CN104655403B - Luminance uniformity test method of dot-matrix light source - Google Patents

Abstract

The invention discloses a luminance uniformity test method of a dot-matrix light source, and relates to the technical fields of display, illumination and image processing. According to the luminance uniformity test method, the luminance uniformity is divided into brightness uniformity and distribution uniformity; the luminance uniformity of the dot-matrix light source is evaluated through the brightness uniformity, the distribution uniformity and the sum of the brightness uniformity and the distribution uniformity; in the testing process, image geometric distortion is corrected; the dot-matrix structure is analyzed, and the non-light-emitting area is excluded from the evaluation range. Compared with the existing test method, the luminance uniformity test method is more comprehensive, objective, accurate, effective, direct, simple and convenient.

Description

A method for testing the luminous uniformity of a dot matrix light source

technical field

The invention relates to the fields of display, illumination light source and computer image processing, in particular to a test method for a dot matrix light source.

Background technique

A dot matrix light source refers to a collection of multiple active light-emitting points arranged in an orderly manner in rows and columns. The device types of the light source include LCD, LED, FED, and PDP. With the rapid development of information technology, point-matrix light sources have been widely used in terminals of various multimedia devices. It can be used as a lighting source and backlight, and can also be used for screens that display various information such as characters, graphics, and videos. The brightness uniformity of a dot matrix light source is an important factor affecting its lighting or display performance, so it is very necessary to propose an effective test method for the brightness uniformity of a light source to provide guidance for the production and development of related devices.

At present, the test method mainly uses a luminance meter to randomly select a number of sampling points to test the luminance value of the dot matrix light source, and then obtains the final evaluation result through a certain algorithm. This method is one-sided, and each measurement can only obtain the data of one sampling point. , Obtaining test results requires multiple adjustments to the test instrument, which takes a lot of time. It is reported that the luminance information of dot matrix pixels is obtained by the sensor, combined with image processing technology and statistical principles, the gray histogram of the pixel is fitted to a normal distribution curve, and the standard deviation is used to evaluate the luminance uniformity. It is also reported that the brightness image obtained by the sensor is divided into several sub-regions, and the brightness feature factors, spatial position factors and texture detail factors based on HVS (human visual characteristics) of each sub-region are calculated, and the characteristics of these factors are used as sub-regions. The evaluation parameters of each sub-area are used to evaluate the brightness uniformity of the light source by the value of the discrete degree of each sub-region parameter. However, the above methods are all directly testing the acquired images, without correcting the geometric distortion of the image caused by shooting; nor distinguishing between luminous points and non-luminous areas, and including those non-luminous areas that should not participate in the evaluation are also included in the evaluation scope; and , or only test the uniformity of brightness, or only test the uniformity of distribution, which is one-sided and does not comprehensively evaluate the uniformity of light emission; the above deficiencies lead to an incomplete and accurate evaluation of the uniformity of light emission of dot matrix light sources.

Contents of the invention

The purpose of the present invention is to provide a comprehensive and accurate method for testing the luminous uniformity of a dot matrix light source.

In order to achieve the above object, the present invention divides the luminous uniformity into luminance uniformity and distribution uniformity, and evaluates the luminous uniformity of the dot matrix light source from different angles with brightness uniformity and distribution uniformity respectively, and uses brightness uniformity and distribution uniformity To comprehensively evaluate the luminous uniformity of the dot matrix light source, and to correct the geometric distortion of the image during the test, and to exclude the non-luminous area from the evaluation range, the following scheme is specifically adopted:

Obtain the luminescent image of the dot matrix light source by taking pictures, shooting videos and then intercepting video images or reading saved images; select the area to be tested in the obtained image, and the area to be tested can be in any shape. For the convenience of operation, you can It is a rectangle; generate a test image; read the gray value of each pixel in the test image; set the gray threshold to separate the bright spot from the background; set the dot matrix distribution structure, array point shape category and its characteristics Parameters, the array point image is obtained in the test image I; the number of pixels included in a single array point image is counted as the array point area Sp, and the number of bright spot pixels contained in each array point image is counted as the array point bright spot total area Se, if If the ratio of Se to Sp of an array point is greater than the set value (for example, 0.3), it is determined that the array point is an effective spot, otherwise it is regarded as an invalid spot; the brightness uniformity and distribution uniformity of the effective spot are calculated, and the brightness uniformity The calculation formula is:

