JP2000188647A - Image evaluation method and image evaluation system - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide an image evaluation method and an image evaluation system with which an evaluation value of image quality with respect to sharpness can quantitatively be obtained with satisfactory correlation with a human sense. SOLUTION: This image evaluation system 100 is provided with an image storage section 101, that stores an image read by a color image input device 200 as an evaluated image, a display environment relevant conversion means 102 that converts image information of the evaluated image into an image corresponding to environment displayed on an image display device, a uniform color space component calculation means 103 that calculates a uniform color space component, visual perception characteristics correction means 104 that conducts weighting corresponding to the visual perception characteristic of person, a space frequency component calculation means 105 that calculates an evaluation value for each spatial frequency component, an integral value calculation means 106 that conducts integration within a proper spatial frequency range, and a correction means 107 that corrects the image with a prescribed coefficient.

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

[0001] 1. Field of the Invention [0002] The present invention relates to an image evaluation method and an image evaluation apparatus, and more particularly, to an image evaluation method for evaluating image quality with respect to sharpness of an image input by a color image input apparatus using a solid-state image sensor. The present invention relates to a method and an image evaluation device.

[0002]

2. Description of the Related Art Various attempts have been made to evaluate image quality. There are mainly two types of image quality evaluation methods. The first type is an objective (physical) evaluation for measuring a deterioration factor of the image quality of an image itself, and the second type is an image quality evaluation method. It is a subjective (psychological) evaluation that quantifies the sense of human perception of quality.

In the objective evaluation, a quantitative evaluation can be obtained, but the evaluation result does not always have a good correlation with human subjectivity. The subjective evaluation is, for example, an evaluation method performed by comparing an evaluation image having deterioration with an evaluation image having no deterioration and converting the image quality into a score, and is a visual image quality evaluation.

[0004] However, such a subjective evaluation has a disadvantage that the test results are different when the examiners are different or the examiners are tired, and there is a problem that a quantitative and stable evaluation result cannot be obtained. is there.

The following three types of images to be evaluated (hereinafter referred to as “images to be evaluated”) can be considered.

Images output from copiers and printers Images compressed / expanded by irreversible image compression methods Images captured by image input devices such as scanners and digital cameras

The above evaluation target is an output device such as a copy machine or a printer. The image output from each evaluation target machine is captured by an image input device under the same conditions such as a high-definition scanner, and the sharpness, which is the sharpness of the line,
Evaluation is made based on the granularity, which is the degree of scattering of dots, the number of gradations, which is the degree of change in gradation, and color reproducibility. For the subjective evaluation, an image output on paper such as a hard copy is used. In this field, various attempts have been made to obtain an objective evaluation value having a good correlation with the above-described subjective evaluation value. For example, the following are known.

In Japanese Patent Application Laid-Open No. 5-284260, an image information component including two-dimensional position information and optical information is converted into a color component, and the converted two-dimensional information is subjected to two-dimensional orthogonal transformation. After converting into spatial frequency information and converting the two-dimensional spatial frequency information into one-dimensional data, the correction (corresponding to the frequency characteristics of human vision) is performed to reduce noise (brightness, color fluctuation) included in the output image. An image evaluation device for evaluating is disclosed.

In Japanese Patent Application Laid-Open No. 7-325922, after converting the density distribution component of a cross section into spatial frequency information by two-dimensional orthogonal transformation from optical information of an image to be evaluated, correction corresponding to the frequency characteristic of human vision is performed. In addition, there is disclosed an evaluation device that evaluates the sharpness of an edge of an output image. Note that the sharpness of the edge of an image is visually called sharpness.

The above evaluation object is an image compression method.
The image compressed / decompressed in each method with respect to the original image is evaluated. In this field, importance is placed on the evaluation of the "reproducibility" of "how efficiently (by increasing the compression ratio)" faithful reproduction of the original image. For example, the original image per pixel and the processed image Is often evaluated using S / N, which is the logarithm of the difference between. For the subjective evaluation, both an image output on paper and an image displayed on an image display device such as a CRT display are used.

The object to be evaluated is an input device such as a scanner or a digital camera. The same output image is photographed by each evaluation target machine (taken in the case of a scanner) and evaluated. In the subjective evaluation, an image displayed on an image display device is mainly used. As an evaluation method of an image captured by a scanner or a digital camera, for example, Japanese Patent Application Laid-Open No. 9-28
Japanese Patent No. 4429, “Image evaluation device” is known.

