JP2007034648A - Image evaluation method and image evaluation device and program - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To calculate an evaluation value corresponding to a feeling of a view without being affected by any background noise in an image evaluation device for evaluating picture quality related to a space frequency. <P>SOLUTION: A featured value acquiring part 22 calculates a stripe featured value Pe as one example of a featured value P related to a space frequency based on input image data processed by a pre-processing part 210. A stripe space frequency band dividing part 286 divides the input image data processed by the pre-processing part 210 into image data with a plurality of space frequency bands. A stripe correction quantity calculating part 288 calculates a stripe correction value Be based on the image data with the plurality of space frequency bands frequency-developed by a space frequency band dividing part 286. A stripe evaluation value calculating part 290 calculates an evaluation value Qe related to the strength of the stripe defect of the space frequency band under consideration based on the stripe featured value Pe calculated by stripe feature value caluculation part 270 and the stripe correction quantity Be calculated by the stripe correction quantity calculating part 288 of the stripe correction quantity acquiring part 280. <P>COPYRIGHT: (C)2007,JPO&INPIT

Description

  The present invention executes an image evaluation method and image evaluation apparatus for evaluating the image quality of an image output from an image output apparatus (image forming apparatus) such as a color printer, and an image evaluation process using an electronic computer (computer). The present invention relates to an image evaluation program. More specifically, the present invention relates to a mechanism for obtaining an evaluation value for evaluating an image quality related to a spatial frequency such as graininess, sharpness, and a flaw defect.

  Determining the image quality of an image output or displayed by an image display device such as a color printer or a copying machine, or an image display device such as a color display device (the image forming device and the image display device are collectively referred to as an image output device) Regarding factors, tone reproducibility, color reproducibility, sharpness, resolution, graininess, gloss, image defects, and the like are known.

  Originally, the brightness and color band-like unevenness (called vertical stripes and horizontal stripes, so-called streak defects) generated in a uniform color image that is output or displayed with uniform brightness and color is a technology unique to the device. This is one of the image defects generated depending on the image quality, and is used as one of important items in quality control. The feature amount and evaluation value related to the graininess, sharpness, and wrinkle defect of the image element of interest are all related to the spatial frequency.

  There are two methods for evaluating image quality such as streak defects in images output from image output devices: subjective evaluation that evaluates image quality by subjective judgment through human vision, and image quality feature quantities through a measuring instrument. There is an objective evaluation to calculate. Subjective evaluation has a problem of evaluation accuracy, such as variations in evaluation results due to human judgment. In recent years, many objective evaluation methods have been proposed.

  For example, Patent Documents 1 to 3 propose a method for objectively evaluating the image quality of a streak defect.

JP 09-037310 A JP 2000-004313 A JP 2002-139404 A

  For example, in the technique described in Patent Document 1, as a method of inspecting luminance unevenness of a color cathode ray tube, a light emitting surface is imaged with a television camera, and after performing projection processing on the luminance value of the captured image to obtain one-dimensional data. A secondary differential value is obtained to evaluate the strength of the streaks.

  In the technique described in Patent Literature 2, a psychological amount perceived by a human when observing banding, which is a kind of defect related to image quality, is predicted. Specifically, the physical characteristic amount of the image to be evaluated is converted into a spatial frequency, and the function obtained from the relationship between the preset physical characteristic amount and the banding perceptual characteristic function corresponding to this physical characteristic amount is related to the spatial frequency. Integration is performed to calculate a banding perception prediction value including a plurality of spatial frequency components.

  Further, in the technique described in Patent Document 3, as a method for evaluating the luminance unevenness of the color display device, a correction coefficient that converts an image into a spatial frequency spectrum image and increases or decreases it by changing the frequency to a one-dimensional luminance average value. Is used to calculate the corrected luminance average value, and the characteristic unevenness extracted from the corrected luminance average value is compared with a predetermined reference to evaluate the luminance unevenness.

  However, with the techniques described in Patent Documents 1 to 3, when evaluating a streak defect in a spatial frequency band of interest in an image, it is not possible to calculate an evaluation value corresponding to visual perception. Specifically, when evaluating streak defects in an image output by an image output device such as a color printer, there is a lot of noise other than the position of the streak defect being noticed. In particular, since it is affected by the spatial frequency of background noise, it is impossible to calculate an evaluation value corresponding to visual perception with the conventional technology that does not consider this influence.

  For example, in the technique described in Patent Document 1, since the evaluation value is calculated by paying attention only to the strength of the streaks, the influence of the background noise other than the streaks cannot be taken into account, and the streak evaluation that can correspond to the visual feeling. The value cannot be calculated.

  Further, in the technique described in Patent Document 2, since a comprehensive evaluation value of an image defect including a plurality of spatial frequency components is calculated, the image defect in the spatial frequency band of interest and the image defect of interest It is not possible to separate background noise other than, and it is not possible to calculate a streak evaluation value corresponding to visual feeling.

  In the technique described in Patent Document 3, the luminance distribution of a defective product frequency pattern is obtained by subtracting the luminance distribution of a non-defective product frequency pattern registered in advance from the luminance distribution of the inspection object, and the luminance unevenness is inspected. , The image defect of the spatial frequency band of interest and other background noise can be separated, but the relationship between the spatial frequency band of the image defect of interest and the spatial frequency band of the background noise is not considered, Therefore, it is not possible to calculate a streak evaluation value corresponding to visual perception.

  It should be noted that the problem of not being able to calculate an evaluation value that corresponds to visual perception due to not considering the relationship between the spatial frequency band of the image quality element of interest and the spatial frequency band of background noise. This is also true for image quality elements related to other spatial frequencies such as graininess and sharpness, as well as streak defects.

  The present invention has been made in view of the above circumstances, and even when evaluating image quality related to the spatial frequency by paying attention to a certain spatial frequency, it is not affected by background noise, It is an object to provide a mechanism that can calculate a corresponding evaluation value.

  According to the experiment by the present inventor, when an image quality related to the spatial frequency is evaluated by paying attention to a certain spatial frequency, the influence that the spatial frequency of the background noise is closer to the focused spatial frequency has on the visual evaluation. It turns out that becomes larger. In this regard, when adding background noise correction to feature quantities related to image quality related to spatial frequencies (for example, feature quantities acquired based on data in real space), the spatial frequency of interest Therefore, it is important to acquire the background noise component used for correction in consideration of the above component. The present invention has been made paying attention to this point.

  That is, in the mechanism according to the present invention, a feature quantity related to the image quality related to the spatial frequency of the image element of interest is acquired based on the input image data, and the input image data is expanded into a plurality of spatial frequency band components. An evaluation value for extracting the background component for the image element of interest based on each spatial frequency band component that is expanded in frequency and evaluating the image quality of the image element of interest based on the feature amount and the background component Was asked objectively.

  In short, when evaluating the image quality related to the spatial frequency, with regard to the component used to correct the background noise for the feature value that is the base of the evaluation value, the background component is frequency-expanded into a plurality of band components, It is characterized in that a more appropriate correction amount is obtained based on the analysis result.

  Based on the analysis results of multiple band components obtained by frequency expansion, when determining a more appropriate correction amount, the correction amount is stronger for the band component including the spatial frequency of interest among the band components. It is preferable to perform a predetermined weighting calculation. That is, it has a great feature in that the correction component used for correcting the background noise is acquired in consideration of the weighting for the spatial frequency of interest.

  The invention described in the dependent claims defines a further advantageous specific example of the image evaluation mechanism according to the present invention. Furthermore, the program according to the present invention is suitable for realizing the image evaluation mechanism according to the present invention by software using an electronic computer (computer). Note that the program may be provided by being stored in a computer-readable storage medium, or may be provided by distribution via wired or wireless communication means.

  According to the present invention, the correction component used for correcting the background noise with respect to the feature quantity related to the image quality related to the spatial frequency of the image element of interest is converted into a plurality of spatial frequency band components. Since frequency expansion is performed and extraction is performed based on each spatial frequency band component subjected to frequency expansion, a correction component can be acquired in consideration of weighting for the spatial frequency of interest, and the space of interest It is possible to acquire an evaluation value at which the image quality factor of the frequency is not easily affected by the background noise or an evaluation value at which it is not. As a result, an evaluation value for evaluating the image quality related to the spatial frequency, such as graininess, sharpness, and flaw defect, can be obtained so as to be compatible with visual perception.

  Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings.

<System Configuration; First Embodiment>
FIG. 1 is a block diagram showing a schematic configuration of an image evaluation system 1 provided with a first embodiment of an image evaluation apparatus according to the present invention. Here, a functional block diagram according to the processing procedure is shown.

  As shown in the figure, an image evaluation system 1 includes an image evaluation apparatus 10 that evaluates the image quality of an image to be evaluated, and an image output apparatus (image forming apparatus) that generates (forms) an image to be evaluated Image on a predetermined recording medium. ) 50 and an image input device 70 for inputting the input image data D20 obtained by digitizing the image for evaluation Image generated by the image output device 50 to the image evaluation device 10.

  For example, a color printer using an image forming process such as an ink jet method or an electrophotographic method is used as the image output device 50 that generates an image to be evaluated Image to be evaluated. For example, the original image data D10 stored in the image evaluation apparatus 10 is converted into a color signal in a device-dependent color space such as RGB or CMYK to form an image on an output medium such as paper. Of course, the original image data D10 may be acquired from other than the image evaluation apparatus 10 to generate the image for evaluation Image.

