Mobile phone screen color gamut correction method and system

Abstract

The invention belongs to the field of screen color gamut correction, and discloses a mobile phone screen color gamut correction method and system, and the method comprises the steps: S1, obtaining ambient light data and screen content data; s2, acquiring color correction parameters based on the ambient light data and the screen content data; s3, obtaining personalized correction parameters based on the color correction parameters; s4, obtaining partition correction parameters based on the personalized correction parameters; s5, acquiring an adaptive feedback correction parameter based on the partition correction parameter; and S6, obtaining the display color of the screen based on the adaptive feedback correction parameter. The invention provides a nonlinear color gamut mapping model so as to ensure that parameters after color correction can be accurately mapped to a color gamut range capable of being displayed by a screen. In the mapping process, the nonlinear coefficient of the mapping function needs to be dynamically adjusted according to the actual display characteristics of the screen, and the consistency of color display under different brightness and contrast ratios is ensured.

Description

Technical Field

[0001] The present invention relates to the field of screen color gamut correction, and in particular to a method and system for correcting the color gamut of a mobile phone screen. Background Art

[0002] With the popularity of mobile devices, especially smartphones, screen display effects have become one of the important factors affecting user experience. Color gamut correction is a key link in screen display technology, which directly determines the color accuracy and realism of the screen. However, since mobile phones need to face changing ambient light and display content in different usage scenarios, static color gamut correction methods often cannot adapt to these changes, resulting in color distortion or unnatural visual experience. Typical color gamut correction systems usually optimize display content through preset standard color spaces (such as sRGB, Adobe RGB, etc.), but these methods have several significant disadvantages.

[0003] First, most traditional color gamut correction methods are based on static algorithms, which cannot be adjusted in real time according to the external ambient light, dynamic changes in display content, and user's personalized preferences. This means that when users use mobile phones under different ambient light conditions (such as strong light, weak light), the color display effect of the screen is not consistent, especially in highly dynamic content such as videos, games or complex image scenes. The displayed color will deviate from the original setting, causing inaccurate colors or visual fatigue. Secondly, traditional methods lack support for user personalized needs. Different users may have different color preferences in different scenarios. For example, some users prefer highly saturated colors, while others need to reduce blue light to protect their eyes. However, existing technologies are usually unable to make adaptive adjustments based on these personalized needs. They can only manually set a fixed display mode and cannot dynamically respond to user needs.

[0004] In addition, existing color gamut correction systems often ignore the need for energy conservation. When using a mobile phone for a long time, the screen is the main source of energy consumption, especially under high-brightness, high-contrast display content, the power consumption problem is particularly serious. During the color gamut correction process, current technology often processes all screen pixels globally, even in areas that are not visually sensitive, and still performs high-intensity color correction, which leads to a lot of unnecessary energy consumption problems, further affecting the battery life of the mobile phone.

[0005] Although some color gamut correction systems have begun to introduce light sensors in recent years to capture changes in ambient light and adjust screen brightness or color temperature, these technologies are still limited to brightness or simple color temperature adjustment, and have not been deeply optimized for color itself, making it difficult to ensure accurate color restoration in complex environments. At the same time, some existing adaptive systems also lack efficient machine learning mechanisms, resulting in color correction adjustments often lagging behind user needs or environmental changes. Based on these shortcomings, the existing technology has not been able to achieve true dynamic and intelligent adjustment when processing color gamut correction, and it is difficult to meet user needs for high color reproduction, low energy consumption, and personalization. Summary of the invention

[0006] The purpose of the present invention is to disclose a method and system for color gamut correction of a mobile phone screen to solve the technical problems raised in the background technology.

[0007] In order to achieve the above object, the present invention adopts the following technical solution:

[0008] In one aspect, the present invention provides a method for color gamut correction of a mobile phone screen, comprising:

[0009] S1, obtaining ambient light data and screen content data;

[0010] S2, obtaining color correction parameters based on ambient light data and screen content data;

[0011] S3, obtaining personalized correction parameters based on the color correction parameters;

[0012] S4, obtaining a partition correction parameter based on the personalized correction parameter;

[0013] S5, obtaining an adaptive feedback correction parameter based on the partition correction parameter;

[0014] S6, obtaining the display color of the screen based on the adaptive feedback correction parameter;

[0015] Among them, obtaining ambient light data and screen content data includes:

[0016] The ambient light sensor is used to obtain ambient light data. env Represents ambient light data, which includes the intensity of ambient light I env , color temperature of ambient light T env and the spectral distribution of ambient light λ env , then S env Can be expressed as:

[0017] S env = {I env , T env ,λ env} (1)

[0018] The unit of ambient light intensity is Lux, and the unit of ambient light color temperature is K;

[0019] The screen content data is defined as C disp , whose value is the color information of each pixel in CIE XYZ space, C disp It is expressed as:

[0020] C disp ={X, Y, Z} (2)

[0021] Among them, X, Y, and Z represent the three standard chromaticity coordinates in the CIE XYZ color space. The RGB value of each pixel is converted to the CIE XYZ space through the standard color conversion matrix:

[0022]

[0023] R, G, B are the red, green, and blue values ​​of each pixel in the current display content, respectively. The value range of R, G, and B is 0 to 255. The converted X, Y, and Z are processed in the standard color space.

