JP3074925B2 - Image evaluation method and device - Google Patents

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to an image evaluation method for evaluating a color image such as a color hard copy, and an apparatus for implementing the method.

[0002]

2. Description of the Related Art As image quality evaluation methods, there are a psychological evaluation for quantifying the degree of visual perception and a physical evaluation for evaluating the properties of an image structure based on objectively measured quantities. An important factor in image quality is image noise. RMS using the standard deviation of density change as a scale to physically express image noise
Examples include a Wiener spectrum obtained by Fourier transforming a change in granularity and density. There is also an evaluation method in which psychological evaluation and physical evaluation are combined, and as an example, a black-and-white solid image (solid a
rea) For images, refer to the document "NoisePercep
Tion in Electrophotograph
y ”Journal of Applied Pho
graphical Engineering Vo
l. 5: P 190-196 (1979) Roger
P. Dooley and Rodney Shaw
Psychological graininess described in
s) based on the Wiener spectrum and average density measurements. Shaw & Doole
There is an algorithm of y). Wiener Spectrum WS
(F) is the collective average of the square of the Fourier spectrum obtained by Fourier transforming the density variation ΔD (x) from the average density obtained by scanning the image with a microdensitometer. The following equation is used:

[0003]

(Equation 1) Where <> is the collective average, x is the position of the image, Δx is the data sampling interval, u is the spatial frequency, p and q are the width and length of the slit of the density system, N is the number of data, and j is (−1).
Represents ^1/2 . The algorithm of Show and Dawley uses the Wiener spectrum WS (f) and the average density Da.
The psychological granularity (grainin
ess) is expected.

[0004]

(Equation 2) Here, Δf is a basic spatial frequency, and VTF is a spatial frequency characteristic of a visual system. This algorithm is useful for evaluating analog images without repeating patterns, but cannot be used for evaluating digital images. Accordingly, the present inventors have previously proposed an image evaluation apparatus for correctly evaluating image quality without being affected by a repetition pattern of a digital image (Japanese Patent Application Laid-Open No. 1-260884).

[0005]

However, the above-described prior art algorithm is applied only to a monochrome image because it regards noise as a variation in density or brightness. When the above algorithm is applied to a color image, only the brightness information of the color image information is evaluated. Therefore, there is a problem that the value evaluated visually differs greatly from the evaluation value. The present invention provides an image evaluation apparatus capable of evaluating image quality even for a color image in order to solve the above problem.

[0006]

(First Invention) An image evaluation method according to the present invention converts image information to be evaluated including optical information and position information into information representing a color, and converts the converted information into information representing the color. Perform orthogonal transformation to generate information indicating the spatial frequency distribution,
Further, the image evaluation value is calculated by performing a correction using a function representing the spatial frequency characteristic of the human visual system and then integrating the correction.

(Second Invention) As shown in FIG. 1, an image evaluation device according to the present invention comprises an image information input means 11 for inputting an image to be evaluated as image information including optical information and position information. An image information storage unit 12 for storing image information input by the image information input unit 11 and information obtained by performing an arithmetic operation on the image information; and an image information input unit 11 for storing the image information input by the image information input unit 11. Information, saturation information, preprocessing means 13 for performing an arithmetic process for converting into color information comprising hue information, and brightness information, saturation information, and hue information are separately orthogonally transformed to the image information, respectively. Spectrum calculating means 14 for generating information indicating the spatial frequency distribution of each of the brightness information, chroma information, and hue information by calculating the spectrum of Spatial frequency characteristic correction means 15 for performing a function representing the spatial frequency characteristic of the human visual system and arithmetic processing on the output of the information, saturation information, and hue information to correct the spatial frequency characteristic, and the spatial frequency characteristic correction means Brightness information, saturation information,
Image evaluation value calculation means 16 for calculating an image evaluation value for each of the hue information and performing a calculation process with an appropriate weight to the image evaluation value to calculate a comprehensive image evaluation value. is there.

