JP2006067423A - Image quality quantitative evaluation method and its device - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To uniformly quantize poorness of appearance such as stripes and unevenness in different patterns and to enhance judgment precision of the stripes and the unevenness. <P>SOLUTION: The image quality quantitative evaluation method has an image input step for reading a fixed density pattern recorded in a recording medium as two-dimensional image data, an image data conversion step for converting the two-dimensional image data into stimulus value data of an opposite color space, a projection step for calculating one-dimensional stimulus value data profiles, respectively by adding values in each spatial position of the two-dimensional stimulus value data along the specific direction, a differential value calculation step for calculating a differential value expressing distance from a mean value of the whole stimulus value data profile to each stimulus value at positions of each one-dimensional stimulus value data profile and an evaluation value calculation step for calculating summation of values based on the differential value to all the positions of one-dimensional stimulus value data profiles as an image quality evaluation value. <P>COPYRIGHT: (C)2006,JPO&NCIPI

Description

  The present invention relates to an image quality quantitative evaluation method and apparatus for quantitatively evaluating streaks and unevenness of an output image in a digital printer.

  In an output image in a printing apparatus (digital printer or the like), streaks or unevenness (hereinafter referred to as “streaks / unevenness”) may occur visually. For example, in a digital printer, the streaks / unevenness occurs due to periodic fluctuations in backlash between driving gears when writing to the rotating drum, or due to potential unevenness in the rotating drum. Therefore, the presence / absence of streak / unevenness in the image quality (finish) of the output image recorded on the recording medium is important as a quality inspection.

  In the past, this streak / unevenness inspection evaluation was made by subjective judgment by an inspector's visual observation. However, a method of automating using an image processing apparatus has been developed in recent years. A streak / unevenness inspection apparatus using this type of image processing apparatus captures an image by an image input apparatus such as a camera, calculates an evaluation value in the image analysis apparatus, and compares it with a preset reference value. The quality of the inspection object is judged.

  For example, in an image input device, an inspection object is photographed using a CCD camera, and the image data is sent to an image analysis device. Image data is digitized and stored in a density level by an image analyzer. Then, preprocessing such as shading correction is performed in the image processing unit, parameters such as standard deviation are calculated in the evaluation value calculation unit, and comparison with a preset reference value is performed. Is output.

Japanese Patent Laid-Open No. 10-96669

  As described above, a method for objectively quantifying stripes and unevenness using an image processing apparatus has been conventionally performed. However, streaks / unevenness occurring in a digital printer has different streaks / unevenness patterns depending on the generation factor, and the direction in which the streaks are generated may not be constant. In the conventional method, since it is compared with a reference value set in advance for each type of streak / unevenness pattern, it is difficult to make a determination for streak / unevenness of different patterns, for example, in an unspecified direction. It was not possible to automatically detect and quantify streaks.

Further, in the method for quantifying streaks / unevenness using the above-described image processing apparatus, only the density level (the density of the ink density) is measured. Therefore, when multiple colors are mixed, it is not consistent with the visual evaluation. There was a problem.
Furthermore, when the evaluation value is calculated using the standard deviation, for example, when a single streak / unevenness is compared with a streak / unevenness occurring on the entire screen, the latter streak / unevenness is numerically It was evaluated badly, and a deviation from visual evaluation occurred, resulting in a problem in the accuracy of judging the quality of streaks and unevenness.

  The present invention has been made in view of the above circumstances, and provides an image quality quantitative evaluation method and apparatus capable of uniformly quantifying the appearance of streaks and unevenness in different patterns (for example, arbitrary directions). -The purpose is to improve the accuracy of unevenness determination.

The above object is achieved by the following configuration.
(1) An image quality quantitative evaluation method for evaluating image quality on a recording medium on which a pattern having a constant density is recorded, wherein the pattern recorded on the recording medium is used as two-dimensional image data. An image input step for reading, an image data conversion step for converting the two-dimensional image data into stimulus value data (W / K, R / G, B / Y) in the opposite color space, and each of the two-dimensional stimulus value data A projection step of adding a value at a spatial position along a specific direction to obtain a one-dimensional stimulus value data profile, and each stimulus value (W / Ki, R / at the position (i) of each one-dimensional stimulus value data profile gi, for B / Yi), the difference value calculation step of calculating a difference value representing the distance from the stimulus value data profile overall average _{(W / K AV, R /} G AV, B / Y AV), the 1 Image Quality quantitative evaluation method characterized by comprising an evaluation value calculating step of obtaining a sum of values based on the difference value for all positions of the original stimulus value data profile as an image quality evaluation value.

