Method and system for controlling color gamut of mobile phone screen

Abstract

The invention provides a mobile phone screen color gamut control method and system, and the method comprises the steps: collecting the spectral characteristics of ambient light, and constructing a spectrum matrix; constructing a dynamic reference color model based on the spectrum matrix to generate a global color gamut adjustment matrix, and dynamically adjusting the screen color gamut according to the global color gamut adjustment matrix; collecting eye movement data of a user in real time, constructing a fixation area, and performing area color gamut optimization analysis on the fixation area in combination with the dynamic reference color model; according to an optimization analysis result, local compensation is carried out in combination with the global color gamut, and a final local color gamut matrix is generated; the energy consumption of the non-gazing area is reduced, and unnecessary color processing is reduced. The invention provides an intelligent and dynamic color gamut self-adaptive adjusting system by combining environment spectrum detection and eye movement tracking technologies, overcomes the defects of the prior art in the aspects of environment light adaptability, color accuracy and energy efficiency, and has remarkable innovativeness and practical value.

Description

Technical Field

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

[0002] With the widespread popularity of smartphones and the rapid development of display technology, users have higher and higher requirements for the color performance of mobile phone screens. The color gamut control capability of mobile phone screens determines whether they can accurately restore the color performance of image and video content, which directly affects the user's visual experience. Existing mobile phone screens, especially those based on OLED and LCD technology, can achieve high color saturation and contrast, but they still face many technical bottlenecks in color gamut control.

[0003] At present, common mobile phone screen color gamut control methods mainly rely on global adjustments based on brightness and contrast. However, these technologies have significant shortcomings: First, the dynamic changes in ambient light conditions are often ignored. When users use mobile phones in different lighting environments (such as strong natural light, indoor lighting, or low-light environments), the color performance of the screen is difficult to maintain consistency, resulting in color distortion or oversaturation, and unable to truly reflect the original appearance of the content. When dealing with complex ambient light changes, existing technologies usually only make simple compensation by adjusting the screen brightness and contrast, which cannot really solve the problem of color distortion.

[0004] Secondly, most of the current color gamut adjustment methods are based on global unified adjustments, lacking precise optimization of the user's visual focus. In actual usage scenarios, users tend to focus only on a certain local area of ​​the screen, and the existing color gamut control technology of mobile phone screens cannot perform detailed color processing on the gaze area, resulting in the user's viewing experience not being fully optimized. In addition, global color adjustment is often accompanied by higher power consumption, especially when displaying high dynamic range (HDR) content, the device power consumption is further increased, thus affecting the device's battery life performance.

[0005] Furthermore, traditional screen color gamut adjustment lacks intelligence and personalization, and cannot be dynamically adjusted according to user usage habits, scene changes, or content types. For example, when watching videos or browsing images, the system fails to provide the best color performance based on the content characteristics, resulting in loss of color details or poor viewing experience in certain scenes. These existing shortcomings have severely restricted the development of mobile phone display technology and have become a bottleneck for improving user visual experience.

[0006] Therefore, the current mobile phone screen color gamut control technology urgently needs an innovative method that can adapt to changes in ambient light, combine the user's visual focus, and take into account both energy saving and color accuracy, so as to better cope with complex usage scenarios and the needs of different users. Summary of the invention

[0007] The purpose of the present invention is to propose a method and system for color gamut control of a mobile phone screen. By combining environmental spectrum detection and eye tracking technology, dynamic optimization of global and local colors is achieved, which significantly improves the user's visual experience under different lighting conditions and effectively reduces device energy consumption.

[0008] In order to achieve the above object, a method for controlling the color gamut of a mobile phone screen is provided in a first aspect of the present invention, the method comprising:

[0009] S1. Collect the spectral characteristics of ambient light and construct a spectral matrix; wherein the spectral characteristic matrix includes the average color temperature of the current environment Average light intensity of the current environment I avg and the total energy E of the current environment tot , expressed as

[0010] S2. constructing a dynamic reference color model based on the spectrum matrix to generate a global color gamut adjustment matrix, and dynamically adjusting the screen color gamut according to the global color gamut adjustment matrix;

[0011] S3, collect the user's eye movement data in real time, construct the gaze area, and combine the gaze area with the dynamic reference color model to perform regional color gamut optimization analysis;

[0012] S4, based on the results of the optimization analysis, local compensation is performed in combination with the global color gamut to generate a final local color gamut matrix;

[0013] S5. Reduce energy consumption in non-attention areas and reduce unnecessary color processing;

[0014] Wherein, the S3 specifically includes:

[0015] First, use the eye tracking sensor to determine the coordinates of the user's gaze point on the screen, P(x, y). By calculating the neighboring area of ​​the point on the screen, determine the gaze area R(x, y) where the user is looking. Define the gaze area R(x, y) as a circular area with a radius of r and P(x, y) as the center, which represents the visual range where the user focuses his attention. The calculation formula for the gaze area R(x, y) is:

[0016] R(x,y)={(x′,y′)|(x′-x) 2 +(y′-y) 2 ≤r 2}

[0017] Where r is the region radius, which is used to define the range of attention around the visual focus;

[0018] According to the user's gaze area, a local color gamut analysis model A(R(x,y),C(t),B(S(t))) is generated in the area, and the difference between the current color performance of the current area and the reference color is analyzed, which is expressed as:

[0019]

[0020] Where ΔE is the average color difference in the local area, L R ,a R ,b R is the color component in the fixation area R(x,y); L B ,a B ,b B is the corresponding color component of the dynamic reference color model B(S(t));

[0021] According to the average color difference in the local area, a local color optimization judgment mechanism is designed; the local color optimization judgment mechanism is:

[0022] When ΔE>∈, ∈ is the preset color difference threshold, triggering local color gamut optimization, otherwise the globally adjusted basic color is kept unchanged, and the optimization process is aimed at adjusting the color in the gaze area R(x, y), while the non-gaze area remains unchanged;

[0023] The local color optimization feedback adjustment amount is ΔC loc (R(x,y)), superimposed with the global adjustment matrix C(t), updates the final color gamut of the area:

[0024] C loc (t) = C (t) + ΔC loc (R(x,y))

[0025] Where, ΔC loc (R(x,y)) is the local optimization adjustment amount for the color of the gaze area, which is dynamically adjusted based on the analyzed color difference ΔE and the user's gaze point P(x,y).

[0026] Furthermore, the spectral characteristics of the ambient light are collected in real time by a spectral sensor built into the mobile phone. The spectral sensor collects light at preset time intervals to construct an ambient spectral characteristic matrix, and preprocesses the ambient spectral characteristic matrix; wherein the light includes color temperature, light intensity and complete spectral distribution.

