CN102158630A - Image-tone-based adaptive green-noise screening method - Google Patents
Image-tone-based adaptive green-noise screening method Download PDFInfo
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Abstract
The invention relates to an adaptive green-noise screening method, in particular to an image-tone-based adaptive green-noise screening method. In the method, a scanning direction of a scanned line is changed according to the parity of the scanned line, and two error diffusion filters consistent with human visual laws are designed. One of 0 and 1 is randomly generated by a computer to select a corresponding error diffusion filter. Finally, the calculation of optimal hysteresis coefficients on each grayscale tone is highlighted, and a corresponding optimal hysteresis coefficient is selected according to an image tone. Therefore, the method has the advantages of effectively solving the problem of visual nonuniformity of an ordinary green-noise screening algorithm to make halftone image dots uniformly scattered, and discreetly presenting image details to make an image output by equipment can greatly meet the requirements of actual printing digital proof in the level of visual effects and details.
Description
Technical field
The present invention relates to the green noise screening method of a kind of self adaptation, especially relate to the green noise screening method of a kind of self adaptation based on the image contrast.
Background technology
The screening plate-making technology that relates generally to printer and high-grade plate making equipment is duplicated in the hard copy of image.Be used for the screening technology that the image hard copy duplicates and be called the digital picture halftone technique again.The digital halftone technology generally can be divided into two big classes: am screening and fm screening.
Am screening is that the site is arranged in order equably, reflects the depth of contrast by the size that changes the site, is a kind of meshing method of putting gathering.But owing to be easy to produce optic interference during the stack of the am screening of different screen angles, be said in the printing field " moire ", and am screening is easy to produce the saltus step of continuous accent level and loses in continuous level changes, and therefore generally do not adopt am screening in printing and high-grade printing process.
Fm screening be with equirotal site with at random or the mode of approximate random arrange, reflect the depth of contrast by the density that changes site in the unit are, be the discrete meshing method of a kind of point.Screening is carried out in the less and equirotal site of fm screening usable floor area, and these little sites are because girth area ratio is big, so the some gain is apparent in view; And because the site is too small, no marking on some hardware, can occurs and bite, appearance point is lost.
At existing error diffusion at that time and existing these the two kinds of problems of other fm screening methods, document " Green-noise digital halftoning " (D L Lau, G R Arce, N C Gallagher. Proc IEEE, 1998; 86; 2424-2444) green noise screening algorithm is proposed first, it combines the characteristics of amplitude modulation and high frequency halftone technique, the site is assembled producing bunch point, and according to gray scale, change the size of bunch point and the distance between bunch point, thereby improved some gain effectively and put the problem of being brought of losing.In the processing of green noise screening algorithm, (m n) is sequentially handled the pixel g of original image line by line, in the process of handling, each pixel is carried out threshold operation (quantizer Quantizer) with a threshold value T, the result of computing be converted into the respective pixel b of half tone image (m, n).With this pixel b (m as a result, n) and asked the pixel g (m of threshold value, n) compare, and with the difference e (m that relatively tries to achieve, n) diffuse on the current processed pixels not processed pixel on every side with an error diffusion filter, when next processes pixel, the pixel value of being asked threshold value be original copy pixel g (m, n) and be flooded to error on this pixel and; Meanwhile, with this pixel b (m, pixel value and sluggish coefficient h and difference e (m of having exported n) and around it as a result, n) multiply each other and carry out certain matrix operation, obtain output value of feedback d, when next processes pixel, threshold value be original threshold value T with the output value of feedback d's and.Green noise Screening Image quality according to above-mentioned algorithm is realized by the reality test, obtains Fig. 2, and the site is comparatively obvious in high light and shadow part bunch point as can be seen from Figure 2, presents tangible granular sensation, visually causes uneven sense for the people.Bunch point that simultaneously this several sites are formed can be lost some image details also seeming coarse aspect the performance high frequency details.Therefore find out that this green noise screening algorithm exists potential deficiency.
Summary of the invention
The present invention mainly is that solution existing in prior technology site is comparatively obvious in high light and shadow part bunch point, presents tangible granular sensation, visually causes uneven sense for the people.Bunch point that simultaneously this several sites are formed is also seeming coarse aspect the performance high frequency details, can lose the technical problem of some image details etc.; Provide that a kind of can to solve common green noise screening algorithm vision effectively inhomogeneous, the half tone image site is uniformly dispersed, and can show image detail fine and smoothly, make the image of equipment output on visual effect and level of detail, can greatly satisfy the requirement of actual print digital sample printing.
