Color data processing method and system using same

Abstract

The invention discloses a color data processing method and a system applying the same, and relates to the technical field of intelligent display. The color data processing method comprises the following steps: step 1, acquiring and preprocessing data of display effect influence parameters; step 2, scene identification and mode matching; 3, self-adaptive adjustment is carried out, wherein self-adaptive adjustment is carried out when the display mode in the step 2 is executed based on the data obtained in the step 1; step 4, user experience optimization, including smooth transition optimization based on the current application scene and ambient light data; and counting the duration of the user entering any application display mode, obtaining monitoring analysis data of the intelligent wearable device, performing fatigue analysis in combination with historical regulation and control records of the current display mode, and performing optimization adjustment according to an analysis result. The display effect of the screen can be improved, and the user experience is improved.

Description

Technical Field

[0001] The present application relates to the field of intelligent display technology, and in particular to a color data processing method and a system using the same. Background Art

[0002] Traditionally, the brightness and other parameters of the display are fixed. If you want to achieve different display effects, you need to manually operate the remote control or the corresponding function buttons of the display to change various parameters, which is relatively inconvenient to use.

[0003] In response to the above, relevant manufacturers have currently developed technologies that can automatically adjust the screen brightness according to the ambient light intensity based on mobile phones, computers, and tablets on the market, and can even adjust the screen display effect according to whether the user is watching a movie, playing games, or doing daily office work; however, the above display technology has the following shortcomings: the display effect regulation is relatively single, it is relatively easy to cause visual fatigue to the user, and it is easy to be affected by temporary emergencies and disrupt the display effect. Therefore, this application proposes a new solution. Summary of the invention

[0004] In order to improve the display effect of the screen, the present application provides a color data processing method and a system using the same.

[0005] In a first aspect, the present application provides a color data processing method, which adopts the following technical solution: A color data processing method, comprising: Step 1: Data collection and preprocessing of parameters affecting display effects, including: Obtain and analyze the data of the color sensing unit preset in the screen to obtain color system data and screen brightness data; Obtaining and analyzing data from a preset ambient light sensing unit to obtain ambient light data; Step 2: scene recognition and pattern matching, which includes: Identify the application scenario based on the device parameters and ambient light data corresponding to the screen; Search the pre-established database according to the application scenario, call the matching display mode, and output; Step 3: Adaptive adjustment, which includes: adaptively adjusting the display mode of step 2 based on the data obtained in step 1; Step 4: User experience optimization, which includes: Optimize smooth transition based on current application scenario and ambient light data; Count the time users spend in any application display mode, obtain monitoring and analysis data from smart wearable devices, perform fatigue analysis based on the historical control records of the current display mode, and make optimization adjustments based on the analysis results.

[0006] Optionally, the smooth transition optimization based on the current application scenario and ambient light data includes: If the rate of change of the ambient light data is greater than the preset smooth transition trigger threshold, the color temperature and brightness adjustment parameters are analyzed and output according to the preset smooth transition algorithm; If the backlight adjustment conditions are currently met, the backlight adjustment is performed preferentially based on the brightness adjustment parameters.

[0007] Optionally, the fatigue analysis includes: Evaluate user behavior habits based on historical monitoring and analysis data from smart wearable devices; If the current time is the t1 duration before sleep in the behavior habit, the preset blue light optimization process is executed; If the user currently maintains the same display mode for a duration greater than t2, and the matching application scenario and time period meet the preset optimization conditions, the preset warm light optimization process is executed; Among them, t1 and t2 are both preset values.

[0008] Optionally, the step 4, user experience optimization, further includes: If the current application scenario matched by the display mode is watching a movie, the current video data of the device corresponding to the display screen is obtained, and the video type is identified; If the type of film or TV program meets the preset stimulating image conditions, the Blue-ray optimization process is terminated.

[0009] Optionally, if the video type meets the preset stimulating image conditions, then: Call the real-time monitoring and analysis data of smart wearable devices to conduct sentiment analysis; If the user pre-selects the horror suppression mode, the preset brightness optimization process is executed when the emotion analysis results and the user's heart rate meet the preset suppression conditions.