  The image input device 70 only needs to be able to generate input image data D20 (digitized image information) corresponding to the original image data D10 and input it to the image evaluation device 10, such as a flatbed scanner. A device that digitizes and outputs pixel information of the image to be evaluated Image generated by the image output device 50 is used. For example, the output image (image to be evaluated Image) of the original image data D10 generated by the image output device 50 is optically read, and the read input image data D20 is supplied to the image evaluation device 10.

  In this case, the image input apparatus 70 is preferably subjected to input characteristic calibration, driver adjustment, and the like, and it is desirable that the graininess, sharpness, gradation characteristics, and color reproducibility do not change depending on the subject. It is further desirable that automatic processing such as AGC and edge enhancement performed on the image input device 70 side is turned off.

  Although not shown, the image evaluation device 10 includes a control unit that controls each functional unit in the device, and a display unit that notifies the user of guidance information, predetermined information processing results, management information, and the like as image information. In addition, a voice notification unit comprising a built-in or externally installed piezoelectric body or speaker that notifies these as voice information, and an instruction input having a mouse or keyboard for receiving various instruction inputs from the operator to the device A user interface unit including a unit (operation unit).

  The image evaluation apparatus 10 includes an output interface unit 11out and an input interface unit 11in as interface function units for delivering image data, and an image output device 50 to be evaluated as functional units related to execution of the image evaluation process. The original image storage unit 12 that stores the original image data D10 necessary for the evaluation of the input image and the input image storage unit 14 that stores the input image data D20 supplied from the image input device 70 are provided. The output interface unit 11out and the original image storage unit 12 are for supplying the image output device 50 with original image data D10 for generating the image for evaluation Image in the image output device 50. It is not essential that the image evaluation apparatus 10 includes these.

The output interface unit 11out is a device such as sRGB (standard RGB), XYZ, Lab (correctly L ^* a ^* b ^* ) or LCh (correctly L ^* C ^* h ^° ) stored in the original image storage unit 12. The original image data D10 represented by the color signal of the independent color space is output to various devices outside the apparatus. The input interface unit 11in takes in the input image data D20 from various devices outside the apparatus and passes it to the input image storage unit 14.

  Here, the sRGB color space data is a color space defined by the International Electrotechnical Commission (IEC), which is widely used in the fields of peripheral devices for personal computers such as digital cameras, printers, and monitors. The color data conforms to the international standard. By performing color adjustment according to sRGB, the difference in color between input and output can be minimized.

  As the original image data D10, not only a pattern image such as a patch image or a gradation image but also a pattern image such as a photographic image can be used. In the present embodiment, a case where the entire image has a uniform color will be described.

  In calculating the evaluation value related to the streak defect, for example, as an image chart output from the image input device 70, an image chart in which the entire image has a uniform color is used. In particular, in the present embodiment, a case where an image chart of 50% CMY is output from the image input device 70 will be described.

  The image evaluation apparatus 10 also includes a color signal conversion unit 16 that converts image data in a device-dependent color space into image data in a device-independent color space, and input image data D20 (image data) supplied from the image input apparatus 70. And an image quality element evaluation value calculation unit 20 for calculating an input image image quality element evaluation value Q20 based on the above.

  The color signal conversion unit 16 converts the input image data D20 into color data of a uniform perceptual color space, which is a device-independent color space, or tristimulus values XYZ or tristimulus values XYZ (collectively input images). Data D22) is converted.

  The image quality element evaluation value calculation unit 20 may calculate an evaluation value using the conversion result (input image data D22) of the color signal conversion unit 16. Image input by using color data in the uniform perceptual color space converted by the color signal converter 16 and device-independent color signals (color data; D22) that can be converted into tristimulus values XYZ or tristimulus values XYZ The influence of the input color characteristics of the device can be eliminated. For example, even if a certain color (Lab, LCH) is input, RGB values of various input image data can be obtained depending on the image input device. However, an input characteristic profile (color conversion profile) of the image input device is used. By converting to device-independent data, the same Lab and LCh can be obtained even if the image input devices are different.

  The image quality element evaluation value calculation unit 20 obtains an evaluation value Qg related to graininess, an evaluation value Qs related to sharpness, an evaluation value Qt related to gradation characteristics, an evaluation value Qc related to color reproducibility, etc. for the image to be evaluated. In addition, particularly in the configuration of the present embodiment, the streak evaluation value Qe related to the streak defect existing in the image to be evaluated Image can be obtained. The feature amount and evaluation value related to the graininess, sharpness, and wrinkle defect of the image element of interest are all related to the spatial frequency.

  Specifically, the image quality element evaluation value calculation unit 20 acquires a predetermined feature amount P having a relationship with the image quality related to the spatial frequency of the image element of interest based on the input image data, An evaluation value acquisition unit 26 is provided for acquiring an evaluation value Q for evaluating the image quality associated with the spatial frequency of the image element of interest.

  In the present embodiment, the feature amount acquisition unit 22 acquires a feature amount P based on image data in real space. The correction amount acquisition unit 24 once converts the image data in the real space into image data in the frequency space, performs predetermined band processing (so-called filtering processing), and returns the image data in the real space to the correction amount. B is acquired.

  Note that the feature amount acquisition unit 22 may convert the image data in the real space into image data in the frequency space, and acquire the feature amount P based on the band component including the spatial frequency of interest. However, in this case, it is necessary to devise preprocessing so that the feature amount P of only the image quality element of interest at the position of interest can be acquired. This is because when image data is converted into the frequency space, the same frequency component appears as the component on the same frequency axis regardless of the position. In this respect, when the feature amount P is acquired from the image data in the real space, it is easy to perform the process of specifying the position.

  Further, the image quality element evaluation value calculation unit 20 divides input image data into a plurality of spatial frequency band components, that is, frequency of one input image data as a plurality of spatial frequency band components as a characteristic part of the present embodiment. The background component for the image element of interest is extracted based on each spatial frequency band component that is expanded in frequency, and the correction amount for the feature amount Q acquired by the feature amount acquisition unit 22 is extracted from the extracted background component. A correction amount acquisition unit 24 for B is provided. The evaluation value acquisition unit 26 calculates an evaluation value Q based on the feature amount P acquired by the feature amount acquisition unit 22 and the correction amount B acquired by the correction amount acquisition unit 24.

  Here, in the algorithm of the image evaluation method according to the present embodiment, a case will be described in which the image quality related to brightness unevenness (so-called streak defect) in a predetermined direction existing in an image output from an image output device such as a color printer is evaluated. To do.

  In order to evaluate the image quality related to such a streak defect, the feature amount acquisition unit 22 includes a preprocessing unit 210 that performs predetermined preprocessing on the input image data D20 or the input image data D22, and a preprocessing unit 210. And a streak feature amount calculation unit 270 that calculates a streak feature amount Pen, which is an example of the feature amount P related to the spatial frequency, on the basis of the processed input image data.

  The pre-processing unit 210 averages the input image data D20 or the input image data D22 in the device-independent color space processed by the color signal conversion unit 16 in a direction parallel to the streak defect and converts it to a one-dimensional profile. A processing unit 212 and an average line difference calculation unit 214 that acquires a lightness difference profile by calculating a difference between the lightness (L *) profile and the average line of the lightness profile are provided. The streak feature value calculation unit 270 obtains the streak feature value Pe based on the difference profile acquired by the average line difference calculation unit 214.

  Further, the correction amount acquisition unit 24, which is a characteristic part of the present embodiment, expands the frequency of the input image data processed by the preprocessing unit 210 into a plurality of spatial frequency band components, and each spatial frequency band that has been expanded in frequency. A background component for a streak defect, which is an example of the image quality of an image element of interest, is extracted based on the component, and a streak correction amount Be for the streak feature amount Pe obtained by the streak feature amount calculation unit 270 using the extracted background component. A streak correction amount acquisition unit 280 is provided.

  The streak correction amount acquisition unit 280 divides the input image data processed by the preprocessing unit 210 into image data of a plurality of spatial frequency bands, and the spatial frequency band division unit 286 performs frequency expansion. A streak correction amount calculating unit 288 that calculates a streak correction amount Ben based on the plurality of image data in the plurality of spatial frequency bands.

  The spatial frequency band division unit 286 divides (differs in frequency) the difference profile acquired by the average line difference calculation unit 214 into a plurality of difference profiles in the spatial frequency band. The streak correction amount calculation unit 288 obtains a streak correction amount Be based on the brightness difference profiles of a plurality of spatial frequency bands that are frequency-expanded by the spatial frequency band division unit 286.

  The evaluation value acquisition unit 26 determines a streak defect based on the streak feature amount Pe calculated by the streak feature amount calculation unit 270 and the streak correction amount Be calculated by the streak correction amount calculation unit 288 of the streak correction amount acquisition unit 280. A streak evaluation value calculation unit 290 that calculates an evaluation value Qe related to strength is provided. In particular, the streak evaluation value calculation unit 290 of the present embodiment is characterized in that it calculates a streak evaluation value Qe for the spatial frequency band of interest.

<Configuration of color signal converter>
FIG. 2 is a block diagram illustrating a schematic configuration of the color signal conversion unit 16. Here, a functional block diagram according to the processing procedure is shown.