[0024] On the other hand, the present invention provides a mobile phone screen color gamut correction system, including a data acquisition module, a first acquisition module, a second acquisition module, a third acquisition module, a fourth acquisition module and a fifth acquisition module;

[0025] The data acquisition module is used to acquire ambient light data and screen content data;

[0026] The first acquisition module is used to acquire color correction parameters based on ambient light data and screen content data;

[0027] The second acquisition module is used to acquire personalized correction parameters based on the color correction parameters;

[0028] The third acquisition module is used to acquire the partition correction parameter based on the personalized correction parameter;

[0029] The fourth acquisition module is used to acquire the adaptive feedback correction parameter based on the partition correction parameter;

[0030] The fifth acquisition module is used to acquire the display color of the screen based on the adaptive feedback correction parameter;

[0031] Among them, obtaining ambient light data and screen content data includes:

[0032] The ambient light sensor is used to obtain ambient light data. env Represents ambient light data, which includes the intensity of ambient light I env , color temperature of ambient light T env and the spectral distribution of ambient light λenv , then S env Can be expressed as:

[0033] S env = {I env , T env ,λ env} (1)

[0034] The unit of ambient light intensity is Lux, and the unit of ambient light color temperature is K;

[0035] The screen content data is defined as C disp , whose value is the color information of each pixel in CIE XYZ space, C disp It is expressed as:

[0036] C disp ={X, Y, Z} (2)

[0037] Among them, X, Y, and Z represent the three standard chromaticity coordinates in the CIE XYZ color space. The RGB value of each pixel is converted to the CIE XYZ space through the standard color conversion matrix:

[0038]

[0039] R, G, B are the red, green, and blue values ​​of each pixel in the current display content, respectively. The value range of R, G, and B is 0 to 255. The converted X, Y, and Z are processed in the standard color space.

[0040] Beneficial effects:

[0041] Spectral Transformation Neural Network for High-Accuracy Color Correction:

[0042] The present invention introduces a spectrum conversion neural network, which can accurately map and correct colors based on the current display content of the mobile phone screen and the spectrum information of the ambient light. Unlike traditional static correction methods, the spectrum conversion neural network can dynamically analyze the spectrum differences between the screen content and the light conditions, ensuring that the screen colors are highly consistent with the colors in the real world under different lighting conditions. This not only solves the problem that traditional static correction cannot adapt to changes in ambient light, but also greatly improves the color restoration and naturalness of the display, especially in complex and changeable lighting environments.

[0043] Meta-learning based adaptive color gamut optimization for user preferences:

[0044] In order to solve the problem that the existing technology cannot meet the personalized needs of users, the present invention designs a set of adaptive optimization mechanisms based on meta-learning. The system quickly builds and updates a meta-learning model by collecting the color preference data of users in different scenarios over a long period of time, so that the system can quickly and adaptively adjust the correction parameters according to a small amount of new data. This means that no matter when the user is using the mobile phone for video, games, reading or other scenarios, the system can intelligently adjust parameters such as color saturation, brightness and contrast to meet the personalized needs of the user, thereby overcoming the problem that the traditional system needs to manually adjust the display mode and lacks dynamic response.

[0045] Energy-saving color gamut correction based on environmental perception:

[0046] In order to solve the problem of excessive energy consumption caused by the global processing of traditional color gamut correction, the present invention uses an environmental perception module to detect changes in ambient light in real time, and combines the spectrum conversion neural network to grade different screen areas, and dynamically adjust the intensity and range of color gamut correction. The system can reduce unnecessary color correction in non-critical areas according to light changes, and focus on optimizing areas that have a greater impact on the user's visual experience, thereby achieving intelligent energy-saving effects. This not only improves the battery life of the mobile phone, but also ensures that while ensuring color accuracy, unnecessary energy waste is reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0048] Figure 1 The present invention is a schematic diagram of a method for color gamut correction of a mobile phone screen.

[0049] Figure 2 The present invention is a schematic diagram of a mobile phone screen color gamut correction system. DETAILED DESCRIPTION

[0050] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. The components of the embodiments of the present invention generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents the selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making creative work belong to the scope of protection of the present invention.

[0051] The present invention proposes a dynamic color gamut correction system based on spectral conversion neural network and meta-learning, which can achieve accurate correction and adaptive optimization of the color of the mobile phone screen. It can not only cope with the color changes under different ambient light, but also dynamically adjust according to the user's visual preferences, thus overcoming the limitations of the existing technology.

