A color perception based numerical color mapping design method

Abstract

The application discloses a kind of numerical color mapping design methods based on color perception, it is related to data visualization color mapping design technical field, step 1, color difference-perception time consumption relationship curve is obtained by construction;Step 2, the starting color and end color of color mapping are uniformly converted to Lab color space, and color mapping construction is carried out in the space;Step 3, the relationship curve obtained in step 1 is used to calculate the perception time consumption of user in using the mapping relationship generated in step 2 and carries out rationality inspection.The numerical color mapping design method based on color perception, can flexibly adjust mapping track according to actual demand and design constraint, guarantee the consistency of actual data and color perception in data visualization, avoid the problem that the perception jump in traditional color mapping and other scientific mapping system application range is limited, simultaneously introduce the perception relationship curve of color difference-perception speed for the first time, allow designer to quickly evaluate the perception time consumption under a certain specific design, can assist in inspecting the rationality of mapping design.

Description

Technical Field

[0001] This invention relates to the field of data visualization color mapping design technology, specifically a numerical color mapping design method based on color perception. Background Technology

[0002] Color mapping, as a key means of mapping numerical information to visual color information, is widely used in scientific visualization, information display, human-computer interaction interfaces, and complex system monitoring. Jet mapping is a classic pseudo-color mapping that is widely used in scientific visualization and signal processing. Due to the limitations of early display devices, scientific visualization aimed to present the maximum range of brightness and hue changes in a single color image. Therefore, Jet mapping was designed as a high-saturation, high-contrast color mapping that could still distinguish major areas in black and white printing. However, this resulted in the maximum and minimum values ​​of color brightness in Jet mapping not being synchronized with its mapping data, and the perceived brightness changes across hues could easily lead to false numerical changes.

[0003] In existing technologies, researchers and engineers have proposed various perceptually optimized color mapping schemes, such as continuous color mappings like Viridis, Plasma, and Inferno. These mappings typically focus on perceptual consistency and uniformity as core design goals, performing well in terms of brightness monotonicity, color difference continuity, and color perception friendliness. Consequently, they are widely used in general visualization scenarios. However, in specific engineering applications and customized interface design, general color mapping schemes with perceptual uniformity often fail to meet actual design requirements. For example, some information displays require layer overlay on a map, so color mapping design needs to deliberately avoid map colors to prevent users from confusing layer information with the map. On the other hand, some colors have clear semantic references or operational meanings in specific application contexts, such as red indicating danger and green indicating safety. Directly using general color mappings that include these color segments may cause semantic conflicts, leading to user misjudgment and increasing cognitive burden.

[0004] Existing color maps mostly exist in the form of fixed color paths or predefined color levels. Their generation process usually does not consider semantic constraints, overlay constraints, or overall system color matching rules in subsequent applications. When design requirements change, specific color segments can often only be avoided by manually trimming, adjusting, or empirically modifying the existing map. This approach not only relies on the designer's experience but also makes it difficult to systematically guarantee the quality of the adjusted map in terms of perceptual continuity, brightness monotonicity, and overall consistency. It is also not conducive to reuse and expansion in different projects or under different constraints. Summary of the Invention

[0005] The purpose of this invention is to provide a numerical color mapping design method based on color perception, in order to solve the problems found in the background art that existing numerical color mapping design methods based on color perception mostly use fixed color paths, rely on manual experience for adjustment, have difficulty in ensuring perceptual continuity, brightness monotonicity, and recognition efficiency, and lack quantitative verification methods.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a numerical color mapping design method based on color perception, comprising the following steps:

[0007] Step 1: First, collect data using the step method experimental paradigm, with color difference as the independent variable and the user's perception time of color as the dependent variable, capture the quantitative correspondence between color difference and perception speed, and construct the color difference-perception time relationship curve to provide a standard basis for subsequent perception rationality verification.

[0008] Step 2: Construct a color map by converting the starting and ending colors of the color map to the Lab color space. Perform interpolation calculations on the values ​​and colors in the Lab color space and control the continuity and consistency of the color map in visual perception through interpolation rules to obtain the initial value-color correspondence mapping relationship, providing an object to be tested for subsequent rationality verification.

[0009] Step 3: Finally, the initial mapping relationship generated in Step 2 is tested for rationality. The minimum perceptible numerical difference is determined according to the actual application requirements. The color difference corresponding to the minimum perceptible numerical difference is calculated through the mapping relationship. The color difference is then substituted into the color difference-perception time relationship curve obtained in Step 1 to obtain the corresponding perception time. Whether the perception time meets the application requirements is used to test whether the current mapping is reasonable.

[0010] Step 4: If the test fails, adjust the color endpoints or intermediate control points and re-interpolate until the perception efficiency requirements are met.

[0011] In this embodiment, the adaptive ladder method in step 1 is a 3-down-1-up control rule: if three consecutive correct judgments are made, the stimulus presentation time is shortened; if one incorrect judgment is made, the presentation time is extended, so that the presentation time converges to near the user's discrimination threshold.

[0012] In this embodiment, step 1 includes:

[0013] 1.1 Establish a standardized experimental procedure that includes fixation point, pre-masking, stimulus presentation, post-masking, and decision input. This procedure design can eliminate visual afterimages and interference and improve the reliability of experimental results.