In Japanese Patent Application Laid-Open No. 9-284429, a spatial frequency component is obtained from image information captured by a scanner or a camera, a correction corresponding to human visual characteristics is performed, and a subjective evaluation is performed by using the obtained value. An image evaluation device that evaluates accurate image quality matched with a value is disclosed.

The major difference between the above evaluation and the above evaluation is that the size of the image to be evaluated (the number of pixels per image) is different. For example, in recent digital cameras, the number of pixels required for one image (hereinafter referred to as “captured pixel number”) is rapidly changing from a 300,000 pixel (VGA) class to a 1 million pixel (SVGA) class. If the number of users who focus on printing out an image increases, the number of pixels is expected to increase in the future.

[0014]

However, in the above-mentioned Japanese Patent Application Laid-Open No. 9-284429, "Image Evaluation Apparatus"
There is a problem in that it is not possible to evaluate image quality having different numbers of captured pixels.

[0015] Conventionally, the sharpness of a digital camera has been evaluated using the value of the limit resolution represented by a TV book. Here, the resolution represented by a TV book (hereinafter, referred to as “limit resolution”) is represented by the number of lines entering the height direction of a captured image.
The limit resolution of a 0 (w) × 480 (h) VGA class digital camera is 480 TV lines if the performance of the CCD and the signal processing system is good (although images are generally quantized by the processing system, etc.). 480 due to image degradation
It will not be a TV book). However, the critical resolution is a criterion for evaluating the performance of a machine and does not reflect human subjectivity.

The present invention has been made in view of the above, and is an image evaluation method and an image evaluation apparatus capable of obtaining an image quality evaluation value for sharpness with good correlation with human senses and quantitatively. The purpose is to provide.

[0017]

In order to solve the above-mentioned problems, the invention according to claim 1 evaluates image quality with respect to sharpness of an image input by a color image input device using a solid-state imaging device. In the image evaluation method, a first step of performing conversion corresponding to an environment when displayed on an image display device on image information of an image to be evaluated, and calculating a uniform color space component of the converted image to be evaluated Second
And a third step of weighting the calculated uniform color space component of the evaluated image in accordance with human visual characteristics, and evaluating each of the weighted uniform color space components for each spatial frequency component. A fourth step of calculating a value, a fifth step of integrating the calculated evaluation value for each spatial frequency component in an appropriate spatial frequency range, and a step of: And a sixth step of performing correction with a predetermined coefficient.

According to a second aspect of the present invention, there is provided an image evaluation apparatus for evaluating image quality with respect to sharpness of an image input by a color image input apparatus using a solid-state image sensor, wherein image information of an image to be evaluated is Display environment conversion means for performing conversion corresponding to the environment when displayed on an image display device, and uniform color space component calculation means for calculating a uniform color space component of the evaluated image converted by the display environment conversion means. A visual characteristic correction unit that weights the uniform color space component calculated by the uniform color space component calculation unit in accordance with human visual characteristics; and a uniform color space component weighted by the visual characteristic correction unit. A spatial frequency component calculating means for calculating an evaluation value for each frequency component, and an evaluation value for each frequency component calculated by the spatial frequency component calculating means. , The integral value calculating means performs integration at the appropriate spatial frequency range, with respect to the integrated evaluation value by the integral value calculating means, in which a correction means for correcting a predetermined coefficient.

According to a third aspect of the present invention, in the first aspect, in the first step, the chromaticity of each primary color, the chromaticity of a white point, and the gamma characteristic of the image display device are selected. Using at least one as a parameter,
The image information of the evaluated image is subjected to conversion corresponding to the environment when displayed on the image display device.

According to a fourth aspect of the present invention, in the second aspect of the present invention, the display environment compatible conversion means includes a chromaticity of each primary color, a chromaticity of a white point, and a gamma characteristic of an image display device. Using at least one of them as a parameter, the image information of the evaluated image is subjected to conversion corresponding to the environment when displayed on the image display device.

According to a fifth aspect of the present invention, in the first aspect, in the second step, only the lightness component of the uniform color space component of the image to be evaluated is output.