[0027] Furthermore, the preprocessing specifically includes: feature compression and dimension reduction, and smoothing processing based on time series;

[0028] The feature compression and dimension reduction are as follows:

[0029]

[0030] Where, T(t) is the preprocessed spectral matrix, is the average color temperature of the ambient light calculated by the spectral matrix, I avg is the average light intensity of the spectrum, E tot is the total energy of the spectrum, f is the compression function, and S(t) is the initial spectrum matrix;

[0031] in, is the average color temperature of the ambient light calculated by the spectral matrix, expressed as:

[0032]

[0033] Where n is the resolution of the spectral sampling, i is the spectrum i, and λ i Indicates the wavelength of the spectrum, I(λ i ) represents the light intensity at that wavelength;

[0034] I avg is the average light intensity of the spectrum, expressed as:

[0035]

[0036] E tot is the total energy of the spectrum, that is, the cumulative sum of light intensities at all wavelengths, expressed as:

[0037]

[0038] The smoothing process based on time series is:

[0039] Let T′(t) be the feature vector after smoothing, and its update rule is:

[0040] T′(t)=αT(t)+(1-α)T′(t-Δt)

[0041] Wherein, α is the smoothing coefficient; T′(t) is the smoothed feature vector at the current moment; T(t) is the original feature vector at the current moment, and Δt is the preset time interval.

[0042] Furthermore, the S3 specifically includes:

[0043] A global color gamut adjustment matrix is ​​defined to represent the initial color gamut configuration of the screen under standard illumination, and a regularization term is introduced to construct a color gamut adjustment model; wherein the global color gamut adjustment matrix is ​​expressed as:

[0044] C(t)=C 0 +ΔC(t)

[0045] Wherein, C(t) is the global adjustment matrix, ΔC(t) is the color gamut adjustment amount calculated based on the ambient light feature T′(t);

[0046] The color gamut adjustment model is expressed as:

[0047]

[0048] in, Indicates the color temperature according to the environment Adjust the color balance of the screen color gamut; f 2 (I avg ) represents the average light intensity of the ambient light I avg , adjust the contrast and brightness of the screen; f 3 (E tot ) represents the total energy of the spectrum E tot The impact on the color saturation of the screen; λ||T′(t)-T′(t-Δt)|| represents the regularization term, which is used to smooth the color changes over time; λ is the coefficient that controls the smoothing effect;

[0049] Based on the current global color gamut matrix C(t) and the past color gamut adjustment results, a dynamic reference color model B(S(t)) is designed through the smooth integration of historical information, which is expressed as:

[0050] B(S(t))=βC(t)+(1-β)B(S(t-Δt))

[0051] Among them, B(S(t)) is the dynamic reference color model at the current moment, reflecting the cumulative influence of historical spectral characteristics; β is the smoothing coefficient, which is used to balance the influence of the current spectrum on the color gamut and the adjustment of the historical color gamut.

[0052] Furthermore, according to the color temperature of the environment Adjust the color balance of the screen's color gamut A nonlinear adaptive function is designed to process the color adjustment under the change of color temperature; wherein the nonlinear adaptive function is expressed as:

[0053]

[0054] in, is the current ambient color temperature; T ref is the reference value of color temperature; k is the intensity coefficient of color adjustment, and γ is the adjustment rate factor.

[0055] Furthermore, the S4 specifically includes:

[0056] Build a local color gamut enhancement model to dynamically adjust the color gamut of the mobile phone screen by comparing the color difference between the gaze area and the global area;

[0057] A local color compensation mechanism is designed based on the local color gamut enhancement model combined with the dynamic reference color model to ensure that the color difference between the user's gaze area and the non-gaze area is not too large, while providing a natural color transition;

[0058] The local color gamut enhancement model and the local color compensation mechanism are combined to generate the final local color gamut matrix.

[0059] Furthermore, the local color gamut enhancement model is expressed as:

[0060] ΔC enh (t,R(x,y))

[0061] =η s ·S adj (R(x,y))+η c ·C adj (R(x,y))+η l ·L adj (R(x,y))-λ||C loc (t)

[0062] -C(t)||

[0063] Among them, η s ,η c ,η l Control the color saturation, contrast and brightness enhancement intensity respectively; S adj (R(x,y)) adjusts the color saturation C adj (R(x,y)) adjusts the contrast to enhance the contrast difference between dark and bright areas in the region, making the details clearer; L adj (R(x,y)) adjusts the brightness so that the visual brightness in the gaze area matches the user's current focus; the regularization term || C loc (t)-C(t)|| introduces the difference constraint between global and local colors to prevent local enhancement from deviating too much from the global color; λ is the regularization coefficient;

[0064] The local color compensation mechanism is expressed as:

[0065] ΔC comp (t,R(x,y))=γ·(C loc (t)-B(S(t)))-ρ·||R(x,y)-R prev (x,y)|

[0066] Among them, γ is the local compensation coefficient, which is used to adjust the compensation strength; ρ is the penalty term coefficient based on the time dimension, which is used to control the color mutation caused by the rapid switching of the user's line of sight; C loc (t) is the local color gamut adjustment matrix;

[0067] The final local color gamut matrix is ​​expressed as:

[0068]

[0069] The final local color gamut matrix locally enhances the color adjustment amount ΔC enh (t, R(x, y)) and the color adjustment amount after compensation ΔC comp (t, R(x, y)) is merged into the local color gamut matrix C loc In (t), the final optimized local color gamut distribution is generated.

[0070] Furthermore, in S5, a dynamic power consumption optimization model is defined to achieve power consumption optimization by simplifying the color and brightness of the non-attention area, wherein the degree of simplification is affected by the ambient light intensity I avg And the control of the user's gaze area size |R(x,y)| is expressed as:

[0071]

[0072] Among them, α(t) is the color and brightness simplification coefficient of the non-attention area, and its value range is [0,1]. The smaller the value, the higher the simplification degree. avg is the current average intensity of ambient light; I th is the threshold of ambient light intensity, representing the critical point where a significant simplification begins; κ is the adjustment coefficient, controlling the response speed to ambient light intensity; |R(x,y)| is the size of the user's gaze area, and |S| is the total area of ​​the screen;

[0073] Based on the color and brightness simplification coefficient α(t) of the non-attention area, the color and brightness of the non-attention area are adjusted to reduce unnecessary calculation burden.

[0074] Furthermore, the color and brightness of the non-attention area are adjusted as follows:

[0075] Color simplification, expressed as:

[0076]

[0077] in, is the simplified color matrix of the non-attention area; C(t) is the global color gamut adjustment matrix; α(t) controls the degree of color simplification in the non-attention area. The smaller the value, the greater the simplification.

[0078] Brightness adjustment, expressed as:

[0079]

[0080] in, is the brightness adjustment matrix of the non-fixated area; L(t) is the global brightness matrix;

[0081] At the same time, the visual balance regularization term Ω(t) is designed to control the focus area R(x,y) and the non-focus area The transition between is expressed as:

[0082]

[0083] Among them, ζ is the strength adjustment parameter of the regularization term; L R (t) and C R (t) are the brightness matrix and color matrix of the fixation area, respectively;

[0084] When Ω(t) is large, it means that the difference between the fixation area and the non-fixation area is too large, so α(t) is automatically reduced to reduce the degree of simplification; when Ω(t) is small, it means that the transition between the two is natural and the degree of simplification is maintained.