[0052] like Figure 1 In one embodiment shown, the present invention provides a method for color gamut correction of a mobile phone screen, comprising:

[0053] S1, obtaining ambient light data and screen content data;

[0054] S2, obtaining color correction parameters based on ambient light data and screen content data;

[0055] S3, obtaining personalized correction parameters based on the color correction parameters;

[0056] S4, obtaining a partition correction parameter based on the personalized correction parameter;

[0057] S5, obtaining an adaptive feedback correction parameter based on the partition correction parameter;

[0058] S6, obtaining the display color of the screen based on the adaptive feedback correction parameter;

[0059] Among them, obtaining ambient light data and screen content data includes:

[0060] The ambient light sensor is used to obtain ambient light data. env Represents ambient light data, which includes the intensity of ambient light I env , color temperature of ambient light T env and the spectral distribution of ambient light λ env , then S env Can be expressed as:

[0061] S env = {I env , T env ,λenv} (1)

[0062] The unit of ambient light intensity is Lux, and the unit of ambient light color temperature is K;

[0063] The ambient light sensor needs to capture the intensity, color temperature, and spectral distribution of the ambient light in real time. The spectrum information of the ambient light will be used in the subsequent color correction model to ensure that the screen display content is consistent with the actual color under ambient light conditions.

[0064] Spectral distribution, usually the distribution of light intensity at different wavelengths in the visible light range (400nm to 700nm).

[0065] The screen content data is defined as C disp , whose value is the color information of each pixel in CIE XYZ space, C disp It is expressed as:

[0066] C disp ={X, Y, Z} (2)

[0067] Among them, X, Y, and Z represent the three standard chromaticity coordinates in the CIE XYZ color space. The RGB value of each pixel is converted to the CIE XYZ space through the standard color conversion matrix:

[0068]

[0069] R, G, B are the red, green, and blue values ​​of each pixel in the current display content, respectively. The value range of R, G, and B is 0 to 255. The converted X, Y, and Z are processed in the standard color space.

[0070] The color information of the screen display content is also the key to correction. The color information needs to be extracted from the image currently displayed on the mobile phone screen for matching and correction with the ambient light. To do this, the system will extract the RGB value of each pixel from the image frame in real time and convert it to a standardized color space (such as CIE XYZ space) so that color correction can be performed more accurately in subsequent processing.

[0071] Through the above technical innovations, the present invention effectively solves the shortcomings of traditional color gamut correction systems in terms of environmental adaptability, response to user personalized needs, and energy efficiency optimization, and realizes the organic combination of high-precision color restoration, intelligent energy saving, and adaptive optimization.

[0072] Preferably, through the time synchronization controller, control S env and C disp The collection was performed at the same time.

[0073] In order to ensure the real-time and accuracy of color correction, the ambient light data S env And the screen displays data C disp The system needs to collect and process data synchronously. In most smartphones, the screen refresh rate is usually 60Hz to 120Hz, so data collection and processing must be synchronized multiple times per second so that environmental changes can be reflected in the adjustment of screen color in a timely manner. This ensures that subsequent corrections are based on the processing of screen color under real-time ambient light conditions, avoiding data lag.

[0074] During the collection of ambient light data and screen content data, the collected data may be affected by noise due to factors such as sensor accuracy and external noise. In order to improve the stability and accuracy of the data, the system needs to denoise and preprocess the collected data.

[0075] Preferably, for the spectral distribution λ, a multi-scale wavelet transform denoising method is used to reduce λ.

[0076] env env noise, perform wavelet decomposition on λ, divide λ into multiple frequency bands, and perform noise reduction on each frequency band separately.

[0077] env env

[0078] The spectral distribution after noise reduction is expressed as λ′:

[0079] env

[0080]

[0081] is the result after noise reduction processing of the i-th frequency band;

[0082] For C disp , the mean filter algorithm is used to filter the pixels in RGB, and then the color space is converted after filtering. n1 represents the total number of frequency bands.

[0083] Preferably, S2 includes:

[0084] S21, spectral feature extraction:

[0085] F λ =Conv1D(λ env ) (5)

[0086] Among them, F λ is the spectral characteristic, λ env is the spectral distribution of ambient light, Conv1D represents a 1D convolutional neural network; the spectral distribution λ in ambient light is the intensity variation of each wavelength in the light.

[0087] env

[0088] The spectral features will be extracted through a 1D convolutional neural network. The size and number of convolution kernels need to match the characteristics of the spectral distribution.

[0089] S22, screen color feature extraction:

[0090] Use the fully connected layer of the neural network to extract the screen color features. The fully connected layer of the neural network converts the XYZ values ​​of the screen into a high-dimensional feature vector:

[0091] F C =W C ·C disp +b C (6)

[0092] Among them, F C is the screen color characteristic, W C and b C are the weights and biases of the fully connected layer respectively;

[0093] In order to achieve more accurate color correction, the spectral feature F λ and color feature F C The fusion can be performed by concatenation or element-wise multiplication. The specific choice depends on the experimental results. The fused features will be used to generate correction parameters.

[0094] S23, feature fusion:

[0095] Feature fusion formula:

[0096] F fusion =F λ ⊕F C (7)

[0097] Among them, ⊕ represents the feature concatenation operation, F fusion is the joint feature, F fusion Combines spectral characteristics and screen color characteristics;

[0098] S24, generate the spectral nonlinear adjustment term R1(λ intensity ):

[0099]

[0100] α is the adjustment coefficient, λ intensity (i) is the actual measured spectral intensity of the i-th sampling point, is the expected spectral intensity; n2 represents the number of sampling points of the spectral intensity data.

