JP2008192011A - Difference extracting device, image reader, difference extracting method, program and recording medium thereof - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide a difference extracting device capable of extracting the differences among images. <P>SOLUTION: This device comprises an image division processing part 32 which divides first image data as image data of a first image and second image data as image data of a second image into a plurality of blocks, respectively such that the respective blocks of both the image data correspond to each other according to a region on each image, an evaluation value calculation part 33 which calculates an evaluation value indicating the degree of difference between the first image data and the second image data for the blocks corresponding to each other in both the image data, and a difference extraction part 34 which extracts the difference of both the image data by comparing the evaluation value and a threshold value. <P>COPYRIGHT: (C)2008,JPO&INPIT

Description

  The present invention relates to a difference location extraction apparatus that extracts a difference location between images.

  Conventionally, in order to evaluate the performance (particularly output performance) of an image forming apparatus that forms an image on a sheet, an image formed on the sheet and a sample image prepared in advance are read by an image reading apparatus. The similarity (identity or difference) is determined by comparing image data of images. Besides the performance evaluation of the image forming apparatus, there are many scenes where it is necessary to determine the similarity between images.

  By the way, when determining the similarity between images by human eyes, the determination result may change depending on the evaluator, the determination time, etc., and the evaluation criteria tend to be unclear. In addition, an erroneous determination such as missing a difference may occur.

Therefore, Patent Document 1 proposes a similarity determination device for determining the similarity between a pair of digital images without using a human. The similarity determination device of Patent Document 1 creates a histogram indicating the relationship between image data and the frequency for each of a pair of digital images, and evaluation data (similarity between both images based on the histograms of both images). The S / N ratio indicating how close the other image is to one image) is calculated, and the similarity between both images is determined based on the evaluation data.
Japanese Patent Laying-Open No. 2003-58886 (Publication date: February 28, 2003)

  However, although the technique disclosed in Patent Document 1 can determine the similarity of the entire image, there is a problem in that a portion with a low similarity (difference portion) cannot be specified.

  The present invention has been made in view of the above-described problems, and a first object thereof is to provide a difference location extraction device, an image reading device, a difference location extraction method, and a program capable of extracting a difference location between images. And providing a recording medium thereof.

  In order to solve the above problem, the difference location extraction apparatus of the present invention acquires a first image data that is image data of a first image and a second image data that is image data of a second image. A difference location extraction apparatus that extracts a difference location between the first image and the second image based on the first image data and the second image data, the first image data acquired by the data acquisition unit and the first image data An image division processing unit that divides the two image data into a plurality of blocks such that each block of the first image data and each block of the second image data correspond to each other, and the first image data in both the image data And an evaluation value calculation unit that calculates an evaluation value indicating the degree of difference between corresponding blocks of the second image data and a memory that stores a threshold value for evaluating the degree of difference between the corresponding blocks The evaluation value calculated by the evaluation value calculation unit and the threshold value stored in the storage means are compared, and a block whose degree of difference indicated by the evaluation value is greater than or equal to the threshold value or a block larger than the threshold value is different. It is characterized by having a different part extracting unit that extracts as a part.

  According to the above configuration, the image division processing unit corresponds to the first image data and the second image data acquired by the data acquisition unit so that each block of the first image data and each block of the second image data correspond to each other. Each is divided into a plurality of blocks. Then, the evaluation value calculation unit calculates an evaluation value indicating the degree of difference between corresponding blocks of the first image data and the second image data in both image data, and the difference part extraction unit calculates the evaluation value calculation unit. The evaluated value is compared with the threshold value stored in the storage means, and a block whose degree of difference indicated by the evaluation value is equal to or greater than the threshold value or a block larger than the threshold value is extracted as a difference portion. Thereby, the difference part (block containing a difference part) of a 1st image and a 2nd image can be extracted.

  Further, the evaluation value calculation unit may be configured to calculate the evaluation value based on color intensity data of all colors included in both the image data. Here, the color intensity data of all the colors included in both the image data means, for example, the color intensity data of three colors of RGB when the both image data have RGB color intensity data, When both image data have CMY color intensity data, it means CMY three color intensity data, and when both image data have a gray scale as color intensity data, it means this gray scale.

  According to the above configuration, the degree of difference between the first image data and the second image data is appropriately determined by calculating evaluation values based on the color intensity data of all colors included in both image data. be able to.

  Further, the evaluation value calculation unit may use the SN ratio of the other image data when the one image data is a target value as the evaluation value. The SN ratio is an SN ratio used in quality engineering. When one image data is set as a target value, an effective component included in the other image data (a component that substantially matches the one image data). And an invalid component (a component different from one image data).

  According to the above configuration, when the color intensity data included in one image data is the target value, the SN ratio of the color intensity data included in the other image data is used as the evaluation value, and the first image data in each block The degree of difference from the second image data is evaluated. Thereby, the influence of noise can be reduced and the degree of difference between the blocks can be appropriately evaluated.

In addition, the evaluation value calculation unit determines that the color intensity data included in one image data is M _{c, d} , and the color intensity data included in the other image data is y _{c, d} (where M _{c, d} and the above _{c in} y _{c, d} is a natural number assigned sequentially from 1 for each color type, and d is a natural number assigned sequentially from 1 for each pixel), y _{c, d} −β × M _c, the maximum absolute value of _d (provided that the β is y _c, the ideal value of _d is M _c, constant of proportionality when to be proportional to _d) may be used as the evaluation value.

According to the above configuration, the degree of difference between the first image data and the second image data in each block is evaluated using the maximum absolute value of y _{c, d} −β × M _{c, d} as an evaluation value. Thereby, the influence of noise can be reduced and the degree of difference between the blocks can be appropriately evaluated.

  Further, the color intensity data of each pixel included in the first image data of each block and the color intensity data of each pixel included in the second image data of each block are averaged for each area composed of a plurality of pixels. An averaging processing unit may be provided, and the evaluation value calculation unit may calculate the evaluation value based on color intensity data averaged for each area.

  According to the above configuration, the evaluation value calculation process can be simplified by calculating the evaluation value based on the color intensity data averaged for each area. In addition, it is possible to reduce the influence of noise caused by positional deviation at the time of image reading, paper quality variation, and the like, and appropriately evaluate the degree of difference between blocks.

  The image conversion unit converts the image size by adding image data of a frame image to the first image data and / or the second image data, and the evaluation value calculation unit adds the image data after the size conversion to the image data after the size conversion. The evaluation value may be calculated based on the above.

  According to the above configuration, the image conversion unit converts the image size of the image data by adding the image data of the frame image to the first image data and / or the second image data, and the evaluation value calculation unit An evaluation value is calculated based on the image data after size conversion. Thereby, for example, even when the size of the first image is different from the size of the second image, the alignment can be performed based on the central portion of both images.

  Further, the color intensity data of each pixel included in the first image data of each block and the color intensity data of each pixel included in the second image data of each block are averaged for each area composed of a plurality of pixels. An averaging processing unit, wherein the image dividing unit divides the first image data into a plurality of blocks arranged in a matrix in a row direction and a column direction, and the averaging processing unit divides the blocks into the blocks The color intensity data is averaged for each of the divided areas divided into a plurality of areas arranged in the row direction and the column direction, and the image conversion unit has the number of pixels in the row direction and the number of blocks in the row direction. It is a multiple of the product of the number of pixels in the row direction in each area, and the number of pixels in the column direction is a multiple of the product of the number of blocks in the column direction and the number of pixels in the column direction in each area. Up It may be configured to add a frame image.

