JP2008129912A - Image processor, blur detection method, program and recording medium - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide an image processor which highly accurately detects blur of a bit map image with less load. <P>SOLUTION: A CPU 160 of a printer 100 divides image data of a target window into blocks of 8×8 pixels, calculates a coefficient group representing a change in a pixel value in each block and selects from an edge pattern table 181 a basic edge pattern approximating a change shape of the coefficient group. With respect to the basic edge pattern, when adjacent blocks are in the same inclination direction as that of a target block, the CPU 160 executes edge connection processing of cumulatively adding burr widths (widths of a gradient of a change in a pixel value) of the blocks with each other, calculates a blur width of the target block and determines block burr. The CPU 160 accumulates the determination results and performs window burr determination processing. If any focused window is included in a target image, the target image is determined to be a focused image. <P>COPYRIGHT: (C)2008,JPO&INPIT

Description

  The present invention relates to a technique for detecting blur in a bitmap image configured as an aggregate of pixels.

  In recent years, digital still cameras have become widespread, and opportunities for taking photographic images of digital data are increasing. These digital photographic images include blurred images due to subject blur and camera shake, and before the user prints the image, it is necessary to exclude the blurred image from the print target. . In a printer equipped with a small liquid crystal display that has been widely used in recent years, it is possible to print after confirming and selecting an image to be printed by the user on the liquid crystal display. Thus, it was difficult to accurately determine blur. On the other hand, in relation to blur detection technology for eliminating such blurry images, Patent Document 1 below discloses that a captured image is divided into a plurality of blocks each having 8 pixels × 8 pixels and subjected to discrete cosine transform. A technique is described in which frequency analysis is performed for each block, and the sharpness of the image (in other words, the degree of blurring) is determined for each block.

Japanese Patent Laid-Open No. 4-170872 JP-A-10-271516

  However, in an image in a format that does not have discrete frequency component coefficients as data, such as a bitmap, the image is converted to a format that has discrete frequency component coefficients such as JPEG as data, and then disclosed in Patent Document 1. If an attempt is made to analyze the blur of a high-resolution image of millions of pixels or more by the technique described in (1), the amount of calculation becomes enormous, and the amount of memory used increases accordingly. Such a problem is a problem that cannot be overlooked particularly in the case of detecting blur in a small device with limited CPU power or memory capacity, such as a printer, a digital camera, or a photo viewer.

  In addition, since the resolution of an image photographed by a recent digital still camera is a high resolution of several million to 10 million pixels, as described in Patent Document 1, it is within a block composed of only 8 pixels × 8 pixels. It was difficult to determine blur and sharpness only. Specifically, when an image of 6 million pixels (about 3000 pixels wide × 2000 pixels high) is printed on an L-size printing paper (about 130 mm wide × 90 mm long), 8 pixels wide is about 0.00 mm. It corresponds to a width of 35 mm, and it is difficult to objectively determine whether an image is sharp or blurred only within such a narrow width. That is, it has become difficult to directly apply the technique described in Patent Document 1 to high-resolution images taken in recent years.

  In consideration of the various problems described above, the problem to be solved by the present invention is to accurately detect image blur with a small load on a bitmap image that does not have discrete frequency component coefficients as data. It is.

An image processing apparatus of the present invention that solves the above problems is as follows.
An image processing device for detecting blur of a bitmap image configured as a collection of pixels,
An image data input unit for inputting image data for each pixel constituting the image;
At least a part of the image is divided into a plurality of blocks each having a predetermined number of pixels, and the block of the image data is determined based on the input image data of pixels belonging to the block for each block. A block pattern calculation unit for calculating a coefficient group composed of a plurality of coefficients representing a change in a predetermined direction at
A pattern storage unit that classifies and stores a plurality of types of patterns in which representative changes in at least one direction in the block of image data of pixels in the block are represented by the coefficient group as basic patterns;
A pattern matching unit that matches the calculated pattern of change of the coefficient group with the plurality of stored basic patterns and selects a basic pattern to be approximated;
The present invention includes a blur determination unit that determines blur of an image represented by the image data based on the arrangement of the basic patterns selected for the block in the image.

  The image processing apparatus having such a configuration divides an image into a plurality of blocks, calculates a coefficient group representing a change in image data in a predetermined direction, and obtains a basic pattern approximated to the pattern of the coefficient group change from the pattern storage unit. Based on the selected basic pattern and the arrangement of the selected basic pattern in the image, blurring of the image is determined. Therefore, it is possible to efficiently determine blur using the basic pattern.

  The bitmap image is image data configured as an aggregate of pixels having color information. For example, PNG format data, BMP format data, JPEG format data, etc. Various image data.

  In the image processing apparatus having the above configuration, the blur determination unit is adjacent to the first gradient formed by the change of each coefficient of the coefficient group of the selected basic pattern of the block from which the basic pattern is selected, and the block. The continuity test means for testing the continuity with the second gradient formed by the change in each coefficient of the coefficient group of the basic pattern selected by the block to be used and the continuity detection means, the respective gradients are not continuous Sometimes, the gradient width of the block from which the basic pattern is selected is detected as the blur width representing the blur level of the block, and when each gradient continues, it is a series of blocks in a predetermined direction in the image divided into blocks. Along the blur width detecting means for detecting a range in which each gradient continues as a blur width, and the blur of the image represented by the image data based on the length of the detected blur width. It may be that a blur determination means for determining.

  According to such a configuration, the continuity of the gradient formed by the change of each coefficient of the coefficient group of the selected basic pattern is detected along the series of blocks. Thus, the range where the gradient continues is detected as the blur width. Therefore, even when the blur width exceeds the size of one block, it can be determined whether or not the image is blurry with high accuracy.