(Third Invention) As shown in FIG. 6, the configuration of an image evaluation apparatus according to the present invention comprises an image information input means 61 for inputting an image to be evaluated as image information including optical information and position information. An image information storage unit 62 for storing image information input by the image information input unit and information obtained as a result of performing arithmetic processing on the image information;
A pre-processing means 63 for performing an arithmetic process for converting the image information input by step 2 into color information, and performing an arithmetic process for converting the color information into chromaticity information; A spectrum calculating means 64 for generating information indicating a spatial frequency distribution of the chromaticity information by performing conversion and calculating a spectrum, and a function representing a spatial frequency characteristic of a human visual system with respect to an output of the spectrum calculating means 64. An image evaluation value calculating means 65 for calculating an image evaluation value by performing a calculation process to correct a spatial frequency characteristic and integrating the same is provided.

(Fourth Invention) As shown in FIG. 6, an image evaluation apparatus according to the present invention comprises an image information input means 61 for inputting an image to be evaluated as image information including optical information and position information. An image information storage unit 62 for storing image information input by the image information input unit and information obtained as a result of performing arithmetic processing on the image information;
2, a pre-processing unit 63 for performing an arithmetic process for converting the image information input by step 2 into color information, and performing an arithmetic process for converting the color information into color difference information; A spectrum calculating means 64 for generating information indicating a spatial frequency distribution of the color difference information by calculating an applied spectrum; and a function and a calculating process for a spatial frequency characteristic of a human visual system with respect to an output of the spectrum calculating means 64. And an image evaluation value calculating means 65 for calculating the image evaluation value by correcting the spatial frequency characteristics by performing the integration.

[0010]

(Operation of the First Invention) The information of the image to be evaluated includes information on the positional relationship and optical information. The information is converted into information representing a color, for example, color information including lightness information, saturation information, and hue information, and chromaticity information or color difference information obtained from the color information. The chromaticity information is obtained by an operation between elements of information representing a color (for example, Expression (16) described later), and the color difference information is obtained by calculating a difference between an average value of each element of the information representing the color. This is obtained by calculation (for example, Expression (17) described later). Information indicating a spatial frequency distribution, that is, a power spectrum is generated by performing orthogonal transformation on the information representing such colors.
Next, the information indicating the spatial frequency distribution is corrected by a function representing the spatial frequency characteristics of the human visual system, and then integrated. The present invention calculates an image evaluation value by integrating a power spectrum of noise obtained by performing orthogonal transformation on information representing a color of an image to be evaluated. In comparison, a color image can be evaluated more in accordance with human senses, and highly reliable image quality evaluation can be performed.

(Function of the Second Invention) In the present invention, the image information is input by the image information input means 11 while retaining the information on the positional relationship, and is stored in the image information storage means 12. The pre-processing unit 13 performs an arithmetic process for converting into color information. After performing this pre-processing, the spectrum calculation means 14 applies orthogonal transformation to extract the features of the image structure, and obtains the spectrum of the image. Next, the spatial frequency characteristic correction means 15 multiplies the output of the spectrum calculation means 14 by the spatial frequency characteristic of the visual system. Then, the output of the spatial frequency characteristic correction means 15 is integrated by the image evaluation value calculation means 16 to calculate each evaluation value, and the evaluation value is subjected to a calculation process with an appropriate weight, thereby obtaining a comprehensive image evaluation. Calculate the value. As mentioned above,
The present invention performs the calculation of the comprehensive image evaluation value by weighting the color information of the color image, that is, the power spectrum of the noise obtained by performing the orthogonal transformation on each of the lightness, the saturation, and the hue. The comprehensive image evaluation value shows a high correlation with the sensory evaluation value, and a highly reliable image quality can be evaluated for a color image that cannot be realized by the conventional evaluation method.