Above-mentioned technical problem of the present invention is mainly solved by following technical proposals:
The green noise screening method of a kind of self adaptation based on the image contrast is characterized in that, may further comprise the steps:
Step 1, with the pending pixel g of original image (m n) compares with threshold value T, the result of processing be converted to half tone image pixel value b (m, n); Read pending pixel g (m, the scanning direction in the time of n) is selected to carry out according to the parity of m: when m was odd number, the scanning direction was for from right to left; When m was even number, the scanning direction was for from left to right;
Step 2, with b (m, n) with g (m n) compares, try to achieve error amount e (m, n);
Step 3, by the error diffusion filter with e (m n) diffuses to around the current processed pixels on the untreated pixel, promptly the pixel value that is spread equal the original image pixel value with by the propagated error sum;
Step 4, according to current processed pixels g (m, n) and on every side the output valve of processed pixels and optimum sluggish coefficient h calculated threshold value of feedback d change threshold value T;
Step 5, repeating step 1 be to step 4, after all processes pixel are finished on original image, generates the half tone image of original image.
At the above-mentioned green noise screening method of a kind of self adaptation based on the image contrast, in the described step 1, just defining threshold value T is 0.5, back threshold value T=T+d;
At the above-mentioned green noise screening method of a kind of self adaptation based on the image contrast, in the described step 2, error amount e (m, concrete obtaining step n) is as follows:
e(m,n)=g(m,n)-b(m,n);
At the above-mentioned green noise screening method of a kind of self adaptation based on the image contrast, in the described step 3, described error diffusion filter is two, be defined as error diffusion filter A1 and error diffusion filter A2, by producing in 0 or 1 liang of number at random, select corresponding error diffusion filter execution in step 3, concrete diffusing step is as follows:
Step 4.1, when definition scans from left to right, error diffusion filter A1 passes to the coefficient value A1 (0 of the error diffusion filter of current some right pixel, 1)=4/7, passes to the coefficient value A1 (1,0)=3/7 of the error diffusion filter of current some lower pixel, pass to the coefficient value A1 (1 of the error diffusion filter of current some lower left corner pixel,-1)=0, passes to the coefficient value A1 (1,1)=0 of the error diffusion filter of current some lower right corner pixel; Error diffusion filter A2 passes to the coefficient value A1 (0 of the error diffusion filter of current some right pixel, 1)=2/5, pass to the coefficient value A1 (1 of the error diffusion filter of current some lower pixel, 0)=3/5, pass to the coefficient value A1 (1 of the error diffusion filter of current some lower left corner pixel,-1)=0, passes to the coefficient value A1 (1,1)=0 of the error diffusion filter of current some lower right corner pixel;
When scanning from right to left, error diffusion filter A1 passes to the coefficient value A1 (0 of the error diffusion filter of current some left pixel, 1)=4/7, pass to the coefficient value A1 (1 of the error diffusion filter of current some lower pixel, 0)=3/7, passes to the coefficient value A1 (1 ,-1)=0 of the error diffusion filter of current some lower left corner pixel, pass to the coefficient value A1 (1,1)=0 of the error diffusion filter of current some lower right corner pixel; Error diffusion filter A2 passes to the coefficient value A1 (0 of the error diffusion filter of current some left pixel, 1)=2/5, pass to the coefficient value A1 (1 of the error diffusion filter of current some lower pixel, 0)=3/5, pass to the coefficient value A1 (1 of the error diffusion filter of current some lower left corner pixel,-1)=0, passes to the coefficient value A1 (1,1)=0 of the error diffusion filter of current some lower right corner pixel;
Step 4.2 according to the odd even different scanning direction difference of line number, is got (i, the image pixel of j) locating;
Step 4.3, calculate the error of input gray level with respect to output gray level: e (m, n)=g (m, n)-b (m, n);
Step 4.4, use the error diffusion filter A1 or the error diffusion filter A2 of picked at random, (m n) multiply by the coefficient of diffusion filter respectively with error e, then respectively with the original image pixel addition of relevant position, and the result replaced the corresponding pixel value of original image.
