JP2005099892A - Image processor and method therefor - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To enable high-speed error diffusion processing wherein a change of an image is small before and after the processing. <P>SOLUTION: A linearization part 100 removes a characteristic added to input image data to generate linear input image data. An error diffusion processing part 12 performs the error diffusion processing by use of a color value inside a linear color space inputted from a representative color determination part 14. A color space conversion part 102 converts the linear input image data into nonlinear image data. The representative color determination part 14 determines a representative color of a pixel included in the nonlinear image data, and outputs the color value inside the linear color space corresponding to the representative color to the error diffusion processing part 12. <P>COPYRIGHT: (C)2005,JPO&NCIPI

Description

  The present invention relates to an image processing apparatus and method for performing error diffusion processing on image data.

In order to improve the image quality, a method called error diffusion is used.
For example, with the spread of the ICC (International Color Consortium) profile, color image processing belonging to a plurality of types of color spaces is required.
So far, generally, error diffusion processing has been performed without considering which color space the image data to be processed belongs to.

However, the pixel value and the color light intensity have a non-linear relationship in a uniform perceptual color space where the human eyes perceive the color difference almost evenly.
When error diffusion is performed on an image belonging to such a nonlinear color space, the diffused error does not become the original value, so the impression of the image after error diffusion is different from the impression of the image before error diffusion. End up.
In order to solve such problems, for example, Patent Document 1 discloses a method of converting a nonlinear color space image into a linear color space image, performing error diffusion, and returning the image to the nonlinear space image again. doing.
JP 2000-101859 A

The present invention has been made based on the background described above, and an object thereof is to provide an image processing apparatus and method that can reduce changes in images before and after processing.
Another object of the present invention is to speed up error diffusion processing.

[Image processing device]
To achieve the above object, an image processing apparatus according to the present invention includes error diffusion means for performing error diffusion processing on pixels of first image data in a first color space, and first error-diffused first Included in first color space conversion means for converting image data into second image data in a second color space different from the first color space, and in the second image data obtained as a result of the conversion An image processing unit that performs a predetermined process on a pixel to be processed, wherein the error diffusion unit includes a first value corresponding to a value of a pixel included in the second image data subjected to the predetermined process. The error diffusion process is performed by diffusing the pixel value of the image data of one color space to the pixel value included in the first image data.

  Preferably, the image processing means identifies a representative color of a pixel included in the second image data obtained as a result of the conversion, and the first color corresponding to the representative color obtained as a result of the identification. The error diffusion means diffuses the generated pixel value of the first color space into the pixel value included in the first image data, thereby generating the pixel value of the color space. Perform error diffusion processing.

  Preferably, the first color space is a non-linear color space, the second color space is a linear color space, and the first color space conversion means is the first color space in the linear color space. Image data is converted into the second image data in a non-linear color space.

  Preferably, the first color space is a uniform perceptual color space, a relative lightness color space or an equivalent neutral lightness color space, and the second color space is an NTSC, PAL or RGB color space. The first color space conversion means converts the first image data in the uniform perceptual color space, the relative lightness color space, or the equivalent neutral lightness color space into the second color space in the NTSC, PAL, or RGB color space. Convert to image data.

  Preferably, the first color space is an NTSC, PAL, sRGB or RGB linear color space, and the second color space is a uniform perceptual color space, a relative lightness color space or an equivalent neutral lightness color. And the first color space conversion means converts the first image data in an NTSC, PAL, sRGB or RGB linear color space into a color of uniform perceptual color space, relative lightness color space or equivalent neutral lightness. It converts into the said 2nd image data in space.

  Preferably, the image processing apparatus further includes second color space conversion means for converting the second image data in which the representative color is identified into third image data in the first color space.

[Image processing method]
In the image processing method, error diffusion processing is performed on the pixels of the first image data in the first color space, and the first image data subjected to the error diffusion processing is different from the first color space. Is converted into second image data in the color space of the image data, and a predetermined process is performed on the pixels included in the second image data obtained as a result of the conversion. The pixel value of the image data in the first color space corresponding to the value of the pixel included in the second image data subjected to the predetermined processing is diffused to the value of the pixel included in the first image data. .