  In this image quality quantitative evaluation method, evaluation is performed by considering the color difference information by converting to the opposite color space, compared with the conventional evaluation method in which only the density level (the density of the ink density) is measured. Modulation processing according to the visual characteristics of the image is performed more accurately, and the poor appearance of streaks and unevenness can be quantified in a unified manner.

  (2) The image quality quantitative evaluation method according to (1), wherein an image quality evaluation value is obtained by adding a weighting function proportional to a distance from the average value to the sum.

  In this image quality quantitative evaluation method, the value obtained by integrating the weighting functions becomes the image quality evaluation value, so that the evaluation more closely matches the visual characteristics.

  (3) Before the projecting step, the stimulus value data converted by the image data converting step is Fourier-transformed to obtain a power spectrum, a function representing human visual characteristics is superimposed on the power spectrum, and The visual quality adaptation step of performing Fourier transform is performed, The image quality quantitative evaluation method according to (1) or (2).

  In this image quality quantitative evaluation method, the visual characteristic adaptation process is performed before the projection step, that is, the visual characteristic adaptation process is performed while the two-dimensional data is used, and then the one-dimensional data is obtained by the projection process. In comparison, streaks and unevenness distributed in the plane of the recording medium are more accurately emphasized, and an improvement in the S / N ratio is expected, thereby improving the detection accuracy of streaks and unevenness.

  (4) After the projecting step and before the difference calculating step, the stimulus value data is Fourier-transformed to obtain a power spectrum, a function representing human visual characteristics is superimposed on the power spectrum, and an inverse Fourier is further obtained. (1) or (2) image quality quantitative evaluation method characterized by performing the visual characteristic adaptation step to convert.

  In this image quality quantitative evaluation method, the visual characteristic adaptation processing is performed after the projection step and before the difference calculation step, so that the amount of data to be processed is changed from two dimensions to one dimension, the calculation burden is reduced, and high speed is achieved. Processing becomes possible.

  (5) The specific direction obtains a plurality of one-dimensional density profiles by adding the density values of the two-dimensional image data along a plurality of directions set at predetermined angles from an arbitrary direction of the image. The image quality quantitative evaluation method according to any one of (1) to (4), wherein the image quality is set in a direction that maximizes a difference between a maximum value and a minimum value of the profile.

  In this image quality quantitative evaluation method, the one-dimensional averaging process added to the pixels on the line is performed for each predetermined angle, so that streak / unevenness in an unspecified direction can be automatically detected.

  (6) Visual characteristic adaptation processing in which the stimulus value data profile includes lightness stimulus information and color difference stimulus value information, and emphasizes different frequency bands for each of the lightness stimulus information and the color difference stimulus value information. The image quality quantitative evaluation method according to any one of (1) to (5), which is performed.

  In this image quality quantitative evaluation method, the stimulus value data profile includes lightness stimulus information and color difference stimulus value information, and appropriate visual characteristic adaptation processing is performed on each of them.

(7) An image quality quantitative evaluation apparatus that evaluates image quality on a recording medium on which a pattern having a constant density is recorded, wherein the pattern recorded on the recording medium is used as two-dimensional image data. An image input unit for reading, an image data conversion unit for converting the two-dimensional image data into stimulus value data (W / K, R / G, B / Y) in the opposite color space, and each of the two-dimensional stimulus value data A value at a spatial position is added along a specific direction to obtain a one-dimensional stimulus value data profile, and each stimulus value (W / Ki, R / Gi, B at the position (i) of each one-dimensional stimulus value data profile is obtained. / Yi), a difference value representing the distance from the average value (W / K _AV , R / _GAV , B / Y _AV ) of the entire stimulus value data profile is obtained, and the difference value is obtained for all positions of the one-dimensional stimulus value data profile. The difference Image Quality quantitative evaluation apparatus characterized by comprising an image quality evaluation value calculation unit for obtaining the sum of the value-based value as an image quality evaluation value.