At the above-mentioned green noise screening method of a kind of self adaptation based on the image contrast, in the described step 4, current process points is with the concrete obtaining step of the output valve of process points is as follows on every side:
When scanning from left to right, read the output valve of the upper right corner pixel of the top pixel of the top left corner pixel point of the left pixel point of current process points, current process points, current process points, current process points and current process points; When scanning from right to left, read the output valve of the upper right corner pixel of the top pixel of the top left corner pixel point of the right pixel point of current process points, current process points, current process points, current process points and current process points;
According to the scanning direction to current process points with on every side process points totally five output valves store, for after threshold calculations do data and prepare.
At the above-mentioned green noise screening method of a kind of self adaptation based on the image contrast, in the described step 4, before the halftoning screening is handled, ask for the optimum sluggish coefficient h of each gray scale in advance, concrete obtaining step is as follows:
A) gray scale of generation 256 * 256 pixels is 0,10,20,30,40,50,60,70,
80,90,100,110,120,130,140,150,160,170,180,190,200,210,220,230,240,255 have 26 gray level images altogether;
B) sluggish coefficient from 0 to 1.0, changes 0.05 at every turn; The sluggish coefficient of choosing after the change is right
26 gray level images that step a generated have snake scan and the green noise screening processing of Select Error diffusion filter at random, and screening result and former figure are asked for mean square deviation; Wherein the mean square deviation formula is:
(m is former gray-scale map in that ((m n) is gray value after screening is handled to y for m, the n) gray value of position n) to x in the formula;
C) in a gray scale, select the pairing sluggish coefficient of Minimum Mean Square Error, save as this
The optimum sluggish coefficient gray scale 0,10,20,30,40,50,60,70,80 of gray scale, 90,100,110,120,130,140,150,160,170,180,190,200,210,220,230,240,255 optimum sluggish coefficient is respectively: 1,0.95,0.9,0.8,0.7,0.65,0.4,0.3,0.2,0.2,0.25,0.2,0.15,0.2,0.1,0.15,0.1,0.5,0.15,0.2,0.3,0.55,0.6,0.75,0.9,1;
D) above-mentioned data are carried out the one-dimensional linear interpolation, calculate the optimum sluggish coefficient of each gray scale.
At the above-mentioned green noise screening method of a kind of self adaptation based on the image contrast, in the described step 4, threshold feedback value d is by with current process points and the output valve of process points and optimum sluggish coefficient h, error e (m on every side, n) multiply each other, and do computing with transformation matrix and obtain, thereby calculate new threshold value: T=T+d; Wherein, described matrixing concrete operation method is:
1) transformation matrix when scanning from left to right
, conversion when scanning from right to left
Matrix
, it is capable to establish transformation matrix S i, the number of j row be S (i-2, j-2);
2) d=h*(S(-1,-1)*b(m-1,n-1)+S(-1,0)*b(m-1,n)+S(-1,+1)*b(m-1,n
+ 1)+and S (0 ,-1) * b (m, n-1)+(m, n)+S (1,1) * b (m+1, n+1)), (i is current process points and the output valve of process points on every side j) to b to S (0,0) * b.
Therefore, the present invention has following advantage: it is inhomogeneous to solve common green noise screening algorithm vision effectively, the half tone image site is uniformly dispersed, and can show image detail fine and smoothly, make the image of equipment output on visual effect and level of detail, can greatly satisfy the requirement of actual print digital sample printing.
Description of drawings
Fig. 1 is the disclosed green noise screening algorithm principle schematic diagram of documents;
Fig. 2 is common green noise screening algorithm site gradual change schematic diagram;
Fig. 3 is the present invention increases line number on common green noise screening algorithm basis the different site gradual change schematic diagram of odd even different scanning direction;
Fig. 4 is an error diffusion filter schematic among the present invention;
Fig. 5 adds the green noise screening method of Select Error filter contrast gradual change schematic diagram at random;
Fig. 6 is based on the green noise screening method of the self adaptation contrast gradual change schematic diagram of image contrast.
Embodiment
Below by embodiment, and in conjunction with the accompanying drawings, technical scheme of the present invention is described in further detail.
