A method, system, device and medium for quantitative calculation of brightness perception

Abstract

This invention discloses a method, system, device, and medium for quantitative calculation of brightness perception, relating to the field of brightness perception. It involves acquiring a high dynamic range (HDR) image of a target area; extracting brightness and chromaticity information from the HDR image; calculating a brightness index based on the brightness information using an image thresholding method; calculating a contrast index based on the brightness information using a hierarchical sampling method; calculating a color temperature index based on the chromaticity information using a color space transformation matrix; and calculating the perceived brightness value of the target area based on the brightness, contrast, and color temperature indices. This invention achieves quantitative calculation of brightness perception through multi-dimensional image indices.

Description

Technical Field

[0001] This invention relates to the field of brightness perception, and in particular to a method, system, device and medium for quantitative calculation of brightness perception. Background Technology

[0002] In recent years, indoor lighting standards and related guidance documents have been revised and new indicators have been added, such as the minimum illuminance index for room surfaces. Although these indicators have been formally introduced into standards and guidelines, in practice, the illuminance of the work surface in indoor workplaces remains the most prominent indicator, while there are no quantitative standards and indicators for the overall brightness perception of the indoor light environment.

[0003] Methods for brightness perception can be divided into two categories: the first is subjective judgment based on the real scene, and the second is subjective judgment of the lighting environment based on images. However, these methods have the following drawbacks:

[0004] The former method yields relatively reliable experimental results, but it is time-consuming and laborious, and can only qualitatively analyze the impact of different parameters in the light environment on brightness perception.

[0005] The latter, image evaluation, can be further divided into two categories: RGB images and High Dynamic Range Imaging (HDR) images. RGB images extract brightness information from the image and fit it to subjective judgment. However, the brightness range of RGB images is computer-compressed and cannot fully represent the brightness range in real space. While HDR images can represent the range in real space, current technologies use a single metric and do not fully consider the correlation between pixels and the characteristics of human vision. Therefore, they cannot accurately reflect human subjective perception of the lighting environment in complex lighting conditions, and cannot achieve quantitative analysis and calculation of brightness perception. Summary of the Invention

[0006] The purpose of this invention is to provide a method, system, device, and medium for quantitative calculation of brightness perception, which realizes quantitative calculation of brightness perception through multi-dimensional image indicators.

[0007] To achieve the above objectives, the present invention provides the following solution:

[0008] A method for quantitative calculation of brightness perception, the method comprising:

[0009] Acquire high dynamic range imaging images of the target area;

[0010] Extract the brightness and chromaticity information of the high dynamic range imaging image;

[0011] The brightness index is calculated based on the brightness information using the image thresholding method.

[0012] Based on the brightness information, the contrast ratio is calculated using a hierarchical sampling method;

[0013] Based on the chromaticity information, the color temperature index is calculated using a color space transformation matrix;

[0014] The perceived brightness value of the target area is calculated based on the brightness index, the contrast index, and the color temperature index.

[0015] Optionally, the target area includes an environmental surface and a lighting source; the environmental surface refers to objects within the target area other than the lighting source; an image thresholding method is used to calculate a brightness index based on the brightness information, specifically including:

[0016] Based on the brightness information, the high dynamic range imaging image is segmented using the image thresholding method to obtain an environmental surface segmentation image and an illumination source segmentation image.

[0017] The ambient surface brightness is determined based on the segmented image of the ambient surface, and the brightness of the lighting source is determined based on the segmented image of the lighting source.

[0018] The average brightness of the ambient surface is determined based on the ambient surface brightness and the number of pixels in the segmented image of the ambient surface.

[0019] The average brightness of the lighting source is determined based on the brightness of the lighting source and the number of pixels in the segmented image of the lighting source.

[0020] The weight values ​​are calculated based on the number of pixels in the environmental surface segmentation image and the number of pixels in the illumination source segmentation image; the weight values ​​include: environmental surface weight and illumination source weight;

[0021] The brightness index is calculated based on the average brightness of the ambient surface, the average brightness of the lighting source, and the weight value.

[0022] Optionally, the formula for calculating the contrast index is:

[0023]

[0024]

[0025]

[0026] Where C is the contrast ratio; N is the number of levels of brightness information; is the average contrast of level l; W is the width of the high dynamic range image; H is the height of the high dynamic range image; c i,jLet L(i,j) be the contrast of pixel (i,j); i is the x-coordinate of pixel (i,j); j is the y-coordinate of pixel (i,j); L(i,j) is the brightness value of pixel (i,j); L k W represents the brightness value of the neighboring pixels of pixel (i, j); l H is the width of the image in layer l; l α is the height of the image in level l; α is the weight of the eight adjacent pixels; K8 is the eight adjacent pixels; k is the index of the adjacent pixels.