  In this image quality quantitative evaluation device, the color difference in the opposite color space is also considered in comparison with the conventional evaluation device that measures only the luminance level (ink density shading). Processing is performed, and the bad appearance can be quantified uniformly.

  (8) A power spectrum is obtained by performing Fourier transform on the stimulus value data, a function representing human visual characteristics is superimposed on the power spectrum, and a visual characteristic adapting unit that performs inverse Fourier transform to obtain stimulus value data is provided. (7) The image quality evaluation apparatus according to (7).

  In this image quality quantitative evaluation apparatus, stimulus value data matching the visual characteristics can be obtained by superimposing a function representing the visual characteristics on the power spectrum of the stimulus value data and performing inverse Fourier transform back.

  According to the method and apparatus for quantitatively evaluating image quality according to the present invention, the read two-dimensional image data is converted into stimulus value data in the opposite color space, and values at each spatial position of the stimulus value data are added along a specific direction. Thus, the one-dimensional stimulus value data profile is obtained, the difference between the stimulus value and the average value of the entire stimulus value data is obtained, and the sum of these differences for all positions is obtained as the image quality evaluation value. The color difference in the image is also taken into consideration, and the modulation process according to the human visual characteristics is performed, so that the bad appearance can be quantified uniformly. As a result, variations such as evaluation values using standard deviation do not occur, and the streak / unevenness determination accuracy can be improved.

Preferred embodiments of an image quality quantitative evaluation method and apparatus according to the present invention will be described below in detail with reference to the drawings.
FIG. 1 shows a block diagram of an image quality quantitative evaluation apparatus according to an embodiment of the present invention.
The image quality quantitative evaluation apparatus 100 according to the present embodiment is roughly divided into an image input unit 11, an image data conversion unit 13, a visual characteristic adaptation unit 15, and an image quality evaluation value calculation unit 17. Explaining the general functions of these components, the image input unit 11 reads a pattern recorded on a recording medium 19 on which a pattern having a constant density is recorded as two-dimensional image data. The image data converter 13 converts the two-dimensional image data into stimulus value data in the opposite color space. The visual characteristic matching unit 15 obtains a power spectrum by performing Fourier transform on the stimulus value data, superimposes a function representing human visual characteristics on the power spectrum, and further performs inverse Fourier transform. The image quality evaluation value calculation unit 17 operates to obtain an image quality evaluation value based on the stimulus value data.

FIG. 2 is an explanatory diagram showing examples of streaks / unevenness in (a), (b), and (c).
Streaks and unevenness may occur in the recording medium 19 to be subjected to quantitative evaluation of image quality. Examples of the stripe / unevenness include a single stripe in FIG. 2A, a periodic unevenness in FIG. 2B, and a random unevenness in FIG. 2C.

Hereinafter, the procedure of the image quality quantitative evaluation method of this embodiment will be sequentially described.
FIG. 3 is a flowchart showing the procedure of the image quality quantitative evaluation method of this embodiment.
The image input unit 11 refers to a device that obtains a two-dimensional (planar) image of the recording medium 19, and any device can be used as long as it can capture a change in brightness with respect to a position in the plane with an electric signal. Commercially available products include scanners and CCD cameras.
<Image input step>
In the image quality quantitative evaluation method according to the present embodiment, first, an image of the recording medium 19 is input from the image input unit 11 in the process of step 11 (hereinafter abbreviated as S11) shown in FIG.

  The image signal input by the image input unit 11 is sent to the image data conversion unit 13. An image signal of an input image including streaks / unevenness is read by the image input unit 11 and digitized to a density level by A / D conversion, and a digital value image composed of information on the density value and the spatial position. Data (red density value R, green density value G, blue density value B, etc.) is stored in a memory or the like of the image data converter 13.

<Image data conversion step>
Based on the characteristic parameters stored in advance, matrix conversion is performed from the digitized image data according to the equation (1) and converted into tristimulus values (S12). Specifically, the color characteristic values (R, G, B) of the image data and these are converted into tristimulus values (X, Y, Z) based on the relationship between the standardized screen brightness. Here, K is a conversion matrix.