Embodiment:
The green noise screening method of a kind of self adaptation based on the image contrast may further comprise the steps:
Step 1, (m n) compares with threshold value T, and just defining threshold value T is 0.5, back threshold value T=T+d with the pending pixel g of original image; The result who handles be converted to half tone image pixel value b (m, n); Read pending pixel g (m, the scanning direction in the time of n) is selected to carry out according to the parity of m: when m was odd number, the scanning direction was for from right to left; When m was even number, the scanning direction was for from left to right;
Step 2, with b (m, n) with g (m n) compares, try to achieve error amount e (m, n); Error amount e (m, n)=g (m, n)-b (m, n);
Step 3, by the error diffusion filter with e (m n) diffuses to around the current processed pixels on the untreated pixel, promptly the pixel value that is spread equal the original image pixel value with by the propagated error sum; Therefore green noise screening algorithm has the characteristic of certain am screening owing to produced a bunch point, can be at certain several gray scale occurrence law striped.These regular stripeds have not only had a strong impact on the half tone image visual effect of green noise screening, and also may produce moire when polychrome is superimposed, as shown in Figure 3.In order to address this problem, we have designed two error diffusion filters that meet the human vision rule.By one in 0 or 1 liang of number of computer random generation, select the error diffusion filter of correspondence, make the position, site become more even, more at random, as shown in Figure 5.Definition error diffusion filter A1 and error diffusion filter A2 select corresponding error diffusion filter execution in step 3, and concrete diffusing step is as follows:
Step 4.1, when definition scans from left to right, error diffusion filter A1 passes to the coefficient value A1 (0 of the error diffusion filter of current some right pixel, 1)=4/7, passes to the coefficient value A1 (1,0)=3/7 of the error diffusion filter of current some lower pixel, pass to the coefficient value A1 (1 of the error diffusion filter of current some lower left corner pixel,-1)=0, passes to the coefficient value A1 (1,1)=0 of the error diffusion filter of current some lower right corner pixel; Error diffusion filter A2 passes to the coefficient value A1 (0 of the error diffusion filter of current some right pixel, 1)=2/5, pass to the coefficient value A1 (1 of the error diffusion filter of current some lower pixel, 0)=3/5, pass to the coefficient value A1 (1 of the error diffusion filter of current some lower left corner pixel,-1)=0, passes to the coefficient value A1 (1,1)=0 of the error diffusion filter of current some lower right corner pixel;
When scanning from right to left, error diffusion filter A1 passes to the coefficient value A1 (0 of the error diffusion filter of current some left pixel, 1)=4/7, pass to the coefficient value A1 (1 of the error diffusion filter of current some lower pixel, 0)=3/7, passes to the coefficient value A1 (1 ,-1)=0 of the error diffusion filter of current some lower left corner pixel, pass to the coefficient value A1 (1,1)=0 of the error diffusion filter of current some lower right corner pixel; Error diffusion filter A2 passes to the coefficient value A1 (0 of the error diffusion filter of current some left pixel, 1)=2/5, pass to the coefficient value A1 (1 of the error diffusion filter of current some lower pixel, 0)=3/5, pass to the coefficient value A1 (1 of the error diffusion filter of current some lower left corner pixel,-1)=0, passes to the coefficient value A1 (1,1)=0 of the error diffusion filter of current some lower right corner pixel;
Step 4.2 according to the odd even different scanning direction difference of line number, is got (i, the image pixel of j) locating;
Step 4.3, calculate the error of input gray level with respect to output gray level: e (m, n)=g (m, n)-b (m, n);
Step 4.4, use the error diffusion filter A1 or the error diffusion filter A2 of picked at random, (m n) multiply by the coefficient of diffusion filter respectively with error e, then respectively with the original image pixel addition of relevant position, and the result replaced the corresponding pixel value of original image; Current process points is with the concrete obtaining step of the output valve of process points is as follows on every side:
When scanning from left to right, read the output valve of the upper right corner pixel of the top pixel of the top left corner pixel point of the left pixel point of current process points, current process points, current process points, current process points and current process points; When scanning from right to left, read the output valve of the upper right corner pixel of the top pixel of the top left corner pixel point of the right pixel point of current process points, current process points, current process points, current process points and current process points;
According to the scanning direction to current process points with on every side process points totally five output valves store, for after threshold calculations do data and prepare.
