CN104655403B - Luminance uniformity test method of dot-matrix light source - Google Patents
Luminance uniformity test method of dot-matrix light source Download PDFInfo
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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
Technical field
The present invention relates to show, lighting source and Computer Image Processing field, more particularly, to the test of array light source
Method.
Background technology
Array light source refers to the set of multiple active luminous points of ordered arrangement in rows and columns, and the type of device of light source includes
LCD, LED, FED and PDP etc..With the fast development of information technology, array light source has been widely used in all kinds of multimedias
The terminal of equipment.It can serve as lighting source and backlight, it is also possible to for showing all kinds of letters such as character, figure and video
The screen of breath.The brightness uniformity of array light source is to affect the key factor of its illumination or display performance, therefore is highly desirable to
Effective method of testing is proposed to the brightness uniformity of light source, the production and research and development for related device provides guidance.
At present mainly some sampled points are randomly selected to array light source using luminance meter using method of testing tests its brightness
Value, then obtains final assessment result by certain algorithm, and this method has one-sidedness, and every time measurement can only be obtained
The data of one sampled point, obtaining test result needs repeatedly to adjust test instrunment, takes more.Have report by sensor come
The monochrome information of lattice pixels is obtained, with reference to image processing techniquess and Principle of Statistics, the grey level histogram of pixel is fitted to
Normal distribution curve, using standard deviation brightness uniformity is evaluated.Also report proposes that the luminance picture for obtaining sensor is drawn
Be divided into some subregions, calculate all subregion based on HVS（Human-eye visual characteristic）The brightness factor, the locus factor
With the grain details factor, with the feature of these factors as subregion evaluating, it is then discrete with all subregion parameter
Degree value is evaluating the brightness uniformity of light source.But above method is all that the image to obtaining directly is tested, not to clapping
Take the photograph the piecture geometry fault for causing to be corrected；Luminous point and non-luminous region are not distinguished, those should not be participated in evaluate yet
Non-luminous region also include range of value；Also, it is a test brightness uniformity, or only testing distributing homogeneity,
With one-sidedness, uniformity of luminance is not comprehensively evaluated；Above deficiency causes the evaluation to array light source uniformity of luminance not
It is enough comprehensive, accurate.
The content of the invention
It is an object of the invention to provide a kind of method for testing array light source uniformity of luminance comprehensively, exactly.
For achieving the above object, the present invention is divided into brightness uniformity and distributing homogeneity uniformity of luminance, respectively with bright
Degree uniformity, distributing homogeneity are equal with brightness uniformity and distribution from different perspectives evaluating the uniformity of luminance of array light source
Even property sum carrys out the uniformity of luminance of overall merit array light source, and carries out school to piecture geometry fault in test process
Just, non-luminous region is excluded range of value, specifically using following scheme：
With video of taking pictures, shoot intercept again video image or reading preserved image mode obtain array light source light
Image；Obtain image in choose region to be tested, region to be tested can be any one shape, be it is easy to operate, can
Being rectangle；Generate test image；Read the gray value of each pixel in the test image；Setting gray threshold, will
Speck and background segment are opened；Dot matrix distributed architecture, array point shape classification and its characteristic parameter are set, are obtained in test image I
Array dot image；Pixel quantity that single array dot image included is counted as array point area Sp, each array point is counted
Image includes the quantity of speck pixel as array point speck gross area Se, if the ratio of the Se and Sp of certain array point is more than setting
Definite value（Such as 0.3）, then judge that the array point is considered as invalid hot spot as effective light spot, otherwise；Calculate the bright of the effective light spot
Uniformity and distributing homogeneity are spent, the computing formula of brightness uniformity is：
Wherein, γ1, γ2, ……, γnIt is the average gray of each effective light spot, γ0It is all described have
The average gray of effect hot spot, sγIt is grey scale deviation, γ is less than 1 and the numerical value more than 0, for weighing brightness uniformity
Property,γCloser to 1, illustrate that the brightness of hot spot is more uniform,γCloser to 0, illustrate that the brightness uniformity of hot spot is poorer；
The computing formula of distributing homogeneity is：
Wherein, β1, β2, ……, βnIt is that photo is evenly divided into behind n region, the hot spot number in each region, β0It is
The meansigma methodss of regional hot spot number, sβIt is hot spot number standard deviation,βIt is one and is less than 1 and the numerical value more than 0, for weighs
The numerical value of brightness uniformity,βCloser to 1, illustrate that the distribution of hot spot is more uniform；βCloser to 0, the distributing homogeneity of hot spot is illustrated
It is poorer；
Calculating β and γ sums are bigger, and β and γ sums are bigger, illustrate that the uniformity of light source is better；
Image and result of calculation are preserved, or is exported to display, it is also possible to exported to other computers and make further place
Reason.