The image information of the color image includes density information and chromaticity information. In the conventional image evaluation of streaks / unevenness, only density information is mainly used. However, in the image quality quantitative evaluation method of the present invention, an image is also evaluated using chromaticity information. However, human visual characteristics are different for each color, and each color component cannot be evaluated in a unified manner. Therefore, human visual characteristics for each color are evaluated by filtering correction for each color component of the image.
Further, in the present embodiment, by processing using the opposite color space, a result that corresponds well to human subjective evaluation can be obtained. Specifically, the tristimulus values (X 1, Y 2, Z) obtained by the equation (1) are converted into three sets (White-Black (K), Red-Green, Blue-Yellow) by the matrix operation according to the equation (2). ) To the stimulus value in the opposite color space (S13).

<Projection step>
Next, the main appearance direction of streaks / unevenness is set.
FIG. 4 is an explanatory diagram schematically showing how the appearance direction of streaks / unevenness at the spatial position of the opposite color stimulus value data is determined. The specific direction indicating the appearance direction of the stripe / unevenness is set as follows.
First, as shown in FIG. 4, the captured two-dimensional image data 21 is added (projected) along a plurality of directions set for each predetermined angle from an arbitrary direction of the image, and a plurality of one-dimensional profiles 23a and 23b are obtained. , 23c,. Then, the direction in which the difference δ between the maximum value and the minimum value of the one-dimensional profile is maximized is set as a specific direction. One-dimensional averaging (S14) for projecting such pixels is performed at high speed by image processing.

  In other words, a process of rotating the captured two-dimensional image data 21 at a predetermined angle θ on a plane and a process of projecting each pixel value in a predetermined direction are performed. The direction of appearance is automatically detected. The setting of the specific direction may be obtained using the stimulus value in the opposite color space other than the two-dimensional image data 21.

<Visual characteristics adaptation step>
Next, the one-dimensional stimulus value data profile 25a converted into stimulus values (W / K, R / G, B / Y) in the opposite color space by the equation (2) and added (projected) along the specific direction. , 25b, 25c,... Are subjected to a one-dimensional Fourier transform, and the power spectrum of the result of the Fourier transform is calculated to obtain a spatial frequency distribution.

  FIG. 5 is an explanatory diagram showing the one-dimensional stimulus value data profile projected in a specific direction corresponding to the streaks / unevennesses (a), (b), and (c) of FIG. In the following description, the W / K channel data will be described as a representative, but the same processing is performed for the R / G and B / Y channels. FIG. 6 is an explanatory diagram showing the power spectrum after Fourier transform for the one-dimensional stimulus value data profile shown in FIG. 5 corresponding to the streaks, irregularities (a), (b), and (c) of FIG. .

By performing Fourier transform on the one-dimensional stimulus value data profiles 25a, 25b, 25c,..., Power spectra 27a, 27b, 27c,. S15). Then, filtering correction is performed on the power spectrum obtained in S15 using spatial frequency characteristics (MTF) adapted to human visual characteristics (S16).
There are various methods for Fourier transform, but here, FFT (Fast Fourier Transform) is used to simplify the calculation.

  It is known that the spatial frequency characteristics of human vision differ depending on the observation conditions (observation parameters) such as the observation distance, the average density of the image to be evaluated at the time of observation, and the ambient density of the image to be evaluated. Toyohiko “Display Conditions and Visual MTF” No. 61 Plus E, 1984). However, in the conventional image evaluation apparatus, the difference due to such observation conditions is not actually faithfully taken into consideration, and is corrected only with one representative human visual system MTF. In different cases, the subjective evaluation of the image quality evaluation and the objective evaluation are not necessarily consistent. In the image quality quantitative evaluation method of the present invention, such inconvenience is eliminated by performing filtering correction using spatial frequency characteristics (MTF) including color information.

FIG. 7 is an explanatory view showing the MTF of visual characteristics, wherein the bandpass shape of the W / K channel is shown in (a), and the lowpass shapes of the R / G and B / Y channels are shown in (b).
That is, the visual characteristic matching unit 15 performs filtering correction that superimposes the MTF characteristics 29a, 29b,... Corresponding to the observation condition on the power spectrum of the image to be evaluated obtained by Fourier transform. As a result, as shown in FIG. 7, the frequency component that is highly sensitive to humans (about 0.4 cycle / mm is strong for the W / K component) is emphasized more, while the frequency with low sensitivity is high. Attenuate components (especially high frequency components). For this reason, it can convert into the sensitivity of the spatial frequency component which matched human visual characteristics. Further, by preparing information on the MTF characteristics as described above for each observation condition in advance, an appropriate visual MTF filter corresponding to the actual observation condition can be appropriately selected to perform filtering correction, and correction processing is performed. The effect and reliability can be further increased.