  Preferably, the image processing identifies a representative color of a pixel included in the second image data obtained as a result of the conversion, and the first color corresponding to the representative color obtained as a result of the identification. A color space pixel value is generated, and the error diffusion process diffuses the generated first color space pixel value to a pixel value included in the first image data.

[program]
The program according to the present invention includes an error diffusion step for performing error diffusion processing on pixels of the first image data in the first color space, and the first image data subjected to the error diffusion processing as the first image data. A first color space conversion step for converting to second image data in a second color space different from the color space, and a predetermined process for pixels included in the second image data obtained as a result of the conversion are performed. An image processing step for causing a computer to execute the image processing step, wherein the error diffusion step includes image data in the first color space corresponding to a pixel value included in the second image data subjected to the predetermined processing. The error diffusion process is performed by diffusing the value of the pixel to the value of the pixel included in the first image data.

According to the image processing apparatus and method of the present invention, it is possible to reduce the change in image quality before and after error diffusion processing.
Moreover, according to the image processing apparatus and method of the present invention, the error diffusion process can be speeded up.

  The best mode of the present invention will be described below.

[First error diffusion processing apparatus 1]
First, a first error diffusion processing device 1 will be described as a first embodiment of the present invention.
FIG. 1 is a diagram showing a configuration of a first error diffusion processing apparatus 1 according to the present invention.
FIG. 2 is a diagram illustrating a configuration of the computer 16 that executes the error diffusion processing device 1 when the error diffusion processing device 1 shown in FIG. 1 is realized by software.
As shown in FIG. 1, the error diffusion processing device 1 includes an error diffusion processing unit 12, a representative color determination unit 14, a linearization unit 100, a first color space conversion unit 102, and a second color space conversion unit 104. Is done.
The error diffusion processing unit 12 includes an error distribution unit 120 and an error addition unit 122.
The representative color determination unit 14 includes a representative color table 140 and a representative color identification unit 142.

As shown in FIG. 2, the computer 16 includes a computer main body 160 including a CPU 162 and a memory 164, a display / input device 166 including a keyboard / display, an image input / output device 168 for inputting / outputting image data, and a CD. It comprises a recording device 170 such as a device / HDD device.
That is, the computer 16 includes a configuration part as a general computer capable of inputting and outputting image data.

Each component of the error diffusion processing device 1 is realized by dedicated hardware, for example.
Alternatively, each component of the error diffusion processing device 1 is realized as a computer program, for example, supplied to the computer 16 via the recording medium 172, loaded into the memory 164, and executed.
With these components, the error diffusion processing device 1 converts color input image data input from the outside (a scanner or the like (not shown) connected to the computer 16 (FIG. 2) via the image input / output device 168). Error dispersion processing is performed on the image to determine a representative color, which is used as output image data.

The input image data belongs to a non-linear color space such as NTSC, PAL, sRGB, or RGB, for example, is composed of a large number of pixels, and is input to the error diffusion processing apparatus 1 in a non-linear state with a 1 / 2.2 power characteristic. Entered.
The linearization unit 100 converts the input image data according to the attached characteristics, and outputs the input image data to the error diffusion processing unit 12 as linear input image data.
That is, for example, when the input image data is given the 1 / 2.2 power characteristic as described above, the linearization unit 100 raises the non-linear input image data to the second power to generate the linearity. Input image data.

FIG. 3 is a diagram illustrating error diffusion processing by the error diffusion processing unit 12 shown in FIG.
The error diffusion processing unit 12 performs error diffusion processing on the linear input image data input from the linearization unit 100.
In the error diffusion processing unit 12, the error distribution unit 120 includes linear color space image data (color values in the linear color space; FIG. 4) corresponding to the representative color input from the representative color identification unit 142 of the representative color determination unit 14. An error ε between the pixel value (described later with reference) and the pixel value of the linear input image data corresponding to this pixel is diffused to the surrounding pixels p of the linear input image data as shown in FIG. An error ε _p is calculated.