(Function of the Third Invention) In the present invention, the image information is input by the image information input means 61 while retaining the information on the positional relationship, and is stored in the image information storage means 62. The pre-processing unit 63 converts the color information into chromaticity information, and further performs an arithmetic process for obtaining chromaticity information. After performing this pre-processing, the spectrum calculation means 64 applies orthogonal transformation to extract features of the image structure, and obtains a spectrum of chromaticity information. Next, the image evaluation value calculation means 5 calculates the image evaluation value by multiplying the output of the spectrum calculation means 4 by the spatial frequency characteristic of the visual system and integrating it. Since the present invention calculates an image evaluation value from a power spectrum of noise obtained by performing orthogonal transformation on chromaticity information of a color image, the image evaluation value shows a high correlation with a sensory evaluation value. A highly reliable image quality evaluation can be performed on a color image that cannot be realized by the evaluation method. Further, since the evaluation can be performed with a single quantity (chromaticity) obtained by integrating the brightness, hue, and saturation information, the calculation time can be greatly reduced.

(Operation of the Fourth Invention) In the present invention, the image information is input by the image information input means 61 while retaining the information of the positional relationship, and is stored in the image information storage means 62. The pre-processing unit 63 converts the color information into color information, and further performs an arithmetic process for obtaining color difference information. After performing this pre-processing, the spectrum calculation means 64 applies orthogonal transform to extract features of the image structure, and obtains a spectrum of color difference information. Next, the image evaluation value calculation means 5 calculates the image evaluation value by multiplying the output of the spectrum calculation means 4 by the spatial frequency characteristic of the visual system and integrating it. Since the present invention calculates an image evaluation value from a power spectrum of noise obtained by performing orthogonal transformation on color difference information of a color image, the image evaluation value shows a high correlation with a sensory evaluation value, and the conventional evaluation. It is possible to perform highly reliable image quality evaluation on a color image that cannot be realized by the conventional method. In addition, since the brightness, hue, and saturation information can be evaluated with a single quantity, the calculation time can be greatly reduced. A high evaluation value is obtained.

[0014]

FIG. 2 is a block diagram showing the schematic arrangement of a color image quality evaluation apparatus according to a first embodiment of the present invention. In the present invention, the image to be evaluated needs to be input as optical information including position information. In this embodiment, the color matching functions x (λ) and y in the XYZ color system are used.
A scanning colorimeter 21 having the same spectral sensitivity as (λ) and z (λ) was used. As the scanning colorimeter, in this embodiment, a color image automatic inspection apparatus as disclosed in Japanese Patent Application Laid-Open No. 62-299971 modified so as to have a spectral sensitivity equivalent to the color matching function was used. The output of the scanning colorimeter 21 stores a signal indicating the tristimulus value XYZ at each sampling point in such a manner that positional relationship information is held. The noise evaluation operation processing unit 23 executes a series of operations as shown in FIG. 3 on the evaluated image stored in the image memory 22,
An image evaluation value representing a physical quantity of image noise is obtained, and the functions constituted by the execution include an L * a * b * converter 231, an L * C * h ° converter 232, a power spectrum calculator 233, Luminous spatial frequency characteristic correction unit 234,
It comprises an image evaluation value calculation unit 235 and the like. The series of arithmetic processing procedures are stored in the arithmetic processing procedure storage unit 25, and the arithmetic control unit 24 controls the noise evaluation arithmetic processing unit 23 according to the stored procedures. Noise evaluation operation processing unit 23
Is output to an output unit 26 such as a CRT display device.

