JP2000022858A - Image quality prediction method - Google Patents

Abstract

PROBLEM TO BE SOLVED: To predict and evaluate the image quality of an image recorder such as a printer in a short time. SOLUTION: In this image quality prediction method, an evaluation calculation part 3 obtains the position of a dot recordable by image data inputted by the basic specification data of a recording density for evaluation set in a basic using data storage part 2. The reflectivity of the obtained position of recording the dot and the position of the dot recordable by the image data inputted from the basic specification data of the reflectivity distribution of the dot for the evaluation set in the basic using data storage part 2 is calculated and the reflectivity distribution of images to be evaluated is calculated.

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

The present invention relates to an image quality prediction method for quantitatively evaluating the quality of an image output from various image recording apparatuses, and more particularly to a method for shortening the evaluation time of image quality.

[0002]

2. Description of the Related Art The factors of image quality can be divided into two factors: a psychological reaction occurring inside the viewer of the image, ie, a psychological factor, and a physical property of the image, ie, a physical factor. The assessment is to classify and determine these psychological and physical factors and quantify how they relate to each other and affect the overall image quality. As a method of quantifying and evaluating the image quality, for example, Japanese Patent Application Laid-Open No.
As disclosed in Japanese Patent Application Laid-Open No. 0-231914, a spatial frequency component is calculated from an optically read image signal, and the calculated spatial frequency component is corrected with the spatial frequency characteristics of the visual system and integrated to obtain an evaluation value. And This evaluation method weights the frequency components that are conspicuous in the noise,
The correspondence between the image noise (roughness) perceived visually and the objective evaluation amount is improved. As described above, the method of quantitatively evaluating image quality generally improves the correlation between subjective image quality and objective physical quantity by correcting the measured physical quantity with human visual characteristics. A similar method is used for the evaluation of jaggedness (jaggy) and the evaluation of sharpness with the diagonal line of.

[0003]

Although the image quality can be quantitatively evaluated by developing the above-described image quantification method, the image to be evaluated is optically evaluated by a scanner or a microdensitometer. Need to be read. The accuracy of the optical reading greatly affects the evaluation, and therefore the speed of the reading device is limited, and the evaluation often takes a very long time. In addition, an image recording apparatus to be evaluated is required for evaluation, and in product development, it is essential that the development of the recording apparatus has progressed to some extent and the level of image output has been reached. Therefore, at present, the image quality of the recording apparatus cannot be predicted at the beginning of development.

For example, when developing an image processing method for an image recording apparatus under development, the image processing method cannot be evaluated until the image recording apparatus is completed. Development was postponed and had to be developed in a short time. Also, if some changes were made to the specifications, such as a change in the recording density of the developed image recording device, in order to confirm how the image quality could be improved, an image recording device with the changed specifications was actually prototyped. The evaluation must be performed after that, and it takes a lot of time to evaluate when the usage is changed.

SUMMARY OF THE INVENTION It is an object of the present invention to provide a method for estimating an image quality of an image recording apparatus such as a printer, which can solve the above-mentioned disadvantages and can evaluate the quality in a short time.

[0006]

An image quality estimating method according to the present invention has basic specification data of an image recording apparatus to be evaluated, and inputs image data corresponding to an image output from the image recording apparatus to be evaluated. And calculating the reflectance distribution of the image formed by the input image data using the basic specification data.

A second image quality prediction method according to the present invention has basic specification data of an image recording device to be evaluated, and inputs and inputs image data corresponding to an image output from the image recording device to be evaluated. The reflectance distribution of an image formed by image data is calculated using the basic specification data and the recording characteristic variation component data.

As the recording characteristic fluctuation component data, it is preferable to use dot position error of the recording apparatus, dot position error of the recording apparatus and fluctuation data of the reflectance distribution shape of dots, or use spatial frequency characteristics of dot position fluctuation.

Further, it is desirable to obtain a final reflectance distribution by performing a convolution operation on the calculated reflectance distribution with a function corresponding to an image forming process.

Further, an image density or a luminance signal of the image to be evaluated is obtained from the reflectance distribution data of the calculated image,
It is characterized by predicting image quality.