[0027] Optionally, based on the chromaticity information, a color temperature index is calculated using a color space transformation matrix, specifically including:

[0028] Based on the chromaticity information, a color space conversion is performed using a color space conversion matrix to obtain chromaticity conversion information;

[0029] Based on the chromaticity conversion information, determine the chromaticity coordinates corresponding to each pixel.

[0030] Calculate the tangent of the angle between each of the chromaticity coordinates and the coordinates of the set point;

[0031] Based on each of the tangent values, determine the color temperature value of the corresponding pixel.

[0032] Calculate the average of all color temperature values ​​to obtain the color temperature index.

[0033] Optionally, the formula for calculating the perceived brightness value is:

[0034]

[0035] Where B is the perceived brightness value; a is the first fitting coefficient; C is the contrast ratio; L eff For brightness index; T c is the color temperature index; n is the second fitting coefficient.

[0036] A brightness perception quantitative calculation system, the system comprising:

[0037] The image acquisition module is used to acquire high dynamic range imaging images of the target area;

[0038] The information extraction module is used to extract the brightness and chromaticity information of the high dynamic range imaging image;

[0039] The brightness index calculation module is used to calculate the brightness index based on the brightness information using the image threshold segmentation method.

[0040] A contrast ratio calculation module is used to calculate the contrast ratio based on the brightness information using a hierarchical sampling method.

[0041] The color temperature index calculation module is used to calculate the color temperature index based on the chromaticity information through a color space transformation matrix.

[0042] The brightness perception value calculation module is used to calculate the brightness perception value of the target area based on the brightness index, the contrast index, and the color temperature index.

[0043] An electronic device includes a memory and a processor, the memory storing a computer program, and the processor running the computer program to enable the electronic device to perform the brightness perception quantitative calculation method described above.

[0044] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described quantitative calculation method for brightness perception.

[0045] According to specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0046] This invention provides a method, system, device, and medium for quantitative calculation of brightness perception. By employing image thresholding and color space transformation matrix, image indicators are calculated from multiple dimensions, and then brightness perception is calculated based on these image indicators. The image indicators include brightness, contrast, and color temperature. By calculating multi-dimensional image indicators, this invention achieves quantitative calculation of brightness perception, thereby improving the correlation between the calculation results and the subjective brightness perception of the human eye, and thus enhancing the simulation accuracy of the image. Attached Figure Description

[0047] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0048] Figure 1 A flowchart of a method for quantitative calculation of brightness perception provided in an embodiment of the present invention;

[0049] Figure 2 This is an overall diagram of the brightness perception quantitative calculation method provided in the embodiments of the present invention;

[0050] Figure 3 This is a structural diagram of the brightness perception quantitative calculation system provided in an embodiment of the present invention.

[0051] Symbol explanation:

[0052] Image acquisition module-1, information extraction module-2, brightness index calculation module-3, contrast index calculation module-4, color temperature index calculation module-5, brightness perception value calculation module-6. Detailed Implementation

[0053] 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.

[0054] The purpose of this invention is to provide a method, system, device, and medium for quantitative calculation of brightness perception, which realizes quantitative calculation of brightness perception through multi-dimensional image indicators.

[0055] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0056] Example 1

[0057] like Figure 1 As shown, this embodiment of the invention provides a method for quantitative calculation of brightness perception, the method comprising:

[0058] Step 100: Acquire a high dynamic range imaging image of the target area.

[0059] Step 200: Extract the brightness and chromaticity information of the high dynamic range imaging image.

[0060] Step 300: Use the image thresholding method to calculate the brightness index based on the brightness information.

[0061] Step 300 specifically includes:

[0062] Based on the brightness information, the high dynamic range imaging image is segmented using the image thresholding method to obtain the environmental surface segmentation image and the illumination source segmentation image.

[0063] The ambient surface brightness is determined based on the segmented image of the ambient surface, and the brightness of the lighting source is determined based on the segmented image of the lighting source.

[0064] The average brightness of the ambient surface is determined based on the ambient surface brightness and the number of pixels in the segmented image of the ambient surface.

[0065] The average brightness of the lighting source is determined based on the brightness of the lighting source and the number of pixels in the segmented image of the lighting source.

