CN103618845A - Green noise halftone algorithm based on minimum developing error laser printer model - Google Patents

Abstract

The invention discloses a green noise halftone algorithm based on a minimum developing error laser printer model, and belongs to halftone screen dot generating methods in the field of image hard copy copying. Due to the fact that a printer has dot gain and dot loss nonlinear warp, and the nonlinear warp needs to be simulated by using the printer models. Currently, density or the reflecting rate are commonly used by the printer model for converting a measuring result, and due to the fact that the density and the reflecting rate do not conform to the linear changes of the gray level of human vision, the parameters of the printer model can be decided through multiple times of printing amount. According to the green noise halftone algorithm based on the minimum developing error laser printer model, printing output results are converted into Munsell lightness values, the optical parameters of the printer model are calculated, and the laser printer model with the maximum developing error is obtained and applied to the green noise halftone algorithm. Only one time of the printing measurement is needed, and the printer model corresponding to the generated optimal parameters well restrains the nonlinear warp of the printer.

Description

A kind of based on the green noise halftoning algorithm of minimum colour developing error laser printer model

Technical field

The invention belongs to digital image processing field, relate to a kind of halftoning method, be specifically related to a kind of based on the green noise halftoning algorithm of minimum colour developing error laser printer model.

Background technology

Because ink powder physical diffusion and electrostatic charge adsorption capacity are not enough, printer there will be a gain and some Loss, the non-linear distortion of printer that is otherwise known as of these two kinds of phenomenons.The non-linear distortion of printer will cause the gray scale of output image level, the relative situation such as relationship between light and dark variation.In order to suppress the non-linear distortion of printer, researcher prints mechanism by feature and the printer of printer emulation print point both at home and abroad, sets up model printer.

Current model printer, is normally converted to measured value density value or reflectivity, and the relation of the gray value by density value, reflectivity and original image after relatively changing is determined model printer.But due to density value and reflectivity and do not meet human eye vision grey scale change situation, thus can not directly by the method for minimum variance, obtain the optimized parameter of model printer, and need to repeatedly print measurement, by human eye, judge optimized parameter.

Summary of the invention

In order to solve the problems of the technologies described above, the present invention proposes a kind of based on the green noise halftoning algorithm of minimum colour developing error laser printer model, to print out results conversion is Munsell value value, calculate the optimized parameter of model printer, obtain the laser printer model of minimum colour developing error, and be applied in green noise halftoning algorithm, only need to once print measurement, model printer corresponding to optimized parameter generating just suppressed the non-linear distortion of printer preferably.

The technical solution adopted in the present invention is: a kind ofly based on the green noise halftoning algorithm of minimum colour developing error laser printer model, it is characterized in that, comprise the following steps:

Step 1: by calculating the minimum colour developing error of test pattern, obtain corresponding optimized parameter

t ₁, T ₂combination; Its specific implementation comprises following sub-step:

Step 1.1: draw the gray level image comprise by black 20 color lumps to the gray-value variation such as white as test pattern I _s;

Step 1.2: select green noise halftoning algorithm to test pattern I _scarry out halftone process, obtain halftone process result images I _h;

Step 1.3: by halftone process result images I _huse target laser printer to print output, obtain Output rusults image I _p;

Step 1.4: use colorimeter or densimeter measurement Output rusults image I _p, measurement data is converted to gray value, obtain 20 color lumps and measure gray value array arrPG; Wherein said measurement data is converted to gray value, its specific implementation comprises following sub-step:

Step 1.4.1: measurement data is converted to the luminance factor Y in CIE1931XYZ colorimeter system; Wherein:

If use colorimeter to measure printing Output rusults, measure the luminance factor Y of printout result;

If use densitometer to measure printing Output rusults, the density D of deriving according to optical reflection density defined formula and the functional relation of luminance factor Y, calculate luminance factor Y:

$D=\lg [{\mathrm{<figure-callout\; id="0"\; label="Y"\; filenames="HDA0000425085610000021.png,HDA0000425085610000023.png"\; state="\{\{state\}\}">Y</figure-callout>}}_0/\left(Y{\mathrm{\&rho;}}_0\right)]\&DoubleRightArrow;Y=\frac{{\mathrm{<figure-callout\; id="0"\; label="Y"\; filenames="HDA0000425085610000021.png,HDA0000425085610000023.png"\; state="\{\{state\}\}">Y</figure-callout>}}_0}{{10}^D{\mathrm{\&rho;}}_0}$ (formula one)