  As shown in the figure, the color signal conversion unit 16 converts the input image data D20 stored in the input image storage unit 14 into color data (for example, a Lab value), tristimulus value XYZ, or tristimulus value XYZ in a uniform perceptual color space. An input image data color conversion unit 164 for converting into color signals independent of devices such as convertible color data is provided.

  As an example, the input image data color conversion unit 164 temporarily converts the input image data D20 indicated by the RGB values captured from the input image storage unit 14 into XYZ tristimulus values that are color signals in a device-independent color space. Later, the image data is converted into input image data D22 indicated by a Lab value that is a uniform color space or L (lightness) C (saturation) h (hue) value constituting three attributes of color.

  In the uniform perceptual color space, since the color difference has a linear relationship with the color difference when viewed with the human eye, it is possible to calculate an evaluation value that is intuitively understandable. For example, uniform perceptual color space data such as tristimulus values XYZ and Lab values defined by CIE can represent absolute color values that do not depend on device characteristics such as an image input device.

  By creating input image data in such an XYZ color system that represents an absolute color or a color system that can be converted to this, the input color characteristics of the image input device 70 are set in the image evaluation device 10. Even if no consideration is given, it is possible to make comparison as color information that matches the visual sensitivity of the human eye, and it is possible to calculate an evaluation value corresponding to visual perception.

  For example, since the RGB value of the input image data D20 is a device-dependent color signal, the input image data color conversion unit 164 uses the input characteristic profile (color conversion profile) of the image input device 70 to generate a device-independent color. The signal is converted into an XYZ3 stimulation value or Lab, LCh.

  Further, the input image data color conversion unit 164 corrects the input characteristics of the image input device 70 by filtering the input image data D20 with respect to not only color characteristics and gradation characteristics but also spatial frequency (MTF) characteristics. Correct with. After this correction, it is converted into an XYZ3 stimulus value or Lab value or LCh value which is a device independent color signal. That is, in the configuration of the present embodiment, the input image data color converting unit 164 generates input image data (for example, input image data) before generating the input image quality factor evaluation value from the input image. It functions as an input characteristic correction unit that corrects the input characteristics of the image input device 70) that generates the data D20.

<Example of streak defects>
FIG. 3 is a diagram showing an example of a streak defect existing in the image to be evaluated Image handled in the present embodiment. In the figure, the direction from the upper end to the lower end is called the vertical direction, and the direction from the left end to the right end is called the horizontal direction.

  As shown in the figure, there are vertical streak noises of various thicknesses and intensities (gradation differences) in the image to be evaluated Image. Under such circumstances, in the present embodiment, in particular, attention is paid to vertical streak noise extending in the vertical direction at the position indicated by the arrow in the center of the figure, and this is regarded as a streak defect. The vertical streak noise existing in the area is treated as background noise. Although illustration is omitted, attention can be paid to horizontal streak noise extending in the horizontal direction.

<Description of functions of the one-dimensional processing unit>
FIG. 4 is a diagram for explaining the function of the one-dimensionalization processing unit 212, and particularly shows an example of the brightness profile obtained by the one-dimensionalization processing unit 212. In the present embodiment, the processing after the one-dimensionalization processing unit 212 will be described for the case of lightness L, which is a signal in a device-independent color space, but other non-device data such as other color data a, b, C, and h. It can be applied to all signals in the dependent color space.

  The device-independent color space image data handled by the one-dimensional processing unit 212 represents the two-dimensional image Image to be evaluated shown in FIG. It is what contains. In addition, vertical streak noise (streaks defects) exists at substantially the same position in the horizontal direction.

  Therefore, the one-dimensional processing unit 16 converts the device-independent color space image data (data for all columns) representing the two-dimensional image to be evaluated Image into a direction substantially parallel to the streak defect (in this example, the horizontal direction). ), I.e., by averaging the data for all columns at the same horizontal position, the data is converted into one-dimensional profile data (lightness profile data in this example). By doing so, profile data as shown in FIG. 4 is obtained. Note that profile data such as lightness profile data is an example of image data represented in real space.

  In the profile data shown in FIG. 4, in addition to a signal component having a small period representing vertical streak noise, a component having a large period in which the data value gradually decreases from the left end toward the right end, ie, data representing a streak defect. In addition, there is a lightness fluctuation component that is less affected by the evaluation of streak defects with a longer period. The lightness fluctuation of this large period is removed by the average line difference calculation unit 214 provided at the subsequent stage of the one-dimensionalization processing unit 212, as will be described later.

  In the averaging process in the one-dimensional processing unit 212, if averaging is performed in a tilted direction that is not parallel to the streak defect, the value of the streak part of the one-dimensional profile becomes smaller than the original value. It is desirable to average in parallel directions.

  Therefore, the one-dimensionalization processing unit 212 uses the image input device 70 to evaluate the image to be evaluated so that the direction of the streak defect is in the vertical direction (or horizontal direction) with respect to the read input image data D20 (or D22). It is desirable to read. Alternatively, when the direction of the streak defect is inclined with respect to the read input image data D20 or the like and deviated from the vertical direction (or the horizontal direction), the one-dimensional processing unit 212 determines the direction of the streak defect such as the input image data D20. It is desirable that the tilt correction is performed electronically by detecting and averaging in the tilted direction.

<Functional Description of Average Line Difference Calculation Unit>
FIG. 5 is a diagram for explaining the function of the average line difference calculation unit 214, and particularly shows an example of the brightness difference profile obtained by the average line difference calculation unit 214.

  First, the average line difference calculation unit 214 obtains feature line profile data indicating an outline (feature) of the brightness profile data obtained by the one-dimensional processing unit 212. The feature line may be any characteristic line as long as it represents a lightness fluctuation component having a period larger than that of data representing a line defect and having little influence on the evaluation of the line defect.

  For example, an average line is typically used as the feature line. As the average line, for example, a curve obtained by approximating lightness profile data with a quadratic polynomial may be used. Hereinafter, the average line will be described.

  Next, the average line difference calculation unit 214 calculates a difference between the average line profile data and the brightness profile data obtained by the one-dimensional processing unit 212. By performing this difference processing, brightness difference profile data as shown in FIG. 5 is obtained. Note that difference profile data such as brightness difference profile data is an example of image data represented in real space.

  In FIG. 5, the data corresponding to the average line is unified with the value “0” on the vertical axis, and a signal component with a small period representing vertical streak noise appears positively and negatively around “0”. Thereby, in the brightness difference profile data, a brightness fluctuation component having a longer period than that of data representing a streak defect and having less influence on the evaluation of the streak defect is removed.

<Configuration of streak feature amount calculation unit>
FIG. 6 is a block diagram illustrating a schematic configuration of the streak feature amount calculation unit 270. The streak feature amount calculation unit 270 obtains a streak feature amount Pen based on the brightness difference profiles of a plurality of spatial frequency bands acquired by the average line difference calculation unit 214.

  For this reason, specifically, the streak feature amount calculation unit 270 converts the streak position detection unit 412 that detects the streak position of the brightness difference profile, and the streak position information and the brightness difference profile obtained by the streak position detection unit 412. And a streak strength calculation unit 414 that calculates the streak strength based on this.

  The streak position detection unit 412 detects the position information of the streak defect based on the brightness difference profile obtained by the average line difference calculation unit 214. Specifically, in order to detect a noise component of a large vertical streak that will represent the noticed streak defect, a brightness difference profile deviating from the range from the lower threshold value Th1 to the upper threshold value Th2 in the brightness difference profile data. It is detected and this area is determined as the streak position.

  Here, since the two threshold values Th1 and TH2 are used for appropriately detecting the position of the noticeable streak defect, it is preferable that the two threshold values Th1 and TH2 are defined in relation to the visual feeling. For example, the same amount can be set in the positive and negative directions based on the difference from the average line (value of “0” in FIG. 5), and the minimum difference in brightness felt by humans can be used. In this way, signal components within the range from the lower threshold value Th1 to the upper threshold value Th2 are excluded as components that are not felt by humans, and larger components that humans feel (in this example, streak defects are eliminated). It is possible to focus on the component to be expressed. In FIG. 5, there is one region that exceeds the threshold values Th1 and Th2, one streak defect exists, and it is in a state where it is properly detected.

  The streak strength calculation unit 414 calculates the strength of the streak defect (streak strength) based on the streak position information detected by the streak position detection unit 412 and the brightness difference profile obtained by the average line difference calculation unit 214. This streak intensity is defined as a streak feature amount Pen.

  As the streak intensity (the streak feature amount Pen indicating the characteristic of the streak defect), for example, the maximum value (peak intensity) of a larger brightness difference profile that falls outside the range from the threshold Th1 to the threshold Th2 in the brightness difference profile data is used as the streak intensity It is good to use. The streak intensity is not limited to such a maximum value as long as it represents the strength of the target streak defect. For example, the average value of a larger brightness difference profile deviating from the range from the threshold Th1 to the threshold Th2 An integral value can also be used.

<Configuration of spatial frequency band division unit>
FIG. 7 is a block diagram illustrating a schematic configuration of the spatial frequency band dividing unit 286 built in the streak correction amount acquisition unit 280. The spatial frequency band dividing unit 286 divides the brightness difference profiles of the plurality of spatial frequency bands acquired by the average line difference calculation unit 214 into the brightness difference profiles of the plurality of spatial frequency bands. That is, a plurality of brightness difference profiles having different frequencies of interest are generated by frequency-expanding one brightness difference profile.