[0010] Optionally, if the current application scenario matched by the display mode is watching a movie, then: Obtain real-time display effects of film and television data and conduct visual analysis of picture features; If the results of the visual analysis of the picture features do not meet the standards, the brightness optimization process will be executed, and the response priority of the horror suppression mode will be higher.

[0011] Optionally, the warm light optimization process includes increasing the proportion of R in RGB by dm, maintaining the duration of t3, and then recovering and re-timing the application duration of the display mode; wherein dm and t3 are preset values.

[0012] In a second aspect, the present application provides a system for applying a color data processing method, which adopts the following technical solution: A system for applying a color data processing method comprises a controller and an intelligent wearable device, wherein the controller is configured as follows: step one, collecting and preprocessing display effect-affecting parameter data; step two, scene recognition and pattern matching; step three, adaptively adjusting the display mode of step two based on the data obtained in step one; and step four, optimizing the user experience.

[0013] To summarize, the present application includes the following beneficial technical effects: the screens and devices to which the present application is applied, in addition to being able to analyze application scenarios and match display modes for them, can also, on this basis, perform adaptive adjustments based on ambient light and screen light to reduce screen stimulation to the eyes; they can also go a step further and make a smooth transition when there is a large difference between the two, perform fatigue analysis based on the display mode application time and data from smart wearable devices, and make optimization adjustments, thereby improving the display effect of the screen. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 It is a schematic diagram of the steps of the method of the present application; Figure 2 It is a schematic diagram of the structure of the system of this application. DETAILED DESCRIPTION

[0015] The following is combined with Figure 1-2 This application is described in further detail.

[0016] The embodiment of the present application discloses a color data processing method.

[0017] Reference Figure 1 , the color data processing method includes: Step 1: Collection and preprocessing of display effect impact parameter data; Step 2: scene recognition and pattern matching; Step 3: Adaptive adjustment; Step 4: Optimize user experience.

[0018] This method aims to improve the display effect of the screen and ensure that users can get the best visual experience in different usage scenarios while avoiding visual fatigue or other health problems.

[0019] Regarding step one, it includes: S11, acquiring and analyzing data of a color sensing unit preset in the screen to obtain color system data and screen brightness data.

[0020] Among them, the color sensing unit can be a color sensor, which is an electrical component of the display (screen) matched by this method; the color sensor should be able to know the red, green and blue color components and the brightness of the light. It is known that the three primary colors of RGB are combined to form various colors, so they can be used to further estimate the corresponding color temperature, hue, and brightness; how to combine the three primary colors and what effect to achieve is the existing technology and will not be repeated; the above-mentioned color temperature and hue are used as color system data.

[0021] S12, obtaining and analyzing data from a preset ambient light sensing unit to obtain ambient light data.

[0022] Among them, the ambient light sensing unit can be a light intensity sensor, which is installed on the display screen and can contact the ambient light information of the display screen to obtain a light intensity detection value; for example: the top frame of a mobile phone screen.

[0023] Regarding step 2, it includes: S21. Obtain application scenarios based on device parameters and ambient light data corresponding to the screen.

[0024] Example: When the screen brightness is low and the color tone is dark (such as dark blue, black, or dark gray), it is considered that the current scene is a movie viewing scene.

[0025] In addition to screen-related parameters, device parameters can also be corresponding control instructions, such as the command to open a video app. Therefore, the application scenario can also be determined by identifying the instruction and the instruction object. In addition to watching movies, the application scenarios also include competition (games), office work, reading, and nighttime.

[0026] S22. Search a pre-established database according to the application scenario, call a matching display mode, and output it.

[0027] It is understandable that the display mode in this application refers to a screen parameter control scheme, which is tested, selected, uploaded and stored in a database by staff for various application scenarios.

[0028] Example: Movie viewing scene: adjust to low brightness and warm colors; Features: Low brightness, dark tone (cold or warm); Adjustment strategy: Low brightness: Lower the brightness of the light strip (70%-80%). Warm tone: Add yellow and orange light sources and adjust to a low color temperature (3000K - 4000K).