Further scheme of the invention is, after choosing region to be tested in the image for obtaining, to the region to be tested
Image carry out geometry deformity correction, regenerate test image.
After effective light spot is judged, the image of effective light spot, background and non-test region is also used respectively different colours, with
Composograph is intuitively shown, is automatically processed by manual observation, judgement, selection or computer, to lattice parameter, image point
Cut and further optimized and revised with hot spot selection result, obtain more accurate result.
Another further scheme of the invention is to calculate dot matrix distributed architecture, array point shape classification and its feature ginseng
The step of number, acquisition array image, specifically adopts with the following method：According to the true form of array point（Such as circular, oval, side
Shape, rectangle and arbitrary polygon）And characteristic parameter（The such as length of side and radius）The preliminary shape that array point in test image is set
Classification and its characteristic parameter, sketch the contours of single array dot image, then finely tune array point characteristic parameter, should obtain by cross correlation value r
The characteristic parameter of the principle search optimum of maximum, finally determines the shape of single array point,
The computing formula of the r is：
Wherein, P is the set of all sub-pixel points of topography, and I is corresponding with P coordinate positions all in test image
The set of sub-pixel point, IijkIt is for image coordinate in I（i, j, k）Sub-pixel point gray value, IaFor IijkMeansigma methodss,
PijkIt is for image coordinate in P（i, j, k）Sub-pixel point gray value, PaFor PijkMeansigma methodss；
An array point is selected in test image as staring array point, with staring array dot center coordinate as origin,
Lattice parameter is calculated according to the relative position and distance of each array point, the coordinate of each array point is drawn；With each array
Position carries out dilation operation by the shape of single array point centered on point coordinates, obtains array image.
Another further scheme of the invention is to fluctuate feasible value whether to judge grey scale change by noise using gray scale
Cause, concrete scheme is to set gradation of image fluctuation feasible value Tc；If certain pixel is less than Tc with the gray scale difference of neighbouring 8 pixels,
Then judge that grey scale change is caused by noise, with the gray value that the gray scale maximum in neighbouring 8 pixels is the pixel；If having two
There is eight connectivity path of at least one grey scale change less than Tc between the border of the individual speck, then merge into two specks
One speck, all pixels on communication path are also under the jurisdiction of the speck.
Due to adopting such scheme, the present invention to bring following technique effect compared with prior art：Using to test
Image carries out geometry deformity correction, it is possible to reduce image fault；Using hot spot and background segment are opened, those can be removed and should not
Participate in the non-luminous region evaluated；Preferable gray scale threshold is obtained by the way of artificial setting and the automatic calculations incorporated of computer
Value, is accurately opened speck with background segment using gray threshold；Invalid hot spot is excluded using dot matrix image is calculated；Using general
The image of effective light spot, background and non-statistical region shows respectively with different colours with composograph, gives people direct feel, utilizes
Gray scale fluctuates feasible value come the impact that abates the noise；Manual intervention is arranged to dot matrix, image segmentation and hot spot selection result are further
Optimize and revise；Above-mentioned technological means can make test result more objective, accurate；It is equal using calculating brightness uniformity, distribution respectively
Even property and sum of the two evaluating uniformity of luminance, then more fully.Using brightness uniformity, distributing homogeneity and sum of the two
To evaluate uniformity of luminance, more comprehensively, therefore the present invention relative to existing method of testing more comprehensively, it is objective and accurate, and
Effectively, intuitively it is, easy.
Description of the drawings
Fig. 1 is the flow chart of the present invention；
Fig. 2 is the lattice luminous image that embodiment shoots；
Fig. 3 is the test image obtained after selection region and deformity correction；
Fig. 4 is single array point schematic shapes
Fig. 5 is array image schematic diagram.
Specific embodiment
Below in conjunction with the accompanying drawings example elaborates to the present invention.