  In the image processing apparatus having such a configuration, the pattern storage unit includes means for storing the basic pattern in association with the pattern number uniquely assigned to each basic pattern, and the pattern matching unit is calculated. As a basic pattern that approximates the changed shape of the coefficient group, a pattern number is selected from the pattern storage unit, and the continuity test means refers to the selected pattern number and refers to the first gradient and the second Means for testing continuity with the gradient may be provided.

  According to such a configuration, since the pattern number is used to test the continuity of each gradient of the basic pattern, the pattern matching process is accelerated and used as compared with the case of directly testing from the basic pattern. Memory capacity can be reduced.

  Further, in the image processing apparatus having such a configuration, the pattern storage unit further includes means for storing the gradient width of each basic pattern in association with the pattern number, and the blur width detection means is configured so that each gradient is not continuous. When the gradient number associated with the pattern number is read from the pattern storage unit, the blur width is detected, and when each gradient continues, each block in the image divided into blocks is read. It is also possible to provide means for reading the width of the gradient associated with the pattern number from the pattern storage unit along the series of blocks in a predetermined direction, and detecting the blur width as an accumulated value of the gradient width.

  According to such a configuration, since the width of the gradient formed by the change of each coefficient of the coefficient group of each basic pattern is stored in advance in the pattern storage unit, the calculation of the blur width can be speeded up.

  Further, in the image processing apparatus having such a configuration, the blur determination unit totalizes the block blur determination unit that determines the blur situation for each block based on the length of the detected blur width, and the result of the determination. The image blur determining means for determining the blur of the entire image may be provided. According to such a configuration, it is possible to determine the blur impression of the entire image.

  Alternatively, in the image processing apparatus configured as described above, the blur determination unit includes a block blur determination unit that determines a blur situation for each block based on the length of the detected blur width, and a predetermined number of image data. The window blur is determined for each block included in the window area composed of blocks by summing up the blur conditions determined by the block blur determination unit and determining whether or not the window area is blurred based on the total result. When at least one window area that is determined not to be blurred exists as a result of moving the window area in the image data with the determination means, it is determined that the entire image represented by the image data is not blurred. It is good also as what is provided with the image blurring determination means to perform.

  According to such a configuration, since it is possible to determine the presence or absence of blur for each window area constituted by a predetermined number of blocks, it is possible to determine whether or not the image is blurred with an accuracy closer to human sensitivity. Is possible. Further, if even one non-blurred window area is detected, the blur determination process for the remaining window areas can be omitted, so that the processing can be speeded up. The predetermined window area may be, for example, an area of 1 cm × 1 cm or 2 cm × 2 cm assuming that image data is printed on an L-size printing paper. This is because if it is determined that the window area of such a size is not blurred in any part of the image, it can be determined that the area is in focus.

  In the image processing apparatus having such a configuration, the predetermined directions are the horizontal direction and the vertical direction, and the block pattern calculation unit is a first coefficient group representing a change in the horizontal direction in the block of image data as a coefficient group. And a second coefficient group that represents a change in the vertical direction within the block of image data, and the pattern matching unit approximates the first coefficient group and the second coefficient group, respectively. Means for selecting a basic pattern to be performed, the blur detection means includes horizontal / vertical blur width detection means for detecting the blur width in the horizontal direction and the vertical direction, respectively, and the blur determination means is in the horizontal direction and the vertical direction. Horizontal / vertical blur determination means for determining blur of an image represented by image data based on the longer blur width of the calculated blur widths. It may be a thing.

  According to such a configuration, the horizontal blur width and the vertical blur width can be calculated for one block. Therefore, if the larger blur width is used, it is possible to calculate the image with higher accuracy. It becomes possible to determine the blur of the image.

  In the image processing apparatus having such a configuration, the pattern matching unit may not select a basic pattern when a condition that the calculated coefficient group is within a predetermined dispersion range is satisfied.

  According to such a configuration, when it is determined that the change in the pixel value in the block is small due to the calculated coefficient group, the selection of the basic pattern can be omitted, so that the processing can be speeded up.

  In the image processing apparatus having such a configuration, the pattern matching unit may select, from the pattern storage unit, a basic pattern that minimizes the average error with the calculated coefficient group from a plurality of types of basic patterns. .

  According to such a configuration, since the pattern matching unit can select the most basic pattern from a plurality of types of basic patterns, it can detect blur and perform blur determination with high accuracy.

  Alternatively, in the image processing apparatus having such a configuration, the pattern matching unit executes an error calculation process that calculates an average error between the plurality of types of basic edge patterns and the calculated coefficient group in the order of the arrangement of the plurality of types of basic patterns. When the calculated error is equal to or smaller than a predetermined threshold, the error calculation process may be stopped and a basic pattern having the error equal to or smaller than the predetermined threshold may be selected from the pattern storage unit.

  According to such a configuration, the pattern matching unit aborts the error calculation process when detecting a basic pattern within a predetermined error range from a plurality of types of basic patterns, so that the pattern matching process can be speeded up. .

  In the image processing apparatus having such a configuration, the block may be configured with 8 pixels in each of the horizontal direction and the vertical direction.

  According to such a configuration, it is possible to speed up the blur determination process while maintaining an appropriate balance between the processing time required for block pattern calculation and the processing time required for edge pattern matching.

  In the image processing apparatus having such a configuration, the image data is each gradation value in the RGB color space, and the coefficient group is calculated based on the luminance value or brightness for each pixel calculated from each gradation value in the RGB color space. It is good also as what is done.