As shown in FIG. 3, the error ε is relative to the pixels 1 to 4 (p = 1 to 4) adjacent to the right, lower left, lower, and lower right of the pixel in which the error ε is calculated (the hatched pixel). Diffused.
Errors ε _{1 to} ε 4 allocated to these pixels 1 to ₄ are calculated by multiplying the error ε by coefficients w _{1 to} w ₄ , respectively (ε _p = ε × w _p ).
As the coefficients w _{1 to} w ₄ , 7/16, 3/16, 5/16, and 1/16 (by Floyed-Steinberg) are used, respectively, as shown in FIG.

The error adding unit 122 performs error diffusion by adding the error ε _p distributed to the periphery as described above to the surrounding pixel value p.
As a result, the pixel value I _ij output from the error adding unit 122 is [I _ij = I _0ij + Σε _kl × w _ijkl (Expression 1)] with respect to the linear input pixel value I _0ij from the linearization unit 100. It becomes.
However, in Expression 1, ij indicates a pixel position, k = i−1 to i + 1, l = j−1 to j + 1 (i ≠ k, j ≠ l), and w _ijkl is a calculation of the pixel value I _ij . _Therefore , the coefficient multiplied by the error ε _kl of the surrounding pixel value _kl .

The first color space conversion unit 102 describes linear input image data that has been subjected to error diffusion processing by the error diffusion processing unit 12 as color image data in a non-linear color space that is close to human color perception (hereinafter referred to as non-linear image data). ) And output to the representative color determination unit 14.
Hereinafter, unless otherwise specified, a specific example is a case where the color space conversion unit 102 converts linear input image data into image data of a uniform perceptual color space (such as CIELAB).

FIG. 4 is a diagram illustrating the contents of the representative color table 140 shown in FIG.
In the representative color determination unit 14, as shown in FIG. 4, the representative color table 140 includes a representative color number, pixel values of image data in the uniform perceptual color space (color values in the uniform perceptual color space), and uniform perceptual colors. The pixel values (color values in the linear color space) of the image data in the linear color space corresponding to the color values in the space are associated with each other, stored in a table format, and used for reference by the representative color identification unit 142.

The representative color identifying unit 142 refers to the representative color table 140, and each pixel value included in the non-linear image data input from the color space converting unit 102 is converted into a color value (in the uniform perceptual color space) of the representative color table 140 ( Figure 4) is calculated, and for each pixel included in the non-linear image data, the representative color number corresponding to the color value in the uniform perceptual color space that is closest to the value of these pixels is mapped. The value in the linear color space to be output is output to the second color space conversion unit 104 as a pixel value.
That is, the representative color identifying unit 142 determines the color of each pixel included in the non-linear image data as the representative color number corresponding to the value of each of these pixels and the color value in the uniform perceptual color space having the closest Euclidean distance. And the representative color indicated by

As described above, linear image data (hereinafter referred to as representative color data) for which the representative color is determined by the representative color determination unit 14 is output to the color space conversion unit 104.
Further, the representative color identifying unit 142 refers to the representative color table 140, and the pixel value of the linear color space corresponding to the representative color identified for each pixel of the nonlinear image data (color value in the linear color space; FIG. 4). ) Is output to the error distribution unit 120.
The second color space conversion unit 104 converts the representative color data into image data in the same color space as the input image data, and further adds the same characteristics as the input image data to obtain output image data. Output to the image input / output device 168 and the recording device 170 (FIG. 2).

[Overall Operation of Error Diffusion Processing Apparatus 1]
Hereinafter, the overall operation of the error diffusion processing apparatus 1 will be described.
Input image data in a linear color space is input to the error diffusion processing device 1.
The linearization unit 100 performs conversion to remove characteristics (such as a 1 / 2.2 characteristic) attached to the input image data, and outputs the converted data to the error diffusion processing unit 12 as linear input image data.