Among them, γ ₁ , γ ₂ , ..., γ _n is the average gray level of each effective spot, γ ₀ is the average gray level of all the effective spots, s _γ is the gray standard deviation, γ is A value less than 1 and greater than 0 is used to measure the brightness uniformity. The closer γ is to 1, the more uniform the brightness of the spot is, and the closer γ is to 0, the worse the brightness uniformity of the spot;

The formula for calculating the uniformity of distribution is:

Among them, β ₁ , β ₂ , ..., β _n is the number of light spots in each area after the photo is evenly divided into n areas, β ₀ is the average number of light spots in each area, s _β is the standard deviation of the number of light spots , β is a value less than 1 and greater than 0, which is used to measure the uniformity of brightness. The closer β is to 1, the more uniform the distribution of light spots; the closer β is to 0, the worse the uniformity of light spots;

Calculate the greater the sum of β and γ, and the greater the sum of β and γ, the better the uniformity of the light source;

Save images and calculation results, or output to a display, and can also be exported to other computers for further processing.

A further solution of the present invention is that, after selecting the area to be tested in the obtained image, the geometric distortion correction is performed on the image of the area to be tested, and the test image is regenerated.

After judging the effective light spot, the images of the effective light spot, background and non-test area will be visually displayed in a composite image with different colors, and through manual observation, judgment, selection or automatic computer processing, dot matrix parameters, image segmentation and The spot selection results are further optimized and adjusted to obtain more accurate results.

Another further solution of the present invention is to calculate the lattice distribution structure, the shape category of the array point and its characteristic parameters, and the step of obtaining the array image specifically adopts the following method: according to the actual shape of the array point (such as circle, ellipse, square, Rectangle and arbitrary polygon) and feature parameters (such as side length and radius, etc.) to preliminarily set the shape category and feature parameters of the array points in the test image, outline a single array point image, and then fine-tune the array point feature parameters, according to the cross-correlation value r The principle of obtaining the maximum value should be used to search for the optimal characteristic parameters, and finally determine the shape of a single array point.

The formula for calculating r is:

Among them, P is the set of all sub-pixel points of the local image, I is the set of all sub-pixel points corresponding to the coordinate position of P in the test image, and I _ijk is the sub-pixel point with image coordinates (i, j, k) in I The gray value of , I _a is the average value of I _ijk , P _ijk is the gray value of the sub-pixel whose image coordinates are (i, j, k) in P, and P _a is the average value of P _ijk ;

Select an array point in the test image as the initial array point, take the center coordinates of the initial array point as the origin, calculate the lattice parameters according to the relative position and distance of each array point, and obtain the coordinates of each array point; The coordinates of each array point are used as the center position to perform expansion operations according to the shape of a single array point to obtain an array image.

Another further solution of the present invention is to use the allowable value of gray scale fluctuation to judge whether the change in gray scale is caused by noise. The specific solution is to set the allowable value Tc of image gray scale fluctuation; If the difference is less than Tc, it is determined that the grayscale change is caused by noise, and the grayscale value of the pixel is the maximum grayscale value of the adjacent 8 pixels; if there is at least one grayscale line between the boundaries of the two bright spots If the eight-connected path whose change is less than Tc, the two bright spots are merged into one bright spot, and all the pixels on the connected path also belong to the bright spot.

Due to the adoption of the above scheme, compared with the prior art, the present invention can bring the following technical effects: the geometric distortion correction of the test image can be used to reduce image distortion; Non-luminous area; the combination of manual setting and computer automatic calculation is used to obtain the ideal gray threshold, and the gray threshold is used to accurately separate the bright spot from the background; the calculation of the dot matrix image is used to exclude invalid spots; the effective spot is used to The images of , background and non-statistical areas are displayed in different colors as composite images, which gives people an intuitive feeling, and the allowable value of gray scale fluctuation is used to eliminate the influence of noise; manual intervention further optimizes and adjusts the results of dot matrix settings, image segmentation and spot selection; The above-mentioned technical means can make the test results more objective and accurate; it is more comprehensive to evaluate the luminous uniformity by separately calculating the brightness uniformity, distribution uniformity and the sum of the two. It is more comprehensive to evaluate the luminous uniformity by using brightness uniformity, distribution uniformity and the sum of the two, so the present invention is more comprehensive, objective and accurate compared with the existing test method, and is effective, intuitive and simple.

Description of drawings

Fig. 1 is a flowchart of the present invention;

Fig. 2 is the dot matrix luminous image that embodiment takes;

Figure 3 is the test image obtained after region selection and deformity correction;

Figure 4 is a schematic diagram of the shape of a single array point

Fig. 5 is a schematic diagram of an array image.

detailed description

The present invention will be described in detail below in conjunction with the accompanying drawings.