[0029] Competitive scene: If the screen brightness is high and cold colors (such as blue, green) or dynamic content appear, the system will enter the game mode, increase the light source brightness and cool colors to improve clarity; Features: High brightness, cool color tone (blue, white); Adjustment strategy: High brightness: Increase the brightness of the light strip (80%-100%); Cool tones: Use cool light (blue, white) and adjust to a high color temperature (6000K - 7000K).

[0030] Night scene: When the ambient brightness is low (such as at night) and the screen brightness is low, the system will automatically enter night mode, use warm light sources, reduce brightness, and avoid strong light stimulation; Features: low brightness, warm color; Adjustment strategy: Low brightness: Low light strip brightness (30%-50%); High Warm Color Temperature: Warm tones (2500K - 3000K).

[0031] Regarding step three, it includes: based on the data obtained in step one, adaptively adjusting the display mode of step two; specifically: a. Strong ambient light: When the ambient light (relative to the screen light) is strong, the brightness of the (screen) light source will be increased to ensure a moderate contrast between the screen and the ambient light; b. Weak ambient light: When the ambient light is weak, the brightness of the light source will be reduced to avoid excessive contrast that irritates the eyes.

[0032] The above-mentioned enhancement amount and reduction amount have preset data relationships and will not be described in detail.

[0033] Regarding step 4, it includes: 1) Smooth transition optimization based on the current application scenario and ambient light data; 2) Count the time users spend in any application display mode, obtain monitoring and analysis data from smart wearable devices, perform fatigue analysis based on the historical control records of the current display mode, and make optimization adjustments based on the analysis results.

[0034] According to the above settings, the screens and devices that apply this method can not only analyze the application scenarios and match the display modes for them, but also make adaptive adjustments based on the ambient light and screen light on this basis to reduce the stimulation of the screen to the glasses; they can also go a step further and make a smooth transition when there is a large difference between the two, combine the display mode application time and the data of the smart wearable device to do fatigue analysis, and make optimization adjustments, so as to improve the display effect of the screen.

[0035] In another embodiment of the method, the above-mentioned smooth transition optimization based on the current application scenario and ambient light data includes: If the rate of change of the ambient light data is greater than the preset smooth transition trigger threshold, the color temperature and brightness adjustment parameters are analyzed and output according to the preset smooth transition algorithm.

[0036] The reason why the change rate is used here instead of the brightness difference is that this link targets sudden brightness changes and has a strong timeliness; the change rate = brightness difference / instantaneous sampling interval. Smooth transition algorithms, such as gradient algorithms and S-curve curve adjustment, can be found in another application of the applicant with publication number CN116056294A, which will not be repeated here.

[0037] The above settings can prevent the brightness of the display from changing too abruptly when the scene or ambient light changes.

[0038] If the backlight adjustment conditions are currently met, the backlight adjustment is performed preferentially based on the brightness adjustment parameters.

[0039] It is known that in addition to adjusting the brightness of the screen by switching on and off as a whole, backlight conditions can also be made based on the backlight design of the display. Compared with the stimulation of direct light source to the eyes, the backlight is softer, so the user's visual comfort is higher during adjustment.

[0040] In another embodiment of the method, fatigue analysis comprises: Evaluate user behavior habits based on historical monitoring and analysis data from smart wearable devices.

[0041] Among them, smart wearable devices include smart rings, smart watches, and smart bracelets, which should be able to collect users' physiological parameters, such as heart rate; priority should be given to models that can analyze the user's current application scenarios through corresponding functional apps and background analysis.

[0042] The above evaluation of user behavior habits, for example: the history record of the smart wearable device is that the user has fallen asleep between 10 pm and 11 pm in the past month, that is, 10 pm to 11 pm is evaluated as the time period for preparing to fall asleep.

[0043] If the current time is the t1 duration before sleep in the behavioral habit, the preset blue light optimization process is executed.

[0044] Among them, t1 is a parameter set by the staff or user, which can be 1 hour. The blue light optimization process includes: matching a blue light range for each application scenario, and the blue light range is smaller than the blue light range of the same scenario during the daytime, that is, actively reducing blue light output.