[0101] Traditional color correction models often ignore the nonlinear effect of light intensity changes on color under multi-spectral conditions. In order to better capture this effect, a spectral nonlinear adjustment term is introduced in the correction process. This adjustment term is specifically used to compensate for abnormal bands in the spectrum (such as specific wavelengths that are too strong or too weak). The function of this term is to suppress overly strong spectral fluctuations and ensure smoother color correction.

[0102] S25, calculate the color correction parameters using the following formula:

[0103] P corr =W corr ·F fusion +b corr +R1(λ intensity )

[0104] W corr represents the weight matrix of the color correction model, b corr represents the bias of the color correction model; P corr Represents color correction parameters.

[0105] The fused feature F fusion The final color correction parameter P is generated through the fully connected layer corr At the same time, a spectral nonlinear adjustment item is introduced to adjust the output, making the correction parameters more accurate.

[0106] The core task of Spectral Conversion Neural Network (SCNN) is to establish the ambient light S env and screen content C disp The mapping relationship between them generates the corrected color parameter P corr .

[0107] Preferably, S3 includes:

[0108] S31, obtaining the initial value P of the personalized correction parameter user :

[0109] P user =f θ (P corr , D user )(10)

[0110] f θ It is a meta-learning model that learns the adjustment rules of user preferences through parameters θ. user Represents the user's historical preference data; D user Includes historical records of user adjustments to saturation, brightness, and contrast;

[0111] D userContains records of users' personalized usage habits and visual preferences, such as records of users' adjustments to color saturation, brightness, and contrast in different scenarios.

[0112] In order to quickly adapt to the personalized needs of different users, the meta-learning model f is introduced θ The core idea of ​​meta-learning is to quickly adjust the parameters of the model through a small amount of new data to adapt it to new users. corr and D user As input, output is the personalized correction parameter P user .

[0113] Gradient Optimization for Meta-Learning:

[0114] The goal of model training is to minimize the personalized correction error through gradient descent. Define a loss function Its value is the deviation between the correction parameter and the actual visual needs of the user. By quickly adjusting θ through the meta-learning model, the present invention can enable the model to obtain good optimization effects with a small amount of data:

[0115]

[0116] Where η is the learning rate, is the gradient with respect to the model parameters θ, H user Indicates the visual correction requirements actually fed back by users, such as color temperature, brightness, etc. * is the result of adjusting θ.

[0117] S32, obtain personalized regularization items:

[0118]

[0119] γ is the regularization coefficient, H user (i) is the i-th historical adjustment record in the user's historical preference data, and It is the expected value calculated by the system based on the standard settings; n3 represents the total number of historical adjustment records.

[0120] In traditional meta-learning models, the user's personalized data directly affects the optimization of the correction parameters. However, in order to improve the stability of personalized correction and prevent overfitting, a personalized regularization term specifically for user preferences is designed. This regularization term is used to limit the model's excessive reliance on personalized preferences, thereby making the correction process smoother.

[0121] S33, obtaining personalized correction parameters:

[0122] P final =P user +R2(D user ) (13)

[0123] P final Represents the personalized correction parameters of the final output.

[0124] It ensures that the user's visual preferences are met while also guaranteeing the smoothness and stability of the correction.

[0125] Preferably, S4 includes:

[0126] S41, using a preset partitioning strategy to divide the screen into n4 areas;

[0127] For example, the value of n4 is 2, and the two areas include a high-brightness and fast-changing area (such as a dynamic video or game scene) and a low-brightness or static area (such as a text or static picture scene).

[0128] The formula for the partitioning strategy is:

[0129] R zone (i) = Segment(M disp (i))(14)

[0130] Among them, R zone (i) indicates the region number to which the i-th pixel belongs, M disp Represents the pixel information matrix of the screen, through M disp The brightness L in (i) pix and dynamic characteristics D pix Analyze and divide the pixels into corresponding areas; M disp (i) represents the information matrix of the i-th pixel;

[0131] Dynamic characteristics refer to the characteristics of pixels changing over time. They are used to measure the rate at which pixel values ​​(such as brightness and color) change between consecutive frames and to determine whether pixels are dynamic or static.

[0132] Segment is a region division function based on brightness and dynamic characteristics, ensuring that high-brightness and rapidly changing areas are separated from low-brightness or static areas;

[0133] If the brightness of a pixel exceeds the preset high brightness threshold and the dynamic characteristics exceed the dynamic change threshold, the pixel is classified as a high brightness and fast changing area; otherwise, the pixel is classified as a low brightness or static area. Generally, the brightness threshold is set to 200 and the dynamic threshold is set to 50%.

[0134] Traditional full-screen color gamut correction consumes a lot of computing resources and energy. Through regional feature recognition and partition correction strategy, the present invention can focus resources on areas that require high-precision correction, avoiding unnecessary computing overhead, thereby achieving significant energy saving effects.

[0135] Each region is judged by its brightness, dynamic characteristics and user preferences to determine its correction strength and energy efficiency level. All pixels in the region share the same correction strategy.

[0136] S42, for areas with high brightness and rapid changes, the partition correction coefficient is

[0137]

[0138] in, It is an energy efficiency regularization term for areas with high brightness and rapid changes (such as areas in video and game scenes). It is used to ensure that the upper limit of calculation can be controlled even in high energy consumption scenarios to prevent over-calculation.