  According to such a configuration, blur determination can be performed using an edge based on a change in luminance value or brightness, so that blur determination with high accuracy can be performed.

  In addition, the image processing apparatus having such a configuration may further include a presentation unit that presents to the user an image determined to be unblurred by the blur determination unit.

  With such a configuration, the user can confirm only images that have been successfully photographed. As a method of presenting to a user, for example, an image can be displayed on a display device. In addition, a list of images determined not to be blurred may be printed and presented to the user.

  Further, the image processing apparatus having such a configuration may further include a printing unit that prints an image selected by the user from the presented images.

  With such a configuration, the user can easily print a desired image from images that have been successfully shot without being conscious of an image that is blurred due to camera shake or subject blur. .

  In addition to the configuration as the image processing apparatus described above, the present invention can also be configured as a blur detection method in which a computer detects blur of an image and a computer program for detecting blur in an image. Such a computer program may be recorded on a computer-readable recording medium. As the recording medium, for example, various media such as a flexible disk, a CD-ROM, a DVD-ROM, a magneto-optical disk, a memory card, and a hard disk can be used.

Hereinafter, in order to further clarify the operations and effects of the present invention described above, embodiments of the present invention will be described based on examples in the following order.
A. Printer hardware configuration:
B. Printing process:
C. Blur judgment processing:
C-1. Flow of blur determination processing:
C-2. Block pattern calculation:
C-3. Edge pattern matching process:
C-4. Edge connection processing:
C-5. Block blur judgment processing:
C-6. Window blur determination processing:

A. Printer hardware configuration:
FIG. 1 is an explanatory diagram showing an appearance of a printer 100 as an embodiment of the image processing apparatus of the present application. The printer 100 is a so-called multi-function printer, and is connected to devices such as a scanner 110 for optically reading an image, a memory card slot 120 for inserting a memory card MC on which image data is recorded, and a digital camera. A USB interface 130 and the like are provided. The printer 100 can print an image captured by the scanner 110, an image read from the memory card MC, and an image read from the digital camera via the USB interface 130 on the printing paper P. It is also possible to print an image input from a personal computer (not shown) connected by a printer cable or USB cable.

  The printer 100 includes an operation panel 140 for performing various operations related to printing. A liquid crystal display 145 is provided at the center of the operation panel 140. The liquid crystal display 145 is used to display an image read from a memory card MC, a digital camera, or the like, or to display a UI (user interface) when using various functions of the printer 100.

  The printer 100 eliminates a blurred image (hereinafter referred to as “blurred image”) from a plurality of image data input from a memory card MC, a digital camera, or the like, and an image that is in focus at one location (hereinafter referred to as “blurred image”). , “Focused image”), and a function for displaying on the liquid crystal display 145. The user can print only an image suitable for printing by selecting a desired image from the images displayed on the liquid crystal display 145 in this way.

  FIG. 2 is an explanatory diagram showing the internal configuration of the printer 100. As shown in the figure, the printer 100 has a carriage 210 on which an ink cartridge 212 is mounted, a carriage motor 220 that drives the carriage 210 in the main scanning direction, and a printing paper P in the sub-scanning direction as a mechanism for printing on the printing paper P. A paper feed motor 230 and the like are provided.

  The carriage 210 includes a total of six types of ink heads 211 corresponding to the inks representing cyan, magenta, yellow, black, light cyan, and light magenta. An ink cartridge 212 containing these inks is mounted on the carriage 210, and the ink supplied from the ink cartridge 212 to the ink head 211 is ejected onto the printing paper P by driving a piezo element (not shown). The

  The carriage 210 is movably held by a sliding shaft 280 installed in parallel with the axial direction of the platen 270. The carriage motor 220 rotates the drive belt 260 in response to a command from the control unit 150 to reciprocate the carriage 210 in parallel with the axial direction of the platen 270, that is, in the main scanning direction. The paper feed motor 230 rotates the platen 270 to convey the printing paper P perpendicular to the axial direction of the platen 270. That is, the paper feed motor 230 can relatively move the carriage 210 in the sub-scanning direction.

  The printer 100 includes a control unit 150 for controlling operations of the ink head 211, the carriage motor 220, and the paper feed motor 230 described above. The control unit 150 is connected to the scanner 110, the memory card slot 120, the USB interface 130, the operation panel 140, and the liquid crystal display 145 shown in FIG.

  The control unit 150 includes a CPU 160, a RAM 170, and a ROM 180. The ROM 180 stores a control program for controlling the operation of the printer 100, and further stores an edge pattern table 181 used in various processes described later.

  The CPU 160 operates as each functional unit (161 to 166) shown in the figure by expanding and executing the control program stored in the ROM 180 in the RAM 170. The functions of these functional units will be described in detail later. In addition to the work area for executing the control program, the RAM 170 has storage areas called a pattern number buffer 171, a vertical accumulation buffer 172, and a horizontal accumulation buffer 173 shown in the figure.

B. Printing process:
FIG. 3 is a flowchart of print processing executed by the CPU 160 of the printer 100. This printing process is a process for printing by inputting image data from the memory card MC or the like.

  When this printing process is executed according to a predetermined operation of the user using the operation panel 140, first, the CPU 160 uses the image data input unit 161 to read from the memory card MC inserted into the memory card slot 120. Bitmap image data composed of RGB color space data for each pixel is input (step S10). Here, the bitmap image data is input from the memory card MC, but may be input from a digital camera or a computer connected via the USB interface 130.