  According to said structure, since the size of each block after a division | segmentation can be matched, the weight of the information amount per pixel in each block can be made uniform.

  Moreover, it is good also as a structure provided with the notification means to notify the said difference location. The notification means is not particularly limited as long as it can be used to notify the difference portion in a method that can identify the difference portion. For example, a display means for displaying the difference portion, and printing the difference portion on a sheet of paper. An image forming means for outputting, a sound output means for notifying different portions by sound, and the like can be used.

  According to said structure, since the notification means which notifies a difference location is provided, a user can be made to recognize a difference location.

  An image reading apparatus according to the present invention is an image reading apparatus including a document reading unit that reads a document image and generates image data, and any one of the above-described difference location extraction devices, and includes the first image data and the above-described first image data. At least one of the second image data is image data generated by the document reading means.

  According to the above configuration, it is possible to extract a difference between the document image read by the document reading unit and another image different from the document image.

  In order to solve the above-described problem, the difference location extraction method of the present invention is based on first image data that is image data of a first image and second image data that is image data of a second image. Is a difference location extraction method for extracting a difference location between the first image data and the second image, an acquisition step of acquiring the first image data and the second image data, and the first image data and the second image data acquired in the acquisition step. Are divided into a plurality of blocks so that each block of the first image data and each block of the second image data correspond to each other, and the first image data and the second image in both the image data The evaluation value calculation step for calculating an evaluation value indicating the degree of difference between corresponding blocks of data, the evaluation value calculated in the evaluation value calculation step and a threshold value stored in advance in the storage means are compared, The computer for the different part extraction step of extracting a larger block than the block or a threshold degree is equal to or more than a threshold difference evaluation value indicates a different part is characterized by extracting a different part.

  According to the above method, the first image data and the second image data are acquired in the acquisition step, and the first image data and the second image data acquired in the acquisition step are acquired in the image division processing step. Each block is divided into a plurality of blocks such that each block of the second image data corresponds to each other. Then, the evaluation value calculation unit calculates an evaluation value indicating the degree of difference between the corresponding blocks of the first image data and the second image data in both image data, and is calculated in the evaluation value calculation step in the difference portion extraction step. Comparing the evaluated value and a threshold value stored in advance in the storage means, and extracting a block where the degree of difference indicated by the evaluation value is equal to or greater than the threshold value or a block larger than the threshold value as a different point. The difference is extracted depending on the computer to be used. Thereby, the difference part (block containing a difference part) of a 1st image and a 2nd image can be extracted by a computer.

  In addition, the said different location extraction apparatus may be implement | achieved by the computer, In this case, the program which implement | achieves the said different location extraction apparatus with a computer by operating a computer as said each part, and it recorded Computer-readable recording media are also included in the scope of the present invention.

  As described above, the difference location extraction apparatus according to the present invention is configured so that each block of the first image data and each block of the second image data are obtained by combining the first image data and the second image data acquired by the data acquisition unit. An image division processing unit that divides each block into a plurality of blocks so as to correspond to each other, and an evaluation value that calculates an evaluation value that indicates the degree of difference between corresponding blocks of the first image data and the second image data in both the image data The calculation unit, the storage unit storing a threshold value for evaluating the degree of difference between the corresponding blocks, and the evaluation value calculated by the evaluation value calculation unit and the threshold value stored in the storage unit are compared. And a difference location extraction unit that extracts a block whose degree of difference indicated by the evaluation value is greater than or equal to a threshold value or a block larger than the threshold value as a difference location.

  Therefore, the difference between the first image and the second image can be extracted.

  The image reading apparatus of the present invention is an image reading apparatus including an original reading unit that reads an original image and generates image data, and any one of the above-described difference location extraction apparatuses, and the first image data At least one of the second image data is image data generated by the document reading means.

  Therefore, it is possible to extract a difference between the original image read by the original reading unit and another image different from the original image.

  The difference location extraction method of the present invention includes an acquisition step of acquiring the first image data and the second image data, and the first image data and the second image data acquired in the acquisition step of the first image data. An image dividing step for dividing each block and each block of the second image data so as to correspond to each other, and a difference between corresponding blocks of the first image data and the second image data in the both image data The evaluation value calculation step for calculating the evaluation value indicating the degree of the evaluation value, the evaluation value calculated in the evaluation value calculation step and the threshold value stored in advance in the storage means are compared, and the degree of difference indicated by the evaluation value is the threshold value. A difference part is extracted by the computer which performs the difference part extraction process which extracts the block larger than the above block or a threshold value as a difference part.

  Therefore, the difference between the first image and the second image can be extracted by the computer.

Embodiment 1
An embodiment of the present invention will be described. In the present embodiment, an example of a digital color multifunction peripheral (MFP) provided with the difference location extraction apparatus of the present invention will be described. However, the present invention is not limited to this, and the difference location extraction apparatus of the present invention is an image forming apparatus that forms an image according to image data on a sheet of paper, resin, etc., density correction, color correction, and contrast correction for the image data. It may be provided in an image processing apparatus that performs various corrections such as an image reading apparatus that reads an image and acquires image data. Further, the difference location extraction apparatus of the present invention is not limited to the configuration provided in the multifunction peripheral, the image forming apparatus, the image processing apparatus, the image reading apparatus, and the like, but may be realized as a difference location extraction apparatus alone.

(1-1. Configuration of Digital Color Multifunction Machine 1)
FIG. 2 is a block diagram showing a schematic configuration of a digital color multifunction peripheral (difference extraction apparatus, image processing apparatus, image forming apparatus, image reading apparatus) 1 according to the present embodiment. The digital color multifunction peripheral 1 has a copy function, a printer function, a facsimile transmission function, a scanner function, a scan to e-mail function, and the like.

  As shown in FIG. 2, the digital color multifunction peripheral 1 includes a color image input device 2, a color image processing device 3, a color image output device 4, a communication device 5, an operation panel 6, and a storage unit 7.

  A color image input device (image reading device, input unit, color intensity acquisition unit) 2 includes a scanner unit (not shown) including a device that converts optical information into an electrical signal such as a CCD (Charge Coupled Device). Then, the reflected light image from the original is output to the color image processing device 3 as an analog signal of RGB (R: red, G: green, B: blue). The color image input device 2 may acquire image data by camera shooting.

  A color image processing apparatus (difference part extracting apparatus) 3 includes an A / D conversion unit 11, a shading correction unit 12, a similarity determination unit 13, an input tone correction unit 14, a region separation processing unit 15, a color correction unit 16, and a black color. A generation under color removal unit 17, a spatial filter processing unit 18, an output tone correction unit 19, and a tone reproduction processing unit 20 are provided. An analog signal output from the color image input device 2 to the color image processing device 3 is converted into an A / D conversion unit 11, a shading correction unit 12, a similarity determination unit 13, and an input tone correction unit in the color image processing device 3. 14, the region separation processing unit 15, the color correction unit 16, the black generation and under color removal unit 17, the spatial filter processing unit 18, the output gradation correction unit 19, and the gradation reproduction processing unit 20 are sent in this order, and CMYK digital color The signal is output to the color image output device 4 as a signal. In the middle from the A / D conversion unit 11 to the gradation reproduction processing unit 20, writing / reading of an analog signal or a digital color signal to the storage unit 7 may be appropriately performed.

  The A / D (analog / digital) converter 11 converts RGB analog signals into digital signals.