As shown in Fig. 1, the present embodiment is comprised the concrete steps that：
With a 2 shooting luminescence process of array light source 1 is shot, video is sent to by computer by the USB interface of computer 3
3, then the luminescent image of array light source 1 is intercepted from video, one rectangle of selection region to be tested in the luminescent image, such as
Shown in Fig. 2；Geometry deformity correction is carried out to the image in rectangle region to be tested, test image is generated, as shown in Fig. 3；Read
Take the gray value of each pixel in the test image；Setting gradation of image fluctuation feasible value Tc, if certain pixel and neighbouring 8
The gray scale difference of individual pixel is less than Tc, then judge that grey scale change is caused by noise, is with the gray scale maximum in neighbouring 8 pixels
The gray value of the pixel；Artificial setting gradation of image threshold value Tr, to pixel of the gray value less than Tr, judges that it is not belonging to speck,
The global gray threshold and local threshold of test image are further calculated with ostu image segmentation thresholds algorithm, by speck 5 and the back of the body
Scape is separated；If have the presence of eight connectivity path of at least one grey scale change less than Tc between the border of two specks, by two
Individual speck merges into a speck, and all pixels on communication path are also under the jurisdiction of the speck；According to the true form of array point
And feature, array point in test image is set and is shaped as ellipse, it is short including semi-major axis and half that its characteristic parameter is tentatively set
The numerical value of axle, sketches the contours of single array dot image 4, then finely tunes array point characteristic parameter, and by cross correlation value r maximum should be obtained
The optimum characteristic parameter semi-major axis of principle search or semi-minor axis numerical value, finally determine the shape of single array point,
The computing formula of the r is：
Wherein, P is the set of all sub-pixel points of topography, and I is corresponding with P coordinate positions all in test image
The set of sub-pixel point, IijkIt is for image coordinate in I（i, j, k）Sub-pixel point gray value, IaFor IijkMeansigma methodss,
PijkIt is for image coordinate in P（i, j, k）Sub-pixel point gray value, PaFor PijkMeansigma methodss；
An array point P is selected in test image00As the staring array point positioned at the row of the 0th row the 0th, if its coordinate is
(x0, y0), according to the relative position and distance of each array point lattice parameter Lx1, Lx2, Ly1 and Ly2, such as Fig. 5 institutes are calculated
Show, then General Cell point PmnCoordinate be (x0+m*Lx1+n*Lx2, y0+m*Ly1+n*Ly2), wherein m and n be specific integer,
Represent that array point is arranged positioned at m rows n-th（As shown in Figure 5）；Position is by single array point centered on each array point coordinates
Shape carries out dilation operation, obtains array image；Test image is shown with composograph, wherein, effective light spot, background and non-
The image of statistical regions shows respectively with red, blue, green three kinds of colors, by manual observation, judgement, select or computer automatically
Reason, chooses result and further optimizes and revises to dot matrix selection, image segmentation and hot spot；Calculate what single array dot image was included
Pixel quantity is array point area Sp, counts the quantity that each array dot image includes speck pixel total as array point speck
Area Se, if the ratio of the Se and Sp of certain array point is more than 0.3, judges the array point as effective light spot；Test image is equal
It is divided into 9 regions evenly, the gradation uniformity and distributing homogeneity of effective light spot is calculated, with gradation uniformity, distributing homogeneity
And gradation uniformity, distributing homogeneity sum are evaluating uniformity of luminance, the computing formula of the brightness uniformity is：
Wherein, γ1, γ2, ……, γnIt is the average gray of each effective light spot, γ0It is all described have
The average gray of effect hot spot, sγIt is grey scale deviation, γ is the numerical value for weighing brightness uniformity；
The computing formula of the distributing homogeneity is：
Wherein, β1, β2, ……, βnIt is that photo is evenly divided into behind 9 regions, the hot spot number in each region, β0It is
The meansigma methodss of regional hot spot number, sβIt is hot spot number standard deviation；By the image obtained in test process, data by artificial or
Self-timing is preserved on a storage medium, or is exported to display and shown, it is also possible to is exported to other equipment and is made further
Process.
Claims (9)
1. a kind of array light source uniformity of luminance method of testing, it is characterised in that comprise the following steps：
（a）Obtain the image of array light source luminescence process；
（b）Region to be tested is chosen in described image；
（c）Generate test image；
（d）Read the gray value of each pixel in the test image；
（e）Setting gray threshold, speck and background segment are opened；
（f）Dot matrix distributed architecture, array point shape classification and its characteristic parameter are calculated, array image is obtained；
（g）Pixel quantity that single array dot image included is calculated as array point area, each array dot image institute is calculated
Quantity comprising speck pixel as array point the speck gross area, if the speck gross area of certain array point and array point area
Ratio is more than setting value, then judge the array point as effective light spot；
（h）The gradation uniformity and distributing homogeneity of the effective light spot are calculated, with gradation uniformity, distributing homogeneity and ash
Spend uniformity, distributing homogeneity sum to evaluate uniformity of luminance, the computing formula of the gradation uniformity is：
Wherein, γ1, γ2, ……, γnFirst, second ..., the gray scale of effective light spot described in n-th it is average
Value, γ0It is the average gray of all effective light spots, sγIt is grey scale deviation, γ is the number for weighing gradation uniformity
Value；
The computing formula of the distributing homogeneity is：
Wherein, β1, β2, ……, βnThat test image is evenly divided into behind n region, first, second ...,
The hot spot number in n region, β0It is the meansigma methodss of regional hot spot number, sβIt is hot spot number standard deviation, β is to weigh to be evenly distributed
The numerical value of property；
β and γ sums are calculated, β and γ sums are bigger, illustrate that the uniformity of light source is better；
（i）Preserve or export result of calculation.