Wherein, Y ₀for the luminance factor of the 10th grade of lightness of Munsell system, Y ₀=102.75, ρ ₀for the reflectivity of the 10th grade of lightness of Munsell system, ρ ₀=1, D is the optical reflection density in certain region, the luminance factor that Y is this region;

Step 1.4.2: luminance factor Y is converted to Munsell value V, according to the functional relation of luminance factor Y and Munsell value V, calculates Munsell value V:

V=2.217Y ^0.352-1.324 (formulas two)

Step 1.4.3: Munsell value V is converted to gray value G,

$G=\frac{V-V_p}{V_s-V_p}$ (formula three)

Wherein, V is to be converted for Munsell value, V _pfor the white corresponding Munsell value of paper, V _sbe Munsell value corresponding to 100% site printing effect;

Step 1.5: for halftone process result images I _h, in [0,1] scope, take 0.1 as step-length, constantly change the wantonly middle parameter of formula

single pixel segmentation is become to 10 * 10 points, while calculating single pixel exposure at the light energy distribution value P of each point (x _i, y _i):

$P(x_i,y_i)={\exp }^{-\&PartialD;[{(x_i-x_c)}^2+{(y_i-y_c)}^2]/l^2}$ (formula wantonly)

Wherein, (x _c, y _c) be the exposure center of pixel (m, n), (x _i, y _i) be pericentral each point of this pixel exposure, l is the centre-to-centre spacing of two neighbouring device print points of laser printer, l=10;

Step 1.6: the light energy of each point of statistics exposure pixel and eight neighborhoods thereof, the average light energy value A of the region of calculation exposure pixel own, adjacent area, diagonal zones _center, A _nerb, A _diag, its universal calculation equation is:

$A=\frac{{\&Integral;}_{y_i-l/2}^{y_i+l/2}{\&Integral;}_{x_i-l/2}^{x_i+l/2}P(x,y)\mathrm{dxdy}}{l\&times;l}$ (formula 5)

Wherein, (x _i-l/2, x _i+ l/2), (y _i-l/2, y _i+ l/2) be respectively the transverse and longitudinal coordinate span in unit are region;

Step 1.7: pointwise judgement halftone process result images I _hin eight neighborhoods of each pixel whether be print point, calculate the superimposed light energy L on each pixel region:

L=uA _center+ vA _nerb+ wA _diag(formula land)

Wherein, whether u is exposed decision, A by current pixel _centerfor the average light energy value of current pixel in pixel region own, in the adjacent domain that v is current pixel by the number of pixels being exposed, A _nerbfor the print pixel point of the proximal direction average light energy value in current pixel region, in the diagonal zones that w is current pixel by the number of pixels being exposed, A _diagfor the print point at the diagonal angle average light energy value in current pixel region;

Step 1.8: take 0.1 as step-length in [0,3] scope, change successively parameter T in formula seven ₁, T ₂, in substitution formula seven, calculate the simulation output gray level value p of each pixel

$p=\left\{\begin{array}{cc}0& L<T_1\\ \frac{L-T_1}{T_2-T_1}& {\mathrm{<figure-callout\; id="1"\; label="T"\; filenames="HDA0000425085610000021.png"\; state="\{\{state\}\}">T</figure-callout>}}_1\&le;L<{\mathrm{<figure-callout\; id="2"\; label="T"\; filenames="HDA0000425085610000021.png"\; state="\{\{state\}\}">T</figure-callout>}}_2\\ 1& {\mathrm{<figure-callout\; id="2"\; label="T"\; filenames="HDA0000425085610000021.png"\; state="\{\{state\}\}">T</figure-callout>}}_2\&le;L\end{array}\right.$ (formula seven)

Wherein, if in conversion threshold process, T ₁>T ₂, this time conversion is skipped, and directly enters conversion next time;

Step 1.9: calculate the mean value of analog gray scale value p corresponding to all pixels in same gray scale color lump, obtain the analog gray scale value array arrIG of 20 gray scale color lumps;

Step 1.10: the root-mean-square error RMSE between computation and measurement gray value array arrPG and analog gray scale value array arrIG:

$\mathrm{RMSE}=\sqrt{\frac{\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{({\mathrm{arrPG}}_i-{\mathrm{arrIG}}_i)}^2}{n}}$ (formula eight)

Wherein, n is the number of gray scale color lump, n=20;

Step 1.11: judgement, does is RMSE minimum value?