[0011]

DESCRIPTION OF THE PREFERRED EMBODIMENTS An image quality prediction method according to the present invention
When image data for evaluation is input, a position at which a dot is printed based on the basic specification data of a recording density set for evaluation in advance, and a position of a dot that can be printed based on the input image data are obtained. The reflectance of the dot position recordable by the input image data is determined by the distance from the center position of the dot recorded by the basic specification data of the recording density and the reflectance distribution of the dot recorded by the basic specification data. Then, the reflectance of the dot position that can be recorded by the input image data from the dot recording position based on the basic specification data of the recording density obtained and the reflectance distribution basic distribution data of the evaluation dot set in advance is calculated. , The reflectance distribution of the image to be evaluated is calculated.

By calculating the average value of the calculated reflectance distribution, the image density of the image to be evaluated can be obtained, and the gamma characteristic indicating the relationship between the input light amount and the output signal of the image recording apparatus to be evaluated is evaluated. Can be. In addition, it is possible to easily evaluate an image by graphing the reflectance distribution or converting the reflectance distribution into a luminance signal corresponding to the reflectance and displaying the luminance signal on a display device.

[0013]

FIG. 1 is a block diagram showing the configuration of an embodiment of the present invention. As shown in the figure, the image quality evaluation device has an image data input unit 1, a basic specification data storage unit 2, an evaluation value calculation unit 3, and an output unit 4. The image data input unit 1 is for inputting image data indicating in which position the image recording device to be evaluated records dots. When the image recording device to be evaluated is a binary printer, whether the dot is on is determined. When a data string of “0” or “1” indicating OFF is input as image data, and the image recording apparatus to be evaluated is a multi-valued printer, it indicates the size or density of dots to be recorded.
A bit data string is input as image data. The basic specification data storage unit 2 stores basic specifications relating to the recording of the image recording apparatus to be evaluated. Basic specification data 21 of the recording density of the image recording apparatus to be evaluated, that is, generally, recording is performed per inch. The number of possible dots DPI,
Basic specification data 22 of the reflectance distribution of the recorded dots is stored. As the basic specification data 22 of the reflectance distribution of the dot, for example, the reflectance distribution according to the distance from the center of the dot shown in FIG. Data is stored as an up table. Evaluation value calculation unit 3
Has a dot position calculator 31 and a reflectance distribution calculator 32. The dot position calculation unit 31 calculates a position at which a dot is recorded based on the basic specification data 21 of the recording density and a position of a dot that can be recorded based on the input image data. The reflectance distribution calculation unit 32 calculates the reflectance at the position where the dot is recorded with reference to the basic specification data 22 of the reflectance distribution of the dots.

The operation of evaluating the image data of the evaluation image by the image quality evaluation apparatus configured as described above will be described.

The image data for evaluation is input to an image data input unit 1.
, The dot position calculator 3 of the evaluation value calculator 3
In step 1, the position at which a dot is to be recorded and the position of a dot that can be recorded based on the input image data are determined based on the basic specification data 21 of the recording density stored in the basic specification data storage unit 2 and sent to the reflectance distribution calculation unit 32. FIG. 3 shows an example of an image in a case where dots are printed based on the recording density basic specification data 21. The dot is located at a position separated by a dot recording pitch L = (1 / recording density) determined by the recording density basic specification data 21. The dots 5a, 5b, 5c are recorded. The position where the dot of the image to be evaluated is recorded is determined by the input image data. For example, assuming that the positions where dots can be recorded based on the input image data are points 6a and 6b, the dot position calculator 31 calculates the positions of the dots 5a, 5b and 5c and the points 6a and 6b and sends them to the reflectance distribution calculator 32. The reflectance distribution calculation unit 32 receives the dot recording position based on the transmitted recording density basic specification data 21 and the image data input from the dot reflectance distribution basic specification data 22 stored in the basic specification data storage unit 2. To calculate the reflectivity at the position of the dot that can be recorded. That is, the position of a dot that can be recorded by the input image data is determined by the distance from the center position of the dot that is recorded by the basic specification data 21 of the recording density, as shown in FIG. Therefore, the reflectance distribution calculation unit 32 calculates a pitch finer than the dot recording pitch L determined by the recording density, for example, 1/1/3 of the dot recording pitch L as shown in FIG.
The reflectance at the position of the dot that can be recorded is calculated based on the image data input at the reflectance calculation pitch RL of eight pitches. For example, in FIG. 3, when the reflectance of the point 6a where a dot can be recorded is obtained from the input image data,
A function using the distance from the center of the dot 5a closest to the point 6a to the point 6a and the distance from the dot center of the reflectivity distribution stored in the basic use data storage unit 2 or the reflectance from the lookup table to the reflectivity of the point 6a Is calculated. Also, point 6b
In a position affected by both of the dots 5a and 5b as in the above, the reflectance according to the distance from the center of the dot 5a and the reflectance according to the distance from the center of the dot 5b are respectively calculated, and the two calculated values are calculated. The reflectance at point 6b is determined by adding the reflectance. The reflectance at each point where dots can be recorded is sequentially obtained from the input image data to calculate the reflectance distribution of the image to be evaluated. The calculated reflectance distribution is output from the output unit 4 to a display device, a memory, or the like.