  Next, the CPU 160 performs a blur determination process on the bitmap image data input in step S10 (step S20). Details of the blur determination process will be described later in “C. Blur determination process”.

  When the blur determination process is completed for one bitmap image data, the CPU 160 determines whether or not the blur determination process has been performed by inputting all the bitmap image data in the memory card MC (step S30). If it is determined that the blur determination process for all bitmap image data has not been performed by this process (step S30: No), the process returns to step S10, and the next bitmap image data is input.

  When it is determined in step S30 that the blur determination process has been completed for all bitmap image data (step S30: Yes), the CPU 160 determines that the image is not blurred in step S20, that is, in-focus. A list of images is displayed on the liquid crystal display 145 (step S40).

  When the list of focused images is displayed on the liquid crystal display 145, the CPU 160 receives selection of an image to be printed from the user via the operation panel 140 (step S50). Then, the selected image is printed by controlling the ink head 211, the paper feed motor 230, the carriage motor 220, and the like (step S60).

  In the printing process described above, all the bitmap image data in the memory card MC is input. However, when a plurality of folders are generated in the memory card MC, the folder specified by the user. It is possible to input bitmap image data only for.

  The print processing apparatus configured as described above determines whether the image is blurred, displays only the image determined to be a focused image, and accepts a print instruction. Therefore, it is difficult for the user to visually determine the blur. Even in a printing apparatus equipped with a small liquid crystal display, a user can easily print a desired image while confirming only an image that has been successfully photographed.

C. Blur judgment processing:
C-1. Flow of blur determination processing:
FIG. 4 is a flowchart of the blur determination process executed in step S20 of the printing process shown in FIG. When this process is started, first, the CPU 160 moves windows to be subjected to blurring determination (hereinafter referred to as “target window”) along the series (step S100). The first destination is the window that exists at the upper left of the bitmap image data. The attention window moves to the right by the width of the window each time the process of step S100 is executed, and when it reaches the right edge of the image, it returns to the left edge and moves downward by the height of the window. Move to the right. In the present embodiment, the above-described window represents an area of a size of about 1 cm × about 1 cm, which is assumed to be the minimum focusing area, on the L-size printing paper, and this window area is used as a unit. Based on the blur determination result, the blur determination of the entire image is finally performed.

  When the attention window is moved, the CPU 160 calculates the block pattern of the current attention window by using the block pattern calculation unit 162 (step S110). The calculation of the block pattern is a process of dividing the bitmap image data into blocks each having a predetermined number of pixels and calculating a coefficient group representing a change in pixel value for each block (details). The details of the processing will be described later in “C-2. Block pattern calculation”).

  In this embodiment, as shown in FIG. 5A, the above-described block is composed of a total of 64 pixels each consisting of 8 pixels in the horizontal direction and the vertical direction. When an image of 6 million pixels (approximately 3000 pixels wide × approximately 2000 pixels long) is printed on L-size printing paper (approximately 13 cm × approximately 9 cm long), the size of one block of 8 pixels × 8 pixels is approximately 0.2 mm. 35 mm × about 0.35 mm. Therefore, 28 × 28 blocks exist in the window area of about 1 cm × about 1 cm, as shown in FIG. 5B. In this embodiment, as described above, assuming that an image of 6 million pixels is printed on the L-size printing paper, the size of the window is about 1 cm × about 1 cm. Needless to say, the window size is appropriately changed according to the number of pixels of the image, the processing speed of the CPU, and the like. For example, when printing in A4 size, when printing an image of 3 million pixels, or when the processing speed of the CPU can be improved, the window size may be increased, for example, about 2 cm × about 2 cm.

  When the block pattern is calculated, the CPU 160 uses the pattern matching unit 163 to execute an edge pattern matching process (step S120). This processing is performed by changing the coefficient group calculated in step S110 from the edge pattern table 181 in which a plurality of types of basic edge patterns representing representative change shapes of pixel values in the block are represented by coefficient groups. This is a process for selecting a basic edge pattern that approximates the shape from the edge pattern table 181 and storing the pattern number assigned to the basic edge pattern in the pattern number buffer 171 secured in the RAM 170 (for details of processing details) It will be described later in “C-3. Edge pattern matching process”.

  When the edge pattern matching process is performed, the CPU 160 refers to the pattern number buffer 171 and the like using the edge linking unit 164 and executes the edge linking process (step S130). In this edge linking process, if the basic edge pattern selected in step S120 above has the same inclination direction as the target block, the blur width (the gradient width of the change in pixel value) between these blocks is calculated. Cumulative addition is performed to calculate the blur width of the block of interest (detailed processing content will be described later in “C-4. Edge connection processing”).

  When the edge connection process is performed, the CPU 160 uses the block blur determination unit 165 to execute the block blur determination process (step S140). This process is a process for determining whether or not the block of interest is blurred based on the blur width calculated by the edge connection process. In the present embodiment, the block of interest is acquired as a determination result of any one of “blurred block”, “focused block”, and “flat block” as the execution result of such processing. In this embodiment, “flat” means a state in which the color change in the block is small. Since a flat block is difficult to determine blur, such a division is provided in addition to the blur block and the focused block. (Detailed processing contents will be described later in “C-5. Block blur determination processing”).

  When the block blur determination process is determined, the CPU 160 uses the window blur determination unit 166 to perform the window blur determination process for the entire window area of interest (step 150). In this process, the blur determination results for each block made in step S140 are aggregated to perform blur determination as a window area (for detailed processing contents, refer to “C-6. Window blur determination process”). Later).