[0139] For low-light or static areas, the partition correction factor is

[0140]

[0141] α low is the adjustment parameter, α low <1, It is an energy efficiency regularization term for low-brightness or static areas (such as areas in reading mode or static pictures), which is used to ensure that the user's visual needs are still met in low-brightness or static areas;

[0142] The calculation formula of energy efficiency regularization term is:

[0143]

[0144] λ1 is the regularization coefficient, D pix (j) represents the dynamic characteristics of the jth pixel, is the dynamic change value expected by the system, n5 represents the total number of pixel samples of dynamic characteristics when calculating the energy efficiency regularization term, that is, the number of pixels on the screen used to evaluate dynamic characteristics;

[0145] The present invention uses different color correction strategies to balance display effect and energy consumption requirements. In order to make the correction strategy more accurate, the present invention introduces an innovative energy efficiency regularization term, which dynamically adjusts the correction strength according to the display characteristics of each area to optimize energy consumption.

[0146] Traditional color correction methods often ignore the dynamic characteristics of the region, resulting in wasted energy in unnecessary regions. To this end, the present invention introduces an energy efficiency regularization term based on dynamic changes, which can adaptively adjust the amount of calculation according to the degree of dynamic changes in the region.

[0147] The role of this regularization term is to constrain the amount of computation in areas with excessive dynamic changes, ensuring that the energy consumption is not too high when performing high-precision correction in these areas.

[0148] S43, set up dynamic adjustment mechanism:

[0149] The color correction of the screen should not only be optimized in the region, but also adjust the energy efficiency strategy of each partition in real time according to the dynamic changes in the actual usage scenario. To this end, the present invention designs a dynamic adjustment function It updates the correction strategy for each partition in real time as time t passes.

[0150] Dynamic adjustment function formula

[0151]

[0152] Among them, η and μ are weight parameters, represents the rate of change of personalized correction parameters over time, Represents the rate of change of the pixel information matrix over time.

[0153] Through this function, the system can perceive the screen usage scenario in real time (for example, switching from static text reading to fast-changing video), and dynamically adjust the partition correction strategy to ensure optimal energy efficiency.

[0154] Through partition correction and innovative energy efficiency regularization term design, the system can intelligently allocate computing resources to ensure high-precision correction in dynamic and high-brightness areas, while reducing computing intensity in static and low-brightness areas to achieve energy-saving effects. At the same time, through dynamic adjustment functions, the system can adapt to different usage scenarios in real time to further improve energy efficiency.

[0155] Preferably, S5 includes:

[0156] The original calculation formula of the adaptive feedback correction parameter is:

[0157]

[0158] in, is the adaptive feedback correction parameter adjusted at time t based on the original calculation formula; P zone represents the partition correction parameter;

[0159] is the correction increment based on ambient light changes;

[0160] It is a correction increment based on user behavior feedback;

[0161] By comparing the current ambient light data and initial light data Dynamically adjust correction parameters. For example, when the ambient light intensity increases, the system automatically increases the screen brightness and adjusts the color saturation to adapt to the new lighting conditions.

[0162] Based on the user's operation behavior at time t (For example, if the user manually adjusts the brightness or turns on the eye protection mode) to ensure that the user's operation can be reflected in the display effect immediately.

[0163] Ambient light can affect the perceived effect of screen color, so it is necessary to dynamically adjust the correction parameters according to light changes. The present invention defines an ambient light adjustment item The rate at which it changes according to the ambient light intensity to calculate the correction increment.

[0164] The calculation formula is:

[0165]

[0166] Among them, α env is the adjustment factor of ambient light, is the current ambient light data, is the initial ambient light data;

[0167] The user's operation behavior (such as brightness adjustment, color mode switching) will affect their expectations of screen display, so it is necessary to dynamically adjust the color correction parameters according to these operations. The present invention defines a user behavior feedback adjustment item The correction increment is calculated by analyzing the user's operation behavior.

[0168] The calculation formula is:

[0169]

[0170] Among them, β user is the adjustment coefficient of user behavior, It is a function that represents the analysis and quantification results of the user's current behavior;

[0171] For example, the user manually lowers the brightness. This behavior will be fed back and a negative increment will be generated, reducing the brightness part of the correction parameters, making the screen respond instantly to user operations.

[0172] Due to the dynamic nature of screen content (e.g., dynamic changes in video content), the present invention needs to design a screen content feedback adjustment item This item is used to cope with frequent changes in screen display content and ensure that the screen can adjust adaptively in high-dynamic scenes (such as fast-changing video or game scenes).

[0173] Calculate screen content feedback correction parameters

[0174]

[0175] Among them, γ disp is the adjustment coefficient of screen content feedback, It is an analytical function of the dynamic changes of the screen content, and generates a feedback adjustment amount according to the changes of the current screen content. For example, if there are a large number of fast-changing dynamic scenes on the screen (such as high frame rate video), the system will increase the color correction frequency and intensity accordingly.