[0085] In another aspect of the present invention, a system for controlling the color gamut of a mobile phone screen is provided, the system comprising:

[0086] The ambient light collection subsystem is used to collect the spectral characteristics of the ambient light and construct a spectral matrix; wherein the spectral characteristic matrix includes the average color temperature of the current environment Average light intensity of the current environment I avg and the total energy E of the current environment tot , expressed as

[0087] A spectral matrix generation subsystem, used to construct a dynamic reference color model based on the spectral matrix to generate a global color gamut adjustment matrix, and dynamically adjust the screen color gamut according to the global color gamut adjustment matrix;

[0088] The regional color gamut optimization subsystem is used to collect the user's eye movement data in real time, construct the gaze area, and combine the gaze area with the dynamic reference color model to perform regional color gamut optimization analysis;

[0089] The global color gamut optimization subsystem is used to perform local compensation based on the results of the optimization analysis and in combination with the global color gamut to generate the final local color gamut matrix;

[0090] The energy optimization subsystem is used to reduce energy consumption in non-fixated areas and reduce unnecessary color processing;

[0091] The regional color gamut optimization subsystem specifically includes:

[0092] First, use the eye tracking sensor to determine the coordinates of the user's gaze point on the screen, P(x, y). By calculating the neighboring area of ​​the point on the screen, determine the gaze area R(x, y) where the user is looking. Define the gaze area R(x, y) as a circular area with a radius of r and P(x, y) as the center, which represents the visual range where the user focuses his attention. The calculation formula for the gaze area R(x, y) is:

[0093] R(x,y)={(x′,y′)|(x′-x) 2 +(y′-y) 2 ≤r 2}

[0094] Where r is the region radius, which is used to define the range of attention around the visual focus;

[0095] According to the user's gaze area, a local color gamut analysis model A(R(x,y),C(t),B(S(t))) is generated in the area, and the difference between the current color performance of the current area and the reference color is analyzed, which is expressed as:

[0096]

[0097] Where ΔE is the average color difference in the local area, L R ,a R ,b R is the color component in the fixation area R(x,y); L B ,a B ,b B is the corresponding color component of the dynamic reference color model B(S(t));

[0098] According to the average color difference in the local area, a local color optimization judgment mechanism is designed; the local color optimization judgment mechanism is:

[0099] When ΔE>∈, ∈ is the preset color difference threshold, triggering local color gamut optimization, otherwise the globally adjusted basic color is kept unchanged, and the optimization process is aimed at adjusting the color in the gaze area R(x, y), while the non-gaze area remains unchanged;

[0100] The local color optimization feedback adjustment amount is ΔC loc (R(x,y)), superimposed with the global adjustment matrix C(t), updates the final color gamut of the area:

[0101] C loc (t) = C (t) + ΔC loc (R(x,y))

[0102] Where, ΔC loc(R(x,y)) is the local optimization adjustment amount for the color of the gaze area, which is dynamically adjusted based on the analyzed color difference ΔE and the user's gaze point P(x,y).

[0103] The beneficial technical effects of the present invention are at least as follows:

[0104] First, the present invention introduces environmental spectrum detection technology. The spectrum sensor in the mobile phone detects the spectrum composition of the ambient light in real time. The system intelligently adjusts the screen color gamut according to the spectrum differences of different light source conditions (such as natural light, indoor light, low light, etc.). Unlike the traditional brightness / contrast adjustment method, the present invention can dynamically adjust the saturation, brightness and color balance of the color gamut based on the spectrum data of the ambient light, thereby effectively solving the color distortion problem caused by changes in ambient light in the prior art. Whether in strong sunlight or dim light, the screen can present accurate and natural color performance, improving the user's viewing experience.

[0105] Secondly, the present invention utilizes eye tracking technology to monitor the user's gaze point in real time through a front camera or a dedicated eye sensor, and optimizes the local color gamut for the focus area of ​​the user's attention. Unlike traditional global color gamut adjustment, the system can dynamically enhance the color saturation and contrast of the user's gaze area to ensure that the display effect of this area is the most delicate and realistic. For non-gaze areas, the color processing is appropriately simplified to reduce power consumption. This local optimization strategy can not only significantly improve the visual effect, but also effectively reduce unnecessary power consumption and enhance the battery life of the device.

[0106] In addition, the present invention organically combines ambient light detection and eye tracking technology through a linkage control mechanism, so that the global and local adjustments of the color gamut are coordinated with each other. The system first adjusts the global color performance according to the changes in the ambient light spectrum, and then performs local fine adjustments based on the user's gaze position to ensure that the color of the user's visual focus area always maintains the best effect under different ambient light conditions. Through this global and local color gamut adaptive adjustment method, the present invention not only solves the problem of color distortion under complex lighting conditions, but also achieves precise visual enhancement by optimizing the focus area, reduces energy consumption, and improves the endurance of the device.

[0107] In summary, the present invention provides an intelligent and dynamic color gamut adaptive adjustment system by combining ambient spectrum detection and eye tracking technology, which solves the shortcomings of the prior art in terms of ambient light adaptability, color accuracy and energy efficiency, and has significant innovation and practical value. BRIEF DESCRIPTION OF THE DRAWINGS

[0108] The present invention is further described using the accompanying drawings, but the embodiments in the accompanying drawings do not constitute any limitation to the present invention. A person skilled in the art can obtain other drawings based on the following drawings without creative work.

[0109] Figure 1 The present invention is a flowchart of a method for controlling the color gamut of a mobile phone screen according to an embodiment of the present invention.

[0110] Figure 2 This is a system framework diagram of a mobile phone screen color gamut control according to an embodiment of the present invention. DETAILED DESCRIPTION

[0111] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and cannot be understood as limiting the present invention.

[0112] like Figure 1 As shown, an embodiment of the present invention provides a method for controlling the color gamut of a mobile phone screen, the method comprising the following steps:

[0113] S1. Collect the spectral characteristics of ambient light and construct a spectral matrix; wherein the spectral characteristic matrix includes the average color temperature T of the current environment c , the average light intensity of the current environment I avg and the total energy E of the current environment tot , expressed as T(t)=[T c ,I avg ,E tot ].

[0114] Specifically, the present invention collects ambient light in real time through a spectral sensor built into a mobile phone. The light collected by the spectral sensor includes color temperature, light intensity, and complete spectral distribution. A time interval Δt is set (for example, once every 100 milliseconds), representing the time point of each collection, and the collected ambient spectral feature matrix S(t), where t represents the current time. The form of S(t) is as follows:

[0115] S(t)=[λ 1 ,I(λ 1 ),λ 2 ,I(λ 2 ),…,λ n ,I(λ n )]

[0116] Among them, λ i Indicates the wavelength of the spectrum; I(λ i) represents the light intensity at that wavelength; n is the resolution of spectral sampling, which is usually a higher value (such as n=400) to ensure sufficient accuracy to describe the ambient light characteristics; S(t) is an n×2 matrix, which represents the change of the ambient spectrum over time t.