[0014] 1.2. Divide the experimental blocks according to the color difference level, randomize the block order, symmetricize the judgment target, and set a forced rest to avoid the sequence effect, button preference and fatigue interference;

[0015] 1.3. Take the average of the presentation durations of a fixed number of trials at the end of the stable period of each block as the stable discrimination duration under that color difference.

[0016] In this embodiment, step 2 includes:

[0017] 2.1 Convert the color mapping start and end points to CIE-Lab coordinates, and uniformly adopt the D65 standard light source conversion rules to ensure color space consistency;

[0018] 2.2. Discretize the range of values ​​to be visualized into multiple numerical nodes at equal intervals and establish a one-to-one correspondence between numerical values ​​and color points.

[0019] In this embodiment, decoupling interpolation is performed on the three Lab channels in step 2:

[0020] 2.3 The L brightness channel performs strict linear interpolation to ensure that the brightness changes monotonically and smoothly with the value, avoiding false value boundary perception;

[0021] 2.4. The ab chroma channel introduces a custom mapping trajectory with intermediate control points, and maintains equal Euclidean distance between adjacent chroma points to ensure uniform perceptual differences.

[0022] In this embodiment, the minimum perceptible numerical difference in step 3 is the minimum effective resolution preset for the application scenario, which is a fixed input condition for color mapping verification.

[0023] In this embodiment, step 3 includes:

[0024] 3.1 Calculate the CIE-Lab color difference corresponding to the minimum perceptible numerical difference based on the color mapping;

[0025] 3.2 Substitute the color difference into the perception relationship curve to obtain the predicted perception time required for the user to complete the identification;

[0026] 3.3 Compare the predicted perception time with the maximum perception time threshold allowed by the application scenario. If it is lower than the threshold, it is considered qualified.

[0027] In this embodiment, the adjustment method of step 4 includes: changing the starting point and ending point colors of the mapping, adding or deleting intermediate control points, modifying the chromaticity coordinates of the control points, and regenerating a color mapping that meets the perception efficiency and design constraints until the perception efficiency requirements are met.

[0028] In this embodiment, the method simultaneously satisfies the engineering visualization constraints of uniform perception, monotonous brightness, color-friendly design, customizable trajectory, semantic compatibility, and interference-free layer overlay, making it suitable for scientific charts, monitoring interfaces, and map overlay scenarios.

[0029] Compared with the prior art, the beneficial effects of the present invention are:

[0030] This numerical color mapping design method based on color perception flexibly adjusts the mapping trajectory according to actual needs and design constraints, ensuring the consistency between actual data and color perception in data visualization. It avoids the perceptual jumps in traditional color mapping and the limited applicability of other scientific mapping systems. At the same time, it introduces the perceptual relationship curve of color difference-perceptual speed, allowing designers to quickly evaluate the perceptual time consumption under a specific design, which can help verify the rationality of the mapping design.

[0031] 1. Furthermore, this invention is the first to deeply couple adaptive step method psychophysical experiments with color mapping design. Through standardized experimental procedures, it accurately captures the quantitative relationship between color difference and perception time, creatively transforming subjective visual perception into calculable and verifiable objective indicators. This completely changes the shortcomings of traditional color mapping, which relies solely on empirical qualitative adjustments and cannot quantify perception efficiency. It makes the design results reproducible, predictable, and verifiable, providing a scientific quantitative basis for visual color design and breaking through the limitations of traditional color maps that only perform qualitative optimization.

[0032] 2. Furthermore, this invention creatively adopts a decoupled design of linear monotonic interpolation of brightness and equidistant segmented interpolation of chromaticity. In the L channel, it ensures that the brightness changes smoothly with the value, eliminating false numerical boundaries and perceptual jumps from the root. In the ab channels, it supports the free intervention of intermediate control points, which not only strictly ensures the uniformity and consistency of visual perception, but also flexibly avoids map background color, semantic conflict color, and layer interference color. It solves the industry pain point that general color maps cannot adapt to customized engineering constraints and the perception quality is out of control after manual adjustment. It achieves the unity of universality and customization, and constructs a decoupled interpolation architecture based on CIE-Lab space to achieve both perceptual uniformity and custom trajectory.

[0033] 3. Furthermore, this invention overcomes the shortcomings of existing technologies that only design without verification, and creatively establishes a perception rationality verification mechanism driven by minimum perceptible difference. It directly transforms the accuracy requirements of application scenarios into color difference thresholds and perception time thresholds, forming a complete closed-loop design process. Designers can quickly iterate and adjust endpoints and control points based on the verification results without repeated trial and error, which greatly improves the design efficiency and engineering reliability of color mapping in complex visualization systems, such as monitoring interfaces, map overlays, and professional instruments. It constructs a closed-loop system of design-calculation-verification-optimization, filling the technical gap in the verification of visualized color maps.