If not, described step 1.5 is carried out in revolution;

If: order is carried out following step 1.12;

Step 1.12: select minimum colour developing error RMSE _mincorresponding parameter

t ₁, T ₂as optimized parameter;

Step 2: by optimized parameter

t ₁, T ₂be loaded in green noise halftoning algorithm, image is carried out to halftone process; Its specific implementation comprises following sub-step:

Step 2.1: substitution optimized parameter

and calculate respectively single print point at the luminous energy value A of pixel region own, adjacent domain and diagonal zones according to formula four and formula 5 _center, A _nerband A _diag, and preserve;

Step 2.2: establishing pending image is that a width size is the gray level image of M * N, x _m,nrepresent the gray value that the pixel (m, n) of pending image is located, wherein x _m,n∈ [0,1], 0 is black, 1 is white; Around error amount and the error diffusion filter convolution of the processed pixel of eight neighborhoods are obtained to error amount, and the gray value of input pixel of current point and the difference of error amount are as input threshold value quantizing device gray value u _m,n:

$u_{m,n}=x_{m,n}-\underset{k,j\&Element;S}{\mathrm{\&Sigma;}}{F}_{k,j}{e}_{m-k,n-j}$ (formula nine)

Wherein, S is the coefficient template scope of error diffusion filter F (), and k, j are the ranks value of the coefficient template of F ();

Step 2.3: the analog gray scale value that adopts sluggish filters H () to locate adjacent processed pixel to pixel (m, n) is carried out weights computing, then be multiplied by sluggish coefficient h as feedback, add input threshold value quantizing device gray value u _m,nin, the output valve b of pixel (m, n) _m,n:

$b_{m,n}=Q\left(u_{m,n}\right)=\left\{\begin{array}{cc}1& u_{m,n}-h\underset{p,q\&Element;R}{\mathrm{\&Sigma;}}{H}_{p,q}{b}_{m-p,n-q}^{\&prime;}>0.5\\ 0& \mathrm{else}\end{array}\right.$ (formula is picked up)

Wherein, R is the coefficient template scope of sluggish filters H (), and output valve 1 is print point, and 0 is print point not; P, q are the ranks value of the coefficient template of H ();

Step 2.4: by luminous energy value A _center, A _nerband A _diagsubstitution formula land, the light energy superposition value on calculating pixel point (m, n) region, u, the v in its Chinese style land, the value of w are determined by the output valve of processed point in pixel (m, n) eight neighborhoods.

$u=\left\{\begin{array}{cc}1& b_{m,n}=0\\ 0& b_{m,n}=1\end{array}\right.$ (formula 11)

(formula 12)

(formula 13)

Step 2.5: by optimized parameter T ₁, T ₂in substitution formula seven, calculate the analog gray scale value b ' of current point _m,n;

Step 2.6: the error amount e that calculates current point _m,nfor the difference e of analog pixel value with input threshold value quantizing device gray value _m,n:

E _m,n=b ' _m,n-u _m,n(formula 14)

Step 2.7: to described in step 2.6, manage from angle point beginning, pending image upper left, by order left-to-right, from top to bottom, until finish to pending image bottom right angle point by step 2.1.

Innovative point of the present invention is:

(1) measurement data is converted to Munsell value value, Munsell value is converted to analog gray scale;

(2) by loop modification parameter

t ₁, T ₂, ask minimal error, using corresponding parameter as optimized parameter;

(3) gray value optimized parameter simulation being obtained replaces the original gray value error of calculation;

It is Munsell value value that the present invention will print out results conversion, calculate the optimized parameter of model printer, obtain the laser printer model of minimum colour developing error, and be applied in green noise halftoning algorithm, only need to once print measurement, model printer corresponding to optimized parameter generating just suppressed the non-linear distortion of printer preferably.