As shown in Figure 1, the specific steps of this embodiment are:

Use the camera head 2 to shoot the light-emitting process of the dot matrix light source 1, transmit the video to the computer 3 through the USB interface of the computer 3, and then intercept the light-emitting image of the dot-matrix light source 1 from the video, and select a rectangular area to be tested in the light-emitting image , as shown in Figure 2; the image of the rectangular area to be tested is corrected for geometric distortion, and a test image is generated, as shown in Figure 3; the gray value of each pixel in the test image is read; the image is set The allowable value of grayscale fluctuation Tc, if the grayscale difference between a certain pixel and the adjacent 8 pixels is less than Tc, it is determined that the grayscale change is caused by noise, and the grayscale value of the pixel is the grayscale value of the adjacent 8 pixels ; artificially set the image grayscale threshold Tr, determine that it does not belong to bright spots for pixels whose grayscale value is less than Tr, and further calculate the global grayscale threshold and local threshold of the test image with the ostu image segmentation threshold algorithm, and bright spot 5 and The background is separated; if there is at least one eight-connected path between the boundaries of two bright spots whose gray level change is smaller than Tc, then the two bright spots are merged into one bright spot, and all pixels on the connected path also belong to the bright spot. spots; according to the actual shape and characteristics of the array points, set the shape of the array points in the test image to be an ellipse, initially set its characteristic parameters including the values of the semi-major axis and semi-minor axis, outline a single array point image 4, and then fine-tune the array Point feature parameters, according to the principle that the cross-correlation value r should obtain the maximum value, search for the optimal feature parameter semi-major axis or semi-minor axis value, and finally determine the shape of a single array point,

The formula for calculating r is:

Among them, P is the set of all sub-pixel points of the local image, I is the set of all sub-pixel points corresponding to the coordinate position of P in the test image, and I _ijk is the sub-pixel point with image coordinates (i, j, k) in I The gray value of , I _a is the average value of I _ijk , P _ijk is the gray value of the sub-pixel whose image coordinates are (i, j, k) in P, and P _a is the average value of P _ijk ;

Select an array point P ₀₀ in the test image as the initial array point located in row 0 and column 0, set its coordinates as (x0, y0), and calculate the lattice parameter Lx1 according to the relative position and distance of each array point , Lx2, Ly1 and Ly2, as shown in Figure 5, the coordinates of any array point P _mn are (x0+m*Lx1+n*Lx2, y0+m*Ly1+n*Ly2), where m and n are specific Integer, indicating that the array point is located in the mth row and the nth column (as shown in Figure 5); the expansion operation is performed on the center position of each array point according to the shape of a single array point to obtain an array image; the test image is displayed as a composite image, Among them, the images of effective light spot, background and non-statistical area are displayed in three colors of red, blue and green respectively, and are manually observed, judged, selected or automatically processed by computer, and the results of dot matrix selection, image segmentation and light spot selection are further optimized and adjusted ; Calculate the number of pixels included in a single array point image as the array point area Sp, count the number of bright spot pixels contained in each array point image as the total area Se of the array point bright spots, if the ratio of Se and Sp of a certain array point is greater than 0.3, it is determined that the array point is an effective spot; the test image is evenly divided into 9 regions, and the gray uniformity and distribution uniformity of the effective spot are calculated, and the gray uniformity, distribution uniformity, and gray uniformity, The sum of the distribution uniformity is used to evaluate the luminous uniformity, and the calculation formula of the brightness uniformity is:

Among them, γ ₁ , γ ₂ , ..., γ _n is the average gray level of each effective spot, γ ₀ is the average gray level of all the effective spots, s _γ is the gray standard deviation, γ is A measure of brightness uniformity;

The calculation formula of the distribution uniformity is:

Among them, β ₁ , β ₂ , ..., β _n is the number of light spots in each area after the photo is evenly divided into 9 areas, β ₀ is the average number of light spots in each area, s _β is the standard deviation of the number of light spots ; The images and data obtained during the test are manually or automatically saved on the storage medium, or output to the display for display, and can also be output to other devices for further processing.