[0034] 4. Furthermore, the method of the present invention simultaneously satisfies multiple engineering constraints such as monotonous brightness, uniform perception, color-friendly design, semantic compatibility, and interference-free layers, significantly reducing the cognitive burden on users and avoiding misreading and misjudgment. In professional scenarios such as scientific visualization, complex system monitoring, marine / geographic information display, and industrial data panels, it greatly improves the efficiency of visual transmission of data information and the reliability of decision-making. Its highly robust design, which strongly adapts to constraints in multiple scenarios, comprehensively improves the accuracy and efficiency of visual information transmission. Attached Figure Description

[0035] Figure 1 This is a flowchart illustrating the operation of the method of the present invention;

[0036] Figure 2 A schematic diagram of the experimental process for the method of this invention;

[0037] Figure 3 This is a graph showing the relationship between color difference and perception time and the corresponding fitting curve in an example of this invention;

[0038] Figure 4 The present invention provides three color mappings designed using different methods: (a) is a mapping designed using traditional RGB color interpolation, (b) is a mapping designed using Lab color space interpolation, and (c) is a mapping designed in Lab space with control points added. Detailed Implementation

[0039] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0040] This invention provides a numerical color mapping design method based on color perception. Its core lies in collecting data using a stepwise experimental paradigm, with color difference as the independent variable and the user's color discrimination time as the dependent variable, capturing the quantitative correspondence between color difference and perception speed, and constructing a color difference-perception time relationship curve to provide a standard basis for subsequent perception rationality verification. Next, the starting and ending colors of the color mapping are uniformly converted to the Lab color space. Interpolation calculations are performed on the numerical values ​​and colors within the Lab color space, and the continuity and consistency of the color mapping in visual perception are controlled through interpolation rules to obtain an initial numerical-color correspondence mapping relationship, providing an object to be verified for subsequent rationality verification. Finally, the rationality of the initial mapping relationship generated in step 2 is verified, and the final determination is based on actual application requirements. The minimum perceptible numerical difference is calculated using a mapping relationship to determine the corresponding color difference. This color difference is then substituted into the color difference-perception time relationship curve obtained in step 1 to obtain the corresponding perception time. The rationality of the current mapping is verified by checking whether the perception time meets the application requirements. If the verification fails, the color endpoints and intermediate control points are adjusted and re-interpolated until the perception efficiency requirements are met. The solution flexibly adjusts the mapping trajectory according to actual needs and design constraints, ensuring the consistency between actual data and color perception in data visualization. This avoids the perceptual jumps in traditional color mapping and the limited applicability of other scientific mapping systems. At the same time, it introduces the color difference-perception speed relationship curve for the first time, allowing designers to quickly evaluate the perception time under a specific design and assisting in verifying the rationality of the mapping design.

[0041] Example 1: To better understand the above technical solution, the following will provide a detailed description of the technical solution in conjunction with the accompanying drawings and specific implementation methods. (Refer to...) Figure 1 As shown in the figure, this is a schematic diagram of the operation flow of the color perception-based numerical color mapping design method. The color perception-based numerical color mapping design method includes the following steps:

[0042] Step 1: First, collect data using the ladder method experimental paradigm. Specifically, the adaptive ladder method uses a 3-down-1-up control rule: if three consecutive correct judgments are made, the stimulus presentation time is shortened; if one incorrect judgment is made, the presentation time is extended, so that the presentation time converges to near the user's discrimination threshold.

[0043] Specifically, when a subject gives a correct / positive evaluation of the stimulus three times in a row, the experimental procedure automatically shortens the stimulus presentation duration of the next trial and actively increases the difficulty of the color discrimination task to approach the subject's true discrimination limit. When a subject gives an incorrect / negative evaluation once, the experimental procedure automatically extends the stimulus presentation duration of the next trial and appropriately reduces the task difficulty to avoid data invalidation due to excessive task difficulty. Through the above dynamic feedback adjustment mechanism, the stimulus presentation duration can be stably converged to near the subject's color discrimination cognitive threshold, ensuring that the collected perception duration data is real, accurate, and reproducible.

[0044] Using color difference as the independent variable and the user's color discrimination time as the dependent variable, this invention accurately captures the quantitative correspondence between color difference magnitude and visual perception speed. Based on multiple sets of valid experimental data, a color difference-perception time relationship curve is obtained. This curve is the core basis for the quantification of perception in this invention, which can transform abstract visual perception into calculable and comparable objective indicators, providing a unified and stable judgment standard for subsequent color mapping rationality verification. This fundamentally overcomes the shortcomings of traditional design, which relies on subjective experience and cannot be quantitatively verified. The invention first presents the gaze point to lock the user's visual focus, then eliminates preceding visual interference through pre-masking, and immediately uses post-masking to block visual afterimages after the color stimulus is presented under controlled duration. Finally, the user completes the decision input. This experimental process can effectively shield external interference factors such as ambient light, visual persistence, and attention drift, significantly improving the purity and consistency of experimental data, and ensuring that the constructed color difference-perception time curve can truly reflect the color discrimination rules of the human visual system.

[0045] The automated steps for data collection in the evaluation mechanism are as follows:

[0046] 1. Initialization and Warm-up Phase: The experimental program first guides the user through a practice mode, which includes a preset number of practice attempts, such as 6, to familiarize the user with the interaction logic. During the practice attempts, the experimental program provides real-time feedback on the correctness of the user's answer after the user makes a selection. After the warm-up, the experimental program automatically switches to the formal data collection mode. The formal mode has the same process logic as the practice mode, but no longer provides feedback on correctness, in order to obtain the user's most realistic cognitive threshold data.

[0047] 2. The execution logic of a single data acquisition unit (Trial) strictly follows the timing logic below to ensure the accuracy of the trigger signal:

[0048] Start reminder step: Display an auxiliary positioning marker, such as a gaze point, at the center of the display terminal for a preset duration, such as 400ms, to lock the user's visual focus.

[0049] Pre-masking step: Presents a randomly generated visual noise image, such as a mosaic matrix, for 200ms to eliminate visual afterimage interference caused by the initial alert.

