, a computer program product may include a product in the form of a S/W program (e.g., a downloadable app) distributed electronically by the manufacturer of an electronic device or through an electronic marketplace (e.g., Google Play Store, App Store). there is. For electronic distribution, at least part of the S/W program may be stored in a storage medium or temporarily created. In this case, the storage medium may be a manufacturer's server, an electronic market server, or a relay server's storage medium that temporarily stores the SW program.

A computer program product, in a system comprised of a server and a client device, may include a storage medium of a server or a storage medium of a client device. Alternatively, if there is a third device (e.g., a smartphone) in communication connection with the server or client device, the computer program product may include a storage medium of the third device. Alternatively, the computer program product may include the S/W program itself, which is transmitted from a server to a client device or a third device, or from a third device to a client device.

In this case, one of the server, the client device, and the third device may execute the computer program product to perform the method according to the disclosed embodiments. Alternatively, two or more of a server, a client device, and a third device may execute the computer program product and perform the methods according to the disclosed embodiments in a distributed manner.

For example, a server (eg, a cloud server or an artificial intelligence server, etc.) may execute a computer program product stored on the server and control a client device connected to the server to perform the method according to the disclosed embodiments.

Although the embodiments have been described in detail above, the scope of the present invention is not limited thereto, and various modifications and improvements made by those skilled in the art using the basic concept of the present invention defined in the following claims are also included in the scope of the present invention. belongs to

Claims

In the display device 100,

A display 120 including a plurality of LEDs;

a memory 130 that stores one or more instructions; and

By executing the one or more instructions,

Collect data on the LEDs,

By analyzing the collected data, predicting image quality abnormalities of the display and obtaining correction information for the LEDs to prevent the image quality abnormalities,

A display device comprising at least one processor (110) that controls driving of the LEDs based on the correction information.
According to paragraph 1,

The data for the LEDs is,

A display device comprising at least one of characteristic values, driving parameters, calibration data, temperature data, on/off frequency data, and defect status data for each of the LEDs.
According to claim 1 or 2,

The at least one processor 110 executes the one or more instructions,

A display device that collects data about the LEDs periodically or based on the occurrence of a preset event and stores the collected data in the memory.
According to any one of claims 1 to 3,

The at least one processor 110 executes the one or more instructions,

A display device that predicts image quality abnormalities of the display and obtains correction information for the LEDs to prevent image quality abnormalities by analyzing the collected data using an artificial intelligence algorithm.
According to any one of claims 1 to 4,

The display includes a plurality of LED modules including the plurality of LEDs,

The at least one processor 110 executes the one or more instructions,

Based on data about the first LEDs included in the first LED module among the plurality of LED modules and the second LEDs included in the second LED module adjacent to the first LED module, the first LED module and Analyzing the difference in image quality of the second LED module,

A display device that obtains correction information for the first LEDs and the second LEDs to minimize the difference in image quality.
According to clause 5,

The at least one processor 110 executes the one or more instructions,

Obtain correction information for characteristic values of third LEDs adjacent to the second LED module among the first LEDs and characteristic values of fourth LEDs adjacent to the first LED module among the second LEDs,

A display device that corrects characteristic values of the third and fourth LEDs based on the correction information.
According to clause 6,

The at least one processor 110 executes the one or more instructions,

In driving the first LEDs, the display device replaces the driving of any one of the third LEDs with the driving of any one of the remaining LEDs other than the third LEDs.
According to any one of claims 1 to 7,

The at least one processor 110 executes the one or more instructions,

As a result of analyzing the collected data, a display device that identifies a first LED among the LEDs that causes the image quality abnormality and corrects characteristic values of the first LED.
According to clause 8,

The at least one processor 110 executes the one or more instructions,

A display device that changes the color corresponding to the first LED from a first color to a second color.
According to clause 8 or 9,

The at least one processor executes the one or more instructions,

A display device that changes the color corresponding to the LEDs included in the line including the first LED from a first color to a second color.
According to any one of claims 8 to 10,

The first LED is a deteriorated LED in which at least one of usage time and usage frequency exceeds a preset threshold, or includes an LED that does not light up.
In a method of operating a display device,

Collecting data on a plurality of LEDs included in the display;

predicting image quality abnormalities of the display by analyzing the collected data;

Obtaining correction information for the LEDs to prevent image quality abnormalities; and

A method of operating a display device including controlling driving of the LEDs based on the correction information.
According to clause 12,

The data for the LEDs is,

A method of operating a display device, including at least one of characteristic values, driving parameters, calibration data, temperature data, on/off frequency data, and defect status data for each of the LEDs.
According to claim 12 or 13,

The step of collecting data on the plurality of LEDs is,

Collecting data on the LEDs periodically or based on the occurrence of a preset event; and

A method of operating a display device, including the step of storing the collected data.
One or more computer-readable recording media storing a program for performing the method of any one of claims 12 to 14.