According to a sixth aspect of the present invention, in the second aspect, the uniform color space component calculating means includes:
It outputs only the lightness component of the uniform color space component of the evaluated image.

[0023]

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Preferred embodiments of an image evaluation method and an image evaluation apparatus according to the present invention will be described below with reference to the accompanying drawings. The uniform color space component calculation means], the "integral value calculation means", and the "correction means" will be described in this order.

[Image Evaluation Apparatus] FIG. 1 is a block diagram showing a configuration of an image evaluation apparatus according to the present invention. In the figure, 100 indicates an image evaluation device, and 200 indicates a color image input device.

The image evaluation device 100 includes an image storage unit 101 for storing an image read by the color image input device 200 as an image to be evaluated, and an environment for displaying image information of the image to be evaluated on an image display device. The image processing apparatus includes a display environment conversion unit that performs a corresponding conversion, a uniform color space component calculation unit that calculates a uniform color space component, and a visual characteristic correction unit that performs weighting corresponding to human visual characteristics.

The image evaluation apparatus 100 includes a spatial frequency component calculating means 10 for calculating an evaluation value for each spatial frequency component.
5, an integral value calculating means 106 for performing integration in an appropriate spatial frequency range, and a correcting means 107 for performing correction with a predetermined coefficient.
And image evaluation value output means 108 for outputting an image evaluation value
An input image parameter storage unit 109 that stores the lowest value of the spatial frequency of the input image described below, an input parameter storage unit 110 that stores data of the width and height of the input image,
A correction coefficient storage unit 111 for storing a correction coefficient for each image type.

The color image input device 200 is for taking in an input image, and is, for example, a digital camera. An example of an evaluation image captured from the color image input device 200 will be described below.

FIG. 2 shows a series of patterns in which a low reflectance and a high reflectance are periodically repeated (hereinafter referred to as a “ladder pattern”).
). The ladder pattern shown in FIG.
Suitable for evaluating image quality for sharpness.

FIG. 3 shows a series of patterns having various frequency components from low spatial frequency components to high spatial frequency components (hereinafter referred to as "ladder patterns"). The ladder pattern shown in FIG. 2 is suitable for evaluating image quality with respect to sharpness of different spatial frequency components.

FIG. 4 shows a resolution chart (hereinafter, referred to as an "ISO chart") for an "ISO" standard electronic still camera. FIG. 5 shows names of respective parts of the ISO chart of FIG. Ladder patterns: 1 for J1 and K1 in FIG.
From 00 to 2000 TV books are shown. Here, for example, 100 TV lines is the number of black and white lines that fall within 20 cm in the vertical direction of the active area (the white portion in FIG. 3) of the chart (one black and white line group counts as one). .

When the shooting conditions of the input image to be used are determined, the conditions are followed. In the case of the ISO chart, the framing point and the focus setting point for each aspect ratio of the number of pixels captured by the digital camera and the uniformity of the luminance distribution on the chart are defined.

Next, an outline of an image evaluation method by the image evaluation apparatus will be described with reference to a flowchart of FIG. FIG. 5 is a flowchart for explaining an image evaluation method by the image evaluation device. Hereinafter, the case where the ISO chart is used as the image to be evaluated will be described.

First, the color image input device 200
The ISO chart is photographed, and the photographed image is stored in the image storage unit 101 as an image to be evaluated (step S101).

By the way, the normally photographed image is displayed on an image display device such as a CRT display.
The display environment conversion unit 102 converts the image to be evaluated stored in the image storage unit 101 in accordance with the characteristics of the image display device to be used, in order to objectively evaluate the image with good correlation with human subjectivity. Output to the color space component calculation means 103 (step 102). The display environment compatible conversion means 10
The detailed processing contents of 2 will be described later.

The uniform color space component calculation means 103 converts the output of the display environment correspondence conversion means 102 into uniform color space components, and quantitatively expresses the perceptual difference in human color among the converted data. Only the data of the lightness component is output to the visual characteristic correcting unit 104 (step S103). The detailed processing content of the uniform color space component calculation means 103 will be described later.

The visual characteristic correcting means 104 applies a visual characteristic correcting function VTF to the output of the uniform color space component calculating means 103 in order to obtain data having good correlation with human vision.
The visual characteristics are corrected by weighting in (f) and output to the spatial frequency component evaluation value calculation means 105 (step S).
104).