FIG. 8 is an explanatory diagram showing the Fourier spectrum after filtering corresponding to the streaks / unevennesses (a), (b), and (c) of FIG. By performing inverse Fourier transform on the spatial frequency distributions 31a, 31b, 31c,... After filtering processing shown in FIG. 8 obtained in this way, as shown in FIG. 33b, 33c,... Are obtained (S17). FIG. 9 is an explanatory diagram showing the stimulus value data profile after the inverse Fourier transform corresponding to the stripe / unevenness (a), (b), and (c) of FIG.
In this way, the respective stimulus values for (W / K, R / G, B / Y) in the opposite color space are recombined.

  As a result, in the one-dimensional profile after the inverse Fourier transform, the steep shape of one streak is dulled in FIG. 9A in accordance with the visual characteristics, and the periodic unevenness in FIG. In the random unevenness of (c), the amplitude decreases.

<Difference value calculation step / Evaluation value calculation step>
Next, an evaluation value is calculated by the image quality evaluation value calculation unit 17 from the result obtained by inverse Fourier transform (S18). The image quality evaluation value calculation unit 17 roughly corresponds to each stimulus value (W / Ki, R / Gi, B / Yi) at the position (i) of each one-dimensional stimulus value data profile returned in S17. Then, a difference value representing a distance from an average value (W / K _AV , R / G _AV , B / Y _AV ) of the entire stimulus value data profile is obtained, and based on the difference value with respect to all positions of the one-dimensional stimulus value data profile. The sum of the values is obtained as an image quality evaluation value.
The image quality evaluation value Ev, which is a quantitative evaluation value for such streaks / unevenness, can be calculated specifically by equation (3).

The image quality evaluation value Ev is expressed as the sum of products of the weights fi and | ΔXi | (i is an index representing an axial position orthogonal to the projection direction of the captured image). Note that | ΔXi | is expressed by equation (4), fi is expressed by equation (5), and when determining the quality of streaks / unevenness, observe only the region where streaks / unevenness occurs. It is a weighting coefficient formulated based on the hypothesis that
Here, a description will be given with reference to FIG. There are three stimulus values (W / Ki, R / Gi, B / Yi) as stimulus values of opposite colors for the one-dimensional position i obtained in S17 described above. An average value (W / K _AV , R / G _AV , B / Y _AV ) is obtained for each of these three stimulus values, and the opposite color stimulus values (W / Ki, R / Gi, B / at each position i) are obtained. yi) and the average value corresponding to _{(W / K AV, R /} G AV, B / Y AV) and differential value _{(W / Ki -W / K AV} ), (R / Gi-R / G AV) , (B / Yi−B / Y _AV ) are obtained for each of the three stimulus values, and | ΔXi | is obtained by obtaining the square root of the square sum of these difference values based on the equation (4).
Further, the weight fi is a value proportional to the distance from the average value of | ΔXi | and normalized so that Σfi = 1 as shown in the equation (5).

  Accordingly, the evaluation value Ev is obtained for each of the three types of stimulus values by determining the distance from the overall average value of the brightness information (W / K) and the color difference information (R / G, B / Y) of the recorded image, The square root of the sum of squares for these distances is multiplied by a weight fi to obtain a sum.

  Therefore, according to the image quality quantitative evaluation method described above, the color difference information is also taken into consideration by converting into the opposite color space, and the modulation processing according to the human visual characteristics is performed more accurately, and the streak / unevenness is reduced. The bad appearance can be quantitatively evaluated uniformly. As a result, variations such as visual evaluation and evaluation values using standard deviation do not occur, and it is possible to improve the accuracy of determining streak / unevenness.

  In addition, since the projection processing for the captured image is carried out by rotating at every predetermined angle θ, the direction of the streaks / unevenness occurring in an unspecified direction is automatically determined without visually identifying the appearance direction of the streaks / unevenness. Can be identified.

  Furthermore, since the captured image data is converted into the opposite color space (image data conversion step) and the visual characteristic adaptation step is performed after performing the projection processing in a specific direction, the visual characteristic adaptation process can reduce the data amount. In addition, since the processing can be performed in a one-dimensional state, the calculation burden is reduced, higher speed processing is possible, and the tact of evaluation can be improved.