Accompanying drawing explanation

Fig. 1: be the flow chart based on minimum colour developing error optimization calculation of parameter of the present invention.

Fig. 2: be the flow chart based on the green noise halftoning algorithm of minimum colour developing error laser printer model of the present invention

Fig. 3: be the luminance factor Y of the embodiment of the present invention and the transformational relation schematic diagram of Munsell value V.

Fig. 4: 20 the pending gray scale color lumps that are the embodiment of the present invention.

Fig. 5-1: the coefficient template that is the error diffusion filter F () that adopts in the embodiment of the present invention.

Fig. 5-2: the coefficient template that is the sluggish filters H () that adopts in the embodiment of the present invention.

Fig. 6: the lena test pattern that is the embodiment of the present invention.

Fig. 7: be being processed and print scanned lena resolution chart by common green noise halftoning algorithm of the embodiment of the present invention.

Fig. 8: be being processed and print scanned lena resolution chart by the green noise halftoning algorithm of error laser printer model that develops the color based on minimum of the embodiment of the present invention.

Embodiment

Pantum2000 laser printer take below as target printer, to being preced with, the high white manifold paper of board prints output, measuring equipment is Eyeone colorimetric instrument, take lena resolution chart as pending image be example, the common green noise halftoning algorithm of take is contrast, use the high score resolution scan instrument of 1200dpi to scan halftoning printout image, with this, the present invention is further described.

Ask for an interview Fig. 1, Fig. 2, the technical solution adopted in the present invention is: a kind ofly based on the green noise halftoning algorithm of minimum colour developing error laser printer model, it is characterized in that, comprise the following steps:

Step 1: by calculating the minimum colour developing error of test pattern, obtain corresponding optimized parameter

t ₁, T ₂combination; Its specific implementation comprises following sub-step:

Step 1.1: ask for an interview Fig. 4, draw 600 * 600 pixels by 20 gray scale color lumps of 0 to 0.95 every 0.05 grey scale change as test pattern I _s, wherein 0 expression is black, and 1 represents in vain;

Step 1.2: select green noise halftoning algorithm to test pattern I _scarry out halftone process, obtain halftone process result images I _h;

Step 1.3: by halftone process result images I _huse Pantum2000 laser printer to print output, obtain Output rusults image I _p;

Step 1.4: use Eyeone colorimetric instrument to measure Output rusults image I _p, and by the following method measurement data is converted to gray value, obtain 20 color lumps and measure gray value array arrPG: wherein said measurement data is converted to gray value, its specific implementation comprises following sub-step:

Step 1.4.1: measurement data is converted to the luminance factor Y in CIE1931XYZ colorimeter system; Its specific implementation process is: the density D of deriving according to optical reflection density defined formula and the functional relation of luminance factor Y, calculate luminance factor Y:

$D=\lg [{\mathrm{<figure-callout\; id="0"\; label="Y"\; filenames="HDA0000425085610000021.png,HDA0000425085610000023.png"\; state="\{\{state\}\}">Y</figure-callout>}}_0/\left(Y{\mathrm{\&rho;}}_0\right)]\&DoubleRightArrow;Y=\frac{{\mathrm{<figure-callout\; id="0"\; label="Y"\; filenames="HDA0000425085610000021.png,HDA0000425085610000023.png"\; state="\{\{state\}\}">Y</figure-callout>}}_0}{{10}^D{\mathrm{\&rho;}}_0}$ (formula one)

Wherein, Y ₀for the luminance factor of the 10th grade of lightness of Munsell system, Y ₀=102.75, ρ ₀for the reflectivity of the 10th grade of lightness of Munsell system, ρ ₀=1, D is the optical reflection density in certain region, the luminance factor that Y is this region;

Step 1.4.2: luminance factor Y is converted to Munsell value V, asks for an interview Fig. 3, according to the functional relation of luminance factor Y and Munsell value V, calculate Munsell value V:

V=2.217Y ^0.352-1.324 (formulas two)

Step 1.4.3: Munsell value V is converted to gray value G,

$G=\frac{V-V_p}{V_s-V_p}$ (formula three)