The error diffusion processing unit 12 diffuses an error ε between the linear color space color value (FIG. 4) input from the representative color determination unit 14 and the linear image data to surrounding pixels as shown in FIG. And output to the color space conversion unit 102.
The first color space conversion unit 102 converts the linear input image data into nonlinear image data and outputs the nonlinear image data to the representative color determination unit 14.

The representative color determination unit 14 outputs the value in the linear color space (FIG. 4) corresponding to the representative color of the pixel included in the non-linear image data to the second color space conversion unit 104 as representative color data.
Further, the representative color determination unit 14 outputs the color value in the linear color space (FIG. 4) corresponding to the representative color of the pixel included in the non-linear image data to the error diffusion processing unit 12.

FIG. 5 shows a case where the input image data is γ-corrected linear NTSC-RGB, that is, the input to the first color space conversion unit 102 is linear NTSC-RGB, and the output of the color conversion unit 102 is a relative lightness color space. It is a figure which illustrates the color space conversion characteristic in the case of being.
In the above description, the error diffusion processing apparatus 1 is described as a specific example in which a uniform perceptual color space (such as CIELAB) is used as the nonlinear color space. However, as shown in FIG. 5, the first color space is used. When the input to the conversion unit 102 belongs to the linear NTSC-RGB color space, the relative brightness indicating the relative brightness obtained by mapping each of RGB (color space channels) independently from the minimum value to the maximum value. A color space can also be used in the error diffusion processing apparatus 1 instead of the uniform perceptual color space.

Furthermore, in the error diffusion processing device 1, equivalent neutral brightness can be used as the relative brightness.
The equivalent neutral lightness represents the relationship between the lightness of gray and the channel of the color space that expresses the gray.

In the error diffusion processing device 1, when a color space of relative lightness or equivalent neutral lightness is used as a nonlinear color space instead of the uniform perceptual color space, the operation of each component of the error diffusion processing device 1 is as follows. Will be changed as follows.
The first color space conversion unit 102 converts linear input image data in a linear color space into nonlinear image data in a color space with relative lightness or equivalent neutral lightness.

The representative color table 140 of the representative color determination unit 14 represents the color value of the color space of the relative lightness or equivalent neutral lightness, the color value in the linear color space (FIG. 4), and the representative instead of the uniform perceptual color space color value. The color numbers are associated with each other and stored in a table format, and are referred to the representative color identification unit 142.
The representative color identifying unit 142 refers to the representative color table 140 to identify the representative color of the pixel included in the image data of the relative lightness or equivalent neutral lightness color space, and generates representative color data. The linear color space color value corresponding to the data is output to the error diffusion processing unit 12.
The operation of the components of these other error diffusion processing apparatuses 1 is as described above.

[Second error diffusion processing device 2]
Next, a second error diffusion processing device 2 will be described as a second embodiment of the present invention.

FIG. 6 is a diagram conceptually illustrating the color gamut of the CIELAB color space.
In a uniform perceptual color space such as CIELAB, the color gamut is not a rectangular parallelepiped, but is distorted, so that the representative colors are not evenly distributed in the color space.
For example, in the color gamut in the CIELAB color space, the area surrounded by the curve shown in FIG. 6 is the color gamut, and the points in the color gamut correspond to the representative color.

As shown in FIG. 6, in the CIELAB color space, the intervals between the representative colors can be made uniform in the area inside the color gamut.
However, in the CIELAB color space, the distribution of representative colors is particularly uneven in a region near the outer periphery of the color gamut, and it is necessary to add important pure colors such as R, G, and B on the curve.

Even if the intervals between the representative colors can be made uniform, the density of the representative colors varies depending on the color gamut region.
Therefore, in the CIELAB color space, it is not possible to determine the representative color recently by simple calculation. In order to determine the representative color recently, the distance between the pixel value of the nonlinear image data and all the representative colors is calculated and the difference is calculated. Often has to be calculated.
The second error diffusion processing device 2 according to the present invention uses the relative lightness color space instead of the uniform perceptual color space, and performs error diffusion processing at a high speed without significantly impairing the impression of the input image. It is devised to be able to.