Referring to FIG. 3, a flow of a series of processes in the noise evaluation operation processing section 23 which is a main part in the present embodiment will be described. L * a * b * conversion The image information input by the scanning colorimeter 21 is a tristimulus value X
(X), Y (x), and Z (x). Here, x is information indicating a position where each tristimulus value information is sampled. This psycho-physical image information is converted to a uniform color space in order to bring it closer to the human psychological quantity. A well-known method can be adopted for this conversion. In this embodiment, the LIE in the (L *, a *, b *) space of CIE1976 is used.
Conversion to * a * b * was used. From XYZ to L * a * b *
The following equation was used for conversion to. L * (x) = 116 {Y (x) / Yn} ^1/3 -16: Y (x) / Yn> 0.008856 (2) L * (x) = 903. 29 {Y (x) / Yn}: Y (x) /Yn≦0.008856 (3) a * (x) = 200 [{X (x) / Xn} ^1/3 − {Y (x) / Yn} ^1/3 ] b * (x) = 500 [{Y (x) / Yn} ¹ /3-{Z (x) / Zn} ^1/3 ]: X (x) /Xn>0.008856 Y (x) / Yn> 0.008856 Z (x) / Zn> 0.008856 (4) where X (x) / Xn, Y (x ) / Yn, Z (x) /
If there is 0.008856 or less in Zn, the corresponding cubic root term in equation (4) is calculated as 7.787 ｛X (x) /
Xn {+16/116, 7.787 {Y (x) / Yn}
+16/116, 7.787 {Z (x) / Zn} +16
/ 116 to calculate. Here, Xn, Yn,
Zn represents a tristimulus value in the XYZ system on the perfect diffuse reflection surface.

L * C * h ° conversion The image information L * a * b * converted above is converted into L *, C *, h ° representing three psychological attributes of color, lightness, saturation, and hue. I do. The following equation was used as the C *, h ° conversion equation. C * (x) = {a * (x) ² + b * (x) ² } ^1/2 ... (5) h ° (x) = tan ⁻¹ (b * (x) / a * (x)) (6) Next, the image information L * (x), C * obtained above
Power spectra of (x) and h ° (x) were obtained by the following processes.

Power spectrum calculation Image data L * (x), C * processed as described above
Discrete Fourier transform was performed on (x) and h ° (x). Discretely sampled image information L * (x),
The power spectrum for C * (x), h ° (x) is P
_{L (f), P C (} f), was calculated using the same in the following formula and as P _h (f) (1) formula.

[0018]

(Equation 3) However, <> indicates that the average of the spectrum for each section is obtained as a set average as shown in FIG. Also, Δx
Is the data sampling interval, u is the spatial frequency, p and q are the width and length of the slit of the densitometer, N is the number of data in one section, L * 0, C * 0, h ° 0 is the average of all sections. L *
(X), C * (x), h ° (x). In this embodiment, the data sampling interval Δx = 20 μm, the width p of the slit of the densitometer p = 20 μm, the length q = 1000 μm,
With the number of data in one section N = 64, a set average of 15 sections was obtained. Next, a psychometric index of image noise is calculated.

Spatial Frequency Correction Next, a power spectrum P _L representing the spatial frequency characteristic of the screen noise obtained by the calculations of the above equations (7), (8) and (9).
(F), P _C (f), and P _h (f) are each multiplied by the spatial frequency characteristics VTF _L, VTF _{C, and} VTF _h of the visual system of lightness, saturation, and hue as shown in the following equations. By integrating and integrating, a colorimetric information, that is, a metric psychological index serving as an index of image noise of each of lightness, chroma, and hue is calculated.

[0020]

(Equation 4)

In this embodiment, the spatial frequency characteristics VTF _L , VTF _L
Dolly describes in the document "On Inv as TF _C and VTFh.
estimation of the Factors
Influencing the Perceive
d Sharpness of Electrophotot
ophthalmic Lines ": AnnualCo
The following equation described in the reference of SPSE 1979 was used. VTF (i · Δf) = 5.05 exp (◆ 0.843i · Δf) ◆ exp (◆ 1.45i · Δf)｝ i · Δf> 1.0 = 1.00 0 ≦ i · Δf ≦ 1. 0 (13) Here, Δf represents a fundamental spatial frequency and is defined by the following equation. Δf = 1 / (NΔx) (14) In this embodiment, Δf = 0.78 cycles / mm.