Next, another embodiment of the image quality quantitative evaluation method according to the present invention will be described.
In this embodiment, the stimulus value in the opposite color space is obtained from the captured image data, the visual characteristic adaptation process is performed with the two-dimensional data, and then the projection process to one dimension is performed to obtain the image quality evaluation value. ing.
FIG. 11 is a flowchart showing the procedure of the image quality quantitative evaluation method of the present embodiment, and FIG. 12 is an explanatory diagram schematically showing the visual characteristic adaptation processing for two-dimensional stimulus value data.

  In this embodiment, the two-dimensional image illustrated in FIG. 12A recorded on the recording medium 19 is read by the image input unit 11 (see FIG. 1) (S21), and the two-dimensional image data (R, G, B) is converted into tristimulus values by the image data converter 13 (S22), and as shown in FIG. 12B, the stimulus value data (W / K, R / G, B / Y) in the opposite color space (S23).

  The converted stimulus value data (W / K, R / G, B / Y) in the opposite color space is two-dimensionally Fourier transformed by the visual characteristic matching unit 15 as shown in FIG. Power spectra 41a, 41b and 41c are obtained (S24). Next, as shown in FIG. 12 (d), MTFs 43a, 43b, and 43c representing human visual characteristics are superimposed on the power spectra 41a, 41b, and 41c, respectively, and further two-dimensional Fourier inverse transform is performed. Thus, as shown in FIG. 12 (e), the stimulus value data of the opposite color in which streaks / unevenness is modulated in accordance with the visual characteristics is obtained (S26).

  In this embodiment, the one-dimensional averaging process shown in FIG. 12 (f) is performed after the visual characteristic adaptation process shown in FIGS. 12 (c) to 12 (e) (S27). That is, streaks / unevenness is modulated in accordance with the visual characteristics with respect to the two-dimensional stimulus value data in the opposite color space, and then the projection process to one dimension is performed.

  The subsequent processing is the same as in the first embodiment, and the image quality evaluation value calculation unit 17 calculates the difference value between the stimulus value at each position of the stimulus value data in the opposite color space and the average value of the entire stimulus value data. Each is obtained, and a value obtained by multiplying these difference values by a weight function is obtained for all positions, and the sum is taken as an image quality evaluation value Ev.

  According to the image quality quantitative evaluation method of the present embodiment, the visual characteristic adaptation processing is performed with the two-dimensional data as it is, and compared with the case where the recording medium 19 is converted into one-dimensional data by the projection processing. The stripes and unevenness distributed in the plane are more accurately emphasized, and the detection accuracy can be improved. That is, since the projection processing is performed after the stripes and unevenness are emphasized, the S / N ratio of the projection result is improved and the separation of the stripes and the noise becomes easy.

The image quality evaluation value Ev obtained by each of the embodiments described above further improves the linearity with human visual evaluation by taking a logarithmic image quality evaluation value Eva as shown in equation (5). Can do.
Logarithmic image quality evaluation value Eva = lov) (5)

It is a block diagram of the image quality quantitative evaluation apparatus according to the present invention. It is explanatory drawing showing the example of the stripe unevenness to (a), (b), (c). It is a flowchart showing the procedure of the image quality quantitative evaluation method which concerns on 1st Embodiment. It is explanatory drawing which showed typically a mode that the appearance direction of the stripe and nonuniformity in the spatial position of opposite color stimulus value data was determined. It is explanatory drawing which represented the one-dimensional irritation | stimulation value data profile projected in the specific direction corresponding to the stripe | line | column nonuniformity (a), (b), (c) of FIG. It is explanatory drawing which represented the power spectrum after the Fourier-transform with respect to the one-dimensional stimulus value data profile shown in FIG. 5 corresponding to the stripe unevenness (a), (b), (c) of FIG. FIG. 4 is an explanatory diagram showing the MTF of visual characteristics, where the band pass shape of the W / K channel is shown in (a), and the low pass shapes of the R / G and B / Y channels are shown in (b). It is explanatory drawing which represented the Fourier spectrum after filtering corresponding to the stripe nonuniformity (a) of FIG. 2, (b), (c). It is explanatory drawing which represented the one-dimensional irritation | stimulation value data profile after Fourier inverse transformation corresponding to the stripe | line | fluctuation nonuniformity (a), (b), (c) of FIG. Stimulus values (W / Ki, R / Gi , B / Yi) for the average value _{(W / K AV, R /} G AV, B / Y AV) is an explanatory view showing a distance from. It is a flowchart showing the procedure of the image quality quantitative evaluation method which concerns on 2nd Embodiment. It is explanatory drawing which represents roughly the visual characteristic adaptation process with respect to two-dimensional stimulus value data.