  The shading correction unit 12 performs a process for removing various distortions generated in the illumination system, the imaging system, and the imaging system of the color image input apparatus 2 on the digital RGB signal sent from the A / D conversion unit 11. Is. Further, the shading correction unit 12 performs a process of converting color balance adjustment and a signal such as a density signal that can be easily handled by the image processing system employed in the color image processing apparatus 3.

  The similarity determination unit (difference part extraction device) 13 is an image corresponding to the evaluation target image (first image) and the sample image data (second image) corresponding to the evaluation target image data (first image data). Based on the sample image (second image), a difference portion (low similarity portion) between the evaluation target image and the sample image is extracted. As a result, the difference between the evaluation target image and the sample image can be determined without involving human subjectivity. The similarity determination unit 13 outputs the input RGB signal to the subsequent input tone correction unit 14 as it is. Details of the similarity determination unit 13 will be described later.

  The input tone correction unit 14 performs image quality adjustment processing such as removal of background color (background color density component: background density) and contrast on the RGB signal from which various distortions have been removed by the shading correction unit.

  The region separation processing unit 15 separates each pixel in the image data into one of a character region, a halftone dot region, and a photo region from the RGB signal. Based on the separation result, the region separation processing unit 15 generates a region identification signal indicating which region the pixel belongs to, the color correction unit 16, the black generation and under color removal unit 17, the spatial filter processing unit 18, and the gradation reproduction. The output signal is output to the processing unit 20, and the input signal output from the input tone correction unit 14 is output to the subsequent color correction unit 16 as it is.

  The color correction unit 16 performs a process of removing color turbidity based on spectral characteristics of CMY (C: cyan, M: magenta, Y: yellow) color materials including unnecessary absorption components in order to realize faithful color reproduction. Is.

  The black generation and under color removal unit 17 generates black (K) signals from the CMY three-color signals after color correction, and subtracts the K signals obtained by black generation from the original CMY signals to generate new CMY signals. The process to generate is performed. As a result, the CMY three-color signal is converted into a CMYK four-color signal.

  The spatial filter processing unit 18 performs spatial filter processing using a digital filter on the image data of the CMYK signal input from the black generation and under color removal unit 17 to correct the spatial frequency characteristics. As a result, blurring of the output image and deterioration of graininess can be reduced. Similar to the spatial filter processing unit 18, the gradation reproduction processing unit 20 also performs predetermined processing on the image data of the CMYK signal based on the region identification signal.

  For example, the region separated into characters by the region separation processing unit 15 has a high frequency enhancement amount in the sharp enhancement processing in the spatial filter processing by the spatial filter processing unit 18 in order to improve the reproducibility of black characters or color characters. Increased. At the same time, the tone reproduction processing unit 20 selects binarization or multi-value processing on a high-resolution screen suitable for high-frequency reproduction.

  Further, with respect to the region separated into halftone dot regions by the region separation processing unit 15, the spatial filter processing unit 18 performs low-pass filter processing for removing the input halftone component. The output tone correction unit 19 performs an output tone correction process for converting a signal such as a density signal into a halftone dot area ratio that is a characteristic value of the color image output device 4. Then, gradation reproduction processing (halftone generation) is performed so that the image is finally separated into pixels and each gradation is reproduced. For the region separated into photographs by the region separation processing unit 15, binarization or multi-value processing is performed on the screen with an emphasis on gradation reproducibility.

  The image data subjected to the above-described processes is temporarily stored in a storage device (not shown), read out at a predetermined timing, and input to the color image output device 4.

  The color image output device 4 outputs the image data input from the color image processing device 3 onto a recording material (for example, paper). The configuration of the color image output device 4 is not particularly limited, and for example, a color image output device using an electrophotographic method or an inkjet method can be used.

  The communication device (input unit, color intensity acquisition unit) 5 performs data communication with other devices (for example, personal computers, server devices, other digital multi-function peripherals, etc.) that are communicably connected via a network.

  The operation panel 6 includes, for example, a display unit such as a liquid crystal display and setting buttons (none of which are shown), and information corresponding to an instruction from a main control unit (not shown) of the digital color multifunction peripheral 1 is described above. While displaying on a display part, the information input from a user via the said setting button is transmitted to the said main control part. The main control unit includes, for example, a CPU (Central Processing Unit) and the like, and is based on programs and various data stored in a ROM (not shown) and the like, information input from the operation panel 6, and the like. To control the operation.

  The storage unit (storage means) 7 is a storage means for storing various information used in the color image processing apparatus 3.

(1-2. Configuration of similarity determination unit 13)
FIG. 1 is a block diagram illustrating a schematic configuration of the similarity determination unit 13. As shown in this figure, the similarity determination unit 13 includes a color intensity data acquisition unit 31, an image division processing unit 32, an evaluation value calculation unit 33, a different part extraction unit 34, a different part notification unit 35, and a similarity evaluation unit 36. It has.

  The color intensity data acquisition unit (data acquisition unit) 31 acquires R, G, B color image data (color intensity data) input from the shading correction unit 12. In this embodiment, R, G, B color image data (color intensity data) is input to the similarity determination unit 13, but the present invention is not limited to this. For example, C, M, Y color The configuration may be such that image data (color intensity data) is input, or monochrome image data (color intensity data) such as grayscale may be input. The color intensity data acquisition unit 31 generates R, G, B color image data, C, M, Y color image data, or monochrome image data such as gray scale based on data input from the outside. It is good also as composition to do.

  The color intensity data acquisition unit 31 once stores the acquired data in the storage unit 7. The storage format is not particularly limited as long as the color intensity can be stored. For example, a BMP format, a TIFF format, or the like can be used.

  In the present embodiment, the difference between the evaluation target image data read from the evaluation target image by the color image input device (data acquisition unit) 2 and the sample image data read from the sample image by the color image input device 2 is extracted. An example will be described, but the present invention is not limited to this. For example, the evaluation target image data and / or sample image data may be acquired by communication via the communication device 5 (data acquisition means), and read from various storage media by a data reading device (data acquisition means) (not shown). You may get it at

  The image division processing unit 32 includes a plurality of pieces of evaluation target image data and sample image data so that each block of the evaluation target image data and each block of the sample image data correspond to each other according to the area on each image. Divide into blocks. For example, as shown in FIG. 3, the evaluation target image data and the sample image data are divided into vertical direction (column direction) t block × horizontal direction (row direction) s block (s and t are integers in which at least one is 2 or more. And the other is an integer greater than or equal to 1. Note that s = t may be satisfied, or s ≠ t may be satisfied). Further, the image division processing unit 32 causes the storage unit 7 to store the number of divisions in the vertical and horizontal directions of the evaluation object image data and the sample image data. The number of divisions in the vertical direction and the horizontal direction may be set in advance, or may be designated by the user via the operation panel 6. Further, the number of divisions corresponding to the image size, resolution, image type, and the like of the evaluation object image data and the sample image data is stored in the storage unit 7 in advance, and the image division processing unit 32 performs the evaluation object image data and the sample image data. The image size, resolution, image type, and the like may be determined, and an appropriate number of divisions may be selected according to the determination result.

  The evaluation value calculation unit 33 calculates an evaluation value for evaluating the similarity (degree of difference) of each block of the evaluation target image data and the sample image data, and stores the calculation result in the storage unit 7. A method for calculating the evaluation value will be described later.