[0050] Stimulus presentation step: A pair of color samples to be evaluated are symmetrically presented in a preset area of ​​the terminal. The presentation duration of the sample pair is dynamically controlled in real time by the aforementioned adaptive ladder algorithm, 3-down1-up.

[0051] Post-masking step: After the sample disappears, the visual noise image is presented again to prevent visual persistence from affecting the user's judgment.

[0052] Decision Input Step: The experimental program pops up an interactive interface, prompting the user to make a choice between two options regarding the sample depth. The experimental program is configured with specific mapping rules. For example, if the first physical button is detected and the F key is triggered, it is determined that the user has indicated that the sample on the left is deeper; if the second physical button is detected and the J key is triggered, it is determined that the user has indicated that the sample on the right is deeper. If the user still does not provide feedback after 5000ms, the experimental program will remind the user and skip this trial.

[0053] 3. Experimental Sequence Control and Load Balancing: To ensure the scientific validity of the original sample set, the acquisition process was divided into multiple independent blocks, each corresponding to a specific independent variable condition and different color difference values.

[0054] Randomization: The order of blocks is randomly assigned among different users to counteract the order effect.

[0055] Key balance: Preset symmetrical judgment targets within each block, for example, the left and right samples each account for 50% of the frequency, in order to eliminate the influence of user operation preferences and key deviation on the data.

[0056] Fatigue management: Set mandatory interaction interruptions and rest periods between different blocks to ensure that users maintain a stable cognitive level at each stage.

[0057] The experimental blocks were divided according to the color difference level. The block order was randomized, the judgment target was symmetrical, and a forced rest was set to avoid the sequence effect, button preference and fatigue interference. The average of the presentation time of a fixed number of trials at the end of the stable period of each block was taken as the stable discrimination time under that color difference.

[0058] Then, users were invited to participate in the above experiment to construct a curve showing the perceptual relationship between color difference and discrimination time;

[0059] First, multiple subjects were selected based on preset screening criteria, which included: normal visual acuity or corrected visual acuity, no color blindness or color weakness, and normal logical reasoning ability; subjects needed to be well-rested and in good mental condition before the experiment.

[0060] Before the formal experiment, the purpose, evaluation criteria and operation procedures of the experiment were explained to the subjects through standardized instructions. The subjects needed to fully understand the experimental task and conduct the experiment in a comfortable and undisturbed environment. The subjects participated on the experimental laptop, and the experimental data was recorded and saved in the experimental laptop as the original sample set for the subsequent construction of the perception relationship curve.

[0061] Since the stepwise method is in the initial stage of fluctuation and tracking, the data is greatly affected by the learning effect and psychological expectations of the subjects, and cannot accurately reflect its steady-state evaluation criteria. Therefore, in the data preprocessing stage of this scheme, only the presentation duration data corresponding to the last 20 trials after entering the steady-state period in each experimental unit block are extracted as valid samples. This end-stage truncation method aims to eliminate the interference of initial fluctuations and ensure that the data obtained are the core feature values ​​of the subjects after they have reached psychological steady state, thereby improving the purity of the original sample set.

[0062] The 20 key data points extracted above, representing the stable cognitive level of the subjects, were arithmetically averaged to obtain the threshold representation value of the subject under the current experimental conditions. Subsequently, the threshold representation values ​​of all subjects under different color differences were summarized to construct a curve reflecting the relationship between perception duration and color difference. This curve can quantitatively describe the change in the ability to perceive a certain color with color difference.

[0063] Step 2: The starting and ending colors of the color mapping are uniformly converted to the Lab color space. Specifically, the starting and ending colors of the color mapping are converted into CIE-Lab coordinates, and the D65 standard light source conversion rule is uniformly adopted to ensure color space consistency. The range of values ​​to be visualized is discretized into multiple numerical nodes at equal intervals, and a one-to-one correspondence between values ​​and color points is established. The starting and ending colors of the color mapping specified by the user or preset by the method are uniformly converted to the CIE-Lab perceptual uniform color space. The D65 standard light source is used as the color conversion benchmark throughout the process to eliminate the color deviation caused by different devices and different color spaces, and to ensure that all color calculations are performed under a unified benchmark of visual perception equivalence. This provides a stable, reliable, and human-eye-compatible computing environment for subsequent interpolation. After the color space conversion is completed, the continuous range of values ​​to be visualized is discretized into an ordered sequence of numerical nodes at equal intervals, so that each numerical node uniquely corresponds to a color point to be generated, establishing a one-to-one mapping basis between numerical changes and color changes. On this basis, decoupled collaborative interpolation is performed on the three Lab channels.