[0117] Furthermore, since the spectral data has a high dimension, in order to simplify the computational complexity of subsequent processing, the present invention performs feature compression and dimension reduction on the spectral matrix S(t). Considering that the color gamut adjustment mainly depends on the color temperature, intensity and spectral energy distribution of the spectrum, the present invention uses a custom spectral weight transformation method to extract these three core features. The feature compression process is performed by the following formula:

[0118]

[0119] in, It is the average color temperature of the ambient light calculated by the spectral matrix. The specific formula is:

[0120]

[0121] I avg is the average light intensity of the spectrum, expressed as:

[0122]

[0123] E tot is the total energy of the spectrum, that is, the cumulative sum of the light intensities at all wavelengths:

[0124]

[0125] This transformation compresses the high-dimensional spectral data S(t) into a simpler feature vector T(t), which facilitates subsequent color gamut adjustment.

[0126] Furthermore, the ambient spectrum may be affected by external interference (such as occasional occlusion, sudden changes in light sources), which can easily generate noise signals. In order to avoid noise interference in the subsequent global color gamut adjustment, the present invention performs time series smoothing on the spectral feature T(t). The feature vector is processed using a custom spectral smoothing filter algorithm, which not only considers the continuity in the time series, but also combines the historical spectral change trend to eliminate the influence of mutation points. Let T′(t) be the feature vector after smoothing, and its update rule is:

[0127] T′(t)=αT(t)+(1-α)T′(t-Δt)

[0128] Among them, α is the smoothing coefficient, which is usually set at 0.8≤α≤0.9, and is used to control the balance between current features and historical features; T′(t) is the smoothed feature vector at the current moment; T(t) is the original feature vector at the current moment. The processed T′(t), that is, the smoothed spectral feature, will be input as the final output to the next step of the global color gamut adjustment module.

[0129] S2. Construct a dynamic reference color model based on the spectrum matrix to generate a global color gamut adjustment matrix, and dynamically adjust the screen color gamut according to the global color gamut adjustment matrix.

[0130] Specifically, to achieve real-time global color gamut adjustment, a global color gamut adjustment matrix C is defined 0 , represents the initial color gamut configuration of the screen under standard illumination. The change of the global color of the screen over time t is determined by the adjusted color gamut matrix C(t):

[0131] C(t)=C 0 +ΔC(t)

[0132] Among them, ΔC(t) is the color gamut adjustment amount calculated based on the ambient light characteristics T′(t). ΔC(t) is the result of combining color temperature, light intensity, energy characteristics and color adjustment coefficients, as follows:

[0133] Furthermore, in order to better capture the impact of dynamic changes in ambient light on the color gamut, ΔC(t) not only takes into account the impact of color temperature, but also introduces an innovative regularization term to limit extreme fluctuations in color temperature and light intensity. This regularization term not only stabilizes color regulation, but also reduces the problem of excessive fluctuations in screen brightness.

[0134] The color gamut adjustment model is defined as:

[0135]

[0136] in, According to the color temperature of the environment Adjust the color balance of the screen color gamut, especially the adjustment of red and blue. The higher the color temperature, the less red and the more blue; 2 (I avg ) is the average light intensity of the ambient light I avg , adjust the contrast and brightness of the screen. The higher the light intensity, the more brightness will be increased, but the contrast will remain balanced; 3 (E tot ) reflects the total energy of the spectrum E totThe impact on the color saturation of the screen, the higher the energy, the higher the color saturation; λ||T′(t)-T′(t-Δt)|| is a regularization term, which is used to smooth the color changes over time and reduce the color gamut fluctuations caused by drastic changes in lighting. λ is a coefficient that controls the smoothing effect, usually set between 0.5 and 1 to ensure that the screen does not flicker frequently when the ambient light changes rapidly.

[0137] Further, the present invention is to The traditional color gamut adjustment model only linearly maps color temperature and color balance. In the scenario of this patent, the ambient color temperature may change rapidly with indoor and outdoor light sources, so an adaptive mechanism is needed. This invention proposes a nonlinear adaptive function to handle color adjustment under color temperature changes:

[0138]

[0139] Among them, T c is the current ambient color temperature; T ref is the reference value of color temperature (such as the standard white light color temperature of 5000K); k is the intensity coefficient of color adjustment, and γ is the adjustment rate factor; this function can more sensitively adapt to changes in color temperature under different light sources through nonlinear mapping, and make the screen color adjustment smoother.

[0140] Through this design, the present invention can not only handle the linear changes commonly seen in traditional color gamut adjustment, but also cope with the challenge of color balance under complex lighting conditions. The function is particularly suitable for fast response of mobile devices in scenes where outdoor natural light and indoor light are switched.

[0141] Furthermore, in order to ensure the continuity and stability of global color adjustment when the ambient light changes frequently, a dynamic reference color model B(S(t)) is defined. This model is based on the current global color gamut matrix C(t) and the past color gamut adjustment results, and generates a reference color state through smooth integration of historical information for subsequent local color gamut optimization.

[0142] B(S(t))=βC(t)+(1-β)B(S(t-Δt))

[0143] Among them, B(S(t)) is the dynamic reference color model at the current moment, reflecting the cumulative influence of historical spectral characteristics; β is the smoothing coefficient (usually between 0.8 and 0.9), which is used to balance the influence of the current spectrum on the color gamut and the adjustment of the historical color gamut; through this formula, the dynamic reference color model avoids drastic color jitter and ensures that the transition of the screen color gamut remains smooth in scenes with drastic changes in light.

[0144] Furthermore, after being processed by the above-mentioned global color gamut adjustment model and dynamic reference model, the output global color gamut matrix C(t) will be applied to the global color management of the mobile phone screen to ensure that the screen can be adaptively adjusted under different ambient light. At the same time, the generated reference color model B(S(t)) serves as the reference for local color gamut optimization in subsequent steps to ensure that the color optimization of the user's gaze area can further enhance the visual effect on the basis of global adjustment.

[0145] S3. Collect the user's eye movement data in real time, construct the gaze area, and combine the gaze area with the dynamic reference color model to perform regional color gamut optimization analysis.

[0146] Specifically, first, use the eye tracking sensor to determine the coordinates of the user's gaze point on the screen, P(x, y). By calculating the neighboring area of ​​the point on the screen, determine the gaze area R(x, y) that the user is looking at. Here, R(x, y) is defined as a circular area with P(x, y) as the center and a radius of r, which represents the visual range where the user focuses his attention. The calculation formula for R(x, y) is:

[0147] R(x,y)={(x′,y′)|(x′-x) 2 +(y′-y) 2 ≤r 2}

[0148] Where r is the region radius, which is used to define the focus area around the visual focus. This focus area will be used as the target area for subsequent local color gamut optimization.