  Therefore, specifically, the spatial frequency band dividing unit 286 includes a discrete Fourier transform unit 422 that performs a discrete Fourier transform on the brightness difference profiles of a plurality of spatial frequency bands acquired by the average line difference calculation unit 214. A band filter multiplier 424 that multiplies the spatial frequency component obtained by the discrete Fourier transform unit 422 by a band filter, and a spatial frequency component of a plurality of bands obtained by multiplying the band filter by the band filter multiplier 424. An inverse discrete Fourier transform unit 426 that performs inverse discrete Fourier transform is provided.

  The discrete Fourier transform unit 422 performs a discrete Fourier transform on the lightness difference profile obtained by the average line difference calculation unit 214, and calculates a spatial frequency component (lightness difference spectrum) of the lightness difference profile.

  The band filter multiplier 424 multiplies a plurality of band filters including the spatial frequency component of the noticed streak defect to obtain a band-by-band brightness difference spectrum. For example, when the spatial frequency f0 of the noticed streak defect is 1.5 cycle / mm, four band-pass filters, f1 (0.2 cycle / mm or less), f2 (0.2 to 1.0 cycle / mm), f3 (1.0 to 5.0 cycle / mm), f4 (5.0 cycle / mm or more) filter is multiplied.

  The inverse discrete Fourier transform unit 426 performs inverse discrete Fourier transform on the spatial frequency components of the four bands obtained by multiplying the band filters, respectively, and the four band brightness difference profile data L_f1, L_f2, L_f3, and L_f4. Is calculated.

  As described above, the spatial frequency band dividing unit 286 of the present embodiment performs Fourier transform on the processing target image data, which is information in the real space, to convert it into information in the frequency space. By applying filtering processing to expand into a plurality of spatial frequency band components, suppressing unnecessary noise from each spatial frequency band component, by inverse Fourier transforming each spatial frequency band component in which this noise is suppressed, A plurality of spatial frequency band components on the frequency space are returned to information on the real space. In the filtering process, at least the band component including the spatial frequency of interest of the image quality of interest is discriminated from the other band components.

  By doing so, the background component for the feature quantity of the image quality of interest (in this example, the streak feature quantity Pen) is appropriate for the band component including the spatial frequency of interest of the image quality of interest and the other band components. Can be separated.

  FIG. 8 and FIG. $ A7 are diagrams illustrating an example of a plurality of brightness difference profiles (brightness difference profiles by band) obtained by frequency expansion and having different frequencies of interest. Here, an example of four band-specific brightness difference profile data L_f 1, L_f 2, L_f 3, and L_f 4 obtained by performing frequency expansion on the brightness difference profile shown in FIG. 5 by the spatial frequency band dividing unit 286 is shown. ing.

  For example, FIG. 8A shows brightness difference profile data L_f1 focused on the band f1 (0.2 cycle / mm or less), and FIG. 8B focused on the band f2 (0.2 to 1.0 cycle / mm). 9A is the brightness difference profile data L_f3 focused on the band f3 (1.0 to 5.0 cycle / mm), and FIG. 9B is the band f4 (5.5). Brightness difference profile data L_f4 focusing on 0 cycle / mm or more).

  As described above, the characteristics of the spatial frequency component of the input image data can be known by expanding the frequency of one brightness difference profile in different bands. For example, with respect to the brightness difference profile data L_f1, L_f2, and L_f4 that do not include the spatial frequency (straight spatial frequency) f0 component of the noticed streak defect, random noise exists in the frequency space. In the brightness difference profile data L_f3 including the defect streak spatial frequency f0 component, the data value at 1.5 cycle / mm, which is the streak spatial frequency f0 of the noticed streak defect, increases, and random noise is generated at other frequencies. Exists.

<Configuration of streak correction amount calculation unit>
FIG. 10 is a block diagram illustrating a schematic configuration of a streak correction amount calculation unit 288 built in the streak correction amount acquisition unit 280. The streak correction amount calculation unit 288 calculates the streak correction amount Ben based on the brightness profiles of a plurality of different spatial frequency bands obtained by the spatial frequency band dividing unit 286.

  Here, the streak correction amount Ben is a feature amount representing the intensity of background noise other than the noticed streak defect, and is a feature amount that influences when the streak evaluation value Qe regarding the strength of the streak defect is obtained. is there.

  In order to appropriately obtain the intensity of background noise other than streak defects, it is necessary to eliminate the influence of the strength of the streak defect to be noticed from the feature amount of each spatial frequency band component. Therefore, specifically, the streak correction amount calculation unit 288 calculates the standard deviation SD of the brightness profiles of the plurality of spatial frequency bands obtained by the spatial frequency band division unit 286 as the feature amount of each spatial frequency band component. The standard deviation calculating unit 432 and a streak spatial frequency band setting unit 434 that distinguishes the spatial frequency band f0 of the noticed streak defect from other spatial frequency bands are configured.

  The standard deviation calculation unit 432 is an example of a band feature quantity acquisition unit that acquires feature quantities of each spatial frequency band component. The streak spatial frequency band setting unit 434 is an example of an attention spatial frequency band setting unit that sets a spatial frequency band of interest in the feature amount P acquired by the feature amount acquisition unit 22.

  Further, the streak correction amount calculation unit 288 is a weighting factor setting unit that sets the weighting factor k for the feature amount of each spatial frequency band component based on the streak spatial frequency band f that is distinct and set by the streak spatial frequency band setting unit 434. 436 and a weighting calculation using the weighting factor k set by the weighting factor setting unit 436 for each standard deviation SD of the plurality of brightness profiles obtained by the standard deviation calculating unit 432, thereby calculating the line correction amount Ben. And a weighting calculation unit 438.

  The standard deviation calculation unit 432, which is an example of the band feature quantity acquisition unit, performs standard deviations SD_f1,... For each of the plurality of spatial frequency band brightness profile data L_f1, L_f2, L_f3, and L_f4 obtained by the spatial frequency band division unit 286. SD_f2, SD_f3, and SD_f4 are calculated.

  Obtaining the standard deviation means comprehensive evaluation of the overall variation, and the noticed streak defect existing in the brightness difference profile data including the streak spatial frequency f0 component of the noticed streak defect. There is an advantage that the influence of strength can be eliminated.

  This is because in the case of the brightness difference profile data shown in FIG. 5, there is only one streak spatial frequency f0 component, but there are many noise frequency components other than the streak spatial frequency f0 component. That is, when the standard deviation is obtained, the streak spatial frequency f0 and the frequency components in the vicinity thereof can be handled as singular points, and the data can be analyzed without eliminating the streak spatial frequency f0 and the frequency components in the vicinity thereof in advance. Information that is not affected by the streak spatial frequency f0 or the frequency components in the vicinity thereof can be acquired. As a result, it is possible to eliminate the influence of the strength of the line defect to be noticed from the feature amount of each spatial frequency band component.

  The streak spatial frequency band setting unit 434, which is an example of the target spatial frequency band setting unit, sets a band including the streak spatial frequency f0 of the target streak defect (spatial defect spatial frequency band). As the streak spatial frequency band, the same spatial frequency band (f1, f2, f3, f4) developed by the spatial frequency band dividing unit 286 is used. The setting of the streak spatial frequency band may be set manually by the operator, or may be automatically set by estimating from the streak feature amount Pen obtained by the streak feature amount calculation unit 270. Good. In any case, it suffices if the spatial frequency band of interest in the feature quantity P acquired by the feature quantity acquisition unit 22 can be set.

  The weight coefficient setting unit 436 has a plurality of lines obtained by the standard deviation calculation unit 432 according to the streak spatial frequency band (any one of f1, f2, f3, and f4) that is distinctly set and set by the streak spatial frequency band setting unit 434. Each weight coefficient k1, k2, k3, k4 to be multiplied by the standard deviations SD_f1, SD_f2, SD_f3, SD_f4 is set.

  Here, the weighting factor setting unit 436 sets different numerical values according to the streak spatial frequency bands (any one of f1, f2, f3, and f4) that are discriminated and set by the streak spatial frequency band setting unit 434. Set to k2, k3, k4.

  At this time, the weighting factor setting unit 436 sets a weighting factor k such that the weighting increases as the streak spatial frequency band set by the streak spatial frequency band setting unit 434, that is, the band including the streak spatial frequency f0 is closer. . For example, it is important to set a numerical value that gives the highest weighting factor kn when fn is set by the streak spatial frequency band setting unit 434.

  This is because the streak feature amount Pen obtained by the streak feature amount calculation unit 270 is more strongly corrected for a component having the same or near frequency as the spatial frequency band f0 of the streak defect, so that the streak related to the strength of the streak defect is obtained. This is because the evaluation value Qe more closely corresponds to the visual feeling.

  For example, when the band f1 is set, k1 = 1.0, k2 = 0.4, k3 = 0.2, k4 = 0.1, and when the band f2 is set, k1 = 0.3, k2 = 1. 0, k3 = 0.3, k4 = 0.1, band f3 is set, k1 = 0.1, k2 = 0.3, k3 = 1.0, k4 = 0.3, band f4 is set In this case, k1 = 0.1, k2 = 0.2, k3 = 0.4, k4 = 1.0 are set.