[0045] If the user currently maintains the same display mode for a duration greater than t2, and the matching application scenario and time period meet the preset optimization conditions, such as: the scene is watching a movie or reading, and the time period is night, the preset warm light optimization process will be executed.

[0046] The warm light optimization process includes: increasing the R ratio dm in RGB, maintaining the t3 duration, and then recovering and re-timing the application duration of the display mode; the above t2, t3, and dm are all preset values.

[0047] According to the above settings, on the one hand, this method can automatically adjust the blue light ratio of the screen before the user goes to bed. The reduction of blue light helps to relieve eye fatigue and improve sleep quality; at the same time, because the user's habits are analyzed based on the historical data of the smart wearable device, the time period when the dimming optimization occurs is more accurate; on the other hand, this method can increase the proportion of warm colors, simulate natural light, and help relax the eyes, especially when reading or watching at night. Enhancing warm light can effectively avoid dry eyes and fatigue.

[0048] In another embodiment of the method, step 4, user experience optimization, further includes: If the current application scenario for display mode matching is watching a movie, the current film and television data of the device corresponding to the display screen is obtained, and the film and television type is identified; the film and television data can be the name and label of the film and television content, or the evaluation data retrieved from the Internet by the name; film and television types, such as: science fiction, romance, thriller, horror, supernatural, etc.

[0049] If the type of film or TV program meets the preset stimulating image conditions, such as: the type of film or TV program is thriller, horror, or supernatural, the Blu-ray optimization process will be terminated.

[0050] According to the above settings, the method can further analyze the screen display content in a specific application scenario, and thereby change the aforementioned blue light optimization process to reduce interference with the viewing experience.

[0051] Furthermore, if the film or TV type meets the preset stimulating image conditions, then: Real-time monitoring and analysis data of smart wearable devices are called to perform emotion analysis. Emotion analysis based on physiological parameters is a prior art, and this application only describes possible emotions under stimulating image conditions: historical data is called to obtain the user's daily heart rate range. If a psychological surge occurs at the moment, it is considered that the emotion of "fear" is currently occurring.

[0052] If the user pre-selects the horror suppression mode, that is, selects the preference when using the device corresponding to the display screen, then when the emotion analysis result and the user's heart rate meet the preset suppression conditions, for example: the emotion is "fear" and the heart rate rises to more than 100 beats / minute, the preset brightness optimization process is executed; an example of the brightness optimization process at this time is: reduce the screen brightness to 60%-80% of the original; preferably, reduce the contrast.

[0053] Through the above settings, the method can use the smart wearable device to analyze the user's state and automatically dim the screen when the user needs to reduce the horror level of the movie, so that part of the content in the movie is relatively blurred to suppress related emotions and meet viewing needs.

[0054] Furthermore, if the current application scenario for display mode matching is watching a movie, then: Obtain the real-time display effect of film and television data and perform visual analysis of picture features; this link can be achieved in at least the following two ways: 1) The device corresponding to the display screen is a TV. There is a camera in the room corresponding to the TV. The camera is used to obtain real-time images and perform feature recognition and clarity analysis; 2) Import the film and television data into the video editing software, parse it through the software, read the properties of the video, and determine the visual features of the picture based on the properties.

[0055] In this embodiment, the visual analysis of picture features refers to the recognizability of various features in the scene when the film enters a dark scene, or the feature clarity of the corresponding image frame.

[0056] If the results of the visual analysis of the picture features do not meet the standards (based on the above, that is, the clarity is below the threshold), the brightness optimization process is executed, and the response priority of the horror suppression mode is higher.

[0057] According to the above settings, this method can generally automatically analyze the film and television data and adaptively adjust the brightness when watching a movie. For example, if it is too dark to see clearly, the brightness can be increased to enhance the viewing experience. If the user is "afraid" or has a high heart rate, the display effect can be reduced and the picture can be actively blurred to help the user relieve and suppress related emotions, thereby effectively enhancing the viewing experience.