[0149] Furthermore, in order to optimize the color performance of the gaze area R(x,y), it is necessary to combine the global color gamut matrix C(t) and the dynamic reference color model B(S(t)) generated from step 2. According to the user's gaze area, a local color gamut analysis model A(R(x,y), C(t), B(S(t))) is generated in the area to analyze the difference between the current color performance of the area and the reference color. The specific analysis method is to determine whether the color of the area needs to be adjusted by calculating the average color difference ΔE in the local area. The ΔE formula is as follows:

[0150]

[0151] Among them, L R ,a R ,b R is the color component in the fixation area R(x,y) (based on the brightness and chromaticity information of the CIELAB color space); L B ,a B ,b B is the corresponding color component of the reference color model B(S(t)).

[0152] Furthermore, if ΔE exceeds the set threshold ∈, it means that the difference between the area and the reference color is too large, and local color gamut adjustment is required. This step ensures the color accuracy of the area where the user is looking, and ensures that the screen can maintain the optimal color display effect under different ambient light and visual focus switching.

[0153] Furthermore, according to the color difference ΔE analyzed above, the present invention designs a local color optimization judgment mechanism. When ΔE>∈, local color gamut optimization is triggered, otherwise the global adjusted basic color is kept unchanged. The optimization process mainly adjusts the color within the attention area R(x,y), while the non-attention area remains unchanged.

[0154] The local color optimization feedback adjustment amount is ΔC loc (R(x,y)), superimposed with the global adjustment matrix C(t), updates the final color gamut of the area:

[0155] C loc (t) = C (t) + ΔC loc (R(x,y))

[0156] Where, ΔC loc (R(x,y)) is the local optimization adjustment of the color of the gaze area, which is dynamically adjusted based on the analyzed color difference ΔE and the user's gaze point P(x,y); the core of this local color gamut adjustment is to reduce unnecessary global color update burdens, concentrate the main computing resources on the area where the user is currently gazing, and ensure the optimal color expression of the visual focus.

[0157] It can be understood that the output of this step is the optimized local color gamut adjustment matrix C loc (t) represents the final color gamut distribution that has been analyzed and optimized for the user's visual focus. At the same time, the output local color gamut analysis model A(R(x,y),C(t),B(S(t))) provides a basis for subsequent energy consumption optimization. Through this local color gamut optimization, the system can provide personalized and accurate color performance under different ambient light and user interaction, ensuring that the color effect of the screen at the user's visual focus is always in the best state.

[0158] S4. Based on the results of the optimization analysis, local compensation is performed in combination with the global color gamut to generate a final local color gamut matrix.

[0159] Specifically, the goal of this step is to further enhance the local color gamut of the user's gaze area R(x,y) based on the analysis results, and to ensure that the enhanced color does not deviate from the global color through a local compensation mechanism based on a dynamic benchmark to avoid unnatural visual effects. The role of this step in the entire patent solution is to ensure that the user's visual focus area has the best color performance and a smooth transition with the global color.

[0160] Furthermore, in order to improve the color performance of the user's gaze area, the present invention designs a local color gamut enhancement model. This model is not only based on color gamut adjustment, but also introduces enhancement items to dynamically adjust by comparing the color difference between the gaze area and the global area. The formula is as follows:

[0161] ΔC enh (t,R(x,y))=η s ·S adj (R(x,y))+η c ·C adj (R(x,y))+η l ·L adj (R(x,y))-λ||C loc (t)-C(t)||

[0162] Among them, η s ,η c ,η l Control the enhancement intensity of color saturation, contrast and brightness respectively. According to the color characteristics R(x,y) of the user's gaze area, these three items are the main adjustment factors for local color enhancement; S adj (R(x,y)) adjusts the color gamut saturation, optimizes the color richness in the gaze area, and improves color vividness; C adj (R(x,y)) adjusts the contrast to enhance the contrast difference between dark and bright areas in the region, making the details clearer; L adj (R(x,y)) adjusts the brightness so that the visual brightness in the gaze area matches the user's current focus; the regularization term |C loc (t)-C(t)|| introduces the difference constraint between global and local colors to prevent local enhancement from deviating too much from global colors. λ is the regularization coefficient, which is usually set between 0.5 and 0.8 to ensure that local enhancement does not affect the global visual coordination.

[0163] Furthermore, the purpose of designing this enhancement model is: when the color characteristics of the gaze area are greatly different from the global ones, the gaze area is dynamically adjusted by introducing enhancement items to improve the user experience, while ensuring a smooth transition of the enhancement through the regularization items.

[0164] Furthermore, in order to ensure that the local color does not deviate too much from the global color performance after the color gamut is enhanced, the present invention designs a local color compensation mechanism based on the dynamic reference model B(S(t)). This compensation mechanism ensures that the color difference between the user's gaze area and the non-gaze area is not too large, while providing a natural color transition. The specific compensation formula is as follows:

[0165] ΔC comp(t,R(x,y))=γ·(C loc (t)-B(S(t)))-ρ·||R(x,y)-R prev (x,y)||

[0166] In the formula, γ is the local compensation coefficient, which is used to adjust the compensation strength; ρ is the penalty term coefficient based on the time dimension, which is used to control the color mutation caused by the user's rapid switching of the gaze. This term is calculated by comparing the current gaze area R(x, y) with the gaze area R at the previous moment. prev (x, y) position difference. If the user's line of sight switches frequently, this item will suppress excessive color compensation to avoid frequent flickering of screen colors. loc (t) is the local color gamut adjustment matrix; B(S(t)) is the global benchmark color model, which ensures that the local color will not differ too much from the global color.

[0167] It is understandable that the key to this formula design is to introduce an innovative time dimension regularization term, which controls the speed of change of the user's gaze area to avoid overreaction of the color compensation mechanism. This design is particularly suitable for occasions with fast scene switching, such as when mobile phone users quickly browse pages, the screen color can be smoothly transitioned.

[0168] Furthermore, the final local color gamut adjustment result needs to combine the results of the enhancement and compensation parts. The present invention defines the final local color gamut matrix The output of combining local enhancement and baseline compensation is shown below:

[0169]

[0170] This formula converts the local enhanced color adjustment amount ΔC enh (t, R(x, y)) and the color adjustment amount after compensation ΔC comp (t, R(x, y)) is merged into the local color gamut matrix C loc In (t), the final optimized local color gamut distribution is generated. This result ensures that the color of the user's gaze area not only maintains its vividness and visual impact after enhancement, but also naturally connects with the global color through a compensation mechanism to avoid over-adjustment.

[0171] Output the final optimized local color gamut matrix This matrix represents the color performance of the user's gaze area after enhancement and compensation, ensuring the best visual effect in the user's visual focus area. At the same time, the color transition between local and global is smoothed to prevent unnecessary visual differences and unnatural color changes. This output serves as the basis for subsequent steps and also provides a reference for the next step of energy consumption optimization in non-gaze areas, ensuring that the screen maintains power consumption control while optimizing color.