[0064] The Lab three-channel decoupled interpolation process is as follows: The L luminance channel performs strict linear interpolation, ensuring that luminance increases or decreases monotonically and smoothly with numerical values. This fundamentally avoids the false numerical boundaries and visual jumps caused by abrupt changes in luminance in traditional color maps, guaranteeing that luminance changes monotonically and smoothly with numerical values ​​and avoiding the perception of false numerical boundaries. The ab chrominance channels introduce intermediate control points and custom mapping trajectory control points to actively plan color mapping trajectories, avoid map background colors, semantically conflicting colors, or interference colors from overlay layers. Equal Euclidean distances are maintained between adjacent chrominance coordinates to ensure a stable proportional relationship between numerical differences and visual perception differences. Equal Euclidean distances are also maintained between adjacent chrominance points to ensure uniform perceptual differences. Interpolation calculations are performed on numerical values ​​and colors within the Lab color space, and color is controlled through interpolation rules. The continuity and consistency of the mapping in visual perception yields the initial numerical-color correspondence mapping relationship, providing an object to be tested for subsequent rationality verification. The above-mentioned L-channel linear interpolation and ab-channel equidistant interpolation operate synchronously and cooperate with each other: the luminance channel is responsible for ensuring perceptual stability, and the chrominance channel is responsible for satisfying customized constraints and perceptual uniformity. The two complete the coordinate combination on the same numerical node, jointly generating a continuous color mapping sequence that conforms to the laws of human eye perception, and obtaining the initial numerical-color correspondence mapping relationship. This mapping sequence not only strictly follows the perceptual uniformity characteristics of Lab space, but also forms a technical echo with the color difference-perceptual time consumption curve constructed in step 1. It can be directly used as the object to be verified for subsequent perceptual rationality verification, realizing the complete technical link of experimental calibration + constraint interpolation → mapping to be tested.

[0065] When transforming the mapping endpoints to Lab coordinates, the specific operations are as follows:

[0066] First, the method needs to determine the start and end points of the target color mapping scheme and map them uniformly to the CIE-Lab perceptual uniform color space so that subsequent linear or nonlinear interpolation calculations based on human visual characteristics can be performed.

[0067] The specific implementation method is as follows:

[0068] Endpoint acquisition and definition are based on design requirements, with the mapping starting point C pre-defined. s With mapping endpoint C e If the original color data is represented in a device-dependent color space such as RGB, CMYK, or Hex, it needs to be converted into device-independent Lab coordinates using a preset conversion algorithm, such as a conversion matrix based on the D65 standard light source.

[0069] Coordinate quantization specifically refers to: in Lab space, the starting point is represented as (L s ,a s ,b s The endpoint is represented as (L) e ,a e ,b e), where: L component Luminance: represents the lightness dimension of the color, used to control the brightness level of the color mapping in visual perception; a and b components Chromaticity: represent the chromaticity dimensions of the red-green axis and the yellow-blue axis, respectively, used to define the hue shift and saturation range of the color mapping.

[0070] Standardized Benchmark: By limiting the color mapping trajectory to the Lab color space, this embodiment can ensure that the color sequence generated in subsequent steps has a constant rate of change in visual perception, thereby effectively avoiding visual artifacts or feature masking caused by color space non-uniformity.

[0071] This embodiment takes the most widely used sRGB color space as an example to select the starting and ending colors of the mapping.

[0072] Extract the RGB integer values ​​of the selected starting color and normalize them to the [0,1] interval, denoted as (R... s G s B s Similarly, obtain the normalized value (R) of the endpoint color. e G e B e The starting point usually represents the minimum value of the data sequence or the background color, while the ending point represents the maximum value of the data sequence or a high-alert target.

[0073] Since the input sRGB values ​​have undergone gamma compression and cannot be directly used for matrix multiplication, an inverse gamma transformation must first be performed on the normalized RGB values ​​to linearly decompress them, starting from the starting point (R). s G s B s） For example, for any one of the channel components (let's call it C) srgb ), its corresponding linear component C linear The calculation formula is as follows:

[0074]

[0075] After the above calculations, the linear RGB coordinates (R) of the starting point are obtained. linear, G linear B linear ).

[0076] Subsequently, in accordance with the International Commission on Illumination (CIE) standards, using the D65 standard light source parameters, the linear RGB values ​​were converted into CIEXYZ tristimulus values ​​(X...) using a fixed transformation matrix. s ,Y s Z s The matrix multiplication relationships are shown below:

[0077]

[0078] After obtaining the XYZ tristimulus values, the XYZ color values ​​are further converted into LAB values ​​with perceptual uniformity characteristics according to the CIE1976 LAB color space standard. Taking the starting point as an example, the specific conversion formula is as follows:

[0079]

[0080] Among them, X n ,Y n Z n The tristimulus constant for the D65 reference white point is typically set to X. n =0.9505, Y n =1.0000, Z n =1.0890, for nonlinear transfer functions The specific calculation is as follows:

[0081]

[0082] The same mapping calculation is performed on the endpoint. Through the above decoupling and nonlinear transformation logic, the precise coordinates of the mapping starting point (L) are finally extracted. s ,a s ,b s ) and endpoint coordinates (L e ,a e ,b e ).

[0083] After obtaining the color endpoints and evaluation curves, this method constructs a mapping sequence from numerical ranges to color space by decoupling the calculations of the lightness and chroma dimensions. The specific steps are as follows:

[0084] Value range discretization: Discretizing the numerical range [V] to be visualized s V e Divide it into n+2 logical nodes, denoted as {V}. s V1, V2, ... V n V e The numerical step size between adjacent nodes remains constant, i.e., it satisfies: V i+1 -V i It is a constant value, used to establish a linear numerical benchmark.

[0085] Perceptual linear interpolation in the brightness dimension, L channel: To ensure consistent brightness in visual perception, strict linear interpolation is performed in the L dimension. Based on the distribution of numerical nodes, the corresponding brightness sequence {L} is calculated. s L1, L2, ... L n ,L e}, so that it satisfies L i+1-L i This step, by setting a constant value, ensures that the color mapping has a stable lightness gradient.

