A method and system for controlling the color gamut of a mobile phone screen

Abstract

This invention proposes a method and system for controlling the color gamut of a mobile phone screen. The method includes: collecting the spectral characteristics of ambient light and constructing a spectral matrix; constructing a dynamic reference color model based on the spectral matrix to generate a global color gamut adjustment matrix; dynamically adjusting the screen color gamut according to the global color gamut adjustment matrix; collecting the user's eye movement data in real time, constructing a gaze region, and performing regional color gamut optimization analysis by combining the gaze region with the dynamic reference color model; performing local compensation based on the optimization analysis results and the global color gamut to generate a final local color gamut matrix; reducing energy consumption in non-gaze regions and minimizing unnecessary color processing. This invention, by combining ambient spectral detection and eye-tracking technology, provides an intelligent and dynamic color gamut adaptive adjustment system, solving the shortcomings of existing technologies in terms of ambient light adaptability, color accuracy, and energy efficiency, and has significant innovation and practical value.

Description

Technical Field

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

[0002] With the widespread adoption of smartphones and the rapid development of display technology, users have increasingly higher demands for the color performance of mobile phone screens. The color gamut control capability of a mobile phone screen determines its ability to accurately reproduce the color representation of images and videos, thus directly affecting the user's visual experience. Existing mobile phone screens, especially those based on OLED and LCD technologies, while achieving high color saturation and contrast, still face many technical bottlenecks in color gamut control.

[0003] Currently, common methods for controlling the color gamut of mobile phone screens mainly rely on global adjustments based on brightness and contrast. However, these technologies have significant shortcomings: First, dynamic changes in ambient light conditions are often ignored. When users use their phones in different lighting environments (such as strong natural light, indoor lighting, or low-light environments), the screen's color performance is difficult to maintain consistency, leading to color distortion or oversaturation, failing to accurately reflect the original content. Existing technologies, when dealing with complex changes in ambient light, typically only compensate by adjusting screen brightness and contrast, which cannot truly solve the color distortion problem.

[0004] Secondly, most current color gamut adjustment methods are based on a global, uniform adjustment, lacking precise optimization for the user's visual focus. In real-world usage scenarios, users often concentrate only on a specific area of ​​the screen, and existing color gamut control technologies for mobile phone screens cannot perform refined color processing on the area being focused on, resulting in an insufficiently optimized viewing experience. Furthermore, global color adjustment often comes with higher power consumption, especially when displaying high dynamic range (HDR) content, further increasing device power consumption and impacting battery life.

[0005] Furthermore, traditional screen color gamut adjustment lacks intelligence and personalization, failing to dynamically adjust based on user habits, scene changes, or content type. For example, when watching videos or browsing images, the system may fail to provide optimal color performance based on content characteristics, resulting in loss of color detail or a poor viewing experience in certain scenarios. These existing shortcomings severely limit the development of mobile display technology and have become a bottleneck in improving the user's visual experience.

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

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

[0008] To achieve the above objectives, 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. The average light intensity I in the current environment avg and the total energy E of the current environment tot , represented as

[0010] S2. 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;

[0011] S3. Real-time collection of user eye movement data, construction of fixation area, and combination of fixation area with dynamic reference color model for regional color gamut optimization analysis;

[0012] S4. Based on the results of the optimization analysis, local compensation is performed in conjunction with the global color gamut to generate the final local color gamut matrix.

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

[0014] Specifically, S3 includes:

[0015] First, eye-tracking sensors are used to determine the coordinates P(x,y) of the user's gaze point on the screen. Then, the gaze region R(x,y) is determined by calculating the neighborhood of this point on the screen. The gaze region R(x,y) is defined as a circular area with radius r centered at P(x,y), representing the visual range of the user's focused attention. The formula for calculating the gaze region R(x,y) is as follows:

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

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

[0018] Based on the user's gaze area, a local color gamut analysis model A(R(x,y),C(t),B(S(t))) is generated within that area. This model analyzes the difference between the current color representation and the reference color in the current region, and is represented as follows:

[0019]

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

[0021] Based on the average color difference within a local area, a local color optimization judgment mechanism is designed; the local color optimization judgment mechanism is as follows:

[0022] When ΔE>∈, ∈ is the preset color difference threshold, triggering local color gamut optimization; otherwise, the basic color of the global adjustment remains unchanged, and the optimization process is for color adjustment within the gaze region R(x,y), while the non-gaze region 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 this region:

[0024] C loc (t)=C(t)+ΔC loc (R(x,y))

[0025] Where, ΔC loc (R(x,y)) is the local optimization adjustment amount of the color in 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 ambient light spectral characteristics 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 feature matrix and preprocesses the ambient spectral feature matrix. The light includes color temperature, light intensity, and complete spectral distribution.

[0027] Furthermore, the preprocessing specifically includes: feature compression and dimensionality reduction, and time-series-based smoothing.