For example, assuming that the observation distance is 560 mm, the visual characteristic correction function VTF (f) is given by the following equation (1).
Can be represented as The unit of f is cycle / mm
It is. VTF (f) = 5.05 × exp (−1.349 × f) × {1-exp (−0.9774 × f)} (1)

The spatial frequency component evaluation value calculation means 105
An evaluation value for the sharpness of each spatial frequency component is calculated from the output of the visual characteristic correction unit 104, and output to the integral value calculation unit 106 (step S105). As the evaluation value, for example, a CTF (Contrast Tr
ansfer Function) can be used.

[0039]

(Equation 1)

The integral value calculating means 106 calculates the integral value of the evaluation value for each spatial frequency component inputted from the spatial frequency component evaluation value calculating means 105, and outputs it to the correcting means 107 (step S106). Here, the integration range differs depending on the number of pixels to be captured, and the details thereof will be described later.

The correcting means 107 includes an integral value calculating means 106
Is corrected with a predetermined correction coefficient, and the corrected value is used as an image quality evaluation value for sharpness (step S107). The detailed processing contents of the correction unit 107 will be described later.

As described above, in the image evaluation apparatus 100 according to the present embodiment, the display environment correspondence conversion means 102 performs conversion according to the environment when the image to be evaluated is displayed on the image display device, and performs uniform color conversion. The space component calculation means 103 performs conversion to a uniform color space component, and the integral value calculation means 106 performs weighting and integration in accordance with human visual characteristics, and corrects one.
In step 07, the correction is performed using an appropriate correction coefficient, so that it is possible to obtain a stable and high-precision image evaluation value that has a good correlation with the human subjectivity for the sharpness of the image.

Next, the display environment correspondence conversion means 102 and the uniform color space component calculation means 10 of the image evaluation apparatus 100
3. Detailed processing contents of the integral value calculating means 106 and the correcting means 107 will be described.

[Display Environment Conversion Unit] The display environment conversion unit 102 uses the image to be evaluated stored in the image storage unit 101 in order to objectively evaluate the image with good correlation with human subjectivity as described above. The conversion is performed according to the characteristics of the image display device to be performed (step S102 in FIG. 6). The specific processing contents will be described.

The display environment corresponding conversion means 102 stores (R, G, B) 0 to 255 stored in the image storage unit 101.
The value is read out for each pixel of the image data to be evaluated, and for example, a tristimulus value (X,
Y, Z) and outputs it to the uniform color space component calculation means 103. Since the method of deriving the transformation matrix is a well-known technique, the description is omitted here. An example of the conversion matrix is shown below.

For example, in the case of a display calibrated to a color temperature of 9300 K ° (substantially the same as the color temperature of a display of a general personal computer), and in the case of a display having the following conditions, the conversion matrix is represented by the following equation (3). be able to.

The tristimulus values (Xn, Yn,
Zn) = (95.25,100,141.25) RGB xy-chromaticity coordinates (xR, yR, zR) = (0.625,0.339,1-XR-
YR) (xG, yG, zG) = (0.283, 0.606, 1-XG-
YG) (xB, yB, zB) = (0.150, 0.063, 1-XB-
YB) Gamma (input / output characteristics of display system) = 1

[0048]

(Equation 2)

The XYZ data is 9300
The present invention is not limited to the CRT display calibrated to K °, but may use a conversion matrix according to the characteristics of the image display device.

As described above, the display environment conversion means 102 uses at least one of the chromaticity of each primary color, the chromaticity of the white point, and the gamma characteristic of the image display device as a parameter to evaluate Since the conversion corresponding to the environment when the image information of the image is displayed on the image display device is performed, it is possible to obtain an evaluation value of the image quality in consideration of the actual observation environment.

[Equivalent Color Space Component Calculating Means] As described above, the uniform color space component calculating means 103 converts the output of the display environment correspondence converting means 102 into uniform color space components, and outputs the human color among the converted data. Only the data of the lightness component, which is supposed to quantitatively represent the perceptual difference of, is output (step S103 in FIG. 6). The specific processing content will be described in detail.

The uniform color space component calculation means 103 outputs the output of the display environment correspondence conversion means 102 to, for example, L * a * b *
Convert to a uniform color space. A conversion formula for converting to the L * a * b * uniform color space is represented, for example, by the following formula (4). L * a * b * data (CIE1976L * a *
b * data is a color space that reflects human color perception.