[0086] Specifically, based on a discretized numerical node architecture, in [L s ,L e Within a one-dimensional scalar interval, a perceptual brightness gradient with equal steps is constructed. Specifically, this is achieved by calculating the global scalar span of the brightness dimension and performing linear discretization based on the n+1 interval segmentation number, thus strictly constraining the brightness difference between any adjacent nodes. Keep it as a constant .

[0087] After the above-described equidistant dispersion process in one-dimensional space, a perceptual brightness sequence (L) is generated that corresponds one-to-one with the target numerical nodes and contains n+2 precise values. s L1, L2, L3……L n ,L e )

[0088] Multi-point constrained interpolation in the chromaticity dimension, ab channel: In the a (green-red) and b (blue-yellow) dimensions, based on design constraints or user preferences, m control points are introduced between the start and end points, which can be denoted as (a c1 ,b c1 ), (a c2 ,b c2 )……(a cm ,b cm ).

[0089] During sequence recombination, the starting point, control point, and ending point are arranged in the trajectory order to form a chromaticity skeleton;

[0090] Piecewise linear interpolation is performed between two adjacent control points, or between an endpoint and a control point, to generate a chromaticity sequence {a} that corresponds one-to-one with the aforementioned numerical nodes. s ,……,a e} and {b s ,……b e}, but it is necessary to ensure (a i+1 -a i ) 2 +(b i+1 -b i ) 2 It is a constant value, that is, adjacent coordinates (a) i ,b i ) and (a i+1 ,b i+1 The distance is fixed, and the L-dimensional linear interpolation is used to ensure that the perceptual difference between adjacent colors is equidistant.

[0091] Specifically, based on the specific color mapping task requirements, m intermediate control points (m≥0) need to be selectively introduced. For example, when the mapping task is relatively simple and there is no specific color avoidance requirement, m=0 can be set, and the starting point and the ending point can be directly connected by a line segment. If it is necessary to avoid the central low saturation area or achieve a specific color direction, several control points are set as needed, and the starting point, control points and the ending point are connected in sequence using an interpolation algorithm to construct a continuous chromaticity trajectory curve.

[0092] Subsequently, isometric resampling is performed on the chromaticity trajectory in a two-dimensional plane. The total length, straight-line distance, or curve length of the trajectory on the ab plane is calculated. Based on the set sequence framework, it is divided into n+1 equal parts. n+2 discrete chromaticity coordinate nodes are extracted along the trajectory to generate a sequence. (a s ,b s ), (a1,b1), (a2,b2),…(a n ,b n (a e ,b e This segmentation operation ensures that the Euclidean distance between any two adjacent nodes in the sequence remains constant in the two-dimensional ab plane, i.e., the chromaticity difference constant.

[0093] Furthermore, in the CIELAB color space, the color difference perceived by the human visual system is defined as a three-dimensional Euclidean distance. Given that this method forces linear interpolation for the brightness dimension, the brightness difference between adjacent nodes is... The distance is locked to a fixed constant; simultaneously, this step establishes the two-dimensional distance between adjacent nodes in the ab plane. It is also a constant value By combining the aforementioned dimensional decoupling and dimensionality reduction constant compensation, this method achieves, from a mathematical mechanism perspective, that the overall perceived color difference between any adjacent nodes in the final generated color sequence remains constant, thereby realizing a uniform transition of the color mapping gradient in visual perception.

[0094] During color synthesis and space transformation, the independently calculated L, a, and b dimension values ​​are recombined by index to construct the complete color coordinates corresponding to each value node, i.e., V. i Corresponding (L) i ,a i ,b i ), forming a color mapping relationship.

[0095] Step 3: Perform a rationality check on the initial mapping relationship generated in Step 2, and determine the minimum perceptible numerical difference based on the actual application requirements. Specifically, the minimum perceptible numerical difference is the minimum effective resolution preset for the application scenario, which is a fixed input condition for the color mapping check.

[0096] Then, calculate the color difference corresponding to the minimum perceptible numerical difference through the mapping relationship, and then substitute the color difference into the color difference-perception time relationship curve obtained in step 1 to obtain the corresponding perception time. Check whether the current mapping is reasonable by whether the perception time meets the application requirements.

[0097] Entering the closed-loop verification stage of perception rationality, this step takes the actual application scenario as the final verification standard, transforms engineering requirements into quantifiable visual discrimination indicators, and achieves precise alignment between color mapping design and actual use requirements. First, based on actual constraints such as visualization goals, user operation requirements, and system response efficiency, the minimum perceptible numerical difference of the application scenario is clearly defined. This difference is the minimum effective resolution preset for the scenario and is a fixed input condition for the entire verification process. It is used to define the minimum numerical change range that users must quickly and accurately identify, providing a unified benchmark for subsequent color difference calculation and perception time determination.

[0098] Using the minimum perceptible numerical difference as input, the numerical-color mapping relationship generated in step 2 is used for reverse derivation to calculate the actual color difference corresponding to this numerical difference in the CIE-Lab space, achieving a standardized conversion from numerical precision to visual color difference. This color difference is then substituted into the color difference-perception time relationship curve constructed in step 1. The curve is used to directly query and determine the predicted perception time required for the user to distinguish this color difference, completing the quantitative evaluation from visual color difference to perception efficiency. This transforms the abstract visual experience into a comparable and quantifiable objective time indicator.