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

[0029]

[0030] Where T(t) is the preprocessed spectral matrix, The average color temperature of ambient light is calculated using the spectral matrix. avg It 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 spectral matrix;

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

[0032]

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

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

[0035]

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

[0037]

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

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

[0040] T′(t)=αT(t)+(1-α)T′(t-Δt)

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

[0042] Furthermore, S3 specifically includes:

[0043] Define a global color gamut adjustment matrix to represent the initial color gamut configuration of the screen under standard lighting conditions, and introduce a regularization term to construct a color gamut adjustment model; wherein, the global color gamut adjustment matrix is ​​expressed as:

[0044] C(t) = C0 + ΔC(t)

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

[0046] The color gamut adjustment model is represented as follows:

[0047]

[0048] in, Indicates the color temperature based on the environment. Adjust the color balance of the screen's color gamut; f2(I avg () indicates that the average light intensity I of the ambient light is used as a reference. avg Adjust the screen's contrast and brightness; f3(E) tot E represents the total energy of the spectrum. tot The effect on screen color saturation; λ||T′(t)-T′(t-Δt)|| represents the regularization term, used to smooth color changes over time; λ is a coefficient that controls the smoothing effect;

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

[0050] B(S(t))=βC(t)+(1-β)B(S(t-Δt))

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

[0052] Furthermore, based on the color temperature of the environment Adjusting the color balance of the screen's color gamut Design a nonlinear adaptive function to handle color adjustment under color temperature changes; wherein, the nonlinear adaptive function is expressed as:

[0053]

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

[0055] Furthermore, S4 specifically includes:

[0056] A local color gamut enhancement model is constructed, and the color gamut of the mobile phone screen is dynamically adjusted by comparing the color differences between the viewing area and the global area.

[0057] A local color compensation mechanism is designed based on the local color gamut enhancement model and 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] By combining the local color gamut enhancement model and the local color compensation mechanism, the final local color gamut matrix is ​​generated.

[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] Where, η s ,η c ,η l Control the enhancement intensity of color saturation, contrast, and brightness respectively; S adj (R(x,y)) adjusts the color gamut saturation C adj (R(x,y)) adjusts the contrast, enhancing the difference between dark and bright areas within a region, making details clearer; L adj (R(x,y)) adjusts the brightness to match the perceived visual brightness within the gaze area to the user's current focus; the regularization term ||C loc (t)-C(t)|| introduces a difference constraint between global and local colors to avoid excessive deviation of local enhancement from global color; λ is the regularization coefficient;

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

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

[0066] Where γ is the local compensation coefficient, used to adjust the intensity of compensation; ρ is the time-based penalty coefficient, used to control color abrupt changes caused by rapid switching of the user's gaze; 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 will adjust the local color enhancement by ΔC. enh (t,R(x,y)) and the compensated color adjustment amount ΔC comp (t,R(x,y)) are 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, which optimizes power consumption by simplifying the color and brightness of non-focused areas. The degree of simplification is affected by the ambient light intensity I. avg The control of the user's gaze region size |R(x,y)| is expressed as:

[0071]

[0072] Where α(t) is the color and brightness simplification coefficient for the non-fixed region, with a value ranging from [0,1], and the smaller the value, the higher the degree of simplification; I avg This is the current average ambient light intensity; I th κ is the threshold of ambient light intensity, representing the critical point at which 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-focal region, the color and brightness of the non-focal region are adjusted to reduce unnecessary computational burden.

[0074] Furthermore, the adjustment of color and brightness in non-focused areas specifically includes:

[0075] Simplified color representation:

[0076]

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

[0078] Brightness adjustment is represented as:

[0079]

[0080] in, L(t) is the brightness adjustment matrix for the non-focused region; L(t) is the global brightness matrix.

[0081] Simultaneously, a visual balance regularization term Ω(t) is designed to control the fixation area R(x,y) and the non-fixation area. The transition between them is represented as:

[0082]

[0083] Where ζ is the intensity adjustment parameter of the regularization term; L R (t) and C R (t) represent the brightness matrix and color matrix of the gaze region, respectively;

[0084] When Ω(t) is large, it indicates that the difference between the fixation area and the non-fixation area is too large, so α(t) is automatically reduced to decrease the simplification level; when Ω(t) is small, it indicates that the transition between the two is natural and the simplification level 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] An ambient light acquisition subsystem is used to acquire 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. The average light intensity I in the current environment avg and the total energy E of the current environment tot , represented as

[0087] The spectral matrix generation subsystem is used to construct a dynamic reference color model based on the spectral matrix to generate a global color gamut adjustment matrix, and to 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 users' eye movement data in real time, construct the gaze region, and combine the gaze region 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 optimization analysis and the global color gamut, and generate the final local color gamut matrix.