Above-mentioned steps 4.4 programs are as follows:
Begin=0;
nEnd=m-1;
nStep=1;
For(i=0;i<n;i++)
{
For(j=nBegin;j!=nEnd+nStep;x+=nStep)
{ according to the odd even different scanning direction difference of line number
Get (i, the image pixel of j) locating: p=N
Ij;
if(p>T)
B
ij=255;
else
B
ij=0;
Calculate the error of input gray level with respect to output gray level: Err=N
Ij-B
Ij;
if(rand()%2)
Use error diffusion filter A1 multiply by the coefficient of the diffusion filter among Fig. 4 respectively with error E rr, and then respectively with the original image pixel addition of relevant position, and the result replaced the corresponding pixel value of original image.}
else
Use error diffusion filter A2 multiply by the coefficient of the diffusion filter among Fig. 4 respectively with error E rr, and then respectively with the original image pixel addition of relevant position, and the result replaced the corresponding pixel value of original image.}
With current process points with on every side the output valve of process points and sluggish coefficient h=0.5, error E rr multiply each other, and do certain matrixing, calculate and export feedback threshold d, calculate new threshold value: T=T+d;
}
nStep=-nStep;
nTemp=nBegin;
nBegin=nEnd;
nEnd=nTemp;
}
Step 4, according to current processed pixels g (m, n) and on every side the output valve of processed pixels and optimum sluggish coefficient h calculated threshold value of feedback d change threshold value T; Before the halftoning screening is handled, ask for the optimum sluggish coefficient h of each gray scale in advance, concrete obtaining step is as follows:
A) gray scale of generation 256 * 256 pixels is 0,10,20,30,40,50,60,70,
80,90,100,110,120,130,140,150,160,170,180,190,200,210,220,230,240,255 have 26 gray level images altogether;
B) sluggish coefficient from 0 to 1.0, changes 0.05 at every turn; The sluggish coefficient of choosing after the change is right
26 gray level images that step a generated have snake scan and the green noise screening processing of Select Error diffusion filter at random, and screening result and former figure are asked for mean square deviation; Wherein the mean square deviation formula is:
(m is former gray-scale map in that ((m n) is gray value after screening is handled to y for m, the n) gray value of position n) to x in the formula;
C) in a gray scale, select the pairing sluggish coefficient of Minimum Mean Square Error, save as this
The optimum sluggish coefficient gray scale 0,10,20,30,40,50,60,70,80 of gray scale, 90,100,110,120,130,140,150,160,170,180,190,200,210,220,230,240,255 optimum sluggish coefficient is respectively: 1,0.95,0.9,0.8,0.7,0.65,0.4,0.3,0.2,0.2,0.25,0.2,0.15,0.2,0.1,0.15,0.1,0.5,0.15,0.2,0.3,0.55,0.6,0.75,0.9,1;
D) above-mentioned data are carried out the one-dimensional linear interpolation, calculate the optimum sluggish coefficient of each gray scale.
Then, threshold feedback value d by with current process points with on every side the output valve of process points and optimum sluggish coefficient h, error e (m n) multiplies each other, and does computing with transformation matrix and obtain, thereby calculates new threshold value: T=T+d; Wherein, described matrixing concrete operation method is:
1) transformation matrix when scanning from left to right
, conversion when scanning from right to left
Matrix
, it is capable to establish transformation matrix S i, the number of j row be S (i-2, j-2);
2）d=h*(S(-1,-1)*b(m-1,n-1)+S(-1,0)*b(m-1,n)+S(-1,+1)*b(m-1,n
+ 1)+and S (0 ,-1) * b (m, n-1)+(m, n)+S (1,1) * b (m+1, n+1)), (i is current process points and the output valve of process points on every side j) to b to S (0,0) * b.
Step 5, repeating step 1 be to step 4, after all processes pixel are finished on original image, generates the half tone image of original image.
Specific embodiment described herein only is that the present invention's spirit is illustrated.The technical staff of the technical field of the invention can make various modifications or replenishes or adopt similar mode to substitute described specific embodiment, but can't depart from spirit of the present invention or surmount the defined scope of appended claims.
Claims (7)
1. the green noise screening method of the self adaptation based on the image contrast is characterized in that, may further comprise the steps:
Step 1, with the pending pixel g of original image (m n) compares with threshold value T, the result of processing be converted to half tone image pixel value b (m, n); Read pending pixel g (m, the scanning direction in the time of n) is selected to carry out according to the parity of m: when m was odd number, the scanning direction was for from right to left; When m was even number, the scanning direction was for from left to right;
Step 2, with b (m, n) with g (m n) compares, try to achieve error amount e (m, n);
Step 3, by the error diffusion filter with e (m n) diffuses to around the current processed pixels on the untreated pixel, promptly the pixel value that is spread equal the original image pixel value with by the propagated error sum;
Step 4, according to current processed pixels g (m, n) and on every side the output valve of processed pixels and optimum sluggish coefficient h calculated threshold value of feedback d change threshold value T;
Step 5, repeating step 1 be to step 4, after all processes pixel are finished on original image, generates the half tone image of original image.