  The weighting calculation unit 438 applies the weighting factors k1, k2, and k2 set by the weighting factor setting unit 436 to the standard deviations SD_f1, SD_f2, SD_f3, and SD_f4 of the plurality of brightness profiles obtained by the standard deviation calculation unit 432, respectively. The streak correction amount Ben is calculated by performing a weighting operation using k3 and k4.

  Here, in the weighting calculation, for example, as shown in the equation (1), the weight coefficient kn and the standard deviation calculation unit 432 are obtained so as to become higher as the streak spatial frequency f0 set by the weight coefficient setting unit 436 is closer. It is preferable to use a linear sum (weighted linear sum) of each overlapped frequency band component kn * SDn obtained by multiplying the standard deviation SDn.

  When the streak correction amount Ben is obtained using such a weighted linear sum, an appropriate streak that is not affected by the strength of the noticed streak defect according to the spatial frequency band of the noticed streak defect. The correction amount Ben can be calculated.

  In this embodiment, the standard deviation SD is calculated as the feature quantity of each spatial frequency band component, so that it is not affected by the spatial frequency f0 or the frequency components in the vicinity thereof, and the background noise other than the streak defect is strong. The streak correction amount Ben is obtained by properly determining the length, that is, eliminating the influence of the strength of the noticeable streak defect, but represents the strength of the background noise that is not affected by the strength of the noticeable streak defect. Other acquisition techniques can be used as long as they are.

  For example, as in general data analysis, it is also conceivable to use an average value, median value (median), span (difference between the maximum value and the minimum value), etc. as the feature quantity of each spatial frequency band component. However, in these cases, it is difficult to avoid the influence of the strength of the noticeable line defect by a simple calculation method.

  For example, in the case of an average value, if the average value is simply obtained, the noticed line defect component is also included in the calculation. In the case of the median value or span, since the median value or span is obtained using the component of the noticed streak defect as the maximum value (or the minimum value or both), the strength of the noticed streak defect It is difficult to obtain the background component properly. For this reason, it is necessary to obtain an average value or the like after eliminating the streak spatial frequency f0 and the frequency components in the vicinity thereof (after performing a prior elimination process). In this respect, if the standard deviation is used, it is advantageous in that information that is not affected by the streak spatial frequency f0 or the frequency components in the vicinity thereof can be obtained without performing such prior exclusion processing.

  In addition, the weighting calculation unit 438 calculates the stripe correction amount Ben by using a weighted linear sum as shown in the equation (1), but the streak spatial frequency band which is an example of the spatial frequency band setting unit of interest. As long as the band component including the noticed streak spatial frequency set by the setting unit 434 can be obtained, the streak correction amount Ben weighted higher can be obtained, and other acquisition methods can be used as long as it can be obtained. However, when a weighted linear sum as shown in Equation (1) is used, an appropriate streak correction amount Ben considering the noticed streak spatial frequency can be obtained very easily compared to other methods.

<Configuration of streak evaluation value calculation unit>
FIG. 11 is a block diagram illustrating a schematic configuration of the line evaluation value calculation unit 290. The streak evaluation value calculation unit 290 has a noticeable streak defect based on the streak feature amount Pen calculated by the streak feature amount calculation unit 270 and the streak correction amount Ben calculated by the streak correction amount calculation unit 288. A streak evaluation value Qe of the spatial frequency band of the noticed streak defect is obtained by eliminating the influence of the streak spatial frequency f0 other than the position where the streak is located and the frequency components in the vicinity thereof.

  Specifically, the streak evaluation value calculation unit 290 calculates an individual streak evaluation value calculation unit 442 that calculates an individual streak evaluation value Qen related to a streak evaluation value of each streak defect, and calculates an individual streak evaluation value. And a total streak evaluation value calculation unit 444 that calculates a total streak evaluation value Qe_all, which is a comprehensive evaluation value related to streak defects, based on a plurality of individual streak evaluation values Qen obtained by the unit 442. .

  The individual streak evaluation value calculation unit 442 subtracts the streak correction amount Ben obtained by the streak correction amount calculation unit 288 from each streak feature amount Pen obtained by the streak feature amount calculation unit 270, for example. The individual streak evaluation value Qen regarding the length is calculated.

  The individual streak evaluation value Qen only needs to indicate the strength (degree) of the streak defect for each detected streak defect, and the streak evaluation value Qe is not limited to being obtained by such difference processing, For example, it may be obtained by dividing the streak feature amount Pen by the streak correction amount Ben. In any of the difference processing and the division processing, the strength of the line defect that is focused on the background noise is obtained.

  The total streak evaluation value calculation unit 444 calculates a total streak evaluation value Qe_all that is a comprehensive evaluation value related to streak defects by accumulating a plurality of individual streak evaluation values Qen obtained by the individual streak evaluation value calculation unit 442. This is taken as the line evaluation value Qe.

  Note that the total streak evaluation value Qe_all only needs to indicate the total strength of the streak defect. The total streak evaluation value Qe_all is not limited to being obtained by such integration processing, and for example, a plurality of individual streak evaluation values Qen are averaged. You may ask for. Alternatively, as in general data analysis, it is also possible to use a median value (median), a span (difference between the maximum value and the minimum value), or the like.

  FIG. 12 is a diagram for explaining the result of calculating the streak evaluation value Qe for the streak defect of two types of images A and B as the image to be evaluated Image by the image evaluation apparatus 10 of the first embodiment. Here, FIG. 12A shows the lightness difference profile data La of the image A to be evaluated, FIG. 12B shows the lightness difference profile data Lb of the image B to be evaluated, and FIG. It is a graph which shows the result of having calculated the stripe evaluation value. In addition, in the chart shown in FIG. 12C, the streak evaluation value of the prior art is also shown as a comparison.

  The two types of images for evaluation Image (image A and image B) are both images output by an electrophotographic color printer, and the brightness difference profiles thereof are as shown in FIGS. 12 (A) and 12 (B). It has become.

  The streak evaluation value according to the prior art is obtained by obtaining the peak intensity of the streak defect and the standard deviation of the background other than the streak defect from the images having the brightness difference profiles shown in FIGS. The evaluation value is calculated by “deviation”.

  On the other hand, the streak evaluation value of the present embodiment is obtained from the image having the brightness difference profile shown in FIGS. 12A and 12B in accordance with the configuration of the image evaluation apparatus 10 shown in FIG. Peak intensity) and streak correction amount Ben (weighted linear sum of standard deviations of four spatial frequency bands) are obtained, and “streaks feature amount Pen−streas correction amount Ben” is calculated as individual streak evaluation value Qen.

  In calculating the streak evaluation value of the present embodiment, since the spatial frequency of the noticed streak defect is approximately 1.5 cycle / mm, the spatial frequency band dividing unit 286 has four bands, f1 (0.2 cycle / mm Below), f2 (0.2 to 1.0 cycle / mm), f3 (1.0 to 5.0 cycle / mm), and f4 (5.0 cycle / mm or more). Further, the streak correction amount calculation unit 288 sets the band f3 to a spatial frequency band including the streak spatial frequency f0 by the streak spatial frequency band setting unit 434, and the weighting coefficient setting unit 436 uses the coefficients k1 to k1 used for weighting calculation. k4 was set to k1 = 0.1, k2 = 0.3, k3 = 1.0, k4 = 0.3.

  As shown in FIGS. 12A and 12B, the image A and the image B have the same peak defect peak intensity, and the image A has a larger background noise (standard deviation) than the image defect. And the frequency characteristics of background noise are different. For example, in the image A, as can be seen from the brightness difference profile shown in FIG. 12A, the background noise in the spatial frequency band f3 that is the same level as the streak defect is large, and the streak defect is not noticeable. On the other hand, in the image B, as can be seen from the brightness difference profile shown in FIG. 12B, the background noise in the spatial frequency band f3 which is about the same as the streak defect is small, and the streak defect is easily noticeable.

  Actually, in visual evaluation, if a component of the same spatial frequency band as the noticeable streak defect exists in the surroundings as background noise, the noticeable streak defect is misunderstood. The strength of the streak defect is higher in the image B in which the background noise in the spatial frequency band f3 is the same as that of the streak defect than in the image A in which the background noise is large in the band f3.

  In the prior art, when the streak evaluation value is calculated as “peak intensity−background standard deviation”, the standard deviation of the background is slightly larger in the image B than in the image A, and thus the streak indicating the strength of the streak defect is shown. The evaluation value Qe is evaluated lower in the image B than in the image A.

  On the other hand, in the present embodiment, the streak correction amount calculation unit 288 gives high weighting to the streak spatial frequency band so that the correction amount of the component having the same or nearby frequency as the spatial frequency band f0 of the streak defect becomes larger. The streak correction amount Ben is obtained. For this reason, when the background noise is corrected by calculating the streak evaluation value Qe according to “streaks feature amount Pen−streaks correction amount Ben”, the streak feature amount Pen obtained by the streak feature amount calculation unit 270 is the same image. Even in such an image, the streak evaluation value Qe obtained by the streak evaluation value calculation unit 290 is smaller for an image having the same or close frequency component as the spatial frequency band f0 of the streak defect, and the visual evaluation can be dealt with. The streak evaluation value Qe is obtained.

  In comparison between the image A and the image B, even if the peak intensity of the streak defect is the same, the image B having the same background noise in the spatial frequency band f3 as that of the streak defect is similar to the streak defect. The streak evaluation value Qe is larger than that of the image A having a large background noise in the spatial frequency band f3, and the streak defect of the image B is evaluated to be higher than the streak defect of the image A. The value Qe can be calculated.