By calculating the average value of the calculated reflectance distribution, the image density of the image to be evaluated can be obtained, and the gamma characteristic indicating the relationship between the input light quantity and the output signal of the image recording apparatus to be evaluated is evaluated. Can be. In addition, it is possible to easily evaluate an image by graphing the reflectance distribution or converting the reflectance distribution into a luminance signal corresponding to the reflectance and displaying the luminance signal on a display device.

In the above embodiment, the basic specification data 2 of the recording density is used.
The case of calculating the reflectivity of the dot position that can be recorded by the input image data from the basic specification data 22 of the position where the dot is recorded and the reflectance distribution of the dots by dot 1 has been described. Basic specification data should be determined. For example, if the image recording device to be evaluated is a laser printer, the recording density, the characteristics of the laser exposure device that scans and exposes the photoconductor, the light attenuation characteristics of the photoconductor, and the surface potential-reflectance conversion characteristics of the photoconductor are used as basic specification data. Good.

FIG. 4 is a block diagram showing the configuration of an image quality evaluation device when the image recording device to be evaluated is a laser printer. As shown in FIG. 4, the basic specification data storage unit 2 stores basic specification data 21 of the recording density of the laser printer,
The basic specification data 23 of the laser exposure characteristic and the basic specification data 24 of the light attenuation characteristic of the photoreceptor indicating the relationship between the exposure amount and the surface potential of the photoreceptor as shown in FIG.
As shown in FIG. 7, basic specification data 25 of the surface potential-reflectance conversion characteristic obtained by experimentally determining the relationship between the surface potential and the reflectance of the photosensitive member is stored. As the basic specification data 23 of the laser exposure characteristic, the beam diameter of the laser spot for scanning and exposing the photosensitive member, the intensity of the laser, and the scanning speed of the laser are stored. The evaluation value calculation unit 3 includes a dot position setting unit 33, an exposure amount distribution calculation unit 34, a surface potential distribution calculation unit 35, and a reflectance conversion unit 36. The dot position setting unit 33 specifies the position of the dot to be recorded by the basic specification data 21 of the recording density. The exposure distribution calculating unit 34 includes a dot position setting unit 33.
Is calculated when laser recording is performed in accordance with the image data input to the position specified by. The surface potential distribution calculation unit 35 converts the calculated exposure distribution into a surface potential of the photoconductor based on the basic specification data 24 of the light attenuation characteristic of the photoconductor. The reflectance converter 36 converts the calculated surface potential of the photoconductor into the basic specification data 25 of the surface potential-reflectance conversion characteristics.
To reflectivity based on

When the image quality evaluation apparatus configured as described above evaluates an image formed by a laser printer based on the image data input from the image data input section 1, the dot position setting section 33 of the evaluation value calculation section 3 uses the basic specifications. Data storage unit 2
The position of the dot to be recorded by the basic specification data 21 of the recording density stored in the
Send to 4. The exposure distribution calculating section 34 includes the dot position setting section 3
The exposure amount when the laser recording corresponding to the input image data is performed at the position specified in 3 based on the basic specification data 23 of the laser exposure characteristic is calculated. Here, when the laser intensity is P and the laser beam diameter at which the laser intensity is 1 / e ² is scanned and exposed by the laser of Wx and Wy, the exposure amount I (x, y) at the exposure amount calculation point (x, y) is obtained. ) Is given by the following equation (1).

[0020]

(Equation 1)

In the formula (1), the exposure calculation points x and y are Vb, the laser scanning speed, and t ₀ is the laser lighting time. In the case of a binary laser printer, the laser needs to scan the dot pitch. Time. In the case of a multi-value laser printer, the dot size is controlled by changing the time t ₀ according to the 8-bit image data. Further, considering exposure of dots around the exposure amount calculation point (x, y), the total exposure amount can be expressed by the following (2).