[0099] This step, together with steps 1 and 2, forms a strongly collaborative and closed-loop logical system: Step 1 provides a benchmark for judging perception efficiency, Step 2 provides a color mapping carrier to be tested, and Step 3 completes the verification of requirements implementation and result judgment. The predicted perception time is compared with the maximum perception time threshold allowed by the application scenario. If it is lower than the threshold, the current mapping is judged to meet the requirements of perception continuity, recognition speed and reasonable use. If it is higher than the threshold, the mapping is judged to not meet the actual use requirements, and the color mapping iterative optimization mechanism is automatically triggered. The process returns to Step 2 to adjust the color endpoints or intermediate control points, regenerates the mapping and checks it again until both design constraints and application perception requirements are met.

[0100] When converting from task space to color difference space, firstly, extract the minimum effective numerical difference that needs to be determined in the target application scenario, i.e., the task accuracy requirement. Using the mapping function constructed above, the physical color difference corresponding to this numerical difference in the color space is calculated. The calculation formula is as follows: ,in, The generated value-to-color mapping function can convert numerical values ​​into corresponding Lab colors. This represents the Euclidean distance in the CIE-Lab space.

[0101] When assessing and verifying perceived timeliness, the calculated color difference value will be used. Substituting the data into a pre-fitted perceptual relationship curve derived from psychological experiments, we can predict the average perceptual time for users at this color difference level. Compliance determination: the predicted Compare with the real-time threshold preset for this application scenario.

[0102] The feedback mechanism is as follows: If If the color is below the preset threshold, the current color mapping design is deemed acceptable; otherwise, return to step 2.2 to readjust the distribution of control points in dimensions ab or the starting and final color values ​​until the recognition timeliness requirements are met.

[0103] Let's take the design of a heat map for university students as an example to illustrate:

[0104] First, 15 college students were invited to participate in the research. Figure 2 The experimental platform was constructed, and based on the specific implementation methods described above, the following diagrams were drawn: Figure 3 The color difference-perception time curve shown;

[0105] In this example, Pirlon's law is used to fit the color difference-reaction time data captured in the experiment to construct a sensorikinetic model. This model specifically introduces a perception critical term. This is used to characterize the technical characteristic that when the color difference is close to or below the critical value, the subject enters a state of random guessing and the reaction time tends to infinity. This is the model scaling factor. The acceleration decay index characterizing the perceived response as color difference increases. This refers to the physiological limits of time, primarily encompassing nerve conduction, specifically the time from the retina to the brain; combined with existing experience. You can choose 2. The undetermined coefficients in the model are solved using 80% of the minimum reaction time observed in the experiment, and the solution is obtained through nonlinear least squares method. and The color difference-reaction time relationship in this example was calculated as follows: ;

[0106] Taking the construction of a yellow-blue mapping as an example, with the starting point specified as RGB(16,7,136) and the ending point specified as RGB(239,248,33), direct interpolation in the RGB space can yield the following result: Figure 4The color mapping shown in (a) is as follows. Converting the above start and end points into the CIE-Lab space, we get (15.69, +47.34, -65.21) (94.14, -24.41, +88.23).

[0107] This mapping service maps values ​​from 0 to 100, dividing them into 100 equal segments, thus constructing 101 logical nodes as follows: {V s =0,V1=1,V2=2,……V 99 =99,V e =100}, corresponding to the brightness sequence obtained by linear interpolation is {L}. s =15.69,L1=16.47,L2=17.26,……L 99 =93.35,L e =94.14}, since there are no special requirements, we first construct a direct linear interpolation along the ab dimension as follows {a s =47.34, a1=46.62,……,a 99 =-23.69,a e =-24.41},{b s =-65.12, b1=-63.67,……,b 99 =86.69,b e =88.23}, recombining the above sequence data with the indices as the reference, we can obtain the color mapping as follows: Figure 4 As shown in (b).

[0108] In this simulated scenario, The value is 2, and the calculation yields... It is 3.73, which corresponds to... The time is approximately 0.173 seconds, which does not meet the general design requirement (0.15 seconds). Therefore, we attempted to add a control point in the middle of ab to reorganize the color sequence. In this example, (90, 48.26) is used as the control point, which is located at (a). s ,b s ) and (a e ,b e On the perpendicular bisector of ), corresponding to V 50 The advantage of using this control point is that linear interpolation can ensure that the distance between any adjacent coordinates is constant. The color mapping after adding the control point is composed of two segments of linear interpolation, as shown in the table below:

[0109] Modified color value mapping

[0110]

[0111] In this mapping, corresponding It is 5.10, corresponding to The time was approximately 0.103 seconds, which meets the requirements, thus completing the design of the color mapping. Figure 4 (c) is the visual representation of this mapping.