  As described above, according to the method for acquiring the evaluation value (streaks evaluation value Qe) regarding the streak defect in the image evaluation apparatus 10 of the present embodiment, the influence of the background component included in the feature amount (streaks feature amount Pen) regarding the streak defect. Since the correction component (streaks correction amount Ben) for suppressing the streak feature can be increased, the suppression amount for the streak feature amount of the component having the same or near frequency as the spatial frequency band of the target streak defect can be increased. Even when a streak defect in the spatial frequency band of interest is evaluated, it is possible to calculate a streak evaluation value that is compatible with visual feeling without being affected by background noise other than the noticed streak defect.

  In a conventional mechanism, there is one that uses frequency analysis to objectively obtain an evaluation value for evaluating image quality (for example, a mechanism described in Patent Document 3). However, if the evaluation value is simply obtained by using frequency analysis, it is difficult to obtain an evaluation value of only the image quality of interest of the frequency of interest of the image element of interest due to the influence of background noise. This is because if there is a component having the same or a nearby frequency other than the position where the image element of interest exists (that is, around the portion of interest), it cannot be distinguished from the component of interest.

  In this regard, when evaluating the image quality related to the spatial frequency, the background component is frequency-expanded into multiple band components for the component used to correct the background noise for the feature value that is the basis of the evaluation value. Thus, if the correction amount is obtained based on the analysis result, a predetermined weighting calculation can be performed so that the correction amount becomes stronger for the band component including the spatial frequency of interest.

  Then, if correction is performed on the feature amount using the correction amount obtained in this way, the spatial frequency band component of interest that has a strong influence on the evaluation value can be corrected more strongly. As a result, it is possible to eliminate the influence of the spatial frequency band component of interest that has a strong influence on the evaluation value, and it is possible to calculate an evaluation value that corresponds to the visual feeling.

  In the conventional mechanism, the background noise is not corrected in consideration of the weighting on the spatial frequency, which is greatly different from the case where the evaluation value corresponding to the visual feeling cannot be calculated.

<System Configuration; Second Embodiment>
FIG. 13 is a block diagram showing a schematic configuration of an image evaluation system 1 including the second embodiment of the image evaluation apparatus according to the present invention. Here, a functional block diagram according to the processing procedure is shown. The image evaluation apparatus 10 according to the second embodiment performs correction according to the spatial frequency characteristics of the visual system on the input image data in advance when obtaining the feature amount P and the correction amount B, and the image data subjected to this correction. The feature amount P and the correction amount B are acquired using the feature.

  Specifically, the image evaluation apparatus 10 according to the second embodiment includes input image data (average line difference in this example) before the streak feature amount calculation unit 270 and the spatial frequency band division unit 286 in the preprocessing unit 210. A visual spatial frequency characteristic correction unit 216 is provided for correcting the brightness difference profile data obtained by the calculation unit 214 according to the spatial frequency characteristic of the visual system. The visual spatial frequency characteristic correction unit 216 corrects the brightness difference profile data obtained by the average line difference calculation unit 214 according to human visual characteristics.

<Configuration of visual spatial frequency characteristic correction unit>
FIG. 14 is a block diagram illustrating a schematic configuration of the visual spatial frequency characteristic correction unit 216. The visual spatial frequency characteristic correction unit 216 performs a discrete Fourier transform on the brightness difference profile data and multiplies it by a function representing the spatial frequency characteristic of the visual system, that is, multiplies the visual characteristic filter to correct the visual characteristic. In addition, by performing inverse discrete Fourier transform on the spatial frequency component to which the correction of the visual characteristic is added, brightness difference profile data matching the human visual characteristic is generated.

  Therefore, specifically, the visual spatial frequency characteristic correction unit 216 includes a discrete Fourier transform unit 402 that performs discrete Fourier transform on the brightness difference profile data obtained by the average line difference calculation unit 214, and a discrete Fourier transform unit. A visual characteristic filter multiplication unit 404 that performs visual characteristic filter processing by multiplying the spatial frequency component obtained by 402 by a visual transfer function VTF (f) (VTF: Visual Transfer Function) corresponding to the spatial frequency characteristic of human vision. And an inverse discrete Fourier transform unit 406 that performs inverse discrete Fourier transform on the spatial frequency component obtained by multiplying the visual property filter by the visual property filter multiplication unit 404.

  The discrete Fourier transform unit 402 performs a discrete Fourier transform on the lightness difference profile data obtained by the average line difference calculation unit 214, and calculates a spatial frequency component (lightness difference spectrum) of the lightness difference profile.

  The visual characteristic filter multiplication unit 404 performs visual characteristic filter processing by using, for example, a Dooley approximate expression shown in Expression (2) as the visual transfer function VTF (f) corresponding to the spatial frequency characteristic of human vision, A brightness difference spectrum with corrected visibility is acquired. Here, f represents the spatial frequency [cycle / mm].

  The inverse discrete Fourier transform unit 406 performs an inverse discrete Fourier transform on the spatial frequency component (luminance difference spectrum after the visual sensitivity correction) obtained by multiplying the visual characteristic filter by the visual characteristic filter multiplication unit 404, Luminance difference profile data L_vtf corrected for visibility matching human visual characteristics is calculated.

  The streak feature quantity calculation unit 270 and the spatial frequency band division unit 286 following the visual spatial frequency characteristic correction unit 216 use the lightness difference profile data L_vtf thus corrected according to the spatial frequency characteristic of the visual system. Then, a streak feature amount Pen and a streak correction amount Ben are obtained.

  FIG. 15 is an example of brightness difference profile data L_vtf that matches the human visual characteristics obtained by the visual spatial frequency characteristic correction unit 216 for the brightness difference profile data shown in FIG. 5 in the image evaluation apparatus 10 of the second embodiment. FIG. As can be seen from the comparison between FIG. 5 and FIG. 15, when correction according to the spatial frequency characteristic of the visual system is performed, components with high frequency and low visibility are suppressed.

  As in the second embodiment, the line feature value Pen and the line correction amount Ben are obtained after performing the correction in accordance with the spatial frequency characteristics of human vision, thereby obtaining the line evaluation value Qe. It is possible to calculate a streak evaluation value Qe that can correspond to the above.

<Configuration using an electronic computer>
The series of image evaluation processing functions described above can be executed by hardware, but can also be executed by software. That is, each functional unit related to the execution of the image evaluation process is not limited to being configured by hardware, but may be realized by software using a computer (computer) based on a program code that realizes the function. Is possible. For example, similarly to the configuration of a general PC (personal computer) main body, a CPU (Central Processing Unit), a RAM (Random Access Memory), a ROM (Read-Only Memory), and the like can be provided.

  The execution of the image evaluation process described above can be realized by the CPU executing an application program incorporated in the image evaluation apparatus having the computer configuration. Therefore, an image quality evaluation program suitable for realizing the image evaluation method and apparatus according to the present invention by software using an electronic computer (computer) or a computer-readable storage medium storing the image quality evaluation program is extracted as an invention. You can also

  Of course, the configuration is not limited to such a computer, but may be configured by a combination of dedicated hardware that performs each function. By adopting a mechanism for executing processing by software, it is possible to enjoy the advantage that the processing procedure, processing conditions, and the like can be easily changed without changing hardware. On the contrary, if it is configured by hardware, high-speed processing can be realized.

  The recording medium causes a state change of energy such as magnetism, light, electricity, etc. according to the description contents of the program to the reading device provided in the hardware resource of the computer, and in the form of a signal corresponding to the change. The program description can be transmitted to the reader.

  For example, a magnetic disc (including a flexible disc), an optical disc (CD-ROM (Compact Disc-Read Only Memory)), a DVD (which is distributed to provide a program to a user separately from a computer, (Including Digital Versatile Disc), magneto-optical disc (including MD (Mini Disc)), or package media (portable storage media) made of semiconductor memory, etc. It may be configured by a ROM, a hard disk, or the like in which a program is recorded that is provided to the user in a state. Or the program which comprises software may be provided via communication networks, such as a wire communication or radio | wireless.

  When a series of evaluation value calculation processes are executed by software, a program (such as an embedded microcomputer) in which a program constituting the software is incorporated in dedicated hardware, or a CPU, logic circuit, storage device, etc. System on a chip (SOC) that implements a desired system by mounting functions on a single chip, or general-purpose that can execute various functions by installing various programs It is installed from a recording medium in a personal computer or the like.

  For example, a storage medium in which a program code of software for realizing an image evaluation value calculation processing function is supplied to a system or apparatus, and the computer (or CPU or MPU) of the system or apparatus stores the program code in the storage medium The same effect as that achieved by hardware can also be achieved by reading and executing. In this case, the program code itself read from the storage medium realizes an image evaluation value calculation processing function.

  Further, by executing the program code read by the computer, not only the function of performing the image evaluation value calculation process is realized, but also an OS (operating system; operating on the computer) based on the instruction of the program code. Basic software) or the like may perform a part or all of the actual processing, and the function of performing the image evaluation value calculation processing may be realized by the processing.

  Further, after the program code read from the storage medium is written in a memory provided in a function expansion card inserted into the computer or a function expansion unit connected to the computer, the function expansion is performed based on the instruction of the program code. There may be a case where the CPU or the like provided in the card or the function expansion unit performs part or all of the actual processing, and the function of performing the image evaluation value calculation processing is realized by the processing.