[0172] S5. Reduce energy consumption in non-attention areas and reduce unnecessary color processing.

[0173] Specifically, in order to reduce the computational load and energy consumption in the non-attention area, the present invention proposes a dynamic power consumption optimization model. The model achieves power consumption optimization by simplifying the color and brightness of the non-attention area. The degree of simplification is affected by the ambient light intensity I avg And the control of the size of the user's gaze area |R(x,y)|.

[0174] Furthermore, a simplification coefficient α(t) is defined, the purpose of which is to dynamically adjust the degree of simplification of the color and brightness of the non-gaze area based on the size of the user's gaze area and the current ambient light intensity. The formula is as follows:

[0175]

[0176] Among them, α(t) is the color and brightness simplification coefficient of the non-attention area, and its value range is [0,1]. The smaller the value, the higher the simplification degree. avg is the current average intensity of ambient light (obtained from the spectrum detection in step 1); I th is the threshold of ambient light intensity, representing the critical point where a significant simplification begins; κ is the adjustment coefficient, controlling the response speed to ambient light intensity; |R(x,y)| is the size of the user's gaze area, and |S| is the total area of ​​the screen.

[0177] Further, simplify the mechanism: when the ambient light is bright (I avg >I th ) and the fixation area is small, the color and brightness of the non-fixation area are significantly simplified, and the power consumption is greatly reduced. When the ambient light is dark or the fixation area is large, the degree of simplification is relatively weakened to ensure the overall visual consistency of the screen.

[0178] Furthermore, based on α(t), the present invention adjusts the color and brightness of the non-attention area to reduce unnecessary calculation burden. The adjustment process is gradually decreasing and dynamically adjusted according to the ambient light conditions and the size of the attention area. Color simplified formula:

[0179]

[0180] in, is the simplified color matrix of the non-attention area; C(t) is the global color gamut adjustment matrix; α(t) controls the degree of color simplification in the non-attention area. The smaller the value, the greater the simplification.

[0181] Brightness adjustment formula:

[0182]

[0183] in, is the brightness adjustment matrix of the non-attention area; L(t) is the global brightness matrix.

[0184] Through this adjustment method, the color and brightness of the non-attention area are dynamically simplified, ensuring reduced power consumption without affecting the user's overall visual perception.

[0185] Furthermore, in order to prevent the user from having too obvious visual differences between the fixation area and the non-fixation area, the present invention introduces a visual balance regularization term to control the difference between the fixation area R(x, y) and the non-fixation area The transition between. Define the visual balance regularization term Ω(t):

[0186]

[0187] Among them, ζ is the strength adjustment parameter of the regularization term; L R (t) and C R (t) are the brightness matrix and color matrix of the fixation area respectively; this regularization term controls the transition smoothness between the fixation area and the non-fixation area to avoid visual abruptness caused by oversimplification of the non-fixation area.

[0188] When Ω(t) is large, it means that the difference between the fixation area and the non-fixation area is too large, and the system automatically reduces α(t) to reduce the degree of simplification; when Ω(t) is small, it means that the transition between the two is natural and the degree of simplification can be maintained.

[0189] Furthermore, by introducing Ω(t), we can ensure that the visual experience remains consistent while optimizing energy consumption. Especially when the ambient light changes dramatically or the user's line of sight switches quickly, this regularization term can effectively smooth the transition and prevent visual interruption.

[0190] It can be understood that after the above processing, the final color matrix of the non-attention area is output and brightness matrix They represent simplified color and brightness information after dynamic power optimization. At the same time, the regularization term Ω(t) is fed back to the system to dynamically adjust the simplification strategy to ensure the overall visual balance of the screen. Through these optimization measures, the entire screen significantly reduces power consumption while maintaining the visual experience, especially in the non-gaze area. The reduction in power consumption will further extend the battery life of the device.

[0191] The final effect of this step is that by simplifying the color and brightness of the non-attention area, the burden on the processor and display is reduced, while the user's attention area still maintains high-quality color and brightness performance. The entire patented solution thus achieves a balance between visual effect and energy efficiency optimization.

[0192] like Figure 2As shown, in another embodiment of the present invention, a system for controlling the color gamut of a mobile phone screen is provided, and the system includes:

[0193] The ambient light collection subsystem 501 is used to collect the spectral characteristics of the ambient light and construct a spectral matrix; wherein the spectral characteristic matrix includes the average color temperature of the current environment Average light intensity of the current environment I avg and the total energy E of the current environment tot , expressed as

[0194] A spectrum matrix generating subsystem 502 is used to construct a dynamic reference color model based on the spectrum matrix to generate a global color gamut adjustment matrix, and dynamically adjust the screen color gamut according to the global color gamut adjustment matrix;

[0195] The regional color gamut optimization subsystem 503 is used to collect the user's eye movement data in real time, construct the gaze area, and perform regional color gamut optimization analysis on the gaze area in combination with the dynamic reference color model;

[0196] The global color gamut optimization subsystem 504 is used to perform local compensation based on the optimization analysis results and in combination with the global color gamut to generate a final local color gamut matrix;

[0197] An energy consumption optimization subsystem 505 is used to reduce energy consumption in non-attention areas and reduce unnecessary color processing;

[0198] The regional color gamut optimization subsystem specifically includes:

[0199] First, use the eye tracking sensor to determine the coordinates of the user's gaze point on the screen, P(x, y). By calculating the neighboring area of ​​the point on the screen, determine the gaze area R(x, y) where the user is looking. Define the gaze area R(x, y) as a circular area with a radius of r and P(x, y) as the center, which represents the visual range where the user focuses his attention. The calculation formula for the gaze area R(x, y) is:

[0200] R(x,y)={(x′,y′)|(x′-x) 2 +(y′-y) 2 ≤r 2}

[0201] Where r is the region radius, which is used to define the range of attention around the visual focus;

[0202] According to the user's gaze area, a local color gamut analysis model A(R(x,y),C(t),B(S(t))) is generated in the area, and the difference between the current color performance of the current area and the reference color is analyzed, which is expressed as:

[0203]

[0204] Where ΔE is the average color difference in the local area, L R ,a R ,b R is the color component in the fixation area R(x,y); L B ,a B ,b B is the corresponding color component of the dynamic reference color model B(S(t));

[0205] According to the average color difference in the local area, a local color optimization judgment mechanism is designed; the local color optimization judgment mechanism is:

[0206] When ΔE>∈, ∈ is the preset color difference threshold, triggering local color gamut optimization, otherwise the globally adjusted basic color is kept unchanged, and the optimization process is aimed at adjusting the color in the gaze area R(x, y), while the non-gaze area remains unchanged;

[0207] The local color optimization feedback adjustment amount is ΔC loc (R(x,y)), superimposed with the global adjustment matrix C(t), updates the final color gamut of the area:

[0208] C loc (t) = C (t) + ΔC loc (R(x,y))

[0209] Where, ΔC loc (R(x,y)) is the local optimization adjustment amount for the color of the gaze area, which is dynamically adjusted based on the analyzed color difference ΔE and the user's gaze point P(x,y).