  The difference part extraction unit 34 compares the evaluation value of each block calculated by the evaluation value calculation unit 33 with the threshold value TH1 stored in advance in the storage unit 7, and the degree of difference between the two images is greater than or equal to the threshold value TH1. A block larger than TH1 is extracted as a different part (low similarity part). The evaluation value may be an index that increases the degree of difference as the value increases, or may be an index that decreases the degree of difference as the value increases. In any case, it is only necessary to determine whether or not the degree of difference is greater than or equal to the threshold value TH1 (or greater than the threshold value TH1) according to the characteristics of the evaluation value.

  The difference part notifying unit 35 causes the display unit of the operation panel 6 to display information for specifying the block extracted as the difference part by the difference part extracting unit 34. For example, as shown in FIG. 4, the difference part notifying unit 35 displays an evaluation target image and inverts the color of the difference part on the image (or highlights or surrounds it with a frame). May be) In addition, text information for identifying the difference portion (for example, what row and column in which the difference portion is a block) may be displayed. In addition, information (image or text) for specifying the difference portion may be printed on paper and output, or the difference portion may be notified by voice output from a speaker or the like (not shown). The difference notification unit 35 sends information specifying the difference extracted by the difference extraction unit 34 to the main control unit of the digital color multifunction peripheral 1, and the main control unit controls each part of the digital color multifunction peripheral 1. The notification may be displayed on the operation panel 6 based on this information, printed on paper, or output by voice.

  The similarity evaluation unit 36 calculates the similarity of the evaluation target image to the sample image (similarity of the entire image). The similarity calculation method is not particularly limited. For example, the similarity evaluation unit 36 calculates, as the similarity, the ratio of the number of blocks that are not extracted as different portions by the evaluation value calculation unit 33 with respect to the total number of blocks. Alternatively, the similarity may be calculated based on the average value AVE of the evaluation values of each block and the threshold value TH2 stored in the storage unit 7 (for example, similarity = 1− | (AVE−TH2) / TH2 | May be used). Note that the similarity evaluation unit 36 may be omitted when it is not necessary to evaluate the similarity of the evaluation target image data to the sample image data.

(Example 1 of evaluation value calculation method)
The evaluation value calculation unit 33 calculates the SN ratio η based on the following formulas (1) to (10), and stores the calculation result in the storage unit 7 as the evaluation value.

Total degrees of freedom f _T = k × m (2)

Variation of proportional term S _β = L ² / r (5)
Degree of freedom of proportional term f _β = 1 (6)
Error variation S _e = S _T -S _β (7)
Degree of freedom of error f _e = f _T −f _β (8)
Error variance V _e = S _e / f _e (9)
SN ratio η = 10 × log ₁₀ ((1 / r) × (S _β −V _e ) / V _e ) (10)
In the above equations (1) and (4), y is the color intensity data of each pixel included in the evaluation target image data. Further, the subscript c when y _{c, d} is a natural number assigned sequentially from 1 according to the type of color. For example, when there are three color intensity data of RGB, the color intensity data of R is c = 1, the color intensity data of G is c = 2, and the color intensity data of B is c = 3. In the above formulas (1) and (4), m represents the maximum value of c, that is, the number of types of colors. In this embodiment, it is assumed that color intensity data for all colors in the evaluation target image data is used. Therefore, for example, m = 3 in the case of color intensity data of three colors of RGB or CMY, and m = 1 in the case of color intensity data of one color such as gray scale. The subscript d is a numerical value for specifying a pixel. For example, in the case of 6 × 8 image data, a serial number of 1 to 8 is assigned to each pixel from the left to the right of the top line (pixel row), and the left to the right of the second line. A serial number of 9 to 16 is assigned toward the end, and thereafter, serial numbers are similarly assigned up to the last line. In the above formulas (1) and (4), k represents the maximum value of d, that is, the number of pixels (number of color intensity data). For example, in the case of 6 × 8 image data, k = 48. Therefore, for example, the intensity data of the _16th pixel of G is y _2,16 .

  M in the above formulas (3) and (4) is color intensity data of each pixel included in the sample image data. The subscript is the same as the subscript y described above.

(Example 2 of evaluation value calculation method)
You may change Formula (10) in an above-described evaluation value calculation method into following formula (10 ').

SN ratio η = 10 × log ₁₀ ((S _β −V _e ) / V _e ) (10 ′)
(Example 3 of evaluation value calculation method)
Instead of the above-described evaluation value calculation method, the SN ratio η may be calculated based on the following formulas (11) to (26), and the calculation result may be stored in the storage unit 7 as the evaluation value.

Total degrees of freedom f _T = k × 2 × m (12)

Variation of the proportional term _{S β = (L 1 + L} 2) 2 / (2 × r) ··· (16)
Degree of freedom of proportional term f _β = 1 (17)
Fluctuation due to difference in proportionality constant S _{N × β} = (L ₁ ² + L ₂ ² ) / r−S _β (18)
Degree of freedom due to difference of proportionality constant _{fN × β} = n−1 = 2-1 = 1) (19)
Error variation S _e = S _T −S _β −S _{N × β} (20)
Degree of freedom of error f _e = f _T −f _β −f _{N × β} (21)
Fluctuation of total error S _N = S _T −S _β = S _{N × β} + S _e (22)
Of the total error degree of freedom _{f N = f T -f β =} f N × β + f e ··· (23)
Error variance V _e = S _e / f _e (24)
Total error variance V _N = S _N / f _N (25)
SN ratio _{η = 10 × log 10 ((} S β -V e) / V N) ··· (26)
(Example 4 of evaluation value calculation method)
Instead of the above-described evaluation value calculation method, y _{c, d} −β × M _{c, where} y _{c, d is} an ideal value y _{c, d} = β × M _{c, d} (where β is a proportional constant) _{. A} deviation value Z that is the maximum absolute value of _d may be calculated, and the calculation result may be used as the evaluation value.

  For example, the deviation value Z may be calculated based on the following equations (27) to (30), and the calculation result may be stored in the storage unit 7 as the evaluation value.

Inclination β = L / r (29)
Deviation value Z = | _{Zc, d} | _max = | yc _{, d−} β × _{Mc, d} | _max (30)
(Example 5 of evaluation value calculation method)
Instead of the evaluation value calculation method described above, the deviation value Z may be calculated based on the following equations (31) to (35), and the calculation result may be stored in the storage unit 7 as the evaluation value.

Inclination β = (L ₁ + L ₂ ) / (2 × r) (34)
Deviation value Z = | Z _{c, d} | _max = | y _{c, d} −β × M _{c, d} | _max (35)
(Specific example of evaluation value calculation)
Next, a specific example of the evaluation value calculation process will be described. Here, a case will be described in which one block of the evaluation target image data and the sample image data is composed of four pixels (pixels) each having image data (R, G, B color intensity data) shown below.

Sample image color intensity data M
Pixel 1: R = 146, G = 33, B = 172
Pixel 2: R = 105, G = 162, B = 231
Pixel 3: R = 94, G = 248, B = 55
Pixel 4: R = 234, G = 42, B = 84
Color intensity data y of the image to be evaluated
Pixel 1: R = 140, G = 38, B = 180
Pixel 2: R = 95, G = 188, B = 241
Pixel 3: R = 80, G = 252, B = 57
Pixel 4: R = 230, G = 48, B = 86
First, the case where SN ratio (eta) calculated by said Formula (1)-(10) is calculated as an evaluation value is demonstrated.