  When the window blur determination process ends in step S150, the CPU 160 determines whether or not the current window area is a focused window based on the determination result of the determination process (step S160). As a result, if the window is an in-focus window (step S160: Yes), it is determined that the currently input bitmap image is in focus (step S170), and the above-described series of blur determination processing ends. To do. That is, when it is determined that any window area in the image is a focused window, it is determined that the bitmap image input in step S10 in FIG. 3 is a focused image. In this way, if any window area in the image is an in-focus window, it is not necessary to determine the presence or absence of blur for all window areas in the image, so the time required for the blur determination process is increased. be able to.

  On the other hand, when it is determined that the window area subjected to the window blur determination process in step S150 is a blur window (step S160: No), the CPU 160 subsequently determines that the current target window is It is determined whether it is the final window (step S180). As a result, if it is determined that the current window of interest is the final window (step S180: Yes), since it can be determined that no window area is a focused window, the currently input bitmap image is a blurred image. Is determined (step S190), and the above-described series of blur determination processing ends. On the other hand, if the current window of interest is not the final window (step S180: No), the CPU 160 returns the process to step S100, and repeatedly executes the various processes described above for the window at the next position.

  Since the image processing apparatus having such a configuration can calculate the blur width between a plurality of blocks by concatenating the blur width for each block, even when the blur width exceeds the size of one block, It can be determined whether or not the image is blurred with high accuracy. In addition, since it is possible to determine the presence or absence of blur for each window area constituted by a plurality of blocks, it is possible to determine whether or not the image is blurred with an accuracy closer to human sensitivity. This is because if it is determined that the window area of such a size is not blurred in any part of the image, it can be determined that the area is in focus.

  In the present embodiment, each time the process of step S100 is executed, the window of interest moves to the right edge of the image from the window at the top left, which is the first destination, to the right by the width of the window. When it reaches, the setting returns to the left end, moves downward by the height of the window, and moves to the right again. However, the present invention is not limited to this, and various methods can be set. For example, the first destination is a window located in the middle of the image, and the setting is to move to the outer periphery concentrically from there, or the first destination is the window that exists at the bottom right, and from there to the left When it moves and reaches the left end of the image, it can be set to return to the right end and move to the right again. Also, different settings can be made according to the type of target image, for example, a landscape photo image and a portrait photo image. For example, when the target image is a portrait photo image, if the first destination described above is set as the window located in the middle of the image, the region including the person, that is, the focused window is included. Since the region is often near the center of the image, the focusing window can be detected earlier, and the blur determination process can be speeded up.

  In this embodiment, the target window moves sequentially, and finally the target window covers the entire image. However, the present invention is not limited to this, and only a limited area of the image is displayed. It may be moved. For example, in the case of a portrait photo image, as described above, since the region including the focused window is often near the center of the image, if the target window moves only in the center of the image, The processing speed can be increased while maintaining the accuracy of blur determination.

  In this embodiment, the window of interest is moved by the width or height of the window. However, the movement is not limited to this, and the movement width is increased, for example, the window width or height. It may be moved by a width of 1.5 times. In this way, the number of windows for performing the window blur determination process is reduced, and the blur determination process can be speeded up. Conversely, the movement width may be small, for example, it may be moved by several pixels, or it may be moved by half the width or height of the window. In this way, it is possible to accurately perform blur determination even when there is an in-focus area that spans the window of the present embodiment, and the accuracy of blur determination processing can be improved.

  Further, in this embodiment, the blur determination process is performed for each window by summing up the blur determination results for each block. However, the window is divided into smaller sub-windows. The blur determination results may be aggregated, and the results may be further aggregated to perform blur determination for each window. In this way, the buffer used for the blur determination process can be made compact.

  Alternatively, the blur determination process may be performed on an image basis by summing up the blur determination results for each block. In this way, it is possible to determine the blur impression of the entire image.

C-2. Block Pattern Calculation FIG. 6 is a flowchart of block pattern calculation executed in step S110 of the blur determination process shown in FIG. The CPU 160 uses the block pattern calculation unit 162 to execute block pattern calculation processing according to the following procedure.

  When this processing is started, the CPU 160 divides the bitmap image data into blocks each composed of a total of 64 pixels each having 8 pixels in the horizontal direction and the vertical direction, as shown in FIG. (Step S200).

Next, the CPU 160 converts the RGB component of each pixel of the pit map image data into a luminance value for each block divided in step S200 (step S210). Specifically, in the above-described conversion, the Y component (luminance value) of the YCbCr color space is calculated from the gradation values R, G, and B of the RGB color space using the following equation (1).
Y = 0.299R + 0.587G + 0.114B (1)

  Next, the CPU 160 calculates a horizontal luminance pattern and a vertical luminance pattern representing changes in the horizontal and vertical luminance values in the block using the luminance value converted in step S210 (step S220), and further These are normalized to calculate a horizontal direction normalized luminance pattern and a vertical direction normalized luminance pattern (step S230), and the process returns to the blur determination process shown in FIG.

  Steps S220 and S230 will be described in detail with reference to FIG. FIG. 7 is an explanatory diagram illustrating a method of calculating a normalized luminance pattern that represents a luminance change in the horizontal direction or the vertical direction in the block from the luminance value of each pixel in the block. F11 to F88 shown in the figure represent the luminance value of each pixel in the block converted in step S210.