[0058] The embodiment of the present application also discloses a system for applying the color data processing method.

[0059] Reference Figure 2 A system using any of the color data processing methods described above includes a controller and a smart wearable device, wherein the controller is configured as follows: step one, collection and preprocessing of parameter data affecting display effects; step two, scene recognition and pattern matching; step three, adaptive adjustment based on the data obtained in step one when executing the display mode of step two; step four, user experience optimization.

[0060] The controller may be a controller of a device corresponding to the display screen, or may be a cloud server.

[0061] The above are all preferred embodiments of the present application, and the protection scope of the present application is not limited thereto. Therefore, any equivalent changes made according to the structure, shape, and principle of the present application should be included in the protection scope of the present application.

Claims

1. A color data processing method, characterized in that: include: Step 1: Data collection and preprocessing of parameters affecting display effects, including: Obtain and analyze the data of the color sensing unit preset in the screen to obtain color system data and screen brightness data; Obtaining and analyzing data from a preset ambient light sensing unit to obtain ambient light data; Step 2: scene recognition and pattern matching, which includes: Identify the application scenario based on the device parameters and ambient light data corresponding to the screen; Search the pre-established database according to the application scenario, call the matching display mode, and output; Step 3: Adaptive adjustment, which includes: adaptively adjusting the display mode of step 2 based on the data obtained in step 1; Step 4: User experience optimization, which includes: Optimize smooth transition based on current application scenario and ambient light data; Count the time users spend in any application display mode, obtain monitoring and analysis data from smart wearable devices, perform fatigue analysis based on the historical control records of the current display mode, and make optimization adjustments based on the analysis results.

2. The color data processing method according to claim 1, characterized in that: The smooth transition optimization based on the current application scenario and ambient light data includes: If the rate of change of the ambient light data is greater than the preset smooth transition trigger threshold, the color temperature and brightness adjustment parameters are analyzed and output according to the preset smooth transition algorithm; If the backlight adjustment conditions are currently met, the backlight adjustment is performed preferentially based on the brightness adjustment parameters.

3. The color data processing method according to claim 1, characterized in that: The fatigue analysis comprises: Evaluate user behavior habits based on historical monitoring and analysis data from smart wearable devices; If the current time is the t1 duration before sleep in the behavior habit, the preset blue light optimization process is executed; If the user currently maintains the same display mode for a duration greater than t2, and the matching application scenario and time period meet the preset optimization conditions, the preset warm light optimization process is executed; Among them, t1 and t2 are both preset values.

4. The color data processing method according to claim 3, characterized in that: The fourth step, user experience optimization, further includes: If the current application scenario matched by the display mode is watching a movie, the current video data of the device corresponding to the display screen is obtained, and the video type is identified; If the type of film or TV program meets the preset stimulating image conditions, the Blue-ray optimization process is terminated.

5. The color data processing method according to claim 4, characterized in that: If the film or TV type meets the preset stimulating image conditions, then: Call the real-time monitoring and analysis data of smart wearable devices to conduct sentiment analysis; If the user pre-selects the horror suppression mode, the preset brightness optimization process is executed when the emotion analysis results and the user's heart rate meet the preset suppression conditions.

6. The color data processing method according to claim 5, characterized in that: If the current display mode matches the application scenario of watching a movie, then: Obtain real-time display effects of film and television data and conduct visual analysis of picture features; If the results of the visual analysis of the picture features do not meet the standards, the brightness optimization process will be executed, and the response priority of the horror suppression mode will be higher.

7. The color data processing method according to claim 3, characterized in that: The warm light optimization process includes increasing the R proportion dm in RGB, maintaining the duration t3 and then recovering and re-timing the application duration of the display mode; wherein dm and t3 are preset values.

8. A system using the color data processing method according to any one of claims 1 to 7, characterized in that: It includes a controller and a smart wearable device, and the controller is configured as follows: step one, collection and preprocessing of parameter data affecting display effects; step two, scene recognition and pattern matching; step three, adaptive adjustment based on the data obtained in step one when executing the display mode of step two; step four, user experience optimization.

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