L * = 116 f (Y / Yn) ^1/3 −16 a * = 500 Δf (X / Xn) ^1/3 −f (Y / Yn) ^1/3 Δb * = 200 Δf ( Y / Yn) ^1/3 −f (Z / Zn) ^1/3・ ・ ・ (4) Here, (Xn, Yn, Zn) are the tristimulus values at the white point of the display for display. However, correction equation (5) exists for dark colors.

[0054] In the case of X / Xn> 0.008856 (light color), in the case of f (X / Xn) = ( X / Xn) 1/3, X / Xn ≦ 0.008856 ( dark), f (X / Xn ) = 7.787 (X / Xn) ^1/3 +16/116 (5) The same applies to f (Y / Yn) and f (Z / Zn).

After the data of the uniform color space is obtained, the uniform color space component calculating means 103 selects only L *, which is the brightness component to which the human is most sensitive, and selects the visual characteristic correcting means 1.
04.

In this embodiment, the L * a * b uniform color space is used, but the L * a * b
Various methods other than the uniform color space, such as the Luv uniform color space, have been proposed, and their lightness components may be used.

As described above, the uniform color space component calculating means 103 uses only the lightness component, to which the human sense is most sensitive, among the lightness component and the chromaticity component of the uniform color space as the evaluation value. Thus, the evaluation value can be calculated more easily.

[Integral value calculating means] Integral value calculating means 106
As described above, the spatial frequency component evaluation value calculating means 10
Then, the integral value of the evaluation value for each spatial frequency component input from step 5 is calculated (step S106 in FIG. 6). The specific processing content will be described in detail.

The evaluation of the sharpness of a digital camera based on the limit resolution represented by a TV book, which has been conventionally performed, is "how far a fine line can be seen". Since human vision is rather sensitive to low spatial frequencies, it is necessary to take into account the whole possible spatial frequency range instead of such an evaluation method.

In the case of a digital camera, considering the concept of “image quality”, it is appropriate that the unit of the spatial frequency component is “the number of lines (cycle / frame) that falls within the vertical (or horizontal) width of the image to be evaluated. The reason is described below.

For example, the same subject is 640 (w) × 48
0 (h) VGA class digital camera (D-Cam
era1) and 1280 (w) × 960 (h) SVG
Images are taken with an A-class digital camera (D-Camera2), and the images are referred to as Image1 and Image2, respectively. If the zooming of the subject and camera is the same, Image 1, Imag
The area shown in e2 should be the same.

On the other hand, when the photographed images are compared and displayed on the display, Image 1, Image 1 are used in order to make the observation conditions the same.
Observe the image 2 with the same size (that is, magnify the image 1 twice vertically and horizontally). This is because the larger the number of captured pixels at the time of photographing, the sharper the finer parts are.

That is, Image 2 contains higher frequency components than Image 1. In other words, the spatial frequency range (cy
cle / frame) is determined.

Hereinafter, "the number of lines (cycles / frames) included in the vertical width of the image to be evaluated" is taken as the unit of the spatial frequency. In the ISO chart of FIG.
From the book to the 2000 TV books are described. Here, the relationship between the spatial frequency (cycle / frame) and the TV book is represented by the following equation (6).

Spatial frequency (cycle / frame) = (TV lines in vertical direction of screen / 2) (6)

In the case of D-Camera 1, the spatial frequency domain is 50 (cycle / frame) (ISO chart 100).
TV line limit) to 240 (cycle / frame) (Nyquist frequency).

In the case of D-Camera 2, the spatial frequency domain is 50 (cycle / frame) (ISO chart 100).
TV line limit) to 480 (cycle / frame) (Nyquist frequency).

Therefore, when the integration value calculating means 106 integrates the evaluation value for each spatial frequency component input from the spatial frequency component evaluation value calculating means 105, the integration area is as described above, And the spatial frequency of the input image.

The input image parameter storage unit 109 from the color image input device (digital camera) 200
, The lowest value of the spatial frequency range that the ladder pattern as the input image can take is input, and the input parameter storage unit 110 is input with the number of pixels of the width and height of the captured image.