FIG. 7 is a diagram showing the configuration of the second error diffusion processing device 2 according to the present invention.
7 that are substantially the same as the constituent parts of the error diffusion processing device 1 shown in FIG. 1 are assigned the same reference numerals.
As shown in FIG. 7, the second error diffusion processing device 2 includes the representative color determination unit 14, the first color space conversion unit 102, and the second color space of the first error diffusion processing device 1 (FIG. 1). The conversion unit 104 is replaced with the second representative color identification unit 202, the third color space conversion unit 200, and the fourth color space conversion unit 204, respectively.
With these components, the first error diffusion processing device 2 uses the relative lightness color space instead of the uniform perceptual color space, and generates the representative color data and the error in the same manner as the first error diffusion processing device 1. Perform diffusion processing.

For example, color input image data in the RGB color space is input to the error diffusion processing device 2.
In the error diffusion processing device 2, the input image data is first converted into linear input image data by the linearization unit 100 as in the error diffusion processing device 1 (FIG. 1).
The third color space conversion unit 200 converts each of the R, G, and B channels included in the linear input image data input from the error diffusion processing unit 12 into the R, G, and B channels of the relative brightness color space.

The representative color identification unit 202 substitutes the R, G, and B channels of the relative lightness color space into a function l for obtaining a representative value, and represents the representative values R _i , G _j , B _k (= l () for each channel. R), l (G), l (B)).
For example, when the representative values are equally spaced, the function l can simply calculate the representative value by dividing the input by the interval value and rounding off and multiplying by the interval value d (l (x) = nint (x / d) * d (where the function nint rounds to the nearest integer)).
Further, the representative color identification unit 202 returns the representative value in the relative lightness color space to the values R _i ′, G _j ′, B _k ′ in the linear color space, and outputs them to the color space conversion unit 204.
The color space conversion unit 204 converts the representative values R _i , G _j , B _k converted by the representative color identification unit 202 into output image data in the original RGB color space.

[Overall Operation of Second Error Diffusion Processing Apparatus 2]
Hereinafter, the overall operation of the second error diffusion processing device 2 will be described.
Input image data in the RGB color space is input to the error diffusion processing device 2.
The linearizer 100 sets this input image data as linear input image data.
The color space conversion unit 200 converts each of the R, G, and B channels included in the linear input image data into each of the R, G, and B channels of the relative lightness color space using the function l.

The representative color identification unit 202 converts the R, G, and B channels of the relative lightness color space into the representative values R _i , G _j , and B _k using the function l, and converts these into values in the linear color space. The returned R _i ′, G _j ′, and B _k ′ are output to the color space conversion unit 204 and the error diffusion processing unit 12 as representative value data.
The error diffusion processing unit 12 performs error diffusion processing using the representative values R _i ′, G _j ′, B _k ′ input from the representative color identification unit 202.
The color space conversion unit 204 converts the representative value data input from the representative color identification unit 202 from a linear color space to an RGB color space, and outputs it as output image data.

[Modification 1]
Next, a first modification of the second error diffusion processing device 2 will be described.
FIG. 8 is a diagram illustrating a one-dimensional lookup table that can be used by the representative color identification unit 202.
In the above description, the case where the representative color identifying unit 202 converts each channel included in the relative brightness input image data into the function l to convert the channel into a representative value is described. Instead of calculating l, it is also possible to obtain a representative value of each channel included in the relative brightness input image data using a lookup table as shown in FIG.

As described above, when the representative value is obtained using the lookup table, the processing amount of the representative color identifying unit 202 is reduced, so that the process for obtaining the representative value can be further speeded up.
In addition, when the representative value is obtained using the lookup table in this way, the degree of freedom in setting the value of the representative value obtained by the error diffusion processing device 2 is increased according to the application.