Claims (9)

1. a dot matrix light source luminous uniformity testing method, is characterized in that comprising the following steps: (a) Obtain the image of the light emitting process of the dot matrix light source; (b) selecting a region to be tested in said image; (c) generate test images; (d) reading the gray value of each pixel in the test image; (e) Set the grayscale threshold to separate the bright spot from the background; (f) Calculate the lattice distribution structure, the shape category of the array point and its characteristic parameters, and obtain the array image; (g) Calculate the number of pixels contained in a single array point image as the area of the array point, and calculate the number of bright spot pixels contained in each array point image as the total area of the bright spot of the array point. If the total area of the bright spot of an array point is equal to If the ratio of the area of the array point is greater than the set value, the array point is determined to be an effective spot; (h) Calculate the gray level uniformity and distribution uniformity of the effective light spot, evaluate the luminous uniformity by the sum of gray level uniformity, distribution uniformity and gray level uniformity, and distribution uniformity, and the gray level uniformity The calculation formula is: Among them, γ ₁ , γ ₂ , ..., γ _n are the average gray levels of the first, second, ..., nth effective light spots, and γ ₀ is the average gray level of all the effective light spots value, s _γ is the standard deviation of the gray level, and γ is the value to measure the uniformity of the gray level; The calculation formula of the distribution uniformity is: Among them, β ₁ , β ₂ , ..., β _n are the number of light spots in the first, second, ..., nth area after the test image is evenly divided into n areas, and β ₀ is each area The average value of the number of spots, s _β is the standard deviation of the number of spots, and β is the value to measure the uniformity of the distribution; Calculate the sum of β and γ, the larger the sum of β and γ, the better the uniformity of the light source; (i) Save or output calculation results. 2. testing method according to claim 1, is characterized in that step f specifically comprises: According to the actual shape and characteristic parameters of the array points, the shape category and characteristic parameters of the array points in the test image are preliminarily set, and the partial image of a single array point is outlined, and then the characteristic parameters of the array points are fine-tuned, according to the principle that the cross-correlation value should obtain the maximum value Search for the optimal feature parameters, and finally determine the shape of a single array point, The formula for calculating the cross-correlation value is: Among them, r is the cross-correlation value, P is the set of all sub-pixel points of the local image, I is the set of all sub-pixel points corresponding to the coordinate position of P in the test image, I _ijk is the image coordinate in I is (i, j, k) the gray value of the sub-pixel, I _a is the average value of I _ijk , P _ijk is the gray value of the sub-pixel whose image coordinates are (i, j, k) in P, P _a is the value of P _ijk average value; Select an array point in the test image as the initial array point, take the center coordinates of the initial array point as the origin, calculate the lattice parameters according to the relative position and distance of each array point, and obtain the coordinates of each array point; Taking the coordinates of each array point as the center position, the expansion operation is performed according to the shape of a single array point to obtain an array image. 3. according to claim 1 or 2 described test methods, it is characterized in that step e specifically comprises: Set the allowable value of image gray scale fluctuation; If the grayscale difference between a certain pixel and 8 adjacent pixels is less than the fluctuation allowable value, the grayscale value of this pixel is determined by the maximum grayscale value among the 8 adjacent pixels; If there is at least one eight-connected path between the boundaries of the two bright spots whose grayscale change is less than the fluctuation allowable value, then merge the two bright spots into one bright spot, and all the pixels on the connected path also belong to the bright spot. spot. 4. according to the described testing method of claim 1 or 2, it is characterized in that also be provided with following steps between step b and c: Perform geometric distortion correction on the image of the region to be tested. 5. method of testing according to claim 3, is characterized in that being also provided with following steps between step b and c: Perform geometric distortion correction on the image of the region to be tested. 6. according to the described testing method of claim 1 or 2, it is characterized in that also be provided with following steps between step g and h: The images of effective light spots, background and non-statistical areas are displayed in different colors as composite images, and the results of dot matrix selection, image segmentation and light spot selection are further optimized and adjusted. 7. testing method according to claim 3, is characterized in that being also provided with following steps between step g and h: The images of effective light spots, background and non-statistical areas are displayed in different colors as composite images, and the results of dot matrix selection, image segmentation and light spot selection are further optimized and adjusted. 8. method of testing according to claim 4, is characterized in that being also provided with following steps between step g and h: The images of effective light spots, background and non-statistical areas are displayed in different colors as composite images, and the results of dot matrix selection, image segmentation and light spot selection are further optimized and adjusted. 9. method of testing according to claim 5, is characterized in that being also provided with following steps between step g and h: The images of effective light spots, background and non-statistical areas are displayed in different colors as composite images, and the results of dot matrix selection, image segmentation and light spot selection are further optimized and adjusted.