The integral value calculating means 106 stores the minimum value (cycle / fram) of the integral range from the input image parameter storage unit 109.
e) is read, and the number of pixels of the width and height of the image taken by the digital camera is read from the input parameter storage unit 110 to calculate the Nyquist frequency. Then, the integral value calculating means 106 calculates the maximum value (cycle / frame) of the integral range according to the above equation (6), and within this integral range, for each spatial frequency component input from the spatial frequency component evaluation value calculating means 105 The evaluation value is integrated to calculate an integrated value, which is output to the correction unit 107.

Here, for convenience of explanation, a digital camera is used as a color image input device using a solid-state image sensor, but various image input devices such as a scanner may be used. In addition, although the number of lines (cycle / frame) included in the vertical width of the image to be evaluated is taken as a unit of the spatial frequency, the number of lines included in the horizontal width may be used.

As described above, the integral value calculating means 10
6 determines the integration range of the spatial frequency according to the Nyquist frequency of the image to be evaluated input by the color image input device using the solid-state imaging device, so that a more accurate evaluation value according to the evaluation target is calculated. be able to.

As described above, for example, when an input image is photographed with a digital camera and an image to be evaluated is created, it is expected that the larger the number of pixels captured at the time of photographing, the sharper the finer the portion. Therefore, a color image input device using a solid-state imaging device can capture information in a spatial frequency region equal to or lower than the Nyquist frequency.

Therefore, the spatial frequency domain of the evaluation chart must be equal to or less than 240 (cycle / frame) (Nyquist frequency of Image 1) in the case of D-Camera 1 according to the above equation (6). Also, D-Cam
In the case of ra2, 480 (cycle / frame) (Image
2 Nyquist frequency).

As described above, a more accurate evaluation value can be obtained by determining the range of the spatial frequency of the input image according to the Nyquist frequency of the image to be evaluated input to the solid-state imaging device to be evaluated. It becomes possible.

[Correction Means] The correction means 107 corrects the output of the integral value calculation means 106 with a predetermined correction coefficient as described above (step S107 in FIG. 6).
The specific processing contents will be described.

The correction means 107 includes a correction coefficient storage unit 111
Of the correction coefficients stored for each image type, the correction coefficients α and β suitable for the image to be evaluated are read out.
The output of No. 6 is corrected by, for example, an evaluation prediction formula shown in the following equation (7), an evaluation value for predicting image quality with respect to sharpness is calculated, and output to the image evaluation value output unit 108.

(Evaluation predicted value for sharpness) = α + β × (Output of integrated value calculation) (7)

Here, α and β are correction coefficients, which are the outputs of the integrated value calculation means 106 used, that is, the color image input device using the solid-state imaging device to be evaluated, and the display for image display. It depends on the characteristics, the spatial frequency characteristic function of human vision, and the method of calculating the evaluation value for each spatial frequency component.

A specific example of the above correction will be described below. For different image types, evaluation prediction values for sharpness are obtained using different appropriate correction coefficient values.

By the way, the image quality of all the images in the world is
It is very difficult to express the correction coefficients α and β with specific values. Here, a specific example is shown because the correction coefficients for the cafeteria image (N2) of the character image of FIG. 7 and the high-precision color digital standard image (JIS × 9201) are obtained. The character image shown in FIG. 7 is an image composed of Chinese characters and Roman characters, and was used because it is considered that the sharpness of the edge with respect to the line is more strictly evaluated than a natural image of a human.

The cafeteria image (N2) is an image including a circular table and chairs, bricks and stucco, and even small parts of the building. Experience has shown that sharpness is most effective as a factor determining image quality. I used it because I knew it.

The predicted value of evaluation of sharpness of a character image and the predicted value of evaluation of sharpness of a cafeteria image can be expressed by the following equations (8) and (9), respectively. The following equation (8),
Regarding the value on the left side of (9), it is assumed that the value 0 is the value of the image quality of an ideal image, and the larger the value, the worse the image quality.

(Evaluated predicted value of sharpness of character image) = 1.44591−0.00475 × (Output of integrated value calculation) (8) (Predicted estimated value of sharpness of cafeteria image) = 1.1448−0.00441 × ( Output of integral value calculation) (9)

The correction coefficient storage unit 111 stores correction coefficients α = 1.44591 and β = −0.00475 corresponding to the character image shown in FIG.
Is the correction coefficient α corresponding to the cafeteria image (N2).
= 1.1448, β = -0.00441.