Explanation of symbols

DESCRIPTION OF SYMBOLS 11 ... Image input part 13 ... Image data conversion part 15 ... Visual characteristic adaptation part 17 ... Image quality evaluation value calculation part 19 ... Recording medium 100 ... Image quality quantitative evaluation apparatus

Claims (8)

An image quality quantitative evaluation method for evaluating image quality on a recording medium on which a pattern having a constant density is recorded,
An image input step of reading a pattern recorded on the recording medium as two-dimensional image data;
An image data conversion step for converting the two-dimensional image data into stimulus value data (W / K, R / G, B / Y) in the opposite color space;
A projecting step of adding a value at each spatial position of the two-dimensional stimulus value data along a specific direction to obtain a one-dimensional stimulus value data profile;
The average value (W / K _AV , R / G _AV ) of the entire stimulus value data profile for each stimulus value (W / Ki, R / Gi, B / Yi) at position (i) of each one-dimensional stimulus value data profile. , B / Y _AV ), a difference value calculating step for obtaining a difference value representing the distance from
An image quality quantitative evaluation method comprising: an evaluation value calculation step of obtaining a sum of values based on the difference values for all positions of the one-dimensional stimulus value data profile as an image quality evaluation value.   2. The image quality quantitative evaluation method according to claim 1, wherein an image quality evaluation value is obtained by adding a weighting function proportional to a distance from the average value to the sum.   Prior to the projecting step, the stimulus value data converted by the image data converting step is subjected to Fourier transform to obtain a power spectrum, a function representing human visual characteristics is superimposed on the power spectrum, and inverse Fourier transform is further performed. 3. The image quality quantitative evaluation method according to claim 1, wherein a visual characteristic adaptation step is performed.   After the projection step and before the difference calculation step, the stimulus value data is subjected to Fourier transform to obtain a power spectrum, a function representing human visual characteristics is superimposed on the power spectrum, and then the inverse Fourier transform is performed. 3. The image quality quantitative evaluation method according to claim 1, wherein a characteristic adaptation step is performed. The specific direction is
A plurality of one-dimensional density profiles are obtained by adding the density values of the two-dimensional image data along a plurality of directions set at predetermined angles from an arbitrary direction of the image, and the maximum and minimum values of the profiles are obtained. The image quality quantitative evaluation method according to claim 1, wherein the image quality is set in a direction that maximizes a difference between the image quality and the image quality. The stimulus value data profile is
The visual characteristic adaptation process which has a lightness stimulus information and color difference stimulus value information, and emphasizes a different frequency band with respect to each of the lightness stimulus information and the color difference stimulus value information is performed. The image quality quantitative evaluation method according to claim 5. An image quality quantitative evaluation apparatus for evaluating the image quality on a recording medium on which a pattern having a constant density is recorded,
An image input unit for reading a pattern recorded on the recording medium as two-dimensional image data;
An image data converter for converting the two-dimensional image data into stimulus value data (W / K, R / G, B / Y) in the opposite color space;
A value at each spatial position of the two-dimensional stimulus value data is added along a specific direction to obtain a one-dimensional stimulus value data profile, and each stimulus value at position (i) of each one-dimensional stimulus value data profile ( The difference value representing the distance from the average value (W / K _AV , R / G _AV , B / Y _AV ) of the entire stimulation value data profile with respect to (W / Ki, R / Gi, B / Yi) is obtained. An image quality quantitative evaluation apparatus comprising: an image quality evaluation value calculation unit that obtains a sum of values based on the difference values for all positions of a dimension stimulus value data profile as an image quality evaluation value.   A power spectrum is obtained by performing Fourier transform on the stimulus value data, a function representing human visual characteristics is superimposed on the power spectrum, and a visual characteristic adapting unit that performs inverse Fourier transform to obtain stimulus value data is provided. The image quality evaluation apparatus according to claim 7.

Images