  In such an electronic computer, for example, software similar to that in a conventional image forming apparatus (multifunction machine) such as a copying application, a printer application, a facsimile (FAX) application, or a processing program for other applications is incorporated. . A control program for transmitting and receiving data to and from the outside via the network 9 is also incorporated.

  At this time, the program is provided as a file describing a program code for realizing the function of performing the image evaluation value calculation process. In this case, the program is not limited to being provided as a batch program file, and is a system configured by a computer. Depending on the hardware configuration, it may be provided as an individual program module. For example, it may be provided as add-in software incorporated in existing copying apparatus control software or printer control software (printer driver).

  FIG. 16 shows an example of a hardware configuration in which the image evaluation apparatus 10 is configured by software using a CPU and a memory, that is, the image evaluation apparatus 10 is realized by software using an electronic computer such as a personal computer. FIG.

  The computer system 900 constituting the image evaluation apparatus 10 includes data from a controller unit 901 and a predetermined storage medium such as a hard disk device, a flexible disk (FD) drive, a CD-ROM (Compact Disk ROM) drive, or a semiconductor memory controller. And a recording / reading control unit 902 for reading out and recording data. In particular, in the present embodiment, the hard disk device or other storage medium functions as the original image storage unit 12 or the input image storage unit 14 to store the original image data D10 and the like.

  The controller unit 901 includes a CPU (Central Processing Unit) 912, a ROM (Read Only Memory) 913 which is a read-only storage unit, and a RAM (Random Access) which can be written and read at any time and is an example of a volatile storage unit. Memory) 915 and RAM (described as NVRAM) 916 which is an example of a nonvolatile storage unit.

  In the above description, the “volatile storage unit” means a storage unit in a form in which the stored contents are lost when the power of the image output terminal 4 is turned off. On the other hand, the “nonvolatile storage unit” means a storage unit in a form that keeps stored contents even when the main power supply of the apparatus is turned off. Any memory device can be used as long as it can retain the stored contents. The semiconductor memory device itself is not limited to a nonvolatile memory device, and a backup power supply is provided to make a volatile memory device “nonvolatile”. You may comprise as follows. Further, the present invention is not limited to a semiconductor memory element, and may be configured using a medium such as a magnetic disk or an optical disk.

  The computer system 900 also includes an instruction input unit 903 having a keyboard, a mouse, and the like as a function unit that forms a user interface, and a display output unit 904 that presents a user with predetermined information such as a guidance screen and a processing result during operation. An image reading unit (scanner unit) 905 that reads an image to be processed, an image forming unit 906 that outputs a processed image in the image evaluation apparatus 10 to a predetermined output medium (for example, printing paper), and each functional unit And an interface unit 909 that performs an interface function therebetween.

  Examples of the interface unit 909 include a system bus 991 that is a transfer path of processing data (including image data) and control data, a scanner IF unit 995 that functions as an interface with the image reading unit 905, an image forming unit 906, and the like. It has a printer IF unit 996 that functions as an interface with other printers, and a communication IF unit 999 that mediates transfer of communication data with the network 9 such as the Internet. The scanner IF unit 995 and the communication IF unit 999 correspond to the input interface unit 11in, and the printer IF unit 996 corresponds to the output interface unit 11out. Note that the recording / reading control unit 902 also has a function of the input interface unit 11in that captures image data from a predetermined storage medium.

  The display output unit 904 includes a display device for presenting main information such as the read whole image and guidance information. The display device is preferably arranged in the vicinity of the instruction input unit 903 so as to efficiently perform a predetermined input operation while confirming the displayed information.

  The display output unit 904 includes, for example, a display control unit 942 and a display unit 944 made up of a CRT (Cathode Ray Tube) or LCD (Liquid Crystal Display). For example, the display control unit 942 displays guidance information, the entire image captured by the image reading unit 905, and the like on the display unit 944. Note that by using the display unit 944 having the touch panel 945 on the display surface, the instruction input unit 903 for inputting predetermined information with a fingertip or a pen can be configured.

  The computer system 900 is connected as an image reading unit corresponding to the image input device 70 of the above embodiment. The image input device 70 has the function of an image input terminal. For example, by using a full width array of CCD solid-state imaging devices, the image input device 70 irradiates light on the document sent to the reading position, thereby displaying an image on the document. Read and convert analog video signals of red R, green G and blue B representing the read image into digital signals. In particular, in the present embodiment, the read image is stored as input image data D20 in a hard disk device or other storage medium (or RAM 915).

  The computer system 900 is connected to an image forming unit corresponding to the image output device 50 of the above embodiment. This image output device 50 is an electrophotographic type, thermal type, thermal transfer type, ink jet type or similar conventional image obtained by, for example, representing an image represented by an image signal obtained by an image reading unit (image input device 70). Using the forming process, a visible image is formed (printed) on plain paper or thermal paper.

  Therefore, the image forming unit (image output device 50) includes, for example, an image processing unit 52 that generates print output data such as binary signals of yellow Y, magenta M, cyan C, and black K, and the image evaluation device 10. Is equipped with a raster output scan-based print engine 54 for operating as a digital printing system.

  In such a configuration, the CPU 912 controls the entire system via the system bus 991. The ROM 913 stores a control program for the CPU 912 and the like. The RAM 915 is configured by SRAM (Static Random Access Memory) or the like, and stores program control variables, data for various processes, and the like. In addition, the RAM 915 stores an electronic document (not only character data but also image data) acquired by a predetermined application program, and image data acquired by an image reading unit (image input device 70) provided in the own device. In addition, an area for temporarily storing electronic data obtained from the outside is included.

  For example, a program that causes a computer to execute an image evaluation value calculation process is distributed through a recording medium such as a CD-ROM. Alternatively, this program may be stored in the FD instead of the CD-ROM. In addition, an MO drive may be provided to store the program in the MO, or the program may be stored in another recording medium such as a nonvolatile semiconductor memory card such as a flash memory. Furthermore, the program may be downloaded and acquired from another server or the like via the network 9 such as the Internet, or may be updated.

  As a recording medium for providing the program, in addition to FD and CD-ROM, an optical recording medium such as DVD, a magnetic recording medium such as MD, a magneto-optical recording medium such as PD, a tape medium, and a magnetic medium. A semiconductor memory such as a recording medium, an IC card, or a miniature card can be used. Part or all of the image evaluation value calculation processing function in the image evaluation apparatus 10 can be stored in an FD or CD-ROM as an example of a recording medium.

  Also, the hard disk device stores data for various processes by the control program, temporarily stores a large amount of image data acquired by the image reading unit (image input device 70), print data acquired from the outside, and the like. Or the area to be. The hard disk device, FD drive, or CD-ROM drive is used, for example, for registering program data for causing the CPU 912 to execute processing such as content acquisition, address acquisition, or address setting by software. .

  It should be noted that a processing circuit 908 may be provided in which not all processing of each functional part related to the image evaluation value calculation processing of the image evaluation apparatus 10 is performed by software, but a part of these functional parts is performed by dedicated hardware. Good. In this case, as illustrated, an image quality element evaluation value calculation unit corresponding to a color signal conversion unit 984 corresponding to the color signal conversion unit 16 and an image quality element evaluation value calculation unit 20 (arbitrary functional units therein). 986 or the like may be provided individually.

  Although the mechanism performed by software can flexibly cope with parallel processing and continuous processing, the processing time becomes longer as the processing becomes complicated, so that a reduction in processing speed becomes a problem. On the other hand, it is possible to construct an accelerator system with a higher speed by using a hardware processing circuit. Even if the processing is complicated, the accelerator system can prevent a reduction in processing speed and can calculate a high throughput.

  As mentioned above, although this invention was demonstrated using embodiment, the technical scope of this invention is not limited to the range as described in the said embodiment. Various changes or improvements can be added to the above-described embodiment without departing from the gist of the invention, and embodiments to which such changes or improvements are added are also included in the technical scope of the present invention.

  Further, the above embodiments do not limit the invention according to the claims (claims), and all combinations of features described in the embodiments are not necessarily essential to the solution means of the invention. Absent. The embodiments described above include inventions at various stages, and various inventions can be extracted by appropriately combining a plurality of disclosed constituent elements. Even if some constituent requirements are deleted from all the constituent requirements shown in the embodiment, as long as an effect is obtained, a configuration from which these some constituent requirements are deleted can be extracted as an invention.

  For example, the specific mechanism of the above embodiment has been described by focusing on the streak defect as an image quality element. However, the present invention is not limited to this, and the image quality element related to other spatial frequencies, such as graininess and sharpness. In addition, a mechanism of acquiring a correction component used for correcting background noise with respect to a feature amount related to the image quality of interest in consideration of weighting of the spatial frequency of interest can be similarly applied.

  For example, in terms of granularity, if a component in the spatial frequency band similar to the granularity of interest exists in the surroundings as background noise, the granularity of the spatial frequency of interest will be confused with that, so the spatial frequency band of the same degree An image with a small background noise in the same spatial frequency band has a higher granularity of the spatial frequency of interest in terms of visibility than an image with a large background noise.