Computation of Comprehensive Image Evaluation Value Color information, that is, brightness, saturation, and hue are integrated.
Image evaluation, which is a psychometric quantity that is an index of image noise
Value Q_TotalIs calculated by the following equation. Q_Total＝ a ・ Q_L+ B · Q_C+ C · Q_h  ... (15) Here, a, b, and c represent weights by each index. that's all
The noise evaluation calculation processing unit 23 has been described in detail.
(2) to (9) and (13) used in each calculation unit
Replaces other mathematical expressions and approximations with similar results.
Clearly, it can be used. In addition,
Perform the expressions (2) to (9) and (13) stored in the storage unit.
The calculation procedure is based on ordinary computer program technology.
This is a design item that can be configured arbitrarily.
Detailed descriptions of these procedures are omitted. Note that noise
In this embodiment, the evaluation operation processing unit 23 is a computer software.
Although the case of realization by software technology was shown,
Part or all of the hardware configuration using individual circuits
Of course you can.

An example of the result of actually using the image noise evaluation apparatus of the present embodiment described above will be described. Image evaluation values were calculated for many samples of color images created by processes such as electrophotography and printing, and sensory evaluation was also performed. All the image samples to be measured used solid color images. The sensory evaluation used an evaluation value obtained by quantifying the noise level of each image sample by a category evaluation method. As a result, as shown in FIG. 5, a very high correlation was obtained between the image evaluation value and the sensory evaluation value according to the present example.

As described above, in this embodiment, the color information of the color image, that is, the power spectrum of the noise obtained by performing the orthogonal transformation on each of the lightness, the saturation, and the hue is weighted to obtain a comprehensive image. Since the evaluation value is calculated, the overall image evaluation value shows a high correlation with the sensory evaluation value, and a reliable image quality evaluation can be performed for a color image that cannot be realized by the conventional evaluation method. It can be carried out.

(Second and Third Embodiments) In the second embodiment, the color information L * a * b * is converted into chromaticity information E (x), and the power spectrum P _E (f) is obtained. In the third embodiment, the color information L * a * b * is converted to the color difference information ΔE (x).
To obtain the power spectrum PΔ _E (f),
This is a color image quality evaluation device configured to obtain an image evaluation value. Since the schematic configuration of these embodiments is common,
Both will be described together. FIG. 7 is a functional block diagram showing the configuration of the second and third embodiments. The color image quality evaluation apparatus of these embodiments includes a scanning colorimeter 71 for inputting an image to be evaluated as optical information including position information.
An image memory 72 for storing image information input by the scanning colorimeter 71 and information of a result of performing an arithmetic process on the image information; and a series of images to be evaluated stored in the image memory 72. Is performed to obtain an image evaluation value representing a physical quantity of image noise.
And an arithmetic control unit 74 for controlling the noise evaluation arithmetic processing unit 73 in accordance with the procedure stored in the arithmetic processing procedure storage unit 75
An operation processing storage unit 75 for storing a series of operation processing procedures of the noise evaluation operation processing unit 73; and an output unit 76 such as a CRT display device for outputting an operation result of the noise evaluation operation processing unit 73. ing.