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

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

[0092] First, eye-tracking sensors are used to determine the coordinates P(x,y) of the user's gaze point on the screen. Then, the gaze region R(x,y) is determined by calculating the neighborhood of this point on the screen. The gaze region R(x,y) is defined as a circular area with radius r centered at P(x,y), representing the visual range of the user's focused attention. The formula for calculating the gaze region R(x,y) is as follows:

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

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

[0095] Based on the user's gaze area, a local color gamut analysis model A(R(x,y),C(t),B(S(t))) is generated within that area. This model analyzes the difference between the current color representation and the reference color in the current region, and is represented as follows:

[0096]

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

[0098] Based on the average color difference within a local area, a local color optimization judgment mechanism is designed; the local color optimization judgment mechanism is as follows:

[0099] When ΔE>∈, ∈ is the preset color difference threshold, triggering local color gamut optimization; otherwise, the basic color of the global adjustment remains unchanged, and the optimization process is for color adjustment within the gaze region R(x,y), while the non-gaze region 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 this region:

[0101] C loc (t)=C(t)+ΔC loc (R(x,y))

[0102] Where, ΔC loc(R(x,y)) is the local optimization adjustment amount of the color in 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, this invention introduces environmental spectral detection technology. By using a spectral sensor in the phone to detect the spectral composition of ambient light in real time, the system intelligently adjusts the screen's color gamut based on the spectral differences under different light source conditions (e.g., natural light, indoor light, low light). Unlike traditional brightness / contrast adjustment methods, this invention dynamically adjusts the saturation, brightness, and color balance of the color gamut based on ambient light spectral data, effectively solving the color distortion problem caused by changes in ambient light in existing technologies. Whether in strong sunlight or dim lighting, the screen can present accurate and natural color performance, enhancing the user's viewing experience.

[0105] Secondly, this invention utilizes eye-tracking technology, monitoring the user's gaze point in real time via a front-facing camera or a dedicated eye-tracking sensor, and performing local color gamut optimization on the focused area. Unlike traditional global color gamut adjustment, the system dynamically enhances the color saturation and contrast of the user's gaze area, ensuring the most delicate and realistic display effect in that area. Non-gaze areas undergo simplified color processing to reduce power consumption. This local optimization strategy not only significantly improves visual effects but also effectively reduces unnecessary power consumption, enhancing the device's battery life.

[0106] Furthermore, this invention organically combines ambient light detection and eye-tracking technology through a linkage control mechanism, enabling coordinated global and local color gamut adjustments. The system first adjusts the global color representation based on changes in the ambient light spectrum, and then performs fine-tuned local adjustments based on the user's gaze position, ensuring that the color in the user's visual focus area remains optimal under different ambient light conditions. Through this adaptive color gamut adjustment method that combines global and local adjustments, this invention not only solves the color distortion problem under complex lighting conditions but also achieves precise visual enhancement through optimization of the focus area, reducing energy consumption and improving device battery life.

[0107] In summary, this invention provides an intelligent and dynamic color gamut adaptive adjustment system by combining environmental spectral detection and eye-tracking technology. It overcomes the shortcomings of existing technologies in terms of environmental light adaptability, color accuracy, and energy efficiency, and has significant innovation and practical value. Attached Figure Description

[0108] The present invention is further described with reference to the accompanying drawings. However, 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 effort.

[0109] Figure 1 This 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 for mobile phone screen color gamut control according to an embodiment of the present invention. Detailed Implementation

[0111] Embodiments of the present invention are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0112] like Figure 1 As shown in the figure, 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, this invention uses a built-in spectral sensor in the mobile phone to collect ambient light in real time. The light collected by the spectral sensor includes color temperature, light intensity, and a complete spectral distribution. A time interval Δt (e.g., collecting data every 100 milliseconds) is set to represent the time point of each collection. The collected environmental spectral feature matrix S(t) is obtained, where t represents the current time. S(t) has the following form:

[0115] S(t)=[λ1,I(λ1),λ2,I(λ2),…,λ n ,I(λ n )]

[0116] Where, λ i I(λ) represents the wavelength of the spectrum. i) represents the light intensity at that wavelength; n is the resolution of the spectral sampling, which is usually a high value (e.g., n = 400) to ensure sufficient accuracy in describing the characteristics of ambient light; S(t) is an n×2 matrix representing the change of the ambient spectrum with time t.

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

[0118]

[0119] in, The average color temperature of ambient light is calculated using the spectral matrix, and the specific formula is as follows:

[0120]

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

[0122]

[0123] E tot It is the total energy of the spectrum, that is, the cumulative sum of light intensity 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 environmental spectrum is prone to noise signals due to potential external interference (such as occasional occlusion or sudden changes in light sources). To avoid noise interference in subsequent global color gamut adjustment, this invention performs time-series smoothing on the spectral feature T(t). A custom spectral smoothing filtering algorithm is used to process the feature vector. This algorithm not only considers the continuity of the time series but also incorporates historical spectral change trends to eliminate the influence of abrupt changes. Let T′(t) be the smoothed feature vector, and its update rule is as follows:

[0127] T′(t)=αT(t)+(1-α)T′(t-Δt)

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

[0129] S2. Based on the spectral matrix, construct a dynamic reference color model 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 C0 is defined, representing the initial color gamut configuration of the screen under standard lighting conditions. The change of the screen's global color over time t is determined by the adjusted color gamut matrix C(t):

[0131] C(t) = C0 + ΔC(t)

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

[0133] Furthermore, to better capture the impact of dynamic changes in ambient light on the color gamut, ΔC(t) not only considers the influence 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 adjustment but also reduces the problem of excessive screen brightness fluctuations.