In this case, from the above formula (1), the total variation S _T = y _1,1 ² + y _1,2 ² + y _1,3 ² + y _1,4 ² + y _2,1 ² + y _2,2 ² + y _2,3 ² + Y _2,4 ² + y _3,1 ² + y _3,2 ² + y _3,3 ² + y _3,4 ² = 140 ² +95 ² +80 ² +230 ² +38 ² +188 ² +252 ² +48 ² +180 ² +241 ² +57 ² +86 ² = 291647,
From the above equation (2), the total degrees of freedom f _T = 4 × 3 = 12.
From the above formula (3), the effective divisor r = M _1,1 ² + M _1,2 ² + M _1,3 ² + M _1,4 ² + M _2,1 ² + M _2,2 ² + M _2,3 ² + M _2,4 ² + M _3,1 ² + M _3,2 ² + M _3,3 ² + M _3,4 ² = 146 ² +105 ² +94 ² +234 ² +33 ² +162 ² +248 ² +42 ² +172 ² +231 ² +55 ² +84 ² = 279560 ,
From the above formula (4), L = M _1,1 × y _1,1 + M _1,2 × y _1,2 + M _1,3 × y _1,3 + M _1,4 × y _1,4 + M _2,1 × y _2,1 + M _2,2 xy _2,2 + M _2,3 xy _2,3 + M _2,4 xy _2,4 + M _3,1 xy _3,1 + M _3,2 xy _3,2 + M _3,3 × y _3,3 + M _3,4 × y _3,4 = 146 × 140 + 105 × 95 + 94 × 80 + 234 × 230 + 33 × 38 + 162 × 188 + 248 × 252 + 42 × 48 + 172 × 180 + 231 × 241 + 55 × 57 + 84 × 86 = 284967
From the above equation (5), the proportional term variation S _β = L ² / r = 284967 ² /279560=2904788.577,
From the above equation (6), the degree of freedom of proportional term f _β = 1,
From the above equation (7), the error variation S _e = S _T −S _β = 291647-290478.577 = 1168.423,
From the above formula (8), the degree of freedom of error f _e = f _T −f _β = 12-1 = 11,
From the above equation (9), since error variance V _e = S _e / f _e = 1168.423 / 11 = 106.22, from the above equation (10),
SN ratio η = 10 × log ₁₀ ((1 / r) × (S _β −V _e ) / V _e ) = 10 × log ₁₀ ((1/279560) × (2904788.577-106.22) / 106. 22) = − 20.097.

  Therefore, the SN ratio (evaluation value) η for this block is −20.097.

  Next, the case where the deviation value Z calculated by the above equations (27) to (30) is calculated as the evaluation value will be described.

From the above formula (27), the effective divisor r = M _1,1 ² + M _1,2 ² + M _1,3 ² + M _1,4 ² + M _2,1 ² + M _2,2 ² + M _2,3 ² + M _2,4 ² + M _3,1 ² + M _3,2 ² + M _3,3 ² + M _3,4 ² = 146 ² +105 ² +94 ² +234 ² +33 ² +162 ² +248 ² +42 ² +172 ² +231 ² +55 ² +84 ² = 279560 ,
From the above equation (28), L = M _1,1 × y _1,1 + M _1,2 × y _1,2 + M _1,3 × y _1,3 + M _1,4 × y _1,4 + M _2,1 × y _2,1 + M _2,2 xy _2,2 + M _2,3 xy _2,3 + M _2,4 xy _2,4 + M _3,1 xy _3,1 + M _3,2 xy _3,2 + M _3,3 × y _3,3 + M _3,4 × y _3,4 = 146 × 140 + 105 × 95 + 94 × 80 + 234 × 230 + 33 × 38 + 162 × 188 + 248 × 252 + 42 × 48 + 172 × 180 + 231 × 241 + 55 × 57 + 84 × 86 = 284967
From the above equation (29), β = L / r = 284967/279560 = 1.019, so the color intensity deviation value Zc, d for each pixel is as follows.

_{Z 1,1 = | Z 1,1 | =} | 140-1.019 × 146 | = 8.824
_{Z 1,2 = | Z 1,2 | =} | 95-1.019 × 105 | = 12.031
_{Z 1,3 = | Z 1,3 | =} | 80-1.019 × 94 | = 15.818
_{Z 1,4 = | Z 1,4 | =} | 230-1.019 × 234 | = 8.526
_{Z 2,1 = | Z 2,1 | =} | 38-1.019 × 33 | = 4.362
_{Z 2,2 = | Z 2,2 | =} | 188-1.019 × 162 | = 22.867
_{Z 2,3 = | Z 2,3 | =} | 252-1.019 × 248 | = 0.797
_{Z 2,4 = | Z 2,4 | =} | 48-1.019 × 42 | = 5.188
Z _3,1 = | Z _3,1 | = | 180-1.019 × 172 | = 4.673
Z _3,2 = | Z _3,2 | = | 241-1.019 × 231 | = 5.532
_{Z 3,3 = | Z 3,3 | =} | 57-1.019 × 55 | = 0.936
Z _3,4 = | Z _3,4 | = | 86−1.019 × 84 | = 0.375
Therefore, the deviation value Z = | Z _{c, d} | _max = 22.867 from the above equation (30).

(Example 1 of setting method of threshold value TH1)
Next, an example of a method for setting the threshold TH1 used when the different part extraction unit 34 extracts a different part will be described.

First, sample image data and evaluation target image data including differences from the sample image data are prepared. For example, the sample image data shown in FIG. 5A and the evaluation target image data shown in FIG. 5B are prepared. In the example of FIGS. 5A and 5B, the block in the upper right (vertical 4 and horizontal 4 positions shown in the figure) has a different portion (low similarity), while other blocks are There are no differences (high similarity).
In addition, the broken line shown in FIG. 5A and FIG. 5B is for explaining the block, and is not included in the sample image and the evaluation target image.

  Next, the sample image data and the evaluation target image data are divided into a predetermined number of blocks (here, 4 × 4). Then, an S / N ratio and / or a deviation value is calculated for each block.

  For example, it is assumed that the S / N ratio and the deviation value for each block are the values shown in the table below.

  Regarding the S / N ratio, the greater the S / N ratio, the higher the similarity, and the smaller the S / N ratio, the lower the similarity. For this reason, the maximum value of the S / N ratio (in the case of Table 1, −22.54) in a block having a different portion (low similarity) and the S / N ratio in a block having no different portion (high similarity). A threshold value may be set between the minimum value (in the case of Table 1, −20.44).

  In addition, what is necessary is just to set how a threshold value is set within the said range according to a use. For example, when it is desired to reduce the frequency at which a block with a different portion is overlooked (the frequency at which it is erroneously determined that the block has a different portion even though it is a block with a different portion), the threshold is set so that there is no different portion. The minimum value of the S / N ratio in the block (in the case of Table 1, 20.44) may be set to a value close thereto. In addition, when it is desired to reduce the frequency of erroneously determining that the block has a different portion even though the block has a different portion, the threshold value is set to the maximum value of the S / N ratio in the block having the different portion ( In the case of Table 1, it may be set to -22.54) or a value close thereto.

  On the other hand, regarding the deviation value, the smaller the deviation value, the higher the similarity, and the larger the deviation value, the lower the similarity. For this reason, a threshold value is set between the minimum value of the deviation value in the block having the difference portion (24.2 in the case of Table 1) and the maximum value of the deviation value in the block having no difference portion (22.3 in the case of Table 1). Should be set.