The scanning colorimeter 71, the image memory 72, the arithmetic control unit 74, the output unit 76, and the like are provided by the scanning colorimeter 21, the image memory 22, the arithmetic control unit 24, and the output unit of the first embodiment. It is the same as the part 26 and the like. Further, the arithmetic processing procedure storage unit 75 of the present embodiment and the arithmetic processing procedure storage unit 25 of the first embodiment differ only in the arithmetic processing procedure stored. The noise evaluation operation processing unit 73
2 (in the case of the second embodiment) or FIG. 9 (in the case of the third embodiment) on the image to be evaluated stored in An image evaluation value representing a physical quantity is obtained, and the function constituted by the execution is an L * a * b * conversion unit 731, chromaticity information (in the case of the second embodiment) or chrominance information (third embodiment). Conversion operation unit 732, power spectrum operation unit 7)
33, an image evaluation value calculation unit 734 and the like. FIG. 8 shows the second
FIG. 9 is a diagram illustrating a flow of processing of a noise evaluation calculation processing unit 73 in the third embodiment, and FIG. 9 is a diagram illustrating a flow of processing of the noise evaluation calculation processing unit 73 in the third embodiment. FIG.
The flow of a series of processes in the noise evaluation operation processing unit 73 will be described with reference to FIG.

The uniform chrominance space conversion The image information input by the scanning colorimeter 71 is a tristimulus value X
(X), Y (x), and Z (x). Here, x is information indicating a position where each tristimulus value information is sampled. This psychophysical image information is converted to a uniform color difference space in order to more closely approximate the psychological quantity of humans. A well-known method can be used for this conversion. In this embodiment, as in the first embodiment, (L *, a
*, B *) space to L * a * b *. For conversion from XYZ to L * a * b *, the same equations (2) to (4) as described in the description of the first embodiment are used.

Conversion of chromaticity information and chrominance information From the image information L * a * b * converted above, chromaticity information E
(X). The following equation was used as the conversion equation. E (x) = {L * (x) ² + a * (x) ² + b * (x) ² } ^1/2 (16) Also, to convert to color difference information ΔE (x), Used. ΔE (x) = ｛ΔL * (x) ² + Δa * (x) ² + Δb * (x) ² ｝ ^1/2 ΔL * (x) = L * (x) −L * ₀ , Δa * (x) = a * (x) −a * ₀ , Δb * (x) = b * (x) −b * ₀ (17) where L * ₀ , a * ₀ , b * ₀ Is L * (x), a *
Indicates the average value of (x), b * (x) or a value specified in advance.

Power Spectrum Calculation A discrete Fourier transform was performed on the chromaticity information E (x) or color difference information ΔE (x) processed as described above. In the case of chromaticity information, discretely sampled chromaticity information E
The calculation was performed using the following equation in the same manner as equation (1-1), with the power spectrum for (x) as P _E (f).

(Equation 5)

Further, when the color difference information, discretely power spectrum for the sampled color difference information _{ΔE (x) P ■ E (} f) is obtained by the following equation.

(Equation 6) However, <> indicates that the average of the spectrum for each section is obtained as a set average as shown in FIG. Also, Δx
Is the data sampling interval, u is the spatial frequency, p and q are the width and length of the slit of the densitometer, N is the number of data in one section, and E ₀ is the average of the sampling data E (x). In this embodiment, the data sampling interval Δx = 20
μm, densitometer slit width p = 20 μm, length q = 1
With 000 μm and the number of data in one section N = 64, a set average of 15 sections was obtained.

Next, a metric psychological index of image noise is calculated. Calculating then said metering psychological indicators (18) or (19) of the power spectrum P _E representing the spatial frequency characteristics of the screen noise obtained by calculation (f) or, P _{■ E} (f), with respect to, As shown in the following equation, a metric psychological index serving as an index of image noise is calculated by multiplying and integrating the spatial frequency characteristic VTF of the visual system.

[0032]

(Equation 7) In this embodiment, Dolly describes the spatial frequency characteristic VTF as "In Investigation of the VTF".
Factors Influencingthe P
received Sharpness of Ele
crophotographic Lines “:
Annual Conferenceof SPSE
The following equation described in 1979 was used.

[0033]

(Equation 8) Here, Δf represents a fundamental spatial frequency and is defined by the following equation. Δf = 1 / (NΔx) (23) In this embodiment, Δf = 0.78 Cycles / mm.