[0210] It should be noted that the workflow described above is merely illustrative and does not limit the scope of protection of the present invention. In practical applications, technicians in this field can select part or all of them according to actual needs to achieve the purpose of the present embodiment, and no limitation is made here.

[0211] In addition, for technical details that are not described in detail in this embodiment, reference can be made to the parameter operation method provided in any embodiment of the present invention, and will not be repeated here.

[0212] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or system including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or system. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or system including the element.

[0213] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0214] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as a read-only memory / random access memory, a magnetic disk, or an optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to execute the methods described in each embodiment of the present invention.

[0215] The above are only preferred embodiments of the present invention, and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A method for controlling the color gamut of a mobile phone screen, characterized in that: The method comprises: S1. Collect the spectral characteristics of ambient light and construct a spectral matrix; wherein the spectral characteristic matrix includes the average color temperature of the current environment Average light intensity of the current environment I avg and the total energy E of the current environment tot , expressed as S2. constructing a dynamic reference color model based on the spectrum matrix to generate a global color gamut adjustment matrix, and dynamically adjusting the screen color gamut according to the global color gamut adjustment matrix; S3, collect the user's eye movement data in real time, construct the gaze area, and combine the gaze area with the dynamic reference color model to perform regional color gamut optimization analysis; S4, based on the results of the optimization analysis, local compensation is performed in combination with the global color gamut to generate a final local color gamut matrix; S5. Reduce energy consumption in non-attention areas and reduce unnecessary color processing; Wherein, the S3 specifically includes: First, use the eye tracking sensor to determine the coordinates of the user's gaze point on the screen, P(x, y). By calculating the neighboring area of ​​the point on the screen, determine the gaze area R(x, y) where the user is looking. Define the gaze area R(x, y) as a circular area with a radius of r and P(x, y) as the center, which represents the visual range where the user focuses his attention. The calculation formula for the gaze area R(x, y) is: R(x,y)={(x′,y′)|(x′-x) 2 +(y′-y) 2 ≤r 2 } Where r is the region radius, which is used to define the range of attention around the visual focus; According to the user's gaze area, a local color gamut analysis model A(R(x,y),C(t),B(S(t))) is generated in the area, and the difference between the current color performance of the current area and the reference color is analyzed, which is expressed as: Where ΔE is the average color difference in the local area, L R ,a R ,b R is the color component in the fixation area R(x,y); L B ,a B ,b B is the corresponding color component of the dynamic reference color model B(S(t)); According to the average color difference in the local area, a local color optimization judgment mechanism is designed; the local color optimization judgment mechanism is: When ΔE>∈, ∈ is the preset color difference threshold, triggering local color gamut optimization, otherwise the globally adjusted basic color is kept unchanged, and the optimization process is aimed at adjusting the color in the gaze area R(x, y), while the non-gaze area remains unchanged; The local color optimization feedback adjustment amount is ΔC loc (R(x,y)), superimposed with the global adjustment matrix C(t), updates the final color gamut of the area: C loc (t)=C(t)+ΔC loc (R(x,y)) Where, ΔC loc (R(x,y)) is the local optimization adjustment amount for the color of the gaze area, which is dynamically adjusted based on the analyzed color difference ΔE and the user's gaze point P(x,y).

2. A method for controlling the color gamut of a mobile phone screen according to claim 1, characterized in that: The spectral characteristics of the ambient light are collected in real time through a spectral sensor built into the mobile phone. The spectral sensor collects light according to a preset time interval to construct an ambient spectral characteristic matrix, and pre-processes the ambient spectral characteristic matrix; wherein the light includes color temperature, light intensity and complete spectral distribution.

3. A method for controlling the color gamut of a mobile phone screen according to claim 2, characterized in that: The preprocessing specifically includes: feature compression and dimension reduction, and smoothing processing based on time series; Among them, the feature compression and dimension reduction are: Where, T(t) is the preprocessed spectral matrix, is the average color temperature of the ambient light calculated by the spectral matrix, I avg is the average light intensity of the spectrum, E tot is the total energy of the spectrum, f is the compression function, and S(t) is the initial spectrum matrix; in, is the average color temperature of the ambient light calculated by the spectral matrix, expressed as: Where n is the resolution of the spectral sampling, i is the spectrum i, and λ i Indicates the wavelength of the spectrum, I(λ i ) represents the light intensity at that wavelength; I avg is the average light intensity of the spectrum, expressed as: E tot is the total energy of the spectrum, that is, the cumulative sum of light intensities at all wavelengths, expressed as: The smoothing process based on time series is: Let T′(t) be the feature vector after smoothing, and its update rule is: T′(t)=αT(t)+(1-α)T′(t-Δt) Wherein, α is the smoothing coefficient; T′(t) is the smoothed feature vector at the current moment; T(t) is the original feature vector at the current moment, and Δt is the preset time interval.

4. The method for controlling the color gamut of a mobile phone screen according to claim 1, characterized in that: The S3 specifically includes: A global color gamut adjustment matrix is ​​defined to represent the initial color gamut configuration of the screen under standard illumination, and a regularization term is introduced to construct a color gamut adjustment model; wherein the global color gamut adjustment matrix is ​​expressed as: C(t)=C0+ΔC(t) Wherein, C(t) is the global adjustment matrix, ΔC(t) is the color gamut adjustment amount calculated based on the ambient light feature T′(t); The color gamut adjustment model is expressed as: in, Indicates the color temperature of the environment Adjust the color balance of the screen color gamut; f2(I avg ) represents the average light intensity of the ambient light I avg , adjust the contrast and brightness of the screen; f3(E tot ) represents the total energy of the spectrum E tot The impact on the color saturation of the screen; λ||T′(t)-T′(t-Δt)|| represents the regularization term, which is used to smooth the color changes over time; λ is the coefficient that controls the smoothing effect; Based on the current global color gamut matrix C(t) and the past color gamut adjustment results, a dynamic reference color model B(S(t)) is designed through the smooth integration of historical information, which is expressed as: B(S(t))=βC(t)+(1-β)B(S(t-Δt)) Among them, B(S(t)) is the dynamic reference color model at the current moment, reflecting the cumulative influence of historical spectral characteristics; β is the smoothing coefficient, which is used to balance the influence of the current spectrum on the color gamut and the adjustment of the historical color gamut.

5. A method for controlling the color gamut of a mobile phone screen according to claim 4, characterized in that: According to the color temperature of the environment Adjust the color balance of the screen's color gamut A nonlinear adaptive function is designed to process the color adjustment under the change of color temperature; wherein the nonlinear adaptive function is expressed as: in, is the current ambient color temperature; T ref is the reference value of color temperature; k is the intensity coefficient of color adjustment, and γ is the adjustment rate factor.