Wherein, V is to be converted for Munsell value, V _pfor the white corresponding Munsell value of paper, V _sbe Munsell value corresponding to 100% site printing effect;

Step 1.5: for halftone process result images I _h, in [0,1] scope, take 0.1 as step-length, constantly change the wantonly middle parameter of formula

single pixel segmentation is become to 10 * 10 points, while calculating single pixel exposure at the light energy distribution value P of each point (x _i, y _i):

$P(x_i,y_i)={\exp }^{-\&PartialD;[{(x_i-x_c)}^2+{(y_i-y_c)}^2]/l^2}$ (formula wantonly)

Wherein, (x _c, y _c) be the exposure center of pixel (m, n), (x _i, y _i) be pericentral each point of this pixel exposure, l is the centre-to-centre spacing of two neighbouring device print points of laser printer, l=10;

Step 1.6: the light energy of each point of statistics exposure pixel and eight neighborhoods thereof, the average light energy value A of the region of calculation exposure pixel own, adjacent area, diagonal zones _center, A _nerb, A _diag, its universal calculation equation is:

$A=\frac{{\&Integral;}_{y_i-l/2}^{y_i+l/2}{\&Integral;}_{x_i-l/2}^{x_i+l/2}P(x,y)\mathrm{dxdy}}{l\&times;l}$ (formula 5)

Wherein, (x _i-l/2, x _i+ l/2), (y _i-l/2, y _i+ l/2) be respectively the transverse and longitudinal coordinate span in unit are region;

Step 1.7: pointwise judgement halftone process result images I _hin eight neighborhoods of each pixel whether be print point, calculate the superimposed light energy L on each pixel region:

L=uA _center+ vA _nerb+ wA _diag(formula land)

Wherein, whether u is exposed decision, A by current pixel _centerfor the average light energy value of current pixel in pixel region own, in the adjacent domain that v is current pixel by the number of pixels being exposed, A _nerbfor the print pixel point of the proximal direction average light energy value in current pixel region, in the diagonal zones that w is current pixel by the number of pixels being exposed, A _diagfor the print point at the diagonal angle average light energy value in current pixel region;

Step 1.8: take 0.1 as step-length in [0,3] scope, change successively parameter T in formula seven ₁, T ₂, in substitution formula seven, calculate the simulation output gray level value p of each pixel

$p=\left\{\begin{array}{cc}0& L<T_1\\ \frac{L-T_1}{T_2-T_1}& {\mathrm{<figure-callout\; id="1"\; label="T"\; filenames="HDA0000425085610000021.png"\; state="\{\{state\}\}">T</figure-callout>}}_1\&le;L<{\mathrm{<figure-callout\; id="2"\; label="T"\; filenames="HDA0000425085610000021.png"\; state="\{\{state\}\}">T</figure-callout>}}_2\\ 1& {\mathrm{<figure-callout\; id="2"\; label="T"\; filenames="HDA0000425085610000021.png"\; state="\{\{state\}\}">T</figure-callout>}}_2\&le;L\end{array}\right.$ (formula seven)

Wherein, if in conversion threshold process, T ₁>T ₂, this time conversion is skipped, and directly enters conversion next time;

Step 1.9: calculate the mean value of analog gray scale value p corresponding to all pixels in same gray scale color lump, obtain the analog gray scale value array arrIG of 20 gray scale color lumps;

Step 1.10: the root-mean-square error RMSE between computation and measurement gray value array arrPG and analog gray scale value array arrIG:

$\mathrm{RMSE}=\sqrt{\frac{\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{({\mathrm{arrPG}}_i-{\mathrm{arrIG}}_i)}^2}{n}}$ (formula eight)

Wherein, n is the number of gray scale color lump, n=20;

Step 1.11: judgement, does is RMSE minimum value?