The noise evaluation operation processing unit 73 of the second and third embodiments has been described above in detail. The equations (2) to (4) and (16) to (19) used in each operation unit are described in detail. It is clear that other equations and approximate equations may be used for the equation and the equation (22) as long as the same result is obtained. Further, the expressions (2) to (4) stored in the operation procedure storage unit and (1)
Since the procedures for calculating the equations (6) to (19) and (22) are design items that can be arbitrarily configured by ordinary computer program technology, detailed descriptions of those procedures are omitted. In this embodiment, the noise evaluation calculation processing unit 73 is realized by computer software technology. However, it is needless to say that a part or all of the noise evaluation calculation processing unit 73 can be configured by a hardware using an individual circuit.

An example of the result of actually using the image noise evaluation device of the present embodiment described above will be described. As in the first embodiment, image evaluation values were calculated for a large number of samples of color images created by processes such as electrophotography and printing, and sensory evaluation was also performed. All the image samples to be measured used solid color images. Also,
The sensory evaluation employed an evaluation value obtained by quantifying the noise level of each image sample by a category evaluation method. As a result, the image evaluation value and the sensory evaluation value according to the present embodiment are different from the first evaluation value.
A very high correlation almost the same as that of the example was obtained. As described above, in the second and third embodiments, the image evaluation value is calculated from the power spectrum of the noise obtained by performing the orthogonal transform on the chromaticity information or the color difference information of the color image. The evaluation value shows a high correlation with the sensory evaluation value, and a highly reliable image quality can be evaluated for a color image that cannot be realized by the conventional evaluation method.

[0036]

According to the present invention, an image evaluation value is calculated from a power spectrum of noise obtained by performing orthogonal transformation on information representing a color of an image to be evaluated including position information. The image evaluation value shows a high correlation with the sensory evaluation value. In particular, it is possible to evaluate a color image which has not been realized by the conventional evaluation method, and it is possible to evaluate image quality with high reliability. Further, in the present invention, when the color information of a color image, that is, brightness, saturation, and hue are weighted to the power spectrum of noise obtained by performing orthogonal transformation on each of the hue, and a total image evaluation value is calculated. Indicates that the image evaluation value has a higher correlation with the sensory evaluation value. Further, in the present invention, when the image evaluation value is calculated based on the power spectrum of noise obtained by performing orthogonal transformation on the chromaticity information of the color image, the brightness, hue, and chroma information are integrated. Since the evaluation can be performed by the amount, the calculation time can be significantly reduced. Further, in the present invention, when calculating the image evaluation value by the power spectrum of the noise obtained by performing orthogonal transformation on the color difference information of the color image, similar to the case of the chromaticity information, brightness, hue,
Since the saturation information can be evaluated with a single quantity, the calculation time can be greatly reduced, and the brightness, hue,
Since the total value is based on the difference between the pieces of chroma information, a more reliable evaluation value can be obtained.

[Brief description of the drawings]

FIG. 1 is a block diagram showing a main configuration of the present invention (second invention).

FIG. 2 is a block diagram illustrating a schematic configuration of an image evaluation device according to a first embodiment of the present invention.

FIG. 3 is a diagram illustrating a processing flow of a noise evaluation calculation processing unit according to the first embodiment.

FIG. 4 is a diagram illustrating a method of calculating a collective average when calculating a power spectrum.

FIG. 5 is a diagram showing an example of a measurement result showing a correlation between an evaluation value and a sensory evaluation value according to the present invention.

FIG. 6 is a block diagram showing a main configuration of the present invention (third invention).

FIG. 7 is a block diagram illustrating a schematic configuration of an image evaluation device according to a second embodiment (and a third embodiment) of the present invention.

FIG. 8 is a diagram illustrating a processing flow of a noise evaluation calculation processing unit according to the second embodiment.

FIG. 9 is a diagram illustrating a processing flow of a noise evaluation calculation processing unit according to the third embodiment.