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

[0135]

[0136] in, To adjust the color temperature according to the environment Adjust the color balance of the screen's color gamut, especially the red and blue hues. Higher color temperatures reduce red and enhance blue; f2(I avg (I) is based on the average light intensity of ambient light. avg Adjust the screen's contrast and brightness. The higher the light intensity, the greater the brightness should be, but the contrast should remain balanced; f3(E) tot To reflect the total spectral energy E tot Regarding the impact on screen color saturation, higher energy results in higher color saturation. λ||T′(t)-T′(t-Δt)|| is a regularization term used to smooth color changes over time, reducing color gamut fluctuations caused by drastic changes in lighting. λ is a coefficient controlling the smoothing effect, typically set between 0.5 and 1 to ensure the screen does not flicker frequently when ambient light changes rapidly.

[0137] Furthermore, the present invention herein... A detailed design was conducted. Traditional color gamut adjustment models merely linearly map color temperature and color balance. However, in the scenario described in this patent, the ambient color temperature may change rapidly with indoor and outdoor light sources, thus requiring an adaptive mechanism. This invention proposes a nonlinear adaptive function to handle color adjustment under color temperature variations:

[0138]

[0139] Among them, T c This is the current ambient color temperature; T ref γ is a reference value for color temperature (e.g., standard white light color temperature 5000K); k is the intensity coefficient for color adjustment, and γ is the adjustment rate factor. This function, through non-linear mapping, can more sensitively adapt to changes in color temperature under different light sources and make screen color adjustment smoother.

[0140] Through this design, the present invention can not only handle the linear changes commonly found in traditional color gamut adjustment, but also address the challenges of color balance under complex lighting conditions. This function is particularly suitable for the rapid response of mobile devices in scenarios involving switching between outdoor natural light and indoor lighting.

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

[0142] B(S(t))=βC(t)+(1-β)B(S(t-Δt))

[0143] Wherein, B(S(t)) is the dynamic reference color model at the current moment, reflecting the cumulative influence of historical spectral features; β is a smoothing coefficient (usually between 0.8 and 0.9), 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 processing by the aforementioned global color gamut adjustment model and dynamic baseline model, the output global color gamut matrix C(t) will be applied to the global color management of the mobile phone screen, ensuring that the screen can adaptively adjust under different ambient light conditions. Simultaneously, the generated baseline color model B(S(t)) serves as the benchmark for local color gamut optimization in subsequent steps, ensuring that color optimization in the user's viewing area can further enhance the visual effect based on global adjustment.

[0145] S3. Real-time acquisition of user eye movement data, construction of fixation area, and combination of fixation area with dynamic reference color model for regional color gamut optimization analysis.

[0146] Specifically, first, eye-tracking sensors are used to determine the coordinates P(x,y) of the user's gaze point on the screen. The user's gaze region R(x,y) is then determined by calculating the neighborhood of that point on the screen. Here, R(x,y) is defined as a circular region centered at P(x,y) with radius r, representing the visual range of the user's focused attention. The formula for calculating R(x,y) is:

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

[0148] Here, r is the region radius, used to define the area of ​​attention around the visual focus. This gaze area will serve as the target region for subsequent local color gamut optimization.

[0149] Furthermore, to optimize the color performance of the gaze region R(x,y), it is necessary to combine the global color gamut matrix C(t) generated in step 2 with the dynamic reference color model B(S(t)). Based on the user's gaze region, a local color gamut analysis model A(R(x,y),C(t),B(S(t))) is generated within that region to analyze the difference between the current color performance and the reference color. Specifically, the analysis method involves calculating the average color difference ΔE within the local region to determine whether color adjustment is needed. The formula for ΔE is as follows:

[0150]

[0151] Among them, L R ,a R ,b R It refers to the color components within the gaze region R(x,y) (based on the brightness and chromaticity information of the CIELAB color space); L B ,a B ,b B It is the corresponding color component of the baseline color model B(S(t)).

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

[0153] Furthermore, based on the color difference ΔE analyzed above, this invention designs a local color optimization judgment mechanism. When ΔE > 0, local color gamut optimization is triggered; otherwise, the basic color of the global adjustment remains unchanged. The optimization process mainly targets color adjustment within the gaze region R(x,y), while the non-gaze region 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 this region:

[0155] C loc (t)=C(t)+ΔC loc (R(x,y))

[0156] Where, ΔC loc (R(x,y)) is the local optimization adjustment amount of the color in 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 the unnecessary burden of global color updates and concentrate the main computing resources on the area currently being gazed at by the user, so as to ensure the optimal color performance of the visual focus.

[0157] Understandably, this step outputs the optimized local color gamut adjustment matrix C. loc (t) represents the final color gamut distribution after user visual focus analysis and optimization. Simultaneously, 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 interactions, ensuring that the screen's color effect at the user's visual focus point remains optimal.

[0158] S4. Based on the results of the optimization analysis, local compensation is performed in conjunction with the global color gamut to generate the 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 region 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, thus avoiding unnatural visual effects. The role of this step in the overall patent solution is to ensure that the user's visual focus area has the highest quality color performance while maintaining a smooth transition with the global color.