FIG. 8 is a graph showing the correlation between the evaluation predicted value (horizontal axis) for the sharpness predicted by the above equation (9) and the subjective evaluation value (vertical axis) which is a human sense value.

The contribution ratio between the evaluation predicted value and the actual subjective evaluation value for the sharpness obtained by the above equation (9) is 0.9.
81, indicating a high correlation with human sensation. The graph shows that the evaluation value predicted value calculated by the above equation (9) better represents the subjective evaluation value as the points are arranged on a straight line having a slope of “−1”. Here, the contribution ratio is the square of the correlation coefficient. If the contribution ratio is 1, the evaluation prediction formula completely predicts the subjective evaluation value.

As described above, the correction means 107
Since an evaluation value for predicting image quality for sharpness is calculated using a different correction coefficient for each image type, an evaluation value for each image type can be obtained.

In the above-described embodiment, a digital camera using a solid-state image sensor has been described as an example of the color image input device 200. However, it goes without saying that another color image input device may be used. No. Further, in the present embodiment, the case where the ISO chart is used as the input image has been described, but another evaluation chart may be used.
Further, in the present embodiment, a CRT display has been described as an example of an image display device, but the present invention is not limited to this, and another image display device such as a liquid crystal display may be used. Further, in the present embodiment, the visual characteristic correction means 104 uses VT as a visual characteristic correction function of human vision.
Although F (f) is used, the present invention is not limited to this. Further, in the present embodiment, CTF is used as the evaluation value, but the present invention is not limited to this.

The present invention is not limited to the above-described embodiment, but can be appropriately changed without changing the gist of the invention.

[0091]

According to the first aspect of the present invention, the image information of the image to be evaluated is converted according to the environment when displayed on the image display device, and the uniform color space component of the converted image to be evaluated is converted. The calculated uniform color space component of the evaluated image is weighted in accordance with human visual characteristics, and the weighted uniform color space component is evaluated for each spatial frequency component. The evaluation value for each spatial frequency component is integrated in an appropriate spatial frequency range, and the integrated evaluation value is corrected with a predetermined coefficient. It is possible to obtain a stable and highly accurate image evaluation value having a good correlation with the subjectivity.

According to a second aspect of the present invention, the display environment conversion means performs conversion corresponding to the environment when the image information of the evaluated image is displayed on the image display device. Calculating a uniform color space component of the image to be evaluated converted by the display environment compatible conversion means, and the visual characteristic correcting means calculates a human visual characteristic for the uniform color space component calculated by the uniform color space component calculating means. The spatial frequency component calculating means calculates an evaluation value for each frequency component of the uniform color space component weighted by the visual characteristic correcting means, and the integral value calculating means calculates by the spatial frequency component calculating means. The evaluation value for each frequency component is integrated in an appropriate spatial frequency range, and the correction unit corrects the evaluation value integrated by the integration value calculation unit by a predetermined coefficient. Since the the a, it is possible to obtain a high precision image evaluation value good stable correlation with human subjectivity relative sharpness of the image.

According to a third aspect of the present invention, in the first aspect, at least one of the chromaticity of each primary color, the chromaticity of a white point, and the gamma characteristic of the image display device is used as a parameter. Then, the image information of the image to be evaluated is subjected to conversion corresponding to the environment when displayed on the image display device. It is possible to obtain an evaluation value of the image quality in consideration of the environment.

According to a fourth aspect of the present invention, in the second aspect of the present invention, the display environment compatible conversion means includes a chromaticity of each primary color, a chromaticity of a white point, and a gamma characteristic of the image display device. Using at least one as a parameter,
Since the conversion corresponding to the environment when displayed on the image display device is performed on the image information of the evaluated image, the actual observation environment is considered in addition to the effect of the invention described in claim 2. It is possible to obtain the evaluated value of the image quality.

According to a fifth aspect of the present invention, only the lightness component of the uniform color space component of the image to be evaluated is output in the first aspect of the invention. In addition to the effect, the evaluation value can be calculated more easily.

According to a sixth aspect of the present invention, in the second aspect, the uniform color space component calculating means outputs only the lightness component of the uniform color space component of the image to be evaluated. In addition to the effect of the first aspect, the evaluation value can be calculated more easily.