  In addition, in terms of sharpness, if a component of the spatial frequency band similar to the sharpness of interest exists in the surroundings as background noise, the sharpness of the spatial frequency of interest will be confused with it. From the viewpoint of visibility, the sharpness of the spatial frequency of interest is higher in an image with a small background noise in the same spatial frequency band than an image with a large background noise.

  In such a case, a correction component is obtained in consideration of weighting for the spatial frequency of interest, and correction is performed using the correction component for the feature quantity related to the image quality related to the spatial frequency of the image element of interest. By doing so, it is possible to accurately suppress the influence of background noise from the image quality factor of the spatial frequency of interest, and as a result, the evaluation value for evaluating the image quality related to the spatial frequency such as graininess and sharpness, It will be possible to find it so that it can be dealt with visually.

  In addition, when a streak defect is compared with an image quality (granularity or sharpness) related to other spatial frequencies, the streak defect is one of image defects generated depending on the apparatus-specific technology. Since the causal relationship between the defect and the specific inherent technology is relatively known, the evaluation is often performed by paying attention to a part of the streaks in the image, and in particular, when the evaluation value of the streak defect is obtained, The effect obtained by applying the mechanism is high.

  In the above-described embodiment, the method for evaluating an image output from an image output device such as a color printer has been described as an example. However, the output method is not limited to a color printer, but is output from another image output device such as a display, electronic paper, or a projector. Even when the image quality of a color image is evaluated, the mechanism described in the above embodiment can be similarly applied by acquiring image data by, for example, imaging the display surface with a television camera.

It is a block diagram showing a schematic structure of an image evaluation system provided with a first embodiment of an image evaluation apparatus according to the present invention. It is a block diagram explaining the schematic structure of a color signal conversion part. It is a figure which shows an example of the stripe defect which exists in the image for evaluation handled by this embodiment. It is a figure which shows an example of the brightness profile obtained in the one-dimensionalization process part. It is a figure which shows an example of the brightness difference profile obtained in the average line difference calculation part. It is a block diagram explaining the schematic structure of a streak feature-value calculation part. It is a block diagram explaining schematic structure of a spatial frequency band division part. It is FIG. (1) which shows an example of the brightness difference profile according to zone | band. It is FIG. (2) which shows an example of the brightness difference profile according to zone | band. It is a block diagram explaining the schematic structure of a stripe correction amount calculation part. It is a block diagram explaining the schematic structure of a stripe evaluation value calculation part. It is a figure explaining the result of having calculated the stripe evaluation value about the stripe defect of two kinds of images A and B by the image evaluation device of a 1st embodiment. It is a block diagram which shows schematic structure of the image evaluation system provided with 2nd Embodiment of the image evaluation apparatus which concerns on this invention. It is a block diagram explaining schematic structure of a visual spatial frequency characteristic correction | amendment part. It is a figure which shows an example of the brightness difference profile data calculated | required in the visual spatial frequency characteristic correction | amendment part. It is the figure which showed an example of the hardware constitutions in the case of implement | achieving an image evaluation apparatus like software using electronic computers, such as a personal computer.

Explanation of symbols

  DESCRIPTION OF SYMBOLS 1 ... Image evaluation system, 10 ... Image evaluation apparatus, 11out ... Output interface part, 11in ... Input interface part, 12 ... Original image memory | storage part, 14 ... Input image memory | storage part, 16 ... Color signal conversion part, 20 ... Image quality element evaluation Value calculation unit, 22 ... feature amount acquisition unit, 24 ... correction amount acquisition unit, 26 ... evaluation value acquisition unit, 50 ... image output device, 70 ... image input device, 164 ... input image data color conversion unit, 210 ... pre-processing 212, one-dimensionalization processing unit, 214 ... average line difference calculation unit, 216 ... visual spatial frequency characteristic correction unit, 270 ... streak feature amount calculation unit, 280 ... streak correction amount acquisition unit, 284 ... streak feature amount calculation unit 286: Spatial frequency band dividing unit, 288: Line correction amount calculating unit, 290: Line evaluation value calculating unit, 402: Discrete Fourier transform unit, 404: Visual characteristic filter multiplying unit, 406: Inverse discrete Fourier Conversion unit, 412 ... streak position detection unit, 414 ... streak intensity calculation unit, 422 ... discrete Fourier transform unit, 424 ... band filter multiplication unit, 426 ... inverse discrete Fourier transform unit, 432 ... standard deviation calculation unit, 434 ... streak space Frequency band setting unit, 436 ... weighting factor setting unit, 438 ... weighting calculation unit, 442 ... individual streak evaluation value calculation unit, 444 ... total streak evaluation value calculation unit

Claims (12)

An image evaluation method for evaluating image quality related to spatial frequency in an image,
Obtaining a feature quantity related to the image quality related to the spatial frequency of the image element of interest based on the input image data;
The input image data is frequency-expanded into a plurality of spatial frequency band components, and a background component for the image element of interest is extracted based on each spatial frequency band component subjected to the frequency expansion,
An image evaluation method comprising: obtaining an evaluation value for evaluating an image quality of the image element of interest based on the feature amount and the background component. The image evaluation method according to claim 1, wherein the background component is extracted such that the weight of the spatial frequency band component of interest is higher. An image evaluation apparatus for evaluating image quality related to spatial frequency in an image,
A feature amount acquisition unit that acquires a feature amount related to image quality related to the spatial frequency of the image element of interest based on input image data;
The input image data is frequency-expanded into a plurality of spatial frequency band components, a background component for the image element of interest is extracted on the basis of each spatial frequency band component subjected to the frequency expansion, and the extracted background component is the feature. A correction amount acquisition unit as a correction amount for the feature amount acquired by the amount acquisition unit;
An evaluation value acquisition unit that acquires an evaluation value for evaluating the image quality of the image element of interest based on the feature amount acquired by the feature amount acquisition unit and the correction amount acquired by the correction amount acquisition unit; An image evaluation apparatus comprising: The feature amount acquisition unit includes a streak feature amount calculation unit that calculates a streak feature amount having a relation with the strength of a streak defect,
The image evaluation apparatus according to claim 3, wherein the evaluation value acquisition unit includes a line evaluation value calculation unit that calculates a line evaluation value related to the strength of the line defect. The correction amount acquisition unit
A spatial frequency band dividing unit that divides input image data into a plurality of spatial frequency band components;
The image evaluation apparatus according to claim 3, further comprising: a correction amount calculation unit that calculates the correction amount based on a plurality of spatial frequency band components frequency-expanded by the spatial frequency band dividing unit. The spatial frequency band dividing unit is
A discrete Fourier transform unit for performing a discrete Fourier transform on the image;
A band filter multiplier for multiplying the spatial frequency component obtained by the discrete Fourier transform unit by a band filter;
An inverse discrete Fourier transform unit that performs an inverse discrete Fourier transform on spatial frequency components of a plurality of bands obtained by multiplying a bandpass filter by the bandpass filter multiplication unit. 5. The image evaluation apparatus according to 5. The correction amount calculation unit is an attention spatial frequency band setting unit that sets a spatial frequency band of interest in the feature amount acquired by the feature amount acquisition unit;
A band feature amount acquisition unit that acquires the feature amount of each spatial frequency band component while eliminating the influence of the strength of the feature amount acquired by the feature amount acquisition unit;
A weighting factor setting unit that sets a weighting factor for the feature quantity of each spatial frequency band component acquired by the band feature quantity acquisition unit, based on the spatial frequency band of interest set by the spatial frequency band setting unit of interest;
A weighting calculation unit that calculates a correction amount by performing a weighting operation on each spatial frequency band component acquired by the band feature amount acquisition unit using a weighting factor set by the weighting factor setting unit. The image evaluation apparatus according to claim 5, wherein: The image evaluation apparatus according to claim 7, wherein the band feature quantity acquisition unit acquires a standard deviation of a spatial frequency band component as a feature quantity of the spatial frequency band component. The weighting factor setting unit sets each weighting factor such that weighting is higher for a component of the spatial frequency band of interest set by the spatial frequency band of interest setting unit. Image evaluation device. The weighting calculation unit is configured to obtain a linear sum of each overlapped frequency band component obtained by multiplying each spatial frequency band component acquired by the band feature amount acquisition unit by a weighting factor set by the weighting factor setting unit. The image evaluation apparatus according to claim 7, wherein the correction amount is calculated. A visual spatial frequency characteristic correction unit that performs correction according to the spatial frequency characteristic of the visual system for the input image data;
The feature amount acquisition unit acquires the feature amount using the image data subjected to the correction by the visual spatial frequency characteristic correction unit,
The image evaluation apparatus according to claim 3, wherein the correction amount acquisition unit acquires the correction amount using the image data subjected to the correction by the visual spatial frequency characteristic correction unit. A program for performing image evaluation processing for evaluating image quality related to spatial frequency in an image using a computer,
A feature amount acquisition unit that acquires a feature amount related to image quality related to the spatial frequency of the image element of interest based on input image data;
The input image data is frequency-expanded into a plurality of spatial frequency band components, a background component for the image element of interest is extracted on the basis of each spatial frequency band component subjected to the frequency expansion, and the extracted background component is the feature. A correction amount acquisition unit as a correction amount for the feature amount acquired by the amount acquisition unit;
An evaluation value acquisition unit that acquires an evaluation value for evaluating the image quality of the image element of interest based on the feature amount acquired by the feature amount acquisition unit and the correction amount acquired by the correction amount acquisition unit. A program characterized by functioning.