  As in the case of the S / N ratio, how to set the threshold value within the above range may be set according to the application. For example, when it is desired to reduce the frequency at which a block with a different portion is overlooked (the frequency at which it is erroneously determined that the block has a different portion even though it is a block with a different portion), the threshold is set so that there is no different portion. The maximum deviation value in the block (22.3 in the case of Table 1) or a value close thereto may be used. Further, when it is desired to reduce the frequency of erroneously determining that the block has a different portion even though it is a block having a different portion, the threshold is set to the minimum value of the deviation value in the block having the different portion or close to it. Set it to a value.

  The threshold value may be set for each type of image. For example, a threshold value is prepared in advance for each type, such as an image consisting only of characters, an image consisting only of photographs or drawings, or an image including characters and photographs or drawings, and stored in the storage unit 7. Also good. In this case, the digital color multifunction peripheral 1 is provided with a determination unit that determines the type (image type) of the evaluation target image data, and the difference portion extraction unit 34 determines from the threshold values stored in the storage unit 7. The threshold value corresponding to the type of the evaluation target image data determined by the above is selected, and the difference portion may be extracted using the selected threshold value. The method for determining the type of image data to be evaluated is not particularly limited, and various conventionally known methods can be used, for example. Further, the type (image type) may be determined for each block or for each block group including a plurality of blocks, and a threshold value corresponding to the determination result may be used.

  As described above, in the digital color multifunction peripheral 1 according to the present embodiment, the image division processing unit 32 divides the evaluation target image data and the sample image data into a plurality of blocks so that each block of both image data corresponds to each other. Then, the evaluation value calculation unit 33 calculates an evaluation value for each block of the evaluation object image data and the sample image data, and the different part extraction unit 34 calculates the evaluation value of each block calculated by the evaluation value calculation unit 33 and the storage unit 7. Are compared with a threshold value TH1 stored in advance to extract a different portion (different block).

  Thereby, the difference part of an evaluation object image and a sample image can be extracted, without passing through a human subjectivity.

  In this embodiment, evaluation values are calculated based on the color intensity data of all colors (three colors of RGB) included in the evaluation target image data and the sample image data. Thereby, a difference part can be determined accurately.

  In the present embodiment, the SN ratio η or the deviation value Z is used as the evaluation value for each block. Thereby, the influence of the noise with respect to the determination precision of a different location can be reduced, and the similarity of images can be determined with high precision.

[Embodiment 2]
Another embodiment of the present invention will be described. For convenience of explanation, members having the same functions as those in the first embodiment are denoted by the same reference numerals, and description thereof is omitted.

  The digital color MFP 1 according to the present embodiment includes a similarity determination unit 13b illustrated in FIG. 6 instead of the similarity determination unit 13 according to the first embodiment. FIG. 6 is a block diagram illustrating a schematic configuration of the similarity determination unit 13b.

  As shown in this figure, the similarity determination unit 13b includes an averaging processing unit 37 in addition to the units included in the similarity determination unit 13 in the first embodiment.

  The averaging processing unit 37 is based on the following expression (36) and expression (37) for the color intensity data of each area composed of a plurality of pixels in the evaluation object image data and the sample image data acquired by the color intensity data acquisition unit 31. Then, the averaging process is performed, and the processing result is stored in the storage unit 7. As shown in FIG. 7, one area is a plurality of pixels (u × v pixels; u and v are integers of 1 or more, u = v may be satisfied, and u ≠ v may be satisfied). Each block is composed of a plurality of areas (m × n areas; m and n are integers of 1 or more, m = n may be satisfied, and m ≠ n may be satisfied). Further, the number of pixels per area (values of u and v) may be set in advance, or may be designated by the user via the operation panel 6. In addition, the number of pixels corresponding to the image size, resolution, image type, and the like of the sample image data and the evaluation target image data is stored in the storage unit 7 in advance, and the image division processing unit 32 performs the sample image data and the evaluation target image data. The image size, resolution, image type, and the like may be determined, and an appropriate number of divisions may be selected according to the determination result.

M ′ _{c, (f, g)} = (M _{c, ((f−1) × u + 1, (g−1) × v + 1)} +... + M _{c, (f × u, g × v)} ) / (U × v) (36)
y ′ _{c, (f, g)} = (y _{c, ((f−1) × u + 1, (g−1) × v + 1)} +... + y _{c (f × u, g × v)} ) / (U × v) (37)
Note that M ′ in the equation (36) represents color intensity data of one area calculated by averaging the sample image data. The subscript c when M ′ _{c, (f, g)} represents the type of color. For example, when there are three color intensity data of RGB, the color intensity data of R is c = 1, the color intensity data of G is c = 2, and the color intensity data of B is c = 3. F and g represent the positions of the respective areas after the averaging process. For example, an area that is 6 vertical and 8 horizontal (sixth from the left, eighth from the top) after the averaging processing is f = 6 and g = 8. In addition, u and v in Expression (36) indicate the number of pixels in the vertical direction (column direction) and the number of pixels in the horizontal direction (row direction), respectively, included in one area.

  In addition, y ′ in the above equation (37) indicates color intensity data of one area calculated by averaging the evaluation target image data. The subscript is the same as M '.

Thereby, for example, the color intensity data of the second color (G; green) in the vertical 6 and horizontal 8 areas obtained by averaging one area of 4 × 4 pixels is
_{M '2, (6,8) =} (M 2, (21,29) + M 2, (21,30) + M 2, (21,31) + M 2, (21,32) + M 2, (22,29 _{) + M 2, (22,30)} + M 2, (22,31) + M 2, (22,32) + M 2, (23,29) + M 2, (23,30) + M 2, (23,31) + M _2, the _{(23,32) + M 2, (} 24,29) + M 2, (24,30) + M 2, (24,31) + M 2, (24,32)) / (4 × 4).

  The image division processing unit 32, the evaluation value calculation unit 33, the different part extraction unit 34, the different part notification unit 35, and the similarity evaluation unit 36 are the same as the processes performed using the color intensity data M and y in the first embodiment. This process is performed using the color intensity data M ′, y ′ of each area calculated by the averaging processing unit 37. That is, a difference portion is extracted using the color intensity data M ′ and y ′ instead of the color intensity data M and y.

  As described above, in this embodiment, the color intensity data for each pixel in the evaluation object image data and the sample image data is averaged for each area composed of a plurality of pixels, and the average value of the color intensity data for each area is obtained. Is calculated. Then, an evaluation value of each block is calculated based on the average value for each area, and a different portion (different block) is extracted.

  In this way, by performing the averaging process, it is possible to reduce the influence of noise caused by positional deviation at the time of image reading, paper quality variation, and the like on the determination result of the different portion.

[Embodiment 3]
Still another embodiment of the present invention will be described. For convenience of explanation, members having the same functions as those in the first and second embodiments are denoted by the same reference numerals and description thereof is omitted.

  The digital color MFP 1 according to the present embodiment includes a similarity determination unit 13c illustrated in FIG. 8 instead of the similarity determination unit 13b according to the second embodiment. FIG. 8 is a block diagram illustrating a schematic configuration of the similarity determination unit 13c.

  As shown in this figure, the similarity determination unit 13c includes a data size calculation unit 38 and a data size processing unit 39 in addition to the units included in the similarity determination unit 13b in the second embodiment.