[0022]

(Equation 2)

The exposure distribution calculation unit 34 calculates the exposure at each position specified by the dot position setting unit 33 according to the equation (1) or (2), and sends it to the surface potential distribution calculation unit 35.
The surface potential distribution calculation unit 35 converts the transmitted exposure amount into a surface potential of the photoconductor based on the basic specification data 24 of the light attenuation characteristic of the photoconductor in the basic specification data storage unit 2 shown in FIG. Send to 36. The reflectance conversion unit 36 converts the sent surface potential of the photoconductor into a reflectance according to the basic specification data 25 of the surface potential-reflectance conversion characteristic shown in FIG. Such calculation and conversion processing are performed, for example, on each point indicated by the reflectance calculation pitch RL shown in FIG. 3 to obtain the reflectance distribution of the screen corresponding to the input image data. The image can be easily evaluated by the reflectance distribution.

In the above embodiment, the case where the dot position setting unit 33 of the evaluation value calculation unit 3 specifies the position of the dot to be recorded by the basic specification data 21 of the recording density stored in the basic specification data storage unit 2 will be described. However, each dot position fluctuates due to the mechanical accuracy and control accuracy of the image recording apparatus. Therefore, as shown in FIG. 7, the fluctuation amount of each dot position generated due to the mechanical accuracy and control accuracy of the image recording apparatus is input from the recording characteristic input unit 7 as the recording characteristic fluctuation data. When setting the positions of the dots to be recorded by the dot position calculation unit 31, the positions of the dots to be recorded are defined by the basic specification data 21 of the recording density stored in the basic specification data storage unit 2. The corrected dot position is corrected by the dot position variation data which is the recording characteristic variation data input from the recording characteristic input unit 7. As described above, by correcting the positions of dots to be printed by giving as data the amount of change in each dot position generated due to the mechanical accuracy, control accuracy, or the like of the image printing apparatus, as shown in FIG. It is possible to calculate the reflectance distribution when the dots 5a, 5b, 5c are recorded at positions shifted from the position determined by the density.

In the above-described embodiment, a case has been described in which the dot position variation is input as the recording characteristic variation data from the recording characteristic input unit 7. However, an image recording apparatus in which the dot reflectance distribution shape varies for each dot. In such a case, the change data may be input as the change data of the recording characteristics. The variation data of the recording characteristics is not limited to one type,
For example, the dot position and the dot reflectance distribution may be input as the variation data.

In the case where the image recording apparatus is a laser printer, as shown in FIG. 9, the data on the fluctuation of the laser intensity and the fluctuation data on the potential distribution of the photoreceptor are recorded from the recording characteristic input unit 7 at the time of recording each dot. When the exposure amount calculation unit 34 calculates the exposure amount at each position specified by the dot position setting unit 33, the calculated exposure amount is corrected by the laser intensity fluctuation data at the time of recording each dot, When the potential distribution calculation unit 35 converts the exposure amount into the surface potential of the photoconductor based on the basic specification data 24 of the light attenuation characteristics of the photoconductor, the exposure data is corrected by the fluctuation data of the potential distribution of the photoconductor. By converting the corrected potential distribution of the photoconductor into a reflectance, an accurate image quality evaluation value can be obtained.

In the case where the image recording apparatus is an ink jet printer having a plurality of nozzles, the ink amount of the ejected ink droplets is different due to the variation in the shape of each nozzle, and the reflectance distribution of the recorded dots depends on the ink amount of the ink droplets. It changes as shown in the reflectance distribution of FIG. In addition, the variation in the shape of each nozzle also affects the ejection direction of the ejected ink droplet, and causes a dot position error. further,
A dot position error also occurs due to a recording paper feeding error.
As described above, the fluctuation factors affecting the image quality of the ink jet printer are dominated by the dot position fluctuation and the dot reflectance distribution shape. Therefore, as shown in FIG. 11, data of dot position fluctuation and dot reflectivity distribution shape fluctuation are given as recording characteristic fluctuation data from the recording characteristic input unit 7, and the position of each dot is determined from the recording density and dot position fluctuation data. After the calculation, the reflectance distribution is obtained from the basic specification data 22 of the reflectance distribution of the dots and the reflectance distribution shape variation data. Thus, an accurate image quality evaluation value can be obtained.