[0066] The weight values ​​are calculated based on the number of pixels in the environment surface segmentation image and the number of pixels in the illumination source segmentation image; the weight values ​​include: environment surface weight and illumination source weight.

[0067] The brightness index is calculated based on the average brightness of the environmental surface, the average brightness of the lighting source, and the weight value.

[0068] Specifically, let {L(i,j)} denote a high dynamic range (HDR) image with width W and height H, where 1≤i≤W, 1≤j≤H, {L o (i,j)} represents the brightness of the pixel at coordinate (i,j) in {L(i,j)}. See the overall operation flow below. Figure 2 .

[0069] An image thresholding segmentation algorithm is used to separate direct lighting sources and environmental surfaces (such as ceilings, floors, walls, and other furniture) within the target area in HDR images.

[0070] The threshold segmentation method is as follows:

[0071]

[0072]

[0073] {L surf {i,j} represents the brightness information of the environment surface after image thresholding, {L} light {i,j} represents the brightness information of the illumination source after image thresholding, {L} aver} represents the average brightness information of the image. According to {L surf (i,j)} and {L light The average brightness of the ambient surface and the lighting source is calculated from (i,j)}.

[0074]

[0075]

[0076] Where n1 is the number of pixels occupied by the room surface in the image; n2 is the number of pixels occupied by the light source in the image; L surfaver The average brightness of the ambient surface; L lightaver This represents the average brightness of the lighting source.

[0077] Calculate the ratios between the number of pixels on the room surface, the number of pixels from the light source, and the total number of pixels in the image, respectively, as weights:

[0078]

[0079]

[0080] Where ω1 is the environmental surface weight; ω2 is the lighting source weight.

[0081] The brightness index L is obtained by weighting and adding the weighted values ​​and the average brightness separately. eff .

[0082] L eff =ω1L surfaver +ω2L lightaver

[0083] Step 400: Calculate the contrast ratio using a hierarchical sampling method based on the brightness information.

[0084] RAMMG contrast is calculated for HDR images. RAMMG is a contrast algorithm that calculates local brightness variations by applying a multi-level method with a pyramidal subsampling structure. This method takes into account the differences in perceived brightness across various image resolutions.

[0085] The formula for calculating the contrast ratio is:

[0086]

[0087]

[0088]

[0089] Where C is the contrast ratio; N is the number of levels of brightness information; is the average contrast of level l; W is the width of the high dynamic range image; H is the height of the high dynamic range image; c i,j Let L(i,j) be the contrast of pixel (i,j); i is the x-coordinate of pixel (i,j); j is the y-coordinate of pixel (i,j); L(i,j) is the brightness value of pixel (i,j); L k W represents the brightness value of the neighboring pixels of pixel (i, j); l H is the width of the image in layer l; l α is the height of the image in level l; α is the weight of the eight adjacent pixels; K8 is the eight adjacent pixels; k is the index of the adjacent pixels.

[0090] Since the image resolution is halved in each subsequent layer, then:

[0091] W l =W l-1 / 2

[0092] H l =H l-1 / 2

[0093] Assign corresponding weights to the eight adjacent pixels:

[0094]

[0095] Step 500: Calculate the color temperature index based on the chromaticity information using the color space conversion matrix.

[0096] Step 500 specifically includes:

[0097] Based on the chromaticity information, a color space conversion is performed using a color space conversion matrix to obtain the chromaticity conversion information.

[0098] Based on the chromaticity conversion information, determine the chromaticity coordinates corresponding to each pixel.

[0099] Calculate the tangent of the angle between each chromaticity coordinate and the coordinate of the set point.

[0100] The color temperature value of the corresponding pixel is determined based on each tangent value.

[0101] Calculate the average of all color temperature values ​​to obtain the color temperature index.

[0102] Specifically, there is a linear conversion between the CIE 1931 RGB color space and the CIE 1931 XYZ color space, which is performed using matrix M. The most common M matrix is ​​CIE XYZ to sRGB, using the CIE Standard Illuminant D65 standard as the reference white light. After the conversion, signal values ​​X, Y, and Z can be obtained respectively. The chromaticity coordinates (x, y) corresponding to each pixel value are calculated based on the X, Y, and Z parameters, as follows:

[0103]

[0104]

[0105]

[0106] The value of n is then determined based on the chromaticity coordinates. A line is drawn from the chromaticity coordinates of the pixel to a point (0.332, 0.1858) on the Planck locus. This line represents the color temperature, and n represents the tangent of the angle between this line and the y-axis. Using this n value and a coefficient, the color temperature CCT(i,j) of each pixel is approximately estimated. Finally, the average color temperature corresponding to all pixel values ​​is calculated to obtain the color temperature index. The calculation formula is as follows:

[0107]

[0108] CCT(i,j)=449n 3 +3525n 2+6823.3n+5520.33;

[0109]

[0110] Step 600: Calculate the perceived brightness value of the target area based on the brightness index, contrast index, and color temperature index.