  The data size calculation unit (image conversion unit) 38 includes the number of blocks (the horizontal division number s and the vertical division number t) when the image division processing unit 32 performs division processing, and the averaging processing unit 37 performs averaging processing. The number of pixels in each area (vertical number u and horizontal number v), height (vertical length) h and width (horizontal length) w of the evaluation target image data, Based on the height h ′ and width w ′ of the sample image data, the enlargement sizes of the evaluation object image data and the sample image data are determined.

  Specifically, the data size calculation unit 38 calculates the width w and width w ′ of the original data out of multiples of the product of the number of divisions (number of blocks) s in the horizontal direction and the number of pixels u in the horizontal direction in each area. The minimum value exceeding the value is the width W of each image data after enlargement. Further, after expanding the minimum value exceeding the height h and the height h ′ of the original data among multiples of the product of the number of vertical divisions (number of blocks) t and the number of vertical pixels v in each area The height H of each of the image data.

  Note that the number of blocks when the division processing is performed in the image division processing unit 32 and the number of pixels in each area when the averaging processing is performed in the averaging processing unit 37 may be set in advance. May be specified. In addition, values corresponding to the image size, resolution, image type, and the like of the evaluation target image data and the sample image data are stored in the storage unit 7 in advance, and the image division processing unit 32 stores the evaluation target image data and the sample image data. The image size, resolution, image type, and the like may be determined, and an appropriate number of divisions may be selected according to the determination result.

  The data size processing unit (image conversion unit) 39 performs an enlargement process on the evaluation target image data and the sample image data based on the calculation result of the data size calculation unit 38.

  Specifically, as shown in FIG. 9, an image (height h × width w or height h ′ × width w ′) corresponding to the original data is arranged in the center, and frames are added to the top, bottom, left and right. Thus, an enlarged image of the size (height H × width W) calculated by the data size calculator 38 is created. Note that the heights of the frames added to the top and bottom of the original data are the height (Hh) / 2 or (Hh ′) obtained by equally dividing the difference between the height H after enlargement and the height of the original data. ) / 2. Similarly, the widths of the frames added to the left and right of the original data are the width (W−w) / 2 or (W−w ′) / that is obtained by equally dividing the difference between the expanded width W and the width of the original data. 2.

  Further, the color intensity of each pixel in the added frame is assumed to be uniform. For example, each pixel is white (R = G = B = 255 for R, G, B color intensity data; signal value = 255 for gray scale) or black (R, G, B color intensity data). In this case, the color intensity of each pixel is set so that R = G = B = 0. In the case of gray scale, the signal value = 0.

  The averaging processing unit 37, the image division processing unit 32, the evaluation value calculation unit 33, the different part extraction unit 34, the different part notification unit 35, and the similarity evaluation unit 36 are subjected to enlargement processing by the data size processing unit 39. The same processing as that in the second embodiment is performed on the data and the sample image data.

  As described above, in this embodiment, the data size calculation unit 38 exceeds the width of the original data among multiples of the product of the number of horizontal divisions (number of blocks) s and the number of horizontal pixels u in each area. The minimum value is the width W of the image data after enlargement, and is the minimum that exceeds the height of the original data among multiples of the product of the number of vertical divisions (number of blocks) t and the number of vertical pixels v in each area. The enlargement sizes of the evaluation object image data and the sample image data are calculated so that the value of is the height H of the enlarged image data. Then, the data size processing unit 39 performs an enlargement process on the evaluation target image data and the sample image data based on the calculation result of the data size calculation unit 38. Then, the averaging processing unit 37, the image division processing unit 32, the evaluation value calculation unit 33, the different part extraction unit 34, the different part notification unit 35, and the similarity evaluation unit 36 are expanded by the data size processing unit 39. An averaging process, an image division process, an evaluation value calculation process, a different part extraction process, a different part notification process, and a similarity evaluation process are performed on the target image data and the sample image data.

  As a result, the size of each block can be made uniform, so that the weights (degrees of influence) for the difference location extraction process and the similarity evaluation process of the information amount per area can be made uniform.

  In addition, the data size processing unit 39 arranges an image (height h × width w or height h ′ × width w ′) corresponding to the original data in the center, and adds a frame to the upper, lower, left, and right sides of the data size. An enlarged image having a size (height H × width W) calculated by the calculation unit 38 is created.

  Thus, even when the size of the evaluation target image data and the size of the sample image data are slightly different (for example, about several pixels), the two images can be aligned based on the center of the image. When there is a positional shift due to parallel movement, a frame may be added after deleting a width that seems to be a margin, up and down, left and right. Thereby, even if there is a positional shift due to parallel movement, the images can be aligned based on the center of the image.

  In addition, the averaging processing unit 37 performs an averaging process on the image data after adding the frame as described above. As a result, the color intensity data of the area including the edge portion and the frame portion of the image before the frame addition is averaged, so even if a slight deviation remains between both images after the frame addition, It is possible to mitigate the effect of misalignment (reduce the influence of noise).

  Further, the data size calculation unit 38 makes the color intensity of each pixel in the frame uniform. As a result, it can be prevented that the color intensity of the frame becomes noise and lowers the determination accuracy.

In each of the above embodiments, only the SN ratio η or only the deviation value Z is used as the evaluation value. However, the present invention is not limited to this, and both the SN ratio η and the deviation value Z may be used as the evaluation value. Calculating this case, the threshold value TH1 _eta for SN ratio eta, may be stored in advance in the memory unit 7 with the threshold TH1 _Z about misalignment value Z, the different part extraction unit 34, from the evaluation target image data and the sample image data The SN ratio η and the threshold value TH1 _η may be compared, and the deviation value Z calculated from the evaluation target image data and the sample image data may be compared with the TH1 _Z. Then, for example, a block where η ≦ TH1 _η or Z ≧ TH1 _Z , or a block where η ≦ TH1 _η and Z ≧ TH1 _Z may be determined as a different point by the different point extraction unit 34. . Further, in the case where both the SN ratio η and the deviation value Z are used as evaluation values, and a common calculation content (formula) is included in the calculation process of the SN ratio η and the deviation value Z, this common The calculation may be performed by a common calculation process. Thereby, it is possible to improve the efficiency of the arithmetic processing, simplify the algorithm in the program for performing the arithmetic processing, or simplify the arithmetic circuit that performs the arithmetic processing. The evaluation value is not limited to the above-described SN ratio and deviation value, and any value can be used as long as it indicates the degree of difference between the two image data. For example, similarity or difference calculated by various conventionally known methods may be used.

  In each of the above embodiments, each unit (each functional block) constituting the digital color multifunction peripheral 1 (particularly the color image processing device 3 and the main control unit) is realized by software using a processor such as a CPU. That is, the digital color MFP 1 includes a CPU (central processing unit) that executes instructions of a control program that realizes each function, a ROM (read only memory) that stores the program, and a RAM (random access memory) that expands the program. ), A storage device (recording medium) such as a memory for storing the program and various data. An object of the present invention is a recording medium in which a program code (execution format program, intermediate code program, source program) of a control program of the digital color multifunction peripheral 1 which is software for realizing the above-described functions is recorded so as to be readable by a computer. Is achieved by the computer (or CPU or MPU) reading and executing the program code recorded on the recording medium.

  Examples of the recording medium include tapes such as magnetic tapes and cassette tapes, magnetic disks such as floppy (registered trademark) disks / hard disks, and disks including optical disks such as CD-ROM / MO / MD / DVD / CD-R. Card system such as IC card, IC card (including memory card) / optical card, or semiconductor memory system such as mask ROM / EPROM / EEPROM / flash ROM.