If not, described step 1.5 is carried out in revolution;

If: order is carried out following step 1.12;

Step 1.12: select minimum colour developing error RMSE _mincorresponding parameter

t ₁, T ₂as optimized parameter; The present embodiment is selected minimum colour developing error RMSE _min=3.28 o'clock, corresponding parameter t ₁=0.4, T ₂=2.3 as optimized parameter;

Step 2: by optimized parameter

t ₁, T ₂be loaded in green noise halftoning algorithm, image is carried out to halftone process; Its specific implementation comprises following sub-step:

Step 2.1: substitution optimized parameter

and calculate respectively single print point at the luminous energy value A of pixel region own, adjacent domain and diagonal zones according to formula four and formula 5 _center, A _nerband A _diag, and preserve;

Step 2.2: establishing pending image is that a width size is the gray level image of M * N, x _m,nrepresent the gray value that the pixel (m, n) of pending image is located, wherein x _m,n∈ [0,1], 0 is black, 1 is white; Around error amount and the error diffusion filter convolution of the processed pixel of eight neighborhoods are obtained to error amount, and the gray value of input pixel of current point and the difference of error amount are as input threshold value quantizing device gray value u _m,n:

$u_{m,n}=x_{m,n}-\underset{k,j\&Element;S}{\mathrm{\&Sigma;}}{F}_{k,j}{e}_{m-k,n-j}$ (formula nine)

Wherein, S is the coefficient template scope of error diffusion filter F (), and k, j are the ranks value of the coefficient template of F (), ask for an interview Fig. 5-1;

Step 2.3: the analog gray scale value that adopts sluggish filters H () to locate adjacent processed pixel to pixel (m, n) is carried out weights computing, then be multiplied by sluggish coefficient h as feedback, add input threshold value quantizing device gray value u _m,nin, the output valve b of pixel (m, n) _m,n:

$b_{m,n}=Q\left(u_{m,n}\right)=\left\{\begin{array}{cc}1& u_{m,n}-h\underset{p,q\&Element;R}{\mathrm{\&Sigma;}}{H}_{p,q}{b}_{m-p,n-q}^{\&prime;}>0.5\\ 0& \mathrm{else}\end{array}\right.$ (formula is picked up)

Wherein, R is the coefficient template scope of sluggish filters H (), and output valve 1 is print point, and 0 is print point not; P, q are the ranks value of the coefficient template of H (), ask for an interview Fig. 5-2;

Step 2.4: by luminous energy value A _center, A _nerband A _diagsubstitution formula land, the light energy superposition value on calculating pixel point (m, n) region, u, the v in its Chinese style land, the value of w are determined by the output valve of processed point in pixel (m, n) eight neighborhoods.

$u=\left\{\begin{array}{cc}1& b_{m,n}=0\\ 0& b_{m,n}=1\end{array}\right.$ (formula 11)

(formula 12)

(formula 13)

Step 2.5: by optimized parameter T ₁=0.4, T ₂in=2.3 substitution formulas seven, calculate the analog gray scale value b ' of current point _m,n;

Step 2.6: the error amount e that calculates current point _m,nfor the difference e of analog pixel value with input threshold value quantizing device gray value _m,n:

E _m,n=b ' _m,n-u _m,n(formula 14)

Step 2.7: to described in step 2.6, manage from angle point beginning, pending image upper left, by order left-to-right, from top to bottom, until finish to pending image bottom right angle point by step 2.1.

In the present embodiment, measurement data is converted to the luminance factor Y in CIE1931XYZ colorimeter system; Also can use colorimeter to measure printing Output rusults, and measure the luminance factor Y of printout result.

Asking for an interview Fig. 6, is the lena test pattern of the embodiment of the present invention; Asking for an interview Fig. 7, is being processed and print scanned lena resolution chart by common green noise halftoning algorithm of the embodiment of the present invention; Its whole light and shade is obviously partially dark, and the contrast of the flower on its hair and cap is level completely also, cannot differentiate details profile; Ask for an interview Fig. 8, being processed and print scanned lena resolution chart by the green noise halftoning algorithm of error laser printer model that develops the color based on minimum of the embodiment of the present invention, its whole light and shade reduction meets former figure, in shadow part details, still can reproduce preferably, effectively suppress the non-linear distortion of printer.

These are only preferred embodiment of the present invention, be not intended to limit protection scope of the present invention, therefore, all any modifications of doing within the spirit and principles in the present invention, be equal to replacement, improvement etc., within all should being included in protection scope of the present invention.