[0111] The formula for calculating the perceived brightness value is:

[0112]

[0113] Where B is the perceived brightness value; a is the first fitting coefficient; C is the contrast ratio; L eff For brightness index; T c denoted as color temperature; n is the second fitting coefficient. Where a = 1.8, n = 0.8.

[0114] Example 2

[0115] like Figure 3 As shown, this embodiment of the invention provides a brightness perception quantitative calculation system, which includes: an image acquisition module 1, an information extraction module 2, a brightness index calculation module 3, a contrast index calculation module 4, a color temperature index calculation module 5, and a brightness perception value calculation module 6.

[0116] Image acquisition module 1 is used to acquire high dynamic range imaging images of the target area.

[0117] Information extraction module 2 is used to extract the brightness and chromaticity information of high dynamic range imaging images.

[0118] Brightness index calculation module 3 is used to calculate the brightness index based on the brightness information using the image threshold segmentation method.

[0119] The contrast ratio calculation module 4 is used to calculate the contrast ratio based on the brightness information using a hierarchical sampling method.

[0120] The color temperature index calculation module 5 is used to calculate the color temperature index based on the chromaticity information through a color space transformation matrix.

[0121] The brightness perception value calculation module 6 is used to calculate the brightness perception value of the target area based on the brightness index, contrast index, and color temperature index.

[0122] In one embodiment, when B = a·(-L) eff ·C / d)·log(T c When )+b, a=0.0044, b=6.3457.

[0123] The correlation analysis of the perceived brightness value of HDR images obtained using the method of this invention with the subjective perception of the human eye uses an HDR image library simulated based on real-world scenes. This library includes three color temperatures (3000K, 4000K, 5000K) and 13 light distribution scenes, totaling 39 lighting environment scenes. This embodiment uses two commonly used objective parameters as evaluation indicators: the Pearson correlation coefficient (CC) under nonlinear regression conditions and the model prediction bias (R²). 2 The CC index is a relatively simple correlation measure that reflects the accuracy of objective perception methods for image brightness. A value closer to 1 indicates a smaller difference between the objective evaluation score and the subjective evaluation score obtained by the image brightness perception evaluation method; R 2 The coefficient of determination is used to characterize the goodness of fit by observing changes in the data. From the expression above, we know that the normal range for the coefficient of determination is [0, 1]. The closer it is to 1, the stronger the explanatory power of the variables in the equation for y, and the better the fit to the data. Table 1 shows the Pearson correlation coefficient CC and the coefficient of determination R under nonlinear regression conditions. 2 Performance metrics.

[0124] Table 1 Performance Indicators

[0125]

[0126] Example 3

[0127] This invention provides an electronic device, including a memory and a processor. The memory stores a computer program, and the processor runs the computer program to enable the electronic device to perform the brightness perception quantitative calculation method in Embodiment 1.

[0128] In one embodiment, the present invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the brightness perception quantitative calculation method in Embodiment 1.

[0129] Advantages of this invention:

[0130] 1. The method of the present invention takes into account the influence of the color temperature information of the image on the brightness perception, so as to obtain a brightness perception value that is more consistent with human subjective feeling under different color temperature lamps.

[0131] 2. The method of the present invention takes into account the brightness information of different surfaces of the room in the image, thereby calculating the effective brightness information perceived by the human eye, so that the evaluation result is consistent with the subjective feeling of the human.

[0132] 3. The method of the present invention takes into account the influence of the light emission direction distribution in the image on brightness perception, which can effectively improve the correlation between objective evaluation results and subjective perception.

[0133] 4. The method of the present invention takes into account the visual perception characteristics of the human eye, calculates the image contrast under multiple image resolutions, and better reflects the subjective perception of the human eye on the contrast between light and dark in the light environment.

[0134] 5. The method of the present invention comprehensively considers the influence of the brightness index, contrast index, and color temperature index of the light environment on brightness perception, so that the calculation results are more consistent with human subjective feelings and better reflect the brightness perception results of the indoor light environment.