[0176] The final calculation formula of the adaptive feedback correction parameter is:

[0177]

[0178] represents the adjusted adaptive feedback correction parameter at time t obtained based on the final calculation formula.

[0179] The formula combines ambient light, user behavior, and screen content feedback to ensure that changes in each factor are instantly reflected in the color correction parameters, ensuring adaptive adjustment of the display effect.

[0180] Dynamic Feedback Regularization

[0181] In order to avoid excessively frequent adjustments that would lead to unnecessary waste of computing resources, the present invention introduces an innovative dynamic feedback regularization term R5(t), which is used to limit the frequency of corrections and avoid unnecessary adjustments in a short period of time.

[0182] Regularization term formula:

[0183]

[0184] Where λ2 is the regularization strength coefficient, is the system's estimate of the desired correction change, is the time rate of change of the correction parameter. The role of this regularization term is to suppress overly frequent corrections and ensure that the system maintains a stable adjustment frequency in actual scenarios.

[0185] Preferably, S6 includes:

[0186] S61, set the mapping formula:

[0187]

[0188] is the actual displayed color after mapping, φ is the nonlinear color gamut mapping function, and the correction parameter Map to the color range that the screen can actually display;

[0189] The mapping function φ is dynamically adjusted according to the screen display characteristics and brightness range. Specifically, φ is a nonlinear color gamut mapping function that adjusts the color space through gamma correction. Ensure that the color parameters are mapped to the color gamut that the screen can actually display. In addition, the φ function also adjusts the color according to the ambient light. Adjust color saturation to ensure enhanced color performance in bright light and avoid oversaturation in low light.

[0190] The displayed color is the color value finally displayed on the screen, which is the RGB value after color gamut correction.

[0191]

[0192] P max Represents the maximum value of the adaptive feedback correction parameter, which is used to Normalized to the range of [0, 1], γ3 represents the Gamma correction coefficient, which is used to control the nonlinear adjustment of brightness and contrast, and its value range is [2.2, 2.4]. max Indicates the maximum brightness or color value of the screen;

[0193] The actual display color of the screen is affected by both color value and brightness. Gamma correction adjusts the nonlinear characteristics of brightness and contrast to match the display effect.

[0194] It is a nonlinear function based on the color gamut characteristics of the screen. It ensures that the screen does not have color distortion at high brightness and does not lose color details at low brightness. Through different nonlinear coefficients, the system can adapt to color performance under different brightness conditions.

[0195] S62, set the color consistency regularization term C(t):

[0196]

[0197] Among them, γ4 is the weight coefficient for adjusting color consistency, is the actual display color value of the i-th region, is the desired display color value for the area.

[0198] In the actual display process, the screen may have problems with uneven display or unbalanced color correction in certain areas. In order to further improve the uniformity and realism of colors, the present invention designs a color consistency regularization term C(t) to balance the color consistency of each display area and ensure that the display effect of the entire screen in different areas is uniform. This regularization term is used to detect and adjust the color differences in each area to ensure the color consistency of the entire screen display, and the color balance can be guaranteed even under different brightness and ambient light. Through this regularization, the system can reduce color drift caused by brightness, contrast and other reasons in certain areas.

[0199] S63, calculate the final display color of the screen:

[0200]

[0201] in, It is the final display color after mapping and color consistency adjustment.

[0202] From S5, the present invention has obtained the color correction parameters dynamically adjusted through the real-time feedback mechanism These parameters have been adaptively adjusted according to changes in ambient light, user interaction behavior, and dynamic changes in screen content. The color gamut correction of the screen ultimately requires The color gamut of the screen is usually wider than the output parameters of the color correction model. In order to avoid oversaturation or color distortion, the present invention needs to convert the color gamut of the screen into the actual displayed color value to meet the user's demand for display quality in the current environment, while maximizing the true restoration of screen color and energy saving effect. Mapping to the actual displayable color range while maintaining color reproduction accuracy.

[0203] The main function of S6 is to convert the color parameters that have undergone a series of corrections and optimizations into the color values ​​that can actually be displayed on the screen. In this process, through the nonlinear color gamut mapping model and color consistency adjustment mechanism, it ensures that the screen display can maintain the accuracy and uniformity of color reproduction under different environmental conditions and user operations.

[0204] The present invention proposes a nonlinear color gamut mapping model to ensure that the color-corrected parameters can be accurately mapped to the color gamut range that the screen can display. The mapping process needs to dynamically adjust the nonlinear coefficient of the mapping function according to the actual display characteristics of the screen to ensure the consistency of color display under different brightness and contrast.