Claims (1)

1. based on the green noise halftoning algorithm of minimum colour developing error laser printer model, it is characterized in that, comprise the following steps:

Step 1: by calculating the minimum colour developing error of test pattern, obtain corresponding optimized parameter

t ₁, T ₂combination; Its specific implementation comprises following sub-step:

Step 1.1: draw the gray level image comprise by black 20 color lumps to the gray-value variation such as white as test pattern I _s;

Step 1.2: select green noise halftoning algorithm to test pattern I _scarry out halftone process, obtain halftone process result images I _h;

Step 1.3: by halftone process result images I _huse target laser printer to print output, obtain Output rusults image I _p;

Step 1.4: use colorimeter or densimeter measurement Output rusults image I _p, measurement data is converted to gray value, obtain 20 color lumps and measure gray value array arrPG; Wherein said measurement data is converted to gray value, its specific implementation comprises following sub-step:

Step 1.4.1: measurement data is converted to the luminance factor Y in CIE1931XYZ colorimeter system; Wherein:

If use colorimeter to measure printing Output rusults, measure the luminance factor Y of printout result;

If use densitometer to measure printing Output rusults, the density D of deriving according to optical reflection density defined formula and the functional relation of luminance factor Y, calculate luminance factor Y:

$D=\lg [Y_0/\left(Y{\mathrm{\&rho;}}_0\right)]\&DoubleRightArrow;Y=\frac{Y_0}{{10}^D{\mathrm{\&rho;}}_0}$ (formula one)

Wherein, Y ₀for the luminance factor of the 10th grade of lightness of Munsell system, Y ₀=102.75, ρ ₀for the reflectivity of the 10th grade of lightness of Munsell system, ρ ₀=1, D is the optical reflection density in certain region, the luminance factor that Y is this region;

Step 1.4.2: luminance factor Y is converted to Munsell value V, according to the functional relation of luminance factor Y and Munsell value V, calculates Munsell value V:

V=2.217Y ^0.352-1.324 (formulas two)

Step 1.4.3: Munsell value V is converted to gray value G,

$G=\frac{V-V_p}{V_s-V_p}$ (formula three)

Wherein, V is to be converted for Munsell value, V _pfor the white corresponding Munsell value of paper, V _sbe Munsell value corresponding to 100% site printing effect;

Step 1.5: for halftone process result images I _h, in [0,1] scope, take 0.1 as step-length, constantly change the wantonly middle parameter of formula

single pixel segmentation is become to 10 * 10 points, while calculating single pixel exposure at the light energy distribution value P of each point (x _i, y _i):

$P(x_i,y_i)={\exp }^{-\&PartialD;[{(x_i-x_c)}^2+{(y_i-y_c)}^2]/l^2}$ (formula wantonly)

Wherein, (x _c, y _c) be the exposure center of pixel (m, n), (x _i, y _i) be pericentral each point of this pixel exposure, l is the centre-to-centre spacing of two neighbouring device print points of laser printer, l=10;

Step 1.6: the light energy of each point of statistics exposure pixel and eight neighborhoods thereof, the average light energy value A of the region of calculation exposure pixel own, adjacent area, diagonal zones _center, A _nerb, A _diag, its universal calculation equation is:

$A=\frac{{\&Integral;}_{y_i-l/2}^{y_i+l/2}{\&Integral;}_{x_i-l/2}^{x_i+l/2}P(x,y)\mathrm{dxdy}}{l\&times;l}$ (formula 5)

Wherein, (x _i-l/2, x _i+ l/2), (y _i-l/2, y _i+ l/2) be respectively the transverse and longitudinal coordinate span in unit are region;

Step 1.7: pointwise judgement halftone process result images I _hin eight neighborhoods of each pixel whether be print point, calculate the superimposed light energy L on each pixel region:

L=uA _center+ vA _nerb+ wA _diag(formula land)

Wherein, whether u is exposed decision, A by current pixel _centerfor the average light energy value of current pixel in pixel region own, in the adjacent domain that v is current pixel by the number of pixels being exposed, A _nerbfor the print pixel point of the proximal direction average light energy value in current pixel region, in the diagonal zones that w is current pixel by the number of pixels being exposed, A _diagfor the print point at the diagonal angle average light energy value in current pixel region;

Step 1.8: take 0.1 as step-length in [0,3] scope, change successively parameter T in formula seven ₁, T ₂, and in substitution formula seven, calculate the simulation output gray level value p of each pixel