[Modification 2]
Next, a second modification of the second error diffusion processing device 2 will be described.
FIG. 9 is a diagram showing a configuration of a modified example (third error diffusion processing device 3) of the second error diffusion processing device 2 shown in FIG.
Also in FIG. 9, the same reference numerals are given to the components substantially the same as the components of the first error diffusion processing device 1 (FIG. 1) among the components of the third error diffusion processing device 3. It is.
As shown in FIG. 9, the third error diffusion processing device 3 includes a linearization unit 100, an error diffusion processing unit 12, a first representative color identification / color space conversion unit 300, and a second representative color identification / color space. The conversion unit 302 is configured.

For example, the process of the third color space conversion unit 200 of the second error diffusion processing device 2 (FIG. 7) is represented by a function g, and the process of the representative color identification unit 202 is represented by a function g−1 (l). Then, the process of the fourth color space conversion unit 204 can be expressed by a function f-1.
Therefore, the processing of the color space conversion unit 200, the representative color identification unit 202, and the color space conversion unit 204 uses these functions g, l, and f ⁻¹ to obtain a function f ⁻¹ (g ⁻¹ (l (g)) ).
The processing of the color space conversion unit 200 and the representative color identification unit 202 is expressed as a function g ⁻¹ (l (g)) using these functions g, l, and f ⁻¹ .

Accordingly, the representative color identification / color space conversion unit 300 performs processing of the function f ⁻¹ (g ⁻¹ (l (g))), and the representative color identification / color space conversion unit 302 causes the function g ⁻¹ ( By performing the processing of l (g)), it is possible to cause the third error diffusion processing device 3 to execute the same processing as that of the second error diffusion processing device 2 (FIG. 7).
The processing of the first and second representative color identification / color space conversion units 300 and 302 uses a one-dimensional lookup table as in the processing of the representative color identification unit 202 in the second error diffusion processing device 2. By using the look-up table in the representative color identification / color space conversion units 300 and 302, the processing of the error diffusion processing device 3 can be further speeded up.

As described above, in the error diffusion processing device according to the present invention, the input image data in the linear color space is converted into a non-linear uniform perceptual color space, etc. Near representative colors (recent representative colors) are identified.
On the other hand, error diffusion processing (error calculation and error distribution) is performed in a linear color space.
Thus, by obtaining the representative color in the nonlinear color space, the most natural representative color for the human eye can be mapped to each pixel.

  The present invention can be used for processing image data.

FIG. 1 is a diagram showing a configuration of a first error diffusion processing apparatus 1 according to the present invention. FIG. 2 is a diagram illustrating a configuration of the computer 16 that executes the error diffusion processing device 1 when the error diffusion processing device 1 shown in FIG. 1 is realized by software. FIG. 3 is a diagram illustrating error diffusion processing by the error diffusion processing unit 12 shown in FIG. FIG. 4 is a diagram illustrating the contents of the representative color table 140 shown in FIG. FIG. 5 is a diagram illustrating a relative lightness color space when the input image data is linear NTSC-RGB. FIG. 6 is a diagram conceptually illustrating the color gamut of the CIELAB color space. FIG. 7 is a diagram showing the configuration of the second error diffusion processing device 2 according to the present invention. FIG. 8 is a diagram illustrating a one-dimensional lookup table that can be used by the representative color identification unit 202. FIG. 9 is a diagram showing a configuration of a modified example (third error diffusion processing device 3) of the second error diffusion processing device 2 shown in FIG.

Explanation of symbols

1-3 ... error diffusion processing device,
100 ... linearization part,
102, 104, 200, 204 ... color space conversion unit,
12: Error diffusion processing unit,
120 ... error distribution unit,
122... Error adding unit,
14: representative color determining unit,
140 ... representative color table,
142, 202 ... representative color identification unit,
300, 302 ... representative color identification / color space conversion unit,

Claims (14)