[0205] like Figure 2In one embodiment shown, the present invention provides a mobile phone screen color gamut correction system, including a data acquisition module, a first acquisition module, a second acquisition module, a third acquisition module, a fourth acquisition module and a fifth acquisition module;

[0206] The data acquisition module is used to acquire ambient light data and screen content data;

[0207] The first acquisition module is used to acquire color correction parameters based on ambient light data and screen content data;

[0208] The second acquisition module is used to acquire personalized correction parameters based on the color correction parameters;

[0209] The third acquisition module is used to acquire the partition correction parameter based on the personalized correction parameter;

[0210] The fourth acquisition module is used to acquire the adaptive feedback correction parameter based on the partition correction parameter;

[0211] The fifth acquisition module is used to acquire the display color of the screen based on the adaptive feedback correction parameter;

[0212] Among them, obtaining ambient light data and screen content data includes:

[0213] The ambient light sensor is used to obtain ambient light data. env Represents ambient light data, which includes the intensity of ambient light I env , color temperature of ambient light T env and the spectral distribution of ambient light λ env , then S env Can be expressed as:

[0214] S env = {I env , T env ,λ env} (1)

[0215] The unit of ambient light intensity is Lux, and the unit of ambient light color temperature is K;

[0216] The screen content data is defined as C disp , whose value is the color information of each pixel in CIE XYZ space, C disp It is expressed as:

[0217] C disp ={X, Y, Z} (2)

[0218] Among them, X, Y, and Z represent the three standard chromaticity coordinates in the CIE XYZ color space. The RGB value of each pixel is converted to the CIE XYZ space through the standard color conversion matrix:

[0219]

[0220] R, G, B are the red, green, and blue values ​​of each pixel in the current display content, respectively. The value range of R, G, and B is 0 to 255. The converted X, Y, and Z are processed in the standard color space.

[0221] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A method for color gamut correction of a mobile phone screen, characterized in that: include: S1, obtaining ambient light data and screen content data; S2, obtaining color correction parameters based on ambient light data and screen content data; S3, obtaining personalized correction parameters based on the color correction parameters; S4, obtaining a partition correction parameter based on the personalized correction parameter; S5, obtaining an adaptive feedback correction parameter based on the partition correction parameter; S6, obtaining the display color of the screen based on the adaptive feedback correction parameter; Among them, obtaining ambient light data and screen content data includes: The ambient light sensor is used to obtain ambient light data. env Represents ambient light data, which includes the intensity of ambient light I env , color temperature of ambient light T env and the spectral distribution of ambient light λ env , then S env Can be expressed as: S env ={I env ,T env ,λ env } (1) The unit of ambient light intensity is Lux, and the unit of ambient light color temperature is K; The screen content data is defined as C disp , whose value is the color information of each pixel in CIE XYZ space, C disp It is expressed as: C disp ={X,Y,Z} (2) Among them, X, Y, and Z represent the three standard chromaticity coordinates in the CIE XYZ color space. The RGB value of each pixel is converted to the CIE XYZ space through the standard color conversion matrix: R, G, B are the red, green, and blue values ​​of each pixel in the current display content, respectively. The value range of R, G, and B is 0 to 255. The converted X, Y, and Z are processed in the standard color space.

2. A method for color gamut correction of a mobile phone screen according to claim 1, characterized in that: Through the time synchronization controller, control S env and C disp The collection was performed at the same time.

3. A method for color gamut correction of a mobile phone screen according to claim 1, characterized in that: For the spectral distribution λ env , the denoising method of multi-scale wavelet transform is used to env To reduce noise, env Perform wavelet decomposition and transform λ env Divide into multiple frequency bands, and perform noise reduction on each frequency band. The spectral distribution after noise reduction is expressed as λ′ env : is the result of noise reduction processing on the i-th frequency band; For C disp , the mean filter algorithm is used to filter the pixels in RGB, and then the color space is converted after filtering. n1 represents the total number of frequency bands.

4. A method for color gamut correction of a mobile phone screen according to claim 1, characterized in that S2 include: S21, spectral feature extraction: F λ =Conv1D(λ env ) (5) Among them, F λ is the spectral characteristic, λ env is the spectral distribution of ambient light, Conv1D represents a 1D convolutional neural network; S22, screen color feature extraction: Use the fully connected layer of the neural network to extract the screen color features. The fully connected layer of the neural network converts the XYZ values ​​of the screen into a high-dimensional feature vector: F C =W C ·C disp +b C (6) Among them, F C is the screen color characteristic, W C and b C are the weights and biases of the fully connected layer respectively; S23, feature fusion: Feature fusion formula: in, represents the feature concatenation operation, F fusion is the joint feature, F fusion Combines spectral characteristics and screen color characteristics; S24, generate the spectral nonlinear adjustment term R1(λ intensity ): α is the adjustment coefficient, λ intensity (i) is the actual measured spectral intensity of the i-th sampling point, is the expected spectral intensity; n2 represents the number of sampling points of spectral intensity data; S25, calculate the color correction parameters using the following formula: P corr =W corr ·F fusion +b corr +R1(λ intensity ) W corr represents the weight matrix of the color correction model, b corr represents the bias of the color correction model; P corr Represents color correction parameters.

5. A method for color gamut correction of a mobile phone screen according to claim 4, characterized in that S3 include: S31, obtaining the initial value P of the personalized correction parameter user : P user =f θ (P corr ,D user ) (10) f θ It is a meta-learning model that learns the adjustment rules of user preferences through parameters θ. user Represents the user's historical preference data; D user Includes historical records of user adjustments to saturation, brightness, and contrast; S32, obtain personalized regularization items: γ is the regularization coefficient, H user (i) is the i-th historical adjustment record in the user's historical preference data, and It is the expected value calculated by the system based on the standard setting; n3 represents the total number of historical adjustment records; S33, obtaining personalized correction parameters: P final =P user +R2(D user ) (13) P final Represents the personalized correction parameters of the final output.