[0160] Furthermore, to improve color performance in the user's gaze area, this invention designs a local color gamut enhancement model. This model not only adjusts the color gamut but also introduces an enhancement term, dynamically adjusting by comparing the color differences 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] Where, η s ,η c ,η l These parameters control the enhancement intensity of color saturation, contrast, and brightness, respectively. Based on the color characteristics R(x,y) of the user's gaze area, these three are the main adjustment factors for local color enhancement; S adj (R(x,y)) adjusts the color gamut saturation, optimizing the color richness within the viewing area and improving color vividness; C adj (R(x,y)) adjusts the contrast, enhancing the difference between dark and bright areas within a region, making details clearer; L adj (R(x,y)) adjusts the brightness to match the perceived visual brightness within the gaze area to the user's current focus; the regularization term |C loc The expression (t)-C(t)|| introduces a constraint on the difference between global and local colors to prevent local enhancements from deviating excessively from the global color. λ is a regularization coefficient, typically set between 0.5 and 0.8, to ensure that local enhancements do not affect the overall visual harmony.

[0163] Furthermore, the design purpose of this enhancement model is to improve the user experience by dynamically adjusting the gaze region when the color characteristics of the gaze region differ significantly from the global color features, while ensuring a smooth transition of the enhancement through regularization.

[0164] Furthermore, to ensure that local colors do not deviate excessively from the global color representation after color gamut enhancement, this invention designs a local color compensation mechanism based on a dynamic baseline model B(S(t)). This compensation mechanism ensures that the color difference between the user's gaze area and 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)||It

[0166] In this context, γ is the local compensation coefficient, used to adjust the intensity of compensation; ρ is a time-based penalty term coefficient, used to control color abrupt changes caused by rapid switching of the user's gaze. This term is calculated by comparing the current gaze region R(x,y) with the previous gaze region R... prev The positional difference at (x, y) will suppress excessively rapid color compensation and prevent frequent screen color flickering if the user's gaze frequently shifts. C loc (t) is the local color gamut adjustment matrix; B(S(t)) is the global baseline color model, ensuring that the local color does not differ too much from the global color.

[0167] Understandably, the key to this formula design lies in introducing an innovative time-dimensional regularization term. By controlling the rate of change in the user's gaze area, it avoids overreaction from the color compensation mechanism. This design is particularly suitable for scenarios with rapid scene transitions, such as when mobile users quickly browse pages, allowing for a smooth transition of screen colors.

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

[0169]

[0170] This formula adjusts the localized color enhancement amount ΔC. enh (t,R(x,y)) and the compensated color adjustment amount ΔC comp (t,R(x,y)) are 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 colors in the user's gaze area, after enhancement, not only maintain vividness and visual impact, but also naturally blend with the global color through a compensation mechanism, avoiding 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 processing, ensuring optimal visual effects in the user's visual focus area. Simultaneously, the color transition between local and global areas is smoothed, preventing unnecessary visual discrepancies and unnatural color changes. This output serves as the basis for subsequent steps and also provides a reference for the next step of power consumption optimization in non-gaze areas, ensuring that the screen maintains power consumption control while optimizing color.

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

[0173] Specifically, to ensure reduced computational load and lower energy consumption in non-focused regions, this invention proposes a dynamic power consumption optimization model. This model optimizes power consumption by simplifying the color and brightness of non-focused regions. The degree of simplification is influenced by the ambient light intensity I. avg And control over 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 color and brightness simplification in the non-focused area, based on the size of the user's focused area and the current ambient light intensity. The formula is as follows:

[0175]

[0176] Where α(t) is the color and brightness simplification coefficient for the non-fixed region, with a value ranging from [0,1], and the smaller the value, the higher the degree of simplification; I avg It is the current average ambient light intensity (obtained from the spectral detection in step 1); I th κ is the threshold of ambient light intensity, representing the critical point at which 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] Furthermore, the mechanism is simplified: when the ambient light is bright (I avg >I th Furthermore, the viewing area is smaller, and the color and brightness of the non-viewing area are significantly simplified, resulting in a substantial reduction in power consumption. When the ambient light is dim or the viewing area is large, the simplification is relatively reduced to ensure the overall visual consistency of the screen.

[0178] Furthermore, based on α(t), this invention adjusts the color and brightness of the non-focused region to reduce unnecessary computational burden. This adjustment process is gradual and dynamically adjusted according to ambient light conditions and the size of the focused region. Simplified color formula:

[0179]

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

[0181] Brightness adjustment formula:

[0182]

[0183] in, L(t) is the brightness adjustment matrix for the non-focused region; L(t) is the global brightness matrix.

[0184] This adjustment method dynamically simplifies the color and brightness of non-focused areas, ensuring reduced power consumption without affecting the user's overall visual perception.

[0185] Furthermore, to prevent overly obvious visual differences between the user's gaze area and non-gaze area, this invention introduces a visual balance regularization term to control the relationship between the gaze area R(x,y) and the non-gaze area. The transition between them. Define the visual balance regularization term Ω(t):

[0186]

[0187] Where ζ is the intensity adjustment parameter of the regularization term; L R (t) and C R (t) represents the brightness matrix and color matrix of the gaze region, respectively; this regularization term controls the smoothness of the transition between the gaze region and the non-gaze region, avoiding visual abruptness caused by oversimplification of the non-gaze region.

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

[0189] Furthermore, by introducing Ω(t), we ensure that the visual experience remains consistent while optimizing energy consumption. Especially when ambient light changes drastically or the user's gaze shifts rapidly, this regularization term effectively smooths the transition and prevents visual breaks.

[0190] Understandably, after the above processing, the final color matrix of the non-focused region is output. and brightness matrix These represent simplified color and brightness information after dynamic power optimization. Simultaneously, a regularization term Ω(t) is fed back to the system to dynamically adjust the simplification strategy, ensuring overall visual balance of the screen. Through these optimizations, the entire screen significantly reduces power consumption while maintaining a good visual experience, especially in non-focused areas, further extending the device's battery life.

[0191] The ultimate effect of this step is to reduce the burden on the processor and display by simplifying the color and brightness of non-focused areas, while maintaining high-quality color and brightness performance in the user-focused areas. The entire patented solution thus achieves a balance between visual appeal and energy efficiency optimization.

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

[0193] An ambient light acquisition subsystem 501 is used to acquire 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. The average light intensity I in the current environment avg and the total energy E of the current environment tot , represented as

[0194] The spectral matrix generation subsystem 502 is used to construct a dynamic reference color model based on the spectral matrix to generate a global color gamut adjustment matrix, and to 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 users' eye movement data in real time, construct the gaze region, and combine the gaze region with the dynamic reference color model to perform regional color gamut optimization analysis.

[0196] The global color gamut optimization subsystem 504 is used to perform local compensation based on the results of optimization analysis and the global color gamut, and generate the final local color gamut matrix.

[0197] The energy consumption optimization subsystem 505 is used to reduce energy consumption in non-focused areas and reduce unnecessary color processing.

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

[0199] First, eye-tracking sensors are used to determine the coordinates P(x,y) of the user's gaze point on the screen. Then, the gaze region R(x,y) is determined by calculating the neighborhood of this point on the screen. The gaze region R(x,y) is defined as a circular area with radius r centered at P(x,y), representing the visual range of the user's focused attention. The formula for calculating the gaze region R(x,y) is as follows:

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

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

[0202] Based on the user's gaze area, a local color gamut analysis model A(R(x,y),C(t),B(S(t))) is generated within that area. This model analyzes the difference between the current color representation and the reference color in the current region, and is represented as follows:

[0203]

[0204] Where ΔE is the average color difference within a local area, and L R ,aR ,b R It is the color component within the gaze region R(x,y); L B ,a B ,b B It is the corresponding color component of the dynamic reference color model B(S(t));

[0205] Based on the average color difference within a local area, a local color optimization judgment mechanism is designed; the local color optimization judgment mechanism is as follows:

[0206] When ΔE>∈, ∈ is the preset color difference threshold, triggering local color gamut optimization; otherwise, the basic color of the global adjustment remains unchanged, and the optimization process is for color adjustment within the gaze region R(x,y), while the non-gaze region 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 this region:

[0208] C loc (t)=C(t)+ΔC loc (R(x,y))

[0209] Where, ΔC loc (R(x,y)) is the local optimization adjustment amount of the color in 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 this invention. In practical applications, those skilled in the art can select some or all of the workflow to achieve the purpose of this embodiment according to actual needs, and no restrictions are imposed here.

[0211] In addition, for technical details not described in detail in this embodiment, please refer to the parameter operation method provided in any embodiment of the present invention, which will not be repeated here.

[0212] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or system. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or system that includes that element.

[0213] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

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

[0215] The above are merely preferred embodiments of the present invention and do not limit the patent scope of the present invention. Any equivalent structural or procedural transformations made based on the content of the present invention's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within 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 includes: S1. Collect the spectral characteristics of ambient light and construct a spectral matrix; wherein, the spectral matrix includes the average color temperature of the current environment. The average light intensity I in the current environment avg and the total energy E of the current environment tot , represented as S2. Based on the spectral matrix, construct a dynamic reference color model to generate a global color gamut adjustment matrix, and dynamically adjust the screen color gamut according to the global color gamut adjustment matrix; S3. Real-time collection of user eye movement data, construction of fixation area, and combination of fixation area with dynamic reference color model for regional color gamut optimization analysis. S4. Based on the results of the optimization analysis, local compensation is performed in conjunction with the global color gamut to generate the final local color gamut matrix; S5. Reduce energy consumption in non-focused areas and reduce unnecessary color processing; Specifically, S3 includes: First, eye-tracking sensors are used to determine the coordinates P(x,y) of the user's gaze point on the screen. Then, the gaze region R(x,y) is determined by calculating the neighborhood of this point on the screen. The gaze region R(x,y) is defined as a circular area with radius r centered at P(x,y), representing the visual range of the user's focused attention. The formula for calculating the gaze region R(x,y) is as follows: R(x,y)={(x′,y′)|(x′-x) 2 +(y′-y) 2 ≤r 2 } Where r is the region radius, used to define the area of ​​attention around the visual focus; Based on the user's gaze area, a local color gamut analysis model A(R(x,y),C(t),B(S(t))) is generated within that area. This model analyzes the difference between the current color representation and the reference color in the current region, and is represented as follows: Where ΔE is the average color difference within a local area, and L R ,a R ,b R It is the color component within the gaze region R(x,y); L B ,a B ,b B It is the corresponding color component of the dynamic reference color model B(S(t)); Based on the average color difference within a local area, a local color optimization judgment mechanism is designed; the local color optimization judgment mechanism is as follows: When ΔE>∈, ∈ is the preset color difference threshold, triggering local color gamut optimization; otherwise, the basic color of the global adjustment remains unchanged, and the optimization process is for color adjustment within the gaze region R(x,y), while the non-gaze region 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 this region: C loc (t)=C(t)+ΔC loc (R(x,y)) Where, ΔC loc (R(x,y)) is the local optimization adjustment amount of the color in the gaze area, which is dynamically adjusted based on the analyzed color difference ΔE and the user's gaze point P(x,y); In step S5, a dynamic power consumption optimization model is defined, which optimizes power consumption by simplifying the color and brightness of non-focused areas. The degree of simplification is influenced by the ambient light intensity I. avg The control of the user's gaze region size |R(x,y)| is expressed as: Where α(t) is the color and brightness simplification coefficient for the non-fixed region, with a value ranging from [0,1], and the smaller the value, the higher the degree of simplification; I avg This is the current average ambient light intensity; I th κ is the threshold of ambient light intensity, representing the critical point at which 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-focal region, the color and brightness of the non-focal region are adjusted to reduce unnecessary computational burden; The adjustment of color and brightness in non-focused areas specifically involves: Simplified color representation: in, C(t) is the simplified non-focused region color matrix; C(t) is the global color gamut adjustment matrix; α(t) controls the degree of color simplification in the non-focused region, and the smaller the value, the greater the simplification. Brightness adjustment is represented as: in, L(t) is the brightness adjustment matrix for the non-focused region; L(t) is the global brightness matrix. Simultaneously, a visual balance regularization term Ω(t) is designed to control the fixation area R(x,y) and the non-fixation area. The transition between them is represented as: Where ζ is the intensity adjustment parameter of the regularization term; L R (t) and C R (t) represent the brightness matrix and color matrix of the gaze region, respectively; When Ω(t) is large, it indicates that the difference between the fixation area and the non-fixation area is too large, so α(t) is automatically reduced to decrease the simplification level; when Ω(t) is small, it indicates that the transition between the two is natural and the simplification level is maintained.

2. The method for controlling the color gamut of a mobile phone screen according to claim 1, characterized in that, The ambient light spectral characteristics are collected in real time by the built-in spectral sensor of the mobile phone. The spectral sensor collects light at preset time intervals to construct an ambient spectral feature matrix and preprocesses the ambient spectral feature matrix. The light includes color temperature, light intensity and complete spectral distribution.

3. The 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 dimensionality reduction, and time-series-based smoothing. The feature compression and dimensionality reduction are as follows: Where T(t) is the preprocessed spectral matrix, The average color temperature of ambient light is calculated using the spectral matrix. avg It 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 spectral matrix; in, The average color temperature of ambient light, calculated from the spectral matrix, is expressed as: Where n is the resolution of spectral sampling, i is the spectrum i, and λ i I(λ) represents the wavelength of the spectrum. i () indicates the light intensity at that wavelength; I avg It is the average light intensity of the spectrum, expressed as: E tot It is the total energy of the spectrum, that is, the cumulative sum of light intensity at all wavelengths, expressed as: The smoothing process based on the time series is as follows: Let T′(t) be the smoothed feature vector, and its update rule is: T′(t)=αT(t)+(1-α)T′(t-Δt) Where α is the smoothing coefficient; T′(t) is the smoothed feature vector at the current time; T(t) is the original feature vector at the current time; 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, S3 specifically includes: Define a global color gamut adjustment matrix to represent the initial color gamut configuration of the screen under standard lighting conditions, and introduce a regularization term to construct a color gamut adjustment model; wherein, the global color gamut adjustment matrix is ​​expressed as: C(t) = C0 + ΔC(t) Where C(t) is the global adjustment matrix, and ΔC(t) is the color gamut adjustment amount calculated based on the ambient light feature T′(t); The color gamut adjustment model is represented as follows: in, Indicates the color temperature based on the environment. Adjust the color balance of the screen's color gamut; f2(I avg () indicates that the average light intensity I of the ambient light is used as a reference. avg Adjust the screen's contrast and brightness; f3(E) tot E represents the total energy of the spectrum. tot The effect on screen color saturation; λ||T′(t)-T'(t-Δt)|| represents the regularization term, used to smooth color changes over time; λ is a coefficient that controls the smoothing effect; Based on the current global color gamut matrix C(t) and past color gamut adjustment results, a dynamic baseline color model B(S(t)) is designed through smooth integration of historical information, expressed as: B(S(t))=βC(t)+(1-β)B(S(t-Δt)) Where B(S(t)) is the dynamic reference color model at the current moment, reflecting the cumulative influence of historical spectral features, C(t) is the current global color gamut matrix, and β is the smoothing coefficient, used to balance the influence of the current spectrum on the color gamut and the adjustment of the historical color gamut.