[Brief description of the drawings]

FIG. 1 is a block diagram illustrating a configuration of an image evaluation device according to the present invention.

FIG. 2 is a diagram showing a series of patterns in which a low reflectance and a high reflectance are periodically repeated.

FIG. 3 is a diagram showing a series of patterns having various frequency components from a low spatial frequency component to a high spatial frequency component.

FIG. 4 is a diagram showing an ISO chart.

FIG. 5 is a diagram showing names of respective parts of the ISO chart shown in FIG. 4;

FIG. 6 is a flowchart for explaining an image evaluation method in the image evaluation device of FIG. 1;

FIG. 7 is a diagram illustrating an example of a character image.

FIG. 8 is a graph showing a correlation between a predicted evaluation value for predicted sharpness and a subjective evaluation value that is a human sense value.

[Explanation of symbols]

 REFERENCE SIGNS LIST 100 image evaluation device 101 image storage unit 102 display environment correspondence conversion unit 103 uniform color space component calculation unit 104 visual characteristic correction unit 105 spatial frequency component calculation unit 106 integral value calculation unit 107 correction unit 108 image evaluation value output unit 109 input image parameter Storage unit 110 Input parameter storage unit 111 Correction coefficient storage unit 200 Color image input device

 ──────────────────────────────────────────────────続 き Continued on the front page F term (reference) 5C062 AA01 AC02 AC05 AC56 AE03 5C077 LL11 MP08 NN02 PP09 PP15 PP32 PP36 PP48 PP49 PQ18 5C079 HA16 HA19 HB00 HB01 HB05 HB08 HB09 LA12 MA17 NA04 NA05 5L096 AA02 BA08 DA08 MA01 9A001 HH23 KZ37 KZ42 LL08

Claims (6)

[Claims] An image evaluation method for evaluating image quality with respect to sharpness of an image input by a color image input device using a solid-state imaging device, wherein image information of an image to be evaluated is displayed on an image display device. A first step of performing a conversion corresponding to the environment at the time; a second step of calculating a uniform color space component of the converted evaluated image; and a step of calculating the uniform color space component of the calculated evaluated image. A third step of performing weighting in accordance with human visual characteristics, a fourth step of calculating an evaluation value for each spatial frequency component of the weighted uniform color space component, A fifth step of performing integration on the evaluation value of in an appropriate spatial frequency range, and a sixth step of correcting the integrated evaluation value by a predetermined coefficient. Image evaluation wherein the door. 2. An image evaluation apparatus for evaluating image quality with respect to sharpness of an image input by a color image input apparatus using a solid-state imaging device, wherein image information of an image to be evaluated is displayed on an image display device. A display environment conversion unit that performs conversion corresponding to the environment; a uniform color space component calculation unit that calculates a uniform color space component of the evaluated image converted by the display environment conversion unit; and the uniform color space component calculation. Visual characteristic correction means for weighting the uniform color space component calculated by the means in accordance with human visual characteristics, and an evaluation value for each frequency component of the uniform color space component weighted by the visual characteristic correction means. Spatial frequency component calculating means to be calculated, and an appropriate spatial frequency range for the evaluation value for each frequency component calculated by the spatial frequency component calculating means. And integral value calculating means performs integration for integrated evaluation value by the integral value calculating means,
An image evaluation device, comprising: correction means for performing correction with a predetermined coefficient. 3. The image evaluation method according to claim 1, wherein in the first step, at least one of chromaticity of each primary color, chromaticity of a white point, and gamma characteristic of the image display device is used as a parameter. An image evaluation method for performing conversion corresponding to an environment when the image information of the image to be evaluated is displayed on the image display device. 4. The image evaluation device according to claim 2, wherein the display environment-compatible conversion unit sets at least one of chromaticity of each primary color, chromaticity of a white point, and a gamma characteristic of the image display device as a parameter. Wherein the image evaluation device performs a conversion corresponding to an environment when the image information is displayed on the image display device. 5. The image evaluation method according to claim 1, wherein in the second step, only the lightness component of the uniform color space component of the image to be evaluated is output. 6. The image evaluation device according to claim 2, wherein said uniform color space component calculation means outputs only a lightness component of a uniform color space component of said evaluated image.