[Explanation of symbols]

11, 61 image information input means, 12, 62 image information storage means, 13, 63 preprocessing means, 14, 64 spectral calculation means, 15 spatial frequency characteristic correction means, 1
6, 65 ... image evaluation value calculation means, 21, 71 ... scanning colorimeter, 22, 72 ... image memory, 23, 73 ... noise evaluation calculation processing unit, 24, 74 ... calculation control unit, 25, 75 ...
Arithmetic processing procedure storage unit, 26, 76 ... output unit.

──────────────────────────────────────────────────続 き Continuation of the front page (72) Inventor Tomoyasu Matsuzaki 2274 Hongo, Ebina-shi, Kanagawa Prefecture Fuji Xerox Co., Ltd. Ebina Works (56) References JP-A-4-273650 (JP, A) JP-A-4- 306048 (JP, A) JP-A-5-284260 (JP, A) JP-A-59-43468 (JP, A) JP-A-59-783 (JP, A) JP-A-63-9970 (JP, A) (58) Field surveyed (Int. Cl. ⁷ , DB name) H04N 1/00

Claims (4)

(57) [Claims] 1. An image information to be evaluated including optical information and position information is converted into information representing a color, and the converted information is subjected to an orthogonal transform to generate information indicating a spatial frequency distribution. An image evaluation method, wherein an image evaluation value is calculated by performing a correction using a function representing a spatial frequency characteristic of a visual system, and then integrating the correction. 2. An image information input means for inputting an image to be evaluated as image information including optical information and position information, and image information input by the image information input means and arithmetic processing performed on the image information Image information storage means for storing the resulting information, and preprocessing means for performing an arithmetic processing for converting the image information input by the image information input means into color information comprising lightness information, saturation information, and hue information; The brightness information, the chroma information, and the hue information are separately subjected to orthogonal transformation to calculate respective spectra, thereby generating information indicating the spatial frequency distribution of each of the brightness information, the chroma information, and the hue information. And a function representing the spatial frequency characteristics of the human visual system with respect to the output of each of the brightness information, the saturation information, and the hue information. A spatial frequency characteristic correction unit that performs arithmetic processing to correct the spatial frequency characteristic, and calculates an image evaluation value of each of the brightness information, the saturation information, and the hue information by integrating the output of the spatial frequency characteristic correction unit. An image evaluation apparatus, comprising: an image evaluation value calculation unit that calculates a total image evaluation value by performing a calculation process with an appropriate weight applied to the image evaluation value. 3. An image information input means for inputting an image to be evaluated as image information including optical information and position information, and an image information input by the image information input means and an arithmetic processing performed on the image information. Image information storage means for storing the result information; and arithmetic processing for converting the image information input by the image information input means into color information, and for converting the color information into chromaticity information Pre-processing means for performing the orthogonal transform on the chromaticity information to calculate a spectrum to generate information indicating a spatial frequency distribution of the chromaticity information; On the other hand, there is provided an image evaluation value calculation means for calculating an image evaluation value by correcting a spatial frequency characteristic by performing a function representing a spatial frequency characteristic of a human visual system and an arithmetic process and integrating the function. Image evaluation apparatus according to claim Rukoto. 4. An image information input means for inputting an image to be evaluated as image information including optical information and positional information, and an image information input by the image information input means and arithmetic processing performed on the image information Image information storage means for storing the resulting information; and arithmetic processing for converting the image information input by the image information input means into color information, and converting the color information into color difference information. Pre-processing means for applying; spectral calculation means for performing orthogonal transformation on the color difference information to calculate a spectrum to generate information indicating a spatial frequency distribution of the color difference information; An image evaluation value calculation means is provided for correcting the spatial frequency characteristic by performing a function representing the spatial frequency characteristic of the visual system and performing arithmetic processing, and calculating an image evaluation value by integration. Image evaluation apparatus according to claim Rukoto.