6. A method for color gamut correction of a mobile phone screen according to claim 5, characterized in that S4 include: S41, using a preset partitioning strategy to divide the screen into n4 areas; The formula for the partitioning strategy is: R zone (i)=Segment(M disp (i)) (14) Among them, R zone (i) indicates the region number to which the i-th pixel belongs, M disp Represents the pixel information matrix of the screen, through M disp The brightness L in (i) pix and dynamic characteristics D pix Analyze and divide the pixels into corresponding areas; M disp (i) represents the information matrix of the i-th pixel; Segment is a region division function based on brightness and dynamic characteristics, ensuring that high-brightness and rapidly changing areas are separated from low-brightness or static areas; S42, for areas with high brightness and rapid changes, the partition correction coefficient is in, It is an energy efficiency regularization term for areas with high brightness and rapid changes. It is used to ensure that the upper limit of calculation can be controlled even in high energy consumption scenarios to prevent over-calculation. For low-light or static areas, the partition correction factor is α low is the adjustment parameter, α low <1, It is an energy efficiency regularization term for low-brightness or static areas, which is used to ensure that the user's visual needs are still met in low-brightness or static areas; The calculation formula of energy efficiency regularization term is: λ1 is the regularization coefficient, D pix (j) represents the dynamic characteristics of the jth pixel, is the dynamic change value expected by the system; n5 represents the number of pixels on the screen used to evaluate dynamic characteristics when calculating the energy efficiency regularization term; S43, set up dynamic adjustment mechanism: Dynamic adjustment function formula Among them, η and μ are weight parameters, represents the rate of change of personalized correction parameters over time, Represents the rate of change of the pixel information matrix over time.

7. A method for color gamut correction of a mobile phone screen according to claim 6, characterized in that S5 include: The original calculation formula of the adaptive feedback correction parameter is: in, is the adaptive feedback correction parameter adjusted at time t based on the original calculation formula; P zone represents the partition correction parameter; is the correction increment based on ambient light changes; It is a correction increment based on user behavior feedback; The calculation formula is: Among them, α env is the adjustment factor of ambient light, is the current ambient light data, is the initial ambient light data; The calculation formula is: Among them, β user is the adjustment coefficient of user behavior, It is a function that represents the analysis and quantification results of the user's current behavior; Calculate screen content feedback correction parameters Among them, γ disp is the adjustment coefficient of screen content feedback, It is a parsing function for the dynamic changes of screen content; The final calculation formula of the adaptive feedback correction parameter is: represents the adjusted adaptive feedback correction parameter at time t obtained based on the final calculation formula.

8. The method for color gamut correction of a mobile phone screen according to claim 7, characterized in that S6 include: S61, set the mapping formula: is the actual displayed color after mapping, φ is the nonlinear color gamut mapping function, and the correction parameter Map to the color range that the screen can actually display; P max Represents the maximum value of the adaptive feedback correction parameter, which is used to Normalized to the range of [0,1], γ3 represents the Gamma correction coefficient, which is used to control the nonlinear adjustment of brightness and contrast, and its value range is [2.2,2.4]. max Indicates the maximum brightness or color value of the screen; S62, set the color consistency regularization term C(t): Among them, γ4 is the weight coefficient for adjusting color consistency, is the actual display color value of the i-th region, is the desired display color value of the area; S63, calculate the final display color of the screen: in, It is the final display color after mapping and color consistency adjustment.

9. A mobile phone screen color gamut correction system, characterized in that: It includes a data acquisition module, a first acquisition module, a second acquisition module, a third acquisition module, a fourth acquisition module and a fifth acquisition module; The data acquisition module is used to acquire ambient light data and screen content data; The first acquisition module is used to acquire color correction parameters based on ambient light data and screen content data; The second acquisition module is used to acquire personalized correction parameters based on the color correction parameters; The third acquisition module is used to acquire the partition correction parameter based on the personalized correction parameter; The fourth acquisition module is used to acquire the adaptive feedback correction parameter based on the partition correction parameter; The fifth acquisition module is used to acquire the display color of the screen based on the adaptive feedback correction parameter; Among them, obtaining ambient light data and screen content data includes: The ambient light sensor is used to obtain ambient light data. env Represents ambient light data, which includes the intensity of ambient light I env , color temperature of ambient light T env and the spectral distribution of ambient light λ env , then S env Can be expressed as: S env ={I env ,T env ,λ env } (1) The unit of ambient light intensity is Lux, and the unit of ambient light color temperature is K; The screen content data is defined as C disp , whose value is the color information of each pixel in CIE XYZ space, C disp It is expressed as: C disp ={X,Y,Z} (2) Among them, X, Y, and Z represent the three standard chromaticity coordinates in the CIE XYZ color space. The RGB value of each pixel is converted to the CIE XYZ space through the standard color conversion matrix: R, G, B are the red, green, and blue values ​​of each pixel in the current display content, respectively. The value range of R, G, and B is 0 to 255. The converted X, Y, and Z are processed in the standard color space.

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