  In step S220, as described above, using the luminance value converted in step S210, a horizontal luminance pattern representing a horizontal luminance change in the block, and a vertical luminance change in the block as a vertical luminance pattern. Is calculated. In this embodiment, since the block divided in step S200 is composed of 8 pixels in both the horizontal direction and the vertical direction, the above luminance pattern is a coefficient group composed of 8 element coefficients in both the horizontal direction and the vertical direction. It becomes. A horizontal luminance pattern FLm (m is an integer of 1 to 8) representing a horizontal luminance change in the block is calculated as an average value of luminance values of eight pixels in the vertical direction belonging to the m-th column in the horizontal direction in the block. Is done. For example, as shown in the drawing, the luminance pattern FL1 of the pixels in the first column in the horizontal direction in the block is an average value of the luminance values F11 to F81 of the eight pixels belonging to the first column. Similarly, a vertical luminance pattern FVn (n is an integer of 1 to 8) representing a luminance change in the vertical direction in the block is calculated as an average value of luminance values of eight pixels in the horizontal direction belonging to the nth row in the vertical direction. Is done. For example, as illustrated, the luminance pattern FV2 of the pixels in the second row in the vertical direction is an average value of the luminance values F21 to F28 of the eight pixels belonging to the second row. That is, FLm and FVn (m and n are integers of 1 to 8) are calculated by the following equations (2) and (3).

  In the present embodiment, the horizontal luminance pattern FLm and the vertical luminance pattern FVn are calculated by the above formulas (2) and (3). However, the present invention is not limited to this. For example, a horizontal luminance pattern FLm representing a horizontal luminance change in a block is given as a sum of luminance values of eight pixels in the vertical direction belonging to the m-th column in the horizontal direction. Any calculation method that can be expressed relatively may be used.

  Next, in step S230, as shown in FIG. 7, the horizontal luminance patterns FL1 to FL8 and the vertical luminance patterns FV1 to FV8, which are coefficient groups composed of the eight element coefficients calculated in step S220, are respectively normalized. Turn into. Thus, the same number of coefficient groups, horizontal normalized luminance patterns (examples as the first coefficient group of the present application) RL1 to RL8, vertical normalized luminance patterns (implementation as the second coefficient group of the present application) Example) RV1 to RV8 are calculated. In the present embodiment, the above normalization is performed by the normalization functions of the following expressions (4) and (5).

  By performing normalization in this way, it becomes possible to treat a plurality of luminance patterns in step S220 that have different values of the respective element coefficients but have the same relative change amount as the same pattern. Therefore, it is possible to improve the matching with the basic edge pattern described later.

  In the present embodiment, the normalization functions shown in Equations (4) and (5) are used, but the present invention is not limited to this. For example, another normalization function in which the sum of the element coefficients of the normalized luminance pattern is 0, or the absolute value of each element coefficient is calculated, and the normalization is performed according to the ratio of the magnitudes of the absolute values. You may use the normalization function that the sum of each subsequent element coefficient becomes 1.

  Further, in this embodiment, the blur determination process is performed by appropriately maintaining the balance between the processing time required for block pattern calculation in step S110 in FIG. 4 and the processing time required for edge pattern matching in step S120 and edge connection processing in step S130. In step S200 of FIG. 6, the block is divided into blocks each consisting of a total of 64 pixels each consisting of 8 pixels in the horizontal direction and the vertical direction. However, the present invention is not limited to this. Depending on the performance of the CPU 160, the number of pixels of the image, the size of the printing paper, etc., a smaller block, for example, a block of 16 pixels in total consisting of 4 pixels each in the horizontal and vertical directions, or a larger block, for example, the horizontal direction The vertical direction may be a block of 256 pixels each consisting of 16 pixels. In this case, the number of elements of each coefficient group shown in FIG. 7 also changes according to the number of constituent pixels of the divided block.

  In this embodiment, the Y component (luminance value) of YCbCr is calculated from each gradation value in the RGB color space in step S210 in FIG. 6, and the luminance pattern is calculated in step S220 using this value. However, it is not limited to this. For example, a G component gradation value having the highest spectral sensitivity of the human eye in the RGB color space may be used, or a V component (brightness) in the HSV color space may be used. The V component in the HSV color space is given as the maximum value of each gradation value in the RGB color space.

C-3. Edge pattern matching process:
FIG. 8 is a flowchart of the edge pattern matching process executed in step S120 of the blur determination process shown in FIG. The CPU 160 uses the pattern matching unit 163 to execute the edge pattern matching process according to the following procedure.

  When this process is executed, first, the CPU 160 for the block of interest is a group of coefficients calculated in step S110 of the blur determination process, which are the horizontal direction normalized luminance patterns RL1 to RL8 and the vertical direction normalized luminance patterns RV1 to RV8. Are acquired (step S300). In the edge pattern matching process, the same processing is executed for each of the horizontal direction normalized luminance patterns RL1 to RL8 and the vertical direction normalized luminance patterns RV1 to RV8. Processing for the activated luminance patterns RL1 to RL8 will be described.

When the CPU 160 acquires the horizontal normalized luminance patterns RL1 to RL8 in step S300, the CPU 160 obtains a difference De1 between the maximum value and the minimum value of this coefficient group, and determines whether or not this is equal to or greater than a predetermined threshold value Th1. (Step S310). The difference De1 between the maximum value and the minimum value of the horizontal direction normalized luminance pattern can be obtained by the following equation (6).
De1 = MAX (RLm) −MIN (RLm) (m is an integer of 1 to 8) (6)

  If it is determined in step S310 that the difference De1 between the maximum value and the minimum value of the coefficient group is greater than or equal to a predetermined threshold value Th1 (step S310: Yes), there is some luminance change in the horizontal direction of the block. I can judge. Therefore, the CPU 160 refers to the edge pattern table 181 stored in the ROM 180 (step S350), and there is a basic edge pattern in which the average error E with the horizontal direction normalized luminance patterns RL1 to RL8 is equal to or less than a predetermined threshold Th2. It is determined whether or not (step S360).