$p=\left\{\begin{array}{cc}0& L<T_1\\ \frac{L-T_1}{T_2-T_1}& T_1\&le;L<T_2\\ 1& T_2\&le;L\end{array}\right.$ (formula seven)

Wherein, if in conversion threshold process, T ₁>T ₂, this time conversion is skipped, and directly enters conversion next time;

Step 1.9: calculate the mean value of analog gray scale value p corresponding to all pixels in same gray scale color lump, obtain the analog gray scale value array arrIG of 20 gray scale color lumps;

Step 1.10: the root-mean-square error RMSE between computation and measurement gray value array arrPG and analog gray scale value array arrIG:

$\mathrm{RMSE}=\sqrt{\frac{\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{({\mathrm{arrPG}}_i-{\mathrm{arrIG}}_i)}^2}{n}}$ (formula eight)

Wherein, n is the number of gray scale color lump, n=20;

Step 1.11: judgement, does is RMSE minimum value?

If not, described step 1.5 is carried out in revolution;

If: order is carried out following step 1.12;

Step 1.12: select minimum colour developing error RMSE _mincorresponding parameter

t ₁, T ₂as optimized parameter;

Step 2: by optimized parameter

t ₁, T ₂be loaded in green noise halftoning algorithm, image is carried out to halftone process; Its specific implementation comprises following sub-step:

Step 2.1: substitution optimized parameter

and calculate respectively single print point at the luminous energy value A of pixel region own, adjacent domain and diagonal zones according to formula four and formula 5 _center, A _nerband A _diag, and preserve;

Step 2.2: establishing pending image is that a width size is the gray level image of M * N, x _m,nrepresent the gray value that the pixel (m, n) of pending image is located, wherein x _m,n∈ [0,1], 0 is black, 1 is white; Around error amount and the error diffusion filter convolution of the processed pixel of eight neighborhoods are obtained to error amount, and the gray value of input pixel of current point and the difference of error amount are as input threshold value quantizing device gray value u _m,n:

$u_{m,n}=x_{m,n}-\underset{k,j\&Element;S}{\mathrm{\&Sigma;}}{F}_{k,j}{e}_{m-k,n-j}$ (formula nine)

Wherein, S is the coefficient template scope of error diffusion filter F (), and k, j are the ranks value of the coefficient template of F ();

Step 2.3: the analog gray scale value that adopts sluggish filters H () to locate adjacent processed pixel to pixel (m, n) is carried out weights computing, then be multiplied by sluggish coefficient h as feedback, add input threshold value quantizing device gray value u _m,nin, the output valve b of pixel (m, n) _m,n:

$b_{m,n}=Q\left(u_{m,n}\right)=\left\{\begin{array}{cc}1& u_{m,n}-h\underset{p,q\&Element;R}{\mathrm{\&Sigma;}}{H}_{p,q}{b}_{m-p,n-q}^{\&prime;}>0.5\\ 0& \mathrm{else}\end{array}\right.$ (formula is picked up)

Wherein, R is the coefficient template scope of sluggish filters H (), and output valve 1 is print point, and 0 is print point not; P, q are the ranks value of the coefficient template of H ();

Step 2.4: by luminous energy value A _center, A _nerband A _diagsubstitution formula land, the light energy superposition value on calculating pixel point (m, n) region, u, the v in its Chinese style land, the value of w are determined by the output valve of processed point in pixel (m, n) eight neighborhoods.

$u=\left\{\begin{array}{cc}1& b_{m,n}=0\\ 0& b_{m,n}=1\end{array}\right.$ (formula 11)

(formula 12)

(formula 13)

Step 2.5: by optimized parameter T ₁, T ₂in substitution formula seven, calculate the analog gray scale value b ' of current point _m,n;

Step 2.6: the error amount e that calculates current point _m,nfor the difference e of analog pixel value with input threshold value quantizing device gray value _m,n:

E _m,n=b ' _m,n-u _m,n(formula 14)

Step 2.7: to described in step 2.6, manage from angle point beginning, pending image upper left, by order left-to-right, from top to bottom, until pending image bottom right angle point finishes by step 2.1.

Images