Error diffusion means for performing error diffusion processing on the pixels of the first image data in the first color space;
First color space conversion means for converting the first image data subjected to the error diffusion processing into second image data in a second color space different from the first color space;
An image processing apparatus comprising: an image processing unit that performs predetermined processing on pixels included in the second image data obtained as a result of the conversion,
The error diffusion means includes, in the first image data, a pixel value of the image data in the first color space corresponding to a pixel value included in the second image data subjected to the predetermined processing. An image processing apparatus that performs the error diffusion process by diffusing the pixel value. The image processing means identifies a representative color of a pixel included in the second image data obtained as a result of the conversion, and includes a first color space corresponding to the representative color obtained as a result of the identification. Generate pixel values,
The error diffusion unit performs the error diffusion process by diffusing the generated pixel value of the first color space into the pixel value included in the first image data. Image processing apparatus. The first color space is a non-linear color space;
The second color space is a linear color space;
The image processing apparatus according to claim 1, wherein the first color space conversion unit converts the first image data in a linear color space into the second image data in a non-linear color space. The second color space is a uniform perceptual color space, a relative lightness color space or an equivalent neutral lightness color space;
The first color space is an NTSC, PAL, sRGB or RGB linear color space;
The first color space conversion means converts the first image data in an NTSC, PAL, sRGB or RGB linear color space into the first perceptual color space, relative lightness color space or equivalent neutral lightness color space. The image processing apparatus according to claim 1, wherein the image processing apparatus converts the image data into two image data. The first image data has a predetermined characteristic,
Correction means for correcting the predetermined characteristic attached to the first image data;
The image processing apparatus according to claim 1, wherein the error diffusion unit performs the error diffusion process on the corrected first image data. The second color space conversion means for converting the second image data in which the representative color is identified into third image data in the first color space. Image processing device. Performing error diffusion processing on the pixels of the first image data in the first color space;
Converting the error-diffused first image data into second image data in a second color space different from the first color space;
An image processing method for performing predetermined processing on pixels included in second image data obtained as a result of the conversion,
The error diffusion process includes, in the first image data, a pixel value of the image data in the first color space corresponding to a pixel value included in the second image data on which the predetermined process has been performed. An image processing method that diffuses the pixel values. The image processing identifies a representative color of a pixel included in the second image data obtained as a result of the conversion, and a pixel in the first color space corresponding to the representative color obtained as a result of the identification Generate a value,
The image processing method according to claim 7, wherein the error diffusion processing diffuses the generated pixel value of the first color space into a pixel value included in the first image data. An error diffusion step for performing error diffusion processing on the pixels of the first image data in the first color space;
A first color space conversion step of converting the first image data subjected to the error diffusion processing into second image data in a second color space different from the first color space;
A program for causing a computer to execute an image processing step of performing predetermined processing on pixels included in second image data obtained as a result of the conversion,
The error diffusion step includes, in the first image data, a pixel value of the image data in the first color space corresponding to a pixel value included in the second image data on which the predetermined processing has been performed. A program for performing the error diffusion process by diffusing the pixel value. The image processing step identifies a representative color of a pixel included in the second image data obtained as a result of the conversion, and the first color space corresponding to the representative color obtained as a result of the identification. Generate pixel values,
The error diffusion step performs the error diffusion process by diffusing the generated pixel value of the first color space into the pixel value included in the first image data. Program. The first color space is a linear color space;
The second color space is a non-linear color space;
The program according to claim 9 or 10, wherein the first color space conversion step converts the first image data in a linear color space into the second image data in a non-linear color space. The second color space is a uniform perceptual color space, a relative lightness color space or an equivalent neutral lightness color space;
The first color space is an NTSC, PAL or RGB color space;
The first color space conversion step converts the first image data in an NTSC, PAL, sRGB or RGB linear color space into the first perceptual color space, relative lightness color space or equivalent neutral lightness color space. The program according to claim 9 or 10, wherein the program is converted into image data of 2. The first image data has a predetermined characteristic,
A correction step of correcting the predetermined characteristic attached to the first image data;
The program according to any one of claims 9 to 12, wherein the error diffusion step performs the error diffusion process on the corrected first image data. 14. The computer according to claim 9, further causing a computer to execute a second color space conversion step of converting the second image data in which the representative color is identified into third image data in the first color space. The program described in.

Images