2. the green noise screening method of a kind of self adaptation based on the image contrast according to claim 1 is characterized in that, in the described step 1, just defining threshold value T is 0.5, back threshold value T=T+d.
3. the green noise screening method of a kind of self adaptation based on the image contrast according to claim 1 is characterized in that, in the described step 2, error amount e (m, concrete obtaining step n) is as follows:
e(m,n)=g(m,n)-b(m,n)。
4. the green noise screening method of a kind of self adaptation according to claim 1 based on the image contrast, it is characterized in that, in the described step 3, described error diffusion filter is two, be defined as error diffusion filter A1 and error diffusion filter A2, by producing in 0 or 1 liang of number at random, select corresponding error diffusion filter execution in step 3, concrete diffusing step is as follows:
Step 4.1, when definition scans from left to right, error diffusion filter A1 passes to the coefficient value A1 (0 of the error diffusion filter of current some right pixel, 1)=4/7, passes to the coefficient value A1 (1,0)=3/7 of the error diffusion filter of current some lower pixel, pass to the coefficient value A1 (1 of the error diffusion filter of current some lower left corner pixel,-1)=0, passes to the coefficient value A1 (1,1)=0 of the error diffusion filter of current some lower right corner pixel; Error diffusion filter A2 passes to the coefficient value A1 (0 of the error diffusion filter of current some right pixel, 1)=2/5, pass to the coefficient value A1 (1 of the error diffusion filter of current some lower pixel, 0)=3/5, pass to the coefficient value A1 (1 of the error diffusion filter of current some lower left corner pixel,-1)=0, passes to the coefficient value A1 (1,1)=0 of the error diffusion filter of current some lower right corner pixel;
When scanning from right to left, error diffusion filter A1 passes to the coefficient value A1 (0 of the error diffusion filter of current some left pixel, 1)=4/7, pass to the coefficient value A1 (1 of the error diffusion filter of current some lower pixel, 0)=3/7, passes to the coefficient value A1 (1 ,-1)=0 of the error diffusion filter of current some lower left corner pixel, pass to the coefficient value A1 (1,1)=0 of the error diffusion filter of current some lower right corner pixel; Error diffusion filter A2 passes to the coefficient value A1 (0 of the error diffusion filter of current some left pixel, 1)=2/5, pass to the coefficient value A1 (1 of the error diffusion filter of current some lower pixel, 0)=3/5, pass to the coefficient value A1 (1 of the error diffusion filter of current some lower left corner pixel,-1)=0, passes to the coefficient value A1 (1,1)=0 of the error diffusion filter of current some lower right corner pixel;
Step 4.2 according to the odd even different scanning direction difference of line number, is got (i, the image pixel of j) locating;
Step 4.3, calculate the error of input gray level with respect to output gray level: e (m, n)=g (m, n)-b (m, n);
Step 4.4, use the error diffusion filter A1 or the error diffusion filter A2 of picked at random, (m n) multiply by the coefficient of diffusion filter respectively with error e, then respectively with the original image pixel addition of relevant position, and the result replaced the corresponding pixel value of original image.
5. the green noise screening method of a kind of self adaptation based on the image contrast according to claim 1 is characterized in that, in the described step 4, current process points is with the concrete obtaining step of the output valve of process points is as follows on every side:
When scanning from left to right, read the output valve of the upper right corner pixel of the top pixel of the top left corner pixel point of the left pixel point of current process points, current process points, current process points, current process points and current process points; When scanning from right to left, read the output valve of the upper right corner pixel of the top pixel of the top left corner pixel point of the right pixel point of current process points, current process points, current process points, current process points and current process points;
According to the scanning direction to current process points with on every side process points totally five output valves store, for after threshold calculations do data and prepare.