[0135] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0136] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. Furthermore, those skilled in the art will recognize that, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method for quantitative calculation of brightness perception, characterized in that, The method includes: Acquire high dynamic range imaging images of the target area; Extract the brightness and chromaticity information of the high dynamic range imaging image; The brightness index is calculated based on the brightness information using the image thresholding method. Based on the brightness information, the contrast ratio is calculated using a hierarchical sampling method; Based on the chromaticity information, the color temperature index is calculated using a color space transformation matrix; The perceived brightness value of the target area is calculated based on the brightness index, the contrast index, and the color temperature index. The formula for calculating the perceived brightness value is: Where B is the perceived brightness value; a is the first fitting coefficient; C is the contrast ratio; L eff For brightness index; T c is the color temperature index; n is the second fitting coefficient.

2. The method for quantitative calculation of brightness perception according to claim 1, characterized in that, The target area includes an environmental surface and a lighting source; the environmental surface refers to objects within the target area other than the lighting source. The image thresholding method is used to calculate the brightness index based on the brightness information, specifically including: Based on the brightness information, the high dynamic range imaging image is segmented using the image thresholding method to obtain an environmental surface segmentation image and an illumination source segmentation image. The ambient surface brightness is determined based on the segmented image of the ambient surface, and the brightness of the lighting source is determined based on the segmented image of the lighting source. The average brightness of the ambient surface is determined based on the ambient surface brightness and the number of pixels in the segmented image of the ambient surface. The average brightness of the lighting source is determined based on the brightness of the lighting source and the number of pixels in the segmented image of the lighting source. The weight values ​​are calculated based on the number of pixels in the environmental surface segmentation image and the number of pixels in the illumination source segmentation image; the weight values ​​include: environmental surface weight and illumination source weight; The brightness index is calculated based on the average brightness of the ambient surface, the average brightness of the lighting source, and the weight value.

3. The method for quantitative calculation of brightness perception according to claim 1, characterized in that, The formula for calculating the contrast ratio is: Where C is the contrast ratio; N is the number of levels of brightness information; is the average contrast of level l; W is the width of the high dynamic range image; H is the height of the high dynamic range image; c i,j Let L(i,j) be the contrast of pixel (i,j); i is the x-coordinate of pixel (i,j); j is the y-coordinate of pixel (i,j); L(i,j) is the brightness value of pixel (i,j); L k W represents the brightness value of the neighboring pixels of pixel (i, j); l H is the width of the image in layer l. l α is the height of the image in level l; α is the weight of the eight adjacent pixels; K8 is the eight adjacent pixels; k is the index of the adjacent pixels.

4. The method for quantitative calculation of brightness perception according to claim 1, characterized in that, Based on the chromaticity information, the color temperature index is calculated using a color space transformation matrix, specifically including: Based on the chromaticity information, a color space conversion is performed using a color space conversion matrix to obtain chromaticity conversion information; Based on the chromaticity conversion information, determine the chromaticity coordinates corresponding to each pixel. Calculate the tangent of the angle between each of the chromaticity coordinates and the coordinates of the set point; Based on each of the tangent values, determine the color temperature value of the corresponding pixel. Calculate the average of all color temperature values ​​to obtain the color temperature index.

5. A brightness perception quantitative calculation system, characterized in that, The system includes: The image acquisition module is used to acquire high dynamic range imaging images of the target area; The information extraction module is used to extract the brightness and chromaticity information of the high dynamic range imaging image; The brightness index calculation module is used to calculate the brightness index based on the brightness information using the image threshold segmentation method. A contrast ratio calculation module is used to calculate the contrast ratio based on the brightness information using a hierarchical sampling method. The color temperature index calculation module is used to calculate the color temperature index based on the chromaticity information through a color space transformation matrix. A brightness perception value calculation module is used to calculate the brightness perception value of the target area based on the brightness index, the contrast index, and the color temperature index. The formula for calculating the perceived brightness value is: Where B is the perceived brightness value; a is the first fitting coefficient; C is the contrast ratio; L eff For brightness index; T c is the color temperature index; n is the second fitting coefficient.

6. An electronic device, characterized in that, The device includes a memory and a processor, the memory being used to store a computer program, and the processor running the computer program to cause the electronic device to perform the brightness perception quantitative calculation method as described in any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that, It stores a computer program that, when executed by a processor, implements the quantitative calculation method for brightness perception as described in any one of claims 1 to 4.

Images