2. method of testing according to claim 1, it is characterised in that step f is specifically included：
The shape classification and its feature of array point in test image are tentatively arranged according to the true form and characteristic parameter of array point
Parameter, sketches the contours of the topography of single array point, then finely tunes array point characteristic parameter, and by cross correlation value maximum should be obtained
The optimum characteristic parameter of principle search, finally determines the shape of single array point,
The computing formula of the cross correlation value is：
Wherein, r is cross correlation value, P for all sub-pixel points of topography set, I be in test image with P coordinate positions pair
The set of all sub-pixel points answered, IijkIt is for image coordinate in I（i, j, k）Sub-pixel point gray value, IaFor Iijk
Meansigma methodss, PijkIt is for image coordinate in P（i, j, k）Sub-pixel point gray value, PaFor PijkMeansigma methodss；
An array point is selected in test image as staring array point, with staring array dot center coordinate as origin, according to
The relative position and distance of each array point calculates lattice parameter, draws the coordinate of each array point；
Position carries out dilation operation by the shape of single array point centered on each array point coordinates, obtains array image.
3. method of testing according to claim 1 and 2, it is characterised in that step e is specifically included：
Setting gradation of image fluctuation feasible value；
If certain pixel is less than fluctuation feasible value with the gray scale difference of neighbouring 8 pixels, the gray value of the pixel is by neighbouring 8 pixels
Gray scale maximum determine；
If have the presence of eight connectivity path of at least one grey scale change less than fluctuation feasible value between the border of two specks,
Then two specks are merged into into a speck, all pixels on communication path are also under the jurisdiction of the speck.
4. method of testing according to claim 1 and 2, it is characterised in that following steps are additionally provided between step b and c：
Geometry deformity correction is carried out to the image in the region to be tested.
5. method of testing according to claim 3, it is characterised in that following steps are additionally provided between step b and c：
Geometry deformity correction is carried out to the image in the region to be tested.
6. method of testing according to claim 1 and 2, it is characterised in that following steps are additionally provided between step g and h：
The image of effective light spot, background and non-statistical region is shown respectively with different colours with composograph, dot matrix is selected,
Image segmentation and hot spot are chosen result and are further optimized and revised.
7. method of testing according to claim 3, it is characterised in that following steps are additionally provided between step g and h：
The image of effective light spot, background and non-statistical region is shown respectively with different colours with composograph, dot matrix is selected,
Image segmentation and hot spot are chosen result and are further optimized and revised.
8. method of testing according to claim 4, it is characterised in that following steps are additionally provided between step g and h：
The image of effective light spot, background and non-statistical region is shown respectively with different colours with composograph, dot matrix is selected,
Image segmentation and hot spot are chosen result and are further optimized and revised.
9. method of testing according to claim 5, it is characterised in that following steps are additionally provided between step g and h：
The image of effective light spot, background and non-statistical region is shown respectively with different colours with composograph, dot matrix is selected,
Image segmentation and hot spot are chosen result and are further optimized and revised.
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| CN105243970B (en) * | 2015-11-19 | 2018-10-09 | 广东威创视讯科技股份有限公司 | A kind of LED uniformities appraisal procedure and device |
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| CN111767623B (en) * | 2019-03-14 | 2022-04-29 | 华东交通大学 | Novel method for evaluating full-range illumination uniformity based on human vision |
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| CN111609998A (en) * | 2020-05-11 | 2020-09-01 | 歌尔股份有限公司 | Detection method and detection device for illumination uniformity and readable storage medium |
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| CN112747906B (en) * | 2021-01-28 | 2022-07-22 | 歌尔光学科技有限公司 | Light source detection method, detection device and readable storage medium |
| CN113075135B (en) * | 2021-03-31 | 2022-07-15 | 中国科学院长春光学精密机械与物理研究所 | Method for correcting accurate position of light spot array of snapshot type imaging spectrometer |
| CN113281310B (en) * | 2021-04-06 | 2022-12-02 | 安徽工程大学 | Method for detecting light transmittance and uniformity of optical medium material |
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