6. the green noise screening method of a kind of self adaptation based on the image contrast according to claim 1 is characterized in that, in the described step 4, before the halftoning screening is handled, asks for the optimum sluggish coefficient h of each gray scale in advance, and concrete obtaining step is as follows:
A) gray scale of generation 256 * 256 pixels is 0,10,20,30,40,50,60,70,
80,90,100,110,120,130,140,150,160,170,180,190,200,210,220,230,240,255 have 26 gray level images altogether;
B) sluggish coefficient from 0 to 1.0, changes 0.05 at every turn; The sluggish coefficient of choosing after the change is right
26 gray level images that step a generated have snake scan and the green noise screening processing of Select Error diffusion filter at random, and screening result and former figure are asked for mean square deviation; Wherein the mean square deviation formula is:
(m is former gray-scale map in that ((m n) is gray value after screening is handled to y for m, the n) gray value of position n) to x in the formula;
C) in a gray scale, select the pairing sluggish coefficient of Minimum Mean Square Error, save as this
The optimum sluggish coefficient gray scale 0,10,20,30,40,50,60,70,80 of gray scale, 90,100,110,120,130,140,150,160,170,180,190,200,210,220,230,240,255 optimum sluggish coefficient is respectively: 1,0.95,0.9,0.8,0.7,0.65,0.4,0.3,0.2,0.2,0.25,0.2,0.15,0.2,0.1,0.15,0.1,0.5,0.15,0.2,0.3,0.55,0.6,0.75,0.9,1;
D) above-mentioned data are carried out the one-dimensional linear interpolation, calculate the optimum sluggish coefficient of each gray scale.
7. the green noise screening method of a kind of self adaptation according to claim 1 based on the image contrast, it is characterized in that, in the described step 4, threshold feedback value d is by with current process points and the output valve of process points and optimum sluggish coefficient h, error e (m on every side, n) multiply each other, and do computing with transformation matrix and obtain, thereby calculate new threshold value: T=T+d; Wherein, described matrixing concrete operation method is:
1) transformation matrix when scanning from left to right
, conversion when scanning from right to left
Matrix
, it is capable to establish transformation matrix S i, the number of j row be S (i-2, j-2);
2)d=h*(S(-1,-1)*b(m-1,n-1)+S(-1,0)*b(m-1,n)+S(-1,+1)*b(m-1,n
+ 1)+and S (0 ,-1) * b (m, n-1)+(m, n)+S (1,1) * b (m+1, n+1)), (i is current process points and the output valve of process points on every side j) to b to S (0,0) * b.
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| CN103400355A (en) * | 2013-08-19 | 2013-11-20 | 武汉大学 | Method for extracting edge roughness characteristic of shaftless printing dot |
| CN109271761A (en) * | 2018-09-29 | 2019-01-25 | 南京林业大学 | A kind of generation and recognition methods that the document based on green feature of making an uproar is anti-fake |
| CN113542710A (en) * | 2021-09-15 | 2021-10-22 | 广州匠芯创科技有限公司 | Image processing method, system and medium based on error diffusion Dither algorithm |
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| JP2001082892A (en) * | 1999-09-14 | 2001-03-30 | Calsonic Kansei Corp | Packing sticking apparatus for heat exchanger |
| CN101489023A (en) * | 2009-01-22 | 2009-07-22 | 旭丽电子(广州)有限公司 | Green noise error diffusion method based on tone dependency and related printing apparatus |
| US20100245927A1 (en) * | 2009-03-30 | 2010-09-30 | Wen-Chian Wong | Tone dependent green-noise error diffusion method and printing device thereof |
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| JP2001082892A (en) * | 1999-09-14 | 2001-03-30 | Calsonic Kansei Corp | Packing sticking apparatus for heat exchanger |
| CN101489023A (en) * | 2009-01-22 | 2009-07-22 | 旭丽电子(广州)有限公司 | Green noise error diffusion method based on tone dependency and related printing apparatus |
| US20100245927A1 (en) * | 2009-03-30 | 2010-09-30 | Wen-Chian Wong | Tone dependent green-noise error diffusion method and printing device thereof |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN103400355A (en) * | 2013-08-19 | 2013-11-20 | 武汉大学 | Method for extracting edge roughness characteristic of shaftless printing dot |
| CN103400355B (en) * | 2013-08-19 | 2016-04-13 | 武汉大学 | A kind of method realizing the feature extraction of shaftless printing net-point edge roughness |
| CN109271761A (en) * | 2018-09-29 | 2019-01-25 | 南京林业大学 | A kind of generation and recognition methods that the document based on green feature of making an uproar is anti-fake |
| CN109271761B (en) * | 2018-09-29 | 2021-07-13 | 南京林业大学 | Document anti-counterfeiting generation and identification method based on green noise characteristics |
| CN113542710A (en) * | 2021-09-15 | 2021-10-22 | 广州匠芯创科技有限公司 | Image processing method, system and medium based on error diffusion Dither algorithm |
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