  In the present embodiment, the difference De1 between the maximum value and the minimum value of the horizontal direction normalized luminance patterns RL1 to RL8 according to the above equation (5) is used to determine the presence of the luminance change. It is not a thing. For example, other index values that can determine the magnitude of the luminance change in the block, such as variance values of the horizontal normalized luminance patterns RL1 to RL8, may be used.

  Here, the edge pattern table 181 will be described. FIG. 9 is an explanatory diagram illustrating an example of the edge pattern table 181. In the edge pattern table 181, 16 basic edge patterns (second column in the figure) are recorded in association with pattern numbers (first column in the figure) from “A1” to “A16”. . The horizontal axis of the basic edge pattern represents the position of the coefficient in the block, and the vertical axis represents the coefficient value. That is, the basic edge pattern is data composed of eight coefficient values, like the horizontal normalized luminance pattern.

  These basic edge patterns are obtained by classifying typical shapes of luminance changes in which the sign of inclination does not change within one block into 16 types in advance. In this embodiment, the basic edge patterns are classified into 16 types as described above, but may be classified into more patterns.

  Also, the edge pattern table 181 records three types of parameters associated with the basic edge pattern: left edge width LW, center edge width MW, and right edge width RW. The left edge width LW represents the width of the flat portion present on the left side of the luminance pattern, and the right edge width RW represents the width of the flat portion present on the right side of the luminance pattern. Further, the center edge width MW represents the width of the gradient portion sandwiched between the left edge width LW and the right edge width RW.

  Although not shown in FIG. 9, in addition to the pattern table A in which basic edge patterns from “A1” to “A16” are defined in the edge pattern table 181, as shown in FIG. The pattern table B in which the edge patterns obtained by horizontally inverting the basic edge patterns belonging to these pattern tables A are defined as pattern numbers B1 to B16, and the pattern table C in which the vertically inverted edge patterns are defined as pattern numbers C1 to C16. A pattern table D is defined in which edge patterns inverted up and down and left and right are defined as pattern numbers D1 to D16. That is, 64 types of basic edge patterns are defined in the edge pattern table 181 as a whole.

  Here, the description returns to FIG. If it is determined in step S360 that there is a basic edge pattern in which the average error E with the horizontal normalized luminance patterns RL1 to RL8 is equal to or less than the predetermined threshold Th2 (step S360: Yes), the CPU 160 determines the average error E most. The pattern number associated with the basic edge pattern that decreases is acquired and stored in the pattern number buffer 171 (step S370). On the other hand, if it is determined that there is no basic edge pattern that is less than or equal to the predetermined threshold Th2 (step S360: No), the pattern number “unknown” is acquired and the pattern number buffer is obtained, assuming that the approximate basic edge pattern has not been searched It is stored in 171 (step S380).

  In this embodiment, the average error E between the horizontal normalized luminance patterns RL1 to RL8 and the basic edge pattern Pm (m is an integer from 1 to 8) is calculated using the following equation (7). However, the error may be calculated by other methods.

  If it is determined in step S310 that the difference De1 between the maximum value and the minimum value of the coefficient group is less than the predetermined threshold value Th1 (step S310: No), a large luminance change occurs in the horizontal direction in the block. It can be judged that there is no. Therefore, the CPU 160 determines whether or not the previous block is a flat block and the luminance difference De2 between the target block and the previous block is greater than or equal to a predetermined threshold Th3 (step S320). As a result, if the previous block is a flat block and the luminance difference De2 between the target block and the previous block is greater than or equal to a predetermined threshold Th3 (step S320: Yes), a width between the previous block and the target block is increased. Since it can be determined that there is a short luminance change, it is assumed that there is an edge of width 1, and the pattern number “width 1” is acquired and stored in the pattern number buffer 171 (step S340).

  In this embodiment, the luminance difference De2 between the previous block and the target block is calculated by the following equation (8). However, the present invention is not limited to this, and the luminance difference between the previous block and the target block is calculated as follows. Any method can be used.

  On the other hand, if the previous block is a flat block and the luminance difference De2 between the target block and the previous block is not greater than or equal to the predetermined threshold Th3, there is no significant luminance change in the target block, and there is no gap between the previous block and the previous block. Therefore, it is determined that the target block is flat, the pattern number “flat” is acquired, and stored in the pattern number buffer 171 (step S330). When the pattern number of the block of interest is determined by the above processing, the above-described series of edge pattern matching processing ends, and the process returns to the blur determination processing shown in FIG.

  The edge pattern matching process having such a configuration speeds up the edge pattern matching process because the selection of the basic edge pattern in steps S350 to S370 can be omitted when it is determined that the change in the pixel value in the block is small. be able to.

  In this embodiment, if it is determined in step S360 that there is a basic edge pattern in which the average error E with the horizontal direction normalized luminance patterns RL1 to RL8 is equal to or less than the predetermined threshold Th2 (step S360: Yes). The CPU 160 acquires the pattern number associated with the basic edge pattern with the smallest error, but the present invention is not limited to such a mode. For example, errors are calculated in the order of the arrangement of the basic edge patterns in the edge pattern table 181. If it is determined that there is a basic edge pattern that is equal to or less than a predetermined threshold Th2, the error calculation process is terminated at that point, It is also possible to acquire a pattern number associated with a basic edge pattern whose error is equal to or less than a predetermined threshold Th2. With such an aspect, the speed of the edge pattern matching process can be increased. In this case, it is more effective if the basic edge patterns in the edge pattern table 181 are arranged in the order of the shapes that are found empirically.