The dot position error which causes the dot position fluctuation includes a periodic dot position error due to a feeding error of a recording sheet, a random dot position error due to a processing error of a device part, and recording of a photosensitive member in an electrophotographic system. It is caused by various spatial frequency fluctuations such as low-frequency unevenness of the medium. Therefore, it is preferable to input spatial frequency characteristic data as dot position variation data from the recording characteristic input unit 7 and to define dot positions by variations having various frequency components.
FIG. 12 shows an example of this spatial frequency characteristic. As shown in FIG. 12, the spatial frequency characteristics include low frequency fluctuations, periodic position fluctuations due to paper feed speed fluctuations, and random high frequency fluctuations.

When correcting the position of a dot to be recorded based on the spatial frequency characteristic of the dot position variation, as shown in the processing diagram of FIG.
The position at which a dot is recorded is calculated from 1 and white noise including all frequency components is generated uniformly (steps S1 and S2), and the generated white noise is Fourier-transformed and converted into a frequency space (step S3). . The data after the Fourier transform is multiplied by the spatial frequency characteristic of the dot position variation, which is the variation data of the recording characteristics, and filtered (step S4), and the Fourier inverse transform is performed to obtain the target spatial frequency characteristic. Generate noise (fluctuation) (step S4). This is added to the basic dot position obtained from the recording density to calculate the position of each dot having the dot position fluctuation of the target spatial frequency characteristic (step S6). By correcting the positions of the dots recorded based on the spatial frequency characteristics of the dot position fluctuation, the reflectance of the image can be predicted with higher accuracy.

In the above embodiment, the description has been given of the case where the spatial frequency characteristic of the dot position variation is input as the recording characteristic variation data. However, the periodic dot position error due to the recording paper feed error and the random dot position error due to the processing error of the device parts. A dot position error or the like may be input.

Further, in each of the above-described embodiments, the case where the data of the reflectance distribution of the dots is output as the image quality evaluation value from the evaluation value calculation unit 3 has been described. However, as shown in FIG. The data of the reflectance distribution of the dots calculated by the evaluation value calculation unit 3 is sent to the image quality evaluation unit 8, and the image quality evaluation unit 8 calculates the image quality evaluation amount having a good correlation with the subjective evaluation from the data of the reflectance distribution of the dots. . As the image quality evaluation by the image quality evaluation unit 8, for example,
Various image quality evaluation methods can be applied, such as noise evaluation of an image as disclosed in JP-A-191-191 and obtaining a modulation transfer function MTF from the reflectance distribution of the line pair image 9 as shown in FIG.

Further, as in the electrophotographic system, development, transfer,
In the case of an image recording apparatus that forms an image by a plurality of processes such as fixing, if a blur function corresponding to the deterioration characteristics of each process is provided as data, the characteristics of the blur due to the transfer and the reflectance distribution after development may be reduced. Deterioration such as crushing characteristics due to fixing can be sequentially added by convolution calculation. Therefore, in the case of an electrophotographic image recording apparatus, as shown in FIG. 16, a convolution operation is performed by the arithmetic processing unit 10 on the reflectance distribution of the image output from the evaluation value calculation unit 3 by the transfer. Then, a convolution operation of a crushing function by fixing is performed on the calculated reflectance distribution of the image to obtain a final reflectance distribution of the image. Thus, the reflectance distribution of the image can be accurately predicted.

Although the above embodiment has been described with reference to the electrophotographic image recording apparatus, the present invention is not limited to the electrophotographic system.
The present invention can also be applied to a case where the influence of an optical dot gain on which an image recorded on paper is blurred due to scattering of light in paper is considered.

[0034]

As described above, according to the present invention, the reflectance distribution of an image to be output from an image recording apparatus to be evaluated is calculated based on the basic specifications of the image recording apparatus to be evaluated. Therefore, the quality of an image can be predicted in a short time without actually outputting the image from the image recording apparatus to be evaluated.

Further, by calculating the reflectance distribution of an image that will be output from the image recording apparatus to be evaluated based on the basic specifications of the image recording apparatus to be evaluated and the recording characteristic fluctuation component of the image forming apparatus. It is possible to accurately predict the quality of an image in a short time.

Further, by using the dot position error of the image recording apparatus as the recording characteristic fluctuation component, the quality of the image can be accurately predicted in a short time.