  Alternatively, the digital color multifunction peripheral 1 may be configured to be connectable to a communication network, and the program code may be supplied via the communication network. The communication network is not particularly limited. For example, the Internet, intranet, extranet, LAN, ISDN, VAN, CATV communication network, virtual private network, telephone line network, mobile communication network, satellite communication. A net or the like is available. Also, the transmission medium constituting the communication network is not particularly limited. For example, even in the case of wired such as IEEE 1394, USB, power line carrier, cable TV line, telephone line, ADSL line, etc., infrared rays such as IrDA and remote control, Bluetooth ( (Registered trademark), 802.11 wireless, HDR, mobile phone network, satellite line, terrestrial digital network, and the like can also be used. The present invention can also be realized in the form of a computer data signal embedded in a carrier wave in which the program code is embodied by electronic transmission.

  In addition, each block of the digital color multi-function peripheral 1 is not limited to that realized by using software, but may be configured by hardware logic. Hardware that performs a part of the processing and the hardware It may be a combination of arithmetic means for executing software for performing the above control and residual processing.

  The present invention is not limited to the above-described embodiments, and various modifications can be made within the scope of the claims. That is, embodiments obtained by combining technical means appropriately changed within the scope of the claims are also included in the technical scope of the present invention.

It is a block diagram which shows schematic structure of the similarity determination part with which the difference location extraction apparatus shown in FIG. 2 is equipped. It is a block diagram which shows schematic structure of the difference location extraction apparatus concerning one Embodiment of this invention. It is explanatory drawing of the image division process in the different location extraction apparatus shown in FIG. It is explanatory drawing of a difference location notification process in the difference location extraction apparatus shown in FIG. (A) is explanatory drawing which shows an example of sample image data (sample image), (b) is explanatory drawing which shows an example of evaluation object image data (evaluation object image). It is a block diagram which shows schematic structure of the similarity determination part with which the difference location extraction apparatus concerning other embodiment of this invention is equipped. It is explanatory drawing of the averaging process in the different location extraction apparatus shown in FIG. It is a block diagram which shows schematic structure of the similarity determination part with which the difference location extraction apparatus concerning further another embodiment of this invention is equipped. It is explanatory drawing of the data size process in the different location extraction apparatus shown in FIG.

Explanation of symbols

1 Digital color MFP (Difference part extraction device)
2 Color image input device (data acquisition means)
3 Color image processing device (difference extraction device)
4 Color image output device (notification means)
5. Communication device (data acquisition means)
6 Operation panel (display section, notification means)
7 Storage unit (storage means)
13, 13b, 13c Similarity determination unit (difference part extraction device)
31 Color intensity data acquisition unit (data acquisition means)
32 Image segmentation processing unit 33 Evaluation value calculation unit 34 Difference location extraction unit 35 Difference location notification unit (notification means)
36 Similarity Evaluation Unit 37 Averaging Processing Unit 38 Data Size Calculation Unit (Image Conversion Unit)
39 Data size processor (image converter)

Claims (12)

A data acquisition unit that acquires first image data that is image data of the first image and second image data that is image data of the second image, and the first image based on the first image data and the second image data And a second difference image extracting device for extracting a difference between the second image and the second image,
Image division for dividing the first image data and the second image data acquired by the data acquisition unit into a plurality of blocks so that each block of the first image data and each block of the second image data correspond to each other A processing unit;
An evaluation value calculation unit for calculating an evaluation value indicating a degree of difference between corresponding blocks of the first image data and the second image data in both the image data;
Storage means for storing a threshold value for evaluating the degree of difference between the corresponding blocks;
The evaluation value calculated by the evaluation value calculation unit is compared with the threshold value stored in the storage unit, and a block whose degree of difference indicated by the evaluation value is equal to or greater than the threshold value or a block larger than the threshold value is extracted as a difference portion. And a different part extracting unit.   2. The difference location extracting apparatus according to claim 1, wherein the evaluation value calculation unit calculates the evaluation value based on color intensity data of all colors included in the two image data.   The evaluation value calculation unit uses an SN ratio of color intensity data included in the other image data when the color intensity data included in one image data is a target value as the evaluation value. Item 2. The difference point extraction device according to Item 1. The evaluation value calculation unit calculates the color intensity data included in one image data as M _{c, d} , and the color intensity data included in the other image data as y _{c, d} (however, the above M _{c, d} and y _{c , d} is a natural number assigned sequentially from 1 for each color type, and d is a natural number assigned sequentially from 1 for each pixel), and y _{c, d} −β × M _{c, d} the maximum absolute value of (where the beta is a proportional constant when the y _c, the ideal value of _{d y c, d = β ×} M c, and _d) claims, characterized in that the above evaluation value 1. The difference location extraction apparatus according to 1. Averaging that averages the color intensity data of each pixel included in the first image data of each block and the color intensity data of each pixel included in the second image data of each block for each area composed of a plurality of pixels. With a processing unit,
The difference location extraction apparatus according to claim 1, wherein the evaluation value calculation unit calculates the evaluation value based on color intensity data averaged for each area. An image conversion unit that converts an image size by adding image data of a frame image to the first image data and / or the second image data;
The difference location extraction apparatus according to claim 1, wherein the evaluation value calculation unit calculates the evaluation value based on image data after size conversion. Averaging that averages the color intensity data of each pixel included in the first image data of each block and the color intensity data of each pixel included in the second image data of each block for each area composed of a plurality of pixels. With a processing unit,
The image dividing unit divides the first image data into a plurality of blocks arranged in a matrix in the row direction and the column direction,
The averaging processing unit divides the blocks into a plurality of areas arranged in the row direction and the column direction and averages the color intensity data for each divided area,
In the image conversion unit, the number of pixels in the row direction is a multiple of the product of the number of blocks in the row direction and the number of pixels in the row direction in each area, and the number of pixels in the column direction is the number of blocks in the column direction. 7. The difference location extracting apparatus according to claim 6, wherein the frame image is added so as to be a multiple of a product of the number and the number of pixels in the column direction in each area.   The difference part extraction apparatus according to claim 1, further comprising notification means for notifying the difference part. An image reading apparatus comprising: an original reading unit that reads an original image to generate image data; and the difference location extracting apparatus according to any one of claims 1 to 8,
An image reading apparatus, wherein at least one of the first image data and the second image data is image data generated by the document reading means. A difference location extraction method for extracting a difference location between a first image and a second image based on first image data that is image data of a first image and second image data that is image data of a second image. ,
An acquisition step of acquiring the first image data and the second image data;
An image dividing step of dividing the first image data and the second image data acquired in the acquisition step into a plurality of blocks such that each block of the first image data and each block of the second image data correspond to each other. When,
An evaluation value calculating step of calculating an evaluation value indicating a degree of difference between corresponding blocks of the first image data and the second image data in both the image data;
The evaluation value calculated in the evaluation value calculation step is compared with a threshold value stored in advance in the storage means, and a block whose degree of difference indicated by the evaluation value is equal to or greater than the threshold value or a block larger than the threshold value is extracted as a difference portion. The difference part extraction method characterized by extracting a difference part with the computer which performs the difference part extraction process to perform.   The program for operating the difference location extraction apparatus of any one of Claims 1-8, Comprising: The program for functioning a computer as said each part.   The computer-readable recording medium which recorded the program of Claim 10.

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