  In step S360, when determining whether or not there is a basic edge pattern in which the average error E with the horizontal normalized luminance patterns RL1 to RL8 is equal to or less than the predetermined threshold Th2, the CPU 160 determines the horizontal normalized luminance. Before calculating errors between the patterns RL1 to RL8 and each basic edge pattern stored in the edge pattern table 181, first, S1 and S2 obtained by the following equations (9) and (10) are calculated. It is determined which of the above-described pattern tables A to D corresponds to the horizontal normalized luminance patterns RL1 to RL8, and error calculation is performed on 16 types of basic edge patterns included in the corresponding pattern table. It is good.

S1 = RL1 + RL2 + RL3 + RL4-RL5-RL6-RL7-RL8 (9)
S2 = RL1 + RL2-RL3-RL4 + RL5 + RL6-RL7-RL8 (10)

  If the above equations (9) and (10) are used, as shown in FIG. 10 (b), when S1 and S2 obtained from the horizontal direction normalized luminance patterns RL1 to RL8 are both positive values, It can be determined that the direction normalized luminance patterns RL1 to RL8 correspond to the patterns in the pattern table A. Similarly, when both S1 and S2 are negative values, pattern table B, when S1 is negative and S2 is positive, pattern tables C and S1 are positive and S2 is negative. Can be determined to correspond to the pattern of the pattern table D.

  In this way, any one of the pattern tables A to D may be used to calculate an error with respect to the horizontal normalized luminance patterns RL1 to RL8 for the 64 basic edge patterns included in the pattern tables A to D. Error calculation can be performed for 16 types of basic edge patterns included in one pattern table, and the speed of the edge pattern matching process can be increased.

C-4. Edge connection processing:
FIG. 11 is a flowchart of the edge connection process executed in step S130 of the blur determination process shown in FIG. This process is a process for determining the blur width of the central block (the target block) based on the blocks adjacent in the vertical and horizontal directions. Such processing is performed in the horizontal direction and the vertical direction, but in order to simplify the description, the processing performed in the horizontal direction will be described unless otherwise specified. The CPU 160 uses the edge connection unit 164 to execute edge connection processing according to the following procedure.

  When this process is started, first, the CPU 160 moves the target block to be subjected to the edge connection process along the series (step S500). The first destination is the block that exists at the top left of the window of interest. Each time the processing of step S500 is executed, the block of interest moves to the right, and when it reaches the right edge of the window of interest, it moves back to the left and moves down by the height of the block, and again moves to the right. To do.

  When the target block is moved, the CPU 160 obtains the current pattern number of the target block from the pattern number buffer 171 (step S510).

  Next, the CPU 160 determines whether or not the pattern number acquired in step S510 is “width 1” (step S520). As a result, if the pattern number is “width 1” (step S520: Yes), an edge having a width 1 exists between the block of interest and the block before the block of interest in the edge pattern matching process in step S120. Since this is considered (see step S340 in FIG. 8), the blur width of the block of interest is determined as “1” (step S530). Hereinafter, the determined blur width is referred to as a “determined blur width DW”.

  If it is determined in step S520 that the pattern number is not “width 1” (step S520: No), then the CPU 160 determines whether the pattern number is “unknown” or “flat” (step S520). S540). If the pattern number is “unknown” or “flat” as a result of this determination (step S540: Yes), in the edge pattern matching process in step S120, the horizontal luminance change of the block of interest is any of the basic edge patterns. Is not approximated or determined to be flat (see steps S330 and S380 in FIG. 8). In the present embodiment, even if it does not approximate any of the basic edge patterns, it is treated as “flat”, and the determined blur width DW of the block of interest is determined as “0” (step S550).

  If it is determined in step S540 that the pattern number is not “unknown” or “flat” (step S540: No), the CPU 160 is on the right side of the target block (lower side in the case of vertical edge connection processing). The pattern number of the block adjacent to (hereinafter referred to as “adjacent block”) is acquired from the pattern number buffer 171 (step S560). Then, the pattern number of the target block is compared with the pattern number of the adjacent block, and it is determined whether the inclination directions of the basic edge patterns of these blocks match (step S570).

  FIG. 12 is an explanatory diagram illustrating an example in which the inclination directions of the basic edge patterns are the same between the block of interest and the adjacent block. In the example shown in the drawing, the slopes of both blocks are downwardly inclined to the right. As combinations in which the inclination directions of the target block and the adjacent blocks match, there are the following pattern number combinations. That is, if the combination of the pattern number of the target block and the pattern number of the adjacent block is included in any one of the combinations (1) to (8) shown below, the CPU 160 determines that these inclination directions match. Can do.

Attention block No. Adjacent block No.
(1) A1-A16 A1-A16
(2) A1-A16 D1-D16
(3) B1-B16 B1-B16
(4) B1-B16 C1-C16
(5) C1-C16 C1-C16
(6) C1-C16 B1-B16
(7) D1 to D16 D1 to D16
(8) D1 to D16 A1 to A16

  FIG. 13 is an explanatory diagram illustrating an example in which the inclination directions of the basic edge pattern do not match between the target block and the adjacent block. In the illustrated example, the inclination of the block of interest is lower right, whereas the inclination of the adjacent block is upper right, it can be said that the inclinations of both do not match. Combinations of pattern numbers in which the inclination directions of both blocks do not match are combinations other than (1) to (8) described above.

  If it is determined in step S570 that the inclination directions match (step S570: Yes), the CPU 160 subsequently determines that the