Further, by using the dot position error of the image recording apparatus and the fluctuation data of the reflectance distribution shape of the dots as the recording characteristic fluctuation component data, the error of the position of each dot forming an image like an ink jet printer can be reduced. It is possible to accurately predict the quality of an image that will be output from an image recording device in which the reflectance distribution shape greatly contributes to the image quality.

Further, by using the spatial frequency characteristic of the dot position variation as the recording characteristic variation component data, the quality of the image including the variation having various frequency components can be predicted, and the image recording to be evaluated can be performed. The quality of an image that will be output from the device can be predicted with higher accuracy.

Further, a function corresponding to the image forming process is convolution-operated with respect to the calculated reflectance distribution to obtain a final reflectance distribution. According to the above process, the reflectance distribution of an image that will be output from an image recording apparatus that forms an image can be accurately predicted.

Further, the image evaluation can be easily performed by obtaining the image density and the luminance signal of the image to be evaluated from the reflectance distribution of the image which will be output from the image recording apparatus for forming the image. .

[Brief description of the drawings]

FIG. 1 is a block diagram showing a configuration of an embodiment of the present invention.

FIG. 2 is a characteristic diagram showing a reflectance distribution of dots.

FIG. 3 is an explanatory diagram showing an image on which dots are recorded.

FIG. 4 is a block diagram showing a configuration of a second embodiment.

FIG. 5 is a characteristic diagram illustrating light attenuation characteristics of a photoconductor.

FIG. 6 is a characteristic diagram of the reflectance with respect to the surface potential of the photosensitive member.

FIG. 7 is a block diagram showing a configuration of a third embodiment.

FIG. 8 is an explanatory diagram showing another image on which dots are recorded.

FIG. 9 is a block diagram showing a configuration of a fourth embodiment.

FIG. 10 is a characteristic diagram showing reflectance distributions of dots of different sizes.

FIG. 11 is a block diagram showing a configuration of a fifth embodiment.

FIG. 12 is a characteristic diagram showing a spatial frequency characteristic of a dot position variation.

FIG. 13 is a process chart showing the operation of the sixth embodiment.

FIG. 14 is a block diagram showing a configuration of a seventh embodiment.

FIG. 15 is an explanatory diagram showing a line pair image.

FIG. 16 is a block diagram showing a configuration of an eighth embodiment.

[Explanation of symbols]

 DESCRIPTION OF SYMBOLS 1 Image data input part 2 Basic specification data storage part 3 Evaluation value calculation part 4 Output part 21 Basic specification data of recording density 22 Basic specification data of dot reflectance distribution 31 Dot position calculation part 32 Reflection distribution calculation part

 ──────────────────────────────────────────────────続 き Continued on the front page F term (reference) 2C061 AQ06 KK18 KK22 KK24 KK25 5B057 DA20 DC22 DC30 5C062 AA01 AB17 AB21 AB42 AC21 AC55 AF00 BA00 BA04 5C077 LL11 LL18 MP01 MP04 NN04 NN06 PP15 PP46 PP74 PP75 PQ12P09 CA27 FA69 FA81 GA09 MA01

Claims (7)

[Claims] 1. An image forming apparatus having basic specification data of an image recording apparatus to be evaluated, inputting image data corresponding to an image output from the image recording apparatus to be evaluated, and a reflectance distribution of an image formed by the input image data. Is calculated by using the basic specification data. 2. An image forming apparatus having basic specification data of an image recording apparatus to be evaluated, inputting image data corresponding to an image output from the image recording apparatus to be evaluated, and a reflectance distribution of an image formed by the input image data. Is calculated using the basic specification data and the recording characteristic variation component data. 3. The image quality prediction method according to claim 2, wherein a dot position error of a printing apparatus is used as the printing characteristic fluctuation component data. 4. The image quality predicting method according to claim 2, wherein as the recording characteristic fluctuation component data, dot position error of the recording apparatus and fluctuation data of the reflectance distribution shape of the dots are used. 5. The image quality prediction method according to claim 2, wherein a spatial frequency characteristic of a dot position variation is used as said recording characteristic variation component data. 6. The image quality prediction method according to claim 1, wherein a convolution operation is performed on the calculated reflectance distribution with a function corresponding to an image forming process to obtain a final reflectance distribution. . 7. The image quality prediction method according to claim 1, wherein an image density or a luminance signal of the image to be evaluated is obtained from the calculated reflectance distribution data of the image, and the quality of the image is predicted.