6. A method for controlling the color gamut of a mobile phone screen according to claim 1, characterized in that: The S4 specifically includes: Build a local color gamut enhancement model to dynamically adjust the color gamut of the mobile phone screen by comparing the color difference between the gaze area and the global area; A local color compensation mechanism is designed based on the local color gamut enhancement model combined with the dynamic reference color model to ensure that the color difference between the user's gaze area and the non-gaze area is not too large, while providing a natural color transition; The local color gamut enhancement model and the local color compensation mechanism are combined to generate the final local color gamut matrix.

7. A method for controlling the color gamut of a mobile phone screen according to claim 6, characterized in that: The local color gamut enhancement model is expressed as: ΔC enh (t,R(x,y))=η s ·S adj (R(x,y))+η c ·C adj (R(x,y))+η l ·L adj (R(x,y))-λ||C loc (t)-C(t)|| Among them, η s ,η c ,η l Control the color saturation, contrast and brightness enhancement intensity respectively; S adj (R(x,y)) adjusts the color saturation C adj (R(x,y)) adjusts the contrast to enhance the contrast difference between dark and bright areas in the region, making the details clearer; L adj (R(x,y)) adjusts the brightness so that the visual brightness in the gaze area matches the user's current focus; the regularization term || C loc (t)-C(t)|| introduces the difference constraint between global and local colors to prevent local enhancement from deviating too much from the global color; λ is the regularization coefficient; The local color compensation mechanism is expressed as: ΔC comp (t,R(x,y))=γ·(C loc (t)-B(S(t)))-ρ·||R(x,y)-R prev (x,y)| Among them, γ is the local compensation coefficient, which is used to adjust the compensation strength; ρ is the penalty term coefficient based on the time dimension, which is used to control the color mutation caused by the rapid switching of the user's line of sight; C loc (t) is the local color gamut adjustment matrix; The final local color gamut matrix is ​​expressed as: The final local color gamut matrix locally enhances the color adjustment amount ΔC enh (t, R(x, y)) and the color adjustment amount after compensation ΔC comp (t, R(x, y)) is merged into the local color gamut matrix C loc In (t), the final optimized local color gamut distribution is generated.

8. The method for controlling the color gamut of a mobile phone screen according to claim 1, characterized in that: In the S5, a dynamic power consumption optimization model is defined to achieve power consumption optimization by simplifying the color and brightness of the non-attention area, wherein the degree of simplification is affected by the ambient light intensity I avg And the control of the user's gaze area size |R(x,y)| is expressed as: Among them, α(t) is the color and brightness simplification coefficient of the non-attention area, and its value range is [0,1]. The smaller the value, the higher the simplification degree. avg is the current average intensity of ambient light; I th is the threshold of ambient light intensity, representing the critical point where a significant simplification begins; κ is the adjustment coefficient, controlling the response speed to ambient light intensity; |R(x,y)| is the size of the user's gaze area, and |S| is the total area of ​​the screen; Based on the color and brightness simplification coefficient α(t) of the non-attention area, the color and brightness of the non-attention area are adjusted to reduce unnecessary calculation burden.

9. A method for controlling the color gamut of a mobile phone screen according to claim 8, characterized in that: The color and brightness of the non-attention area are adjusted as follows: Color simplification, expressed as: in, is the simplified color matrix of the non-attention area; C(t) is the global color gamut adjustment matrix; α(t) controls the degree of color simplification in the non-attention area. The smaller the value, the greater the simplification. Brightness adjustment, expressed as: in, is the brightness adjustment matrix of the non-fixated area; L(t) is the global brightness matrix; At the same time, the visual balance regularization term Ω(t) is designed to control the focus area R(x,y) and the non-focus area The transition between is expressed as: Among them, ζ is the strength adjustment parameter of the regularization term; L R (t) and C R (t) are the brightness matrix and color matrix of the fixation area, respectively; When Ω(t) is large, it means that the difference between the fixation area and the non-fixation area is too large, so α(t) is automatically reduced to reduce the degree of simplification; when Ω(t) is small, it means that the transition between the two is natural and the degree of simplification is maintained.

10. A system for controlling the color gamut of a mobile phone screen, characterized in that: The system comprises: The ambient light collection subsystem is used to collect the spectral characteristics of the ambient light and construct a spectral matrix; wherein the spectral characteristic matrix includes the average color temperature of the current environment Average light intensity of the current environment I avg and the total energy E of the current environment tot , expressed as A spectral matrix generation subsystem, used to construct a dynamic reference color model based on the spectral matrix to generate a global color gamut adjustment matrix, and dynamically adjust the screen color gamut according to the global color gamut adjustment matrix; The regional color gamut optimization subsystem is used to collect the user's eye movement data in real time, construct the gaze area, and combine the gaze area with the dynamic reference color model to perform regional color gamut optimization analysis; The global color gamut optimization subsystem is used to perform local compensation based on the results of the optimization analysis and in combination with the global color gamut to generate the final local color gamut matrix; The energy optimization subsystem is used to reduce energy consumption in non-fixated areas and reduce unnecessary color processing; The regional color gamut optimization subsystem specifically includes: First, use the eye tracking sensor to determine the coordinates of the user's gaze point on the screen, P(x, y). By calculating the neighboring area of ​​the point on the screen, determine the gaze area R(x, y) where the user is looking. Define the gaze area R(x, y) as a circular area with a radius of r and P(x, y) as the center, which represents the visual range where the user focuses his attention. The calculation formula for the gaze area R(x, y) is: R(x,y)={(x′,y′)|(x′-x) 2 +(y′-y) 2 ≤r 2 } Where r is the region radius, which is used to define the range of attention around the visual focus; According to the user's gaze area, a local color gamut analysis model A(R(x,y),C(t),B(S(t))) is generated in the area, and the difference between the current color performance of the current area and the reference color is analyzed, which is expressed as: Where ΔE is the average color difference in the local area, L R ,a R ,b R is the color component in the fixation area R(x,y); L B ,a B ,b B is the corresponding color component of the dynamic reference color model B(S(t)); According to the average color difference in the local area, a local color optimization judgment mechanism is designed; the local color optimization judgment mechanism is: When ΔE>∈, ∈ is the preset color difference threshold, triggering local color gamut optimization, otherwise the globally adjusted basic color is kept unchanged, and the optimization process is aimed at adjusting the color in the gaze area R(x, y), while the non-gaze area remains unchanged; The local color optimization feedback adjustment amount is ΔC loc (R(x,y)), superimposed with the global adjustment matrix C(t), updates the final color gamut of the area: C loc (t)=C(t)+ΔC loc (R(x,y)) Where, ΔC loc (R(x,y)) is the local optimization adjustment amount for the color of the gaze area, which is dynamically adjusted based on the analyzed color difference ΔE and the user's gaze point P(x,y).

Images