5. The 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 Adjusting the color balance of the screen's color gamut Design a nonlinear adaptive function to handle color adjustment under color temperature changes; wherein, the nonlinear adaptive function is expressed as: in, This is the current ambient color temperature; T ref γ is the reference value for color temperature; k is the intensity coefficient for color adjustment, and γ is the adjustment rate factor.

6. The method for controlling the color gamut of a mobile phone screen according to claim 1, characterized in that, S4 specifically includes: A local color gamut enhancement model is constructed, and the color gamut of the mobile phone screen is dynamically adjusted by comparing the color differences between the viewing area and the global area. A local color compensation mechanism is designed based on the local color gamut enhancement model and 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. By combining the local color gamut enhancement model and the local color compensation mechanism, the final local color gamut matrix is ​​generated.

7. The 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 represented as follows: ΔC en h(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)|| Where, η s ,η c ,η l Control the enhancement intensity of color saturation, contrast, and brightness respectively; S adj (R(x,y)) adjusts the color gamut saturation; C adj (R(x,y)) adjusts the contrast, enhancing the difference between dark and bright areas within a region, making details clearer; L adj (R(x,y)) adjusts the brightness to match the perceived visual brightness within the gaze area to the user's current focus; the regularization term ||C loc (t)-C(t)||| introduces a difference constraint between global and local colors to avoid excessive deviation of local enhancement from global color; λ is the regularization coefficient. The local color compensation mechanism is expressed as follows: ΔC comp (t,R(x,y))=γ·(C loc (t)-B(S(t)))-ρ·||R(x,y)-R prev (x,y)| Where γ is the local compensation coefficient, used to adjust the intensity of compensation; ρ is the time-based penalty coefficient, used to control color abrupt changes caused by rapid switching of the user's gaze; 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 will adjust the local color enhancement by ΔC. en h(t,R(x,y)) and the compensated color adjustment amount ΔC comp (t,R(x,y)) are merged into the local color gamut matrix C. loc In (t), the final optimized local color gamut distribution is generated.

8. A system for implementing the mobile phone screen color gamut control method according to claim 1, characterized in that, The system includes: An ambient light acquisition subsystem is used to acquire the spectral characteristics of ambient light and construct a spectral matrix; wherein, the spectral matrix includes the average color temperature of the current environment. The average light intensity I in the current environment avg and the total energy E of the current environment tot , represented as The spectral matrix generation subsystem is used to construct a dynamic reference color model based on the spectral matrix to generate a global color gamut adjustment matrix, and to dynamically adjust the screen color gamut according to the global color gamut adjustment matrix. The regional color gamut optimization subsystem is used to collect users' eye movement data in real time, construct the gaze region, and combine the gaze region 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 optimization analysis and the global color gamut, and generate the final local color gamut matrix. The energy consumption optimization subsystem is used to reduce energy consumption in non-focused areas and reduce unnecessary color processing. The regional color gamut optimization subsystem specifically includes the following functions: First, eye-tracking sensors are used to determine the coordinates P(x,y) of the user's gaze point on the screen. Then, the gaze region R(x,y) is determined by calculating the neighborhood of this point on the screen. The gaze region R(x,y) is defined as a circular area with radius r centered at P(x,y), representing the visual range of the user's focused attention. The formula for calculating the gaze region R(x,y) is as follows: R(x,y)={(x′,y′)|(x′-x) 2 +(y′-y) 2 ≤r 2 } Where r is the region radius, used to define the area of ​​attention around the visual focus; Based on the user's gaze area, a local color gamut analysis model A(R(x,y),C(t),B(S(t))) is generated within that area. This model analyzes the difference between the current color representation and the reference color in the current region, and is represented as follows: Where ΔE is the average color difference within a local area, and L R ,a R ,b R It is the color component within the gaze region R(x,y); L B ,a B ,b B It is the corresponding color component of the dynamic reference color model B(S(t)); Based on the average color difference within a local area, a local color optimization judgment mechanism is designed; the local color optimization judgment mechanism is as follows: When ΔE>∈, ∈ is the preset color difference threshold, triggering local color gamut optimization; otherwise, the basic color of the global adjustment remains unchanged, and the optimization process is for color adjustment within the gaze region R(x,y), while the non-gaze region 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 this region: C loc (t)=C(t)+ΔC loc (R(x,y)) Where, ΔC loc (R(x,y)) is the local optimization adjustment amount of the color in the gaze area, which is dynamically adjusted based on the analyzed color difference ΔE and the user's gaze point P(x,y).

Images