[0112] When the perception rationality check in step 3 determines that the current color mapping does not meet the perception efficiency requirements of the application scenario, this invention enters the iterative optimization closed-loop stage. Using the color-perception speed curve from step 1 as the optimization basis, the Lab space interpolation rules from step 2 as the optimization framework, and the check results from step 3 as the optimization check, these three elements work together to drive precise adjustment of the mapping parameters, avoiding aimless manual trial and error, and ensuring that each adjustment can directionally improve perception efficiency and recognition speed. This optimization adopts a directional parameter adjustment strategy. Specific adjustment methods include: changing the starting or ending color of the color mapping to change the overall color gamut range, adding or deleting intermediate control points to reconstruct the color mapping trajectory, and modifying the chromaticity coordinates of the control points to fine-tune the local color difference. All adjustment operations are strictly controlled. The process is performed within the uniform perception space of CIE-Lab and continues to follow the core rules of L-channel linear interpolation and Euclidean distance interpolation such as ab-channel interpolation to ensure that the optimization process does not disrupt perceptual continuity, brightness monotonicity, and visual uniformity. After parameter adjustment, the system automatically re-executes the decoupled interpolation calculation in step 2 to generate a new numerical-color mapping relationship. Then, it synchronously calls the inspection process in step 3 to recalculate the color difference and perception time corresponding to the minimum perceptible numerical difference, forming a fully automatic iterative operation logic of adjustment → interpolation → inspection → judgment. This loop continues to execute until the mapping meets the perceptual efficiency requirements, and finally outputs the optimal color mapping that simultaneously meets the custom design constraints, perceptual uniformity requirements, and actual application timeliness, realizing a closed loop of the entire process from design and inspection to optimization.

[0113] The contents not described in detail in this specification are existing technologies known to those skilled in the art.

[0114] Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A numerical color mapping design method based on color perception, characterized in that, Includes the following steps: Step 1: First, collect data using the step method experimental paradigm, with color difference as the independent variable and the user's perception time of color as the dependent variable, capture the quantitative correspondence between color difference and perception speed, and construct the color difference-perception time relationship curve to provide a standard basis for subsequent perception rationality verification. Step 2: The starting and ending colors of the color mapping are uniformly converted to the Lab color space. Interpolation calculations are performed on the numerical values ​​and colors in the Lab color space. The continuity and consistency of the color mapping in visual perception are controlled by the interpolation rules to obtain the initial numerical-color correspondence mapping relationship, providing an object to be tested for subsequent rationality verification. Step 3: Finally, the initial mapping relationship generated in Step 2 is checked for rationality. The minimum perceptible numerical difference is determined according to the actual application requirements. The color difference corresponding to the minimum perceptible numerical difference is calculated through the mapping relationship. Then, the color difference is substituted into the color difference-perception time relationship curve obtained in Step 1 to obtain the corresponding perception time. Whether the perception time meets the application requirements is used to check whether the current mapping is reasonable. Step 4: If the test fails, adjust the color endpoints and intermediate control points and re-interpolate until the perception efficiency requirements are met.

2. The numerical color mapping design method based on color perception according to claim 1, characterized in that: The adaptive ladder method in step 1 is a 3-down-1-up control rule: if three consecutive correct judgments are made, the stimulus presentation time is shortened; if one incorrect judgment is made, the presentation time is extended, so that the presentation time converges to near the user's discrimination threshold.

3. The numerical color mapping design method based on color perception according to claim 2, characterized in that: Step 1 includes: 1.1 Establish a standardized experimental procedure that includes fixation point, pre-masking, stimulus presentation, post-masking, and decision input. This procedure design can eliminate visual afterimages and interference and improve the reliability of experimental results. 1.

2. Divide the experimental blocks according to the color difference level, randomize the block order, symmetricize the judgment target, and set a forced rest to avoid the sequence effect, button preference and fatigue interference; 1.

3. Take the average of the presentation durations of a fixed number of trials at the end of the stable period of each block as the stable discrimination duration under that color difference.

4. The numerical color mapping design method based on color perception according to claim 1, characterized in that: Step 2 includes: 2.1 Convert the color mapping start and end points to CIE-Lab coordinates, and uniformly adopt the D65 standard light source conversion rules to ensure color space consistency; 2.

2. Discretize the range of values ​​to be visualized into multiple numerical nodes at equal intervals and establish a one-to-one correspondence between numerical values ​​and color points.

5. The numerical color mapping design method based on color perception according to claim 4, characterized in that: In step 2, decoupling interpolation is performed on the three Lab channels: 2.3 The L brightness channel performs strict linear interpolation to ensure that the brightness changes monotonically and smoothly with the value, avoiding false value boundary perception; 2.

4. The ab chroma channel introduces a custom mapping trajectory with intermediate control points, and maintains equal Euclidean distance between adjacent chroma points to ensure uniform perceptual differences.

6. The numerical color mapping design method based on color perception according to claim 1, characterized in that: In step 3, the minimum perceptible numerical difference is the minimum effective resolution preset for the application scenario, which is a fixed input condition for color mapping verification.

7. The numerical color mapping design method based on color perception according to claim 6, characterized in that: Step 3 includes: 3.1 Calculate the CIE-Lab color difference corresponding to the minimum perceptible numerical difference based on the color mapping; 3.2 Substitute the color difference into the perception relationship curve to obtain the predicted perception time required for the user to complete the identification; 3.3 Compare the predicted perception time with the maximum perception time threshold allowed by the application scenario. If it is lower than the threshold, it is considered qualified.

8. The numerical color mapping design method based on color perception according to claim 1, characterized in that: The adjustment method in step 4 includes: changing the starting and ending colors of the mapping, adding or deleting intermediate control points, modifying the chromaticity coordinates of the control points, and regenerating a color mapping that meets the requirements of perception efficiency and design constraints.

9. A numerical color mapping design method based on color perception according to any one of claims 1-8, characterized in that: The method simultaneously satisfies engineering visualization constraints such as uniform perception, monotonous brightness, color-friendly design, customizable trajectory, semantic compatibility, and interference-free layer overlay, making it suitable for applications such as scientific charts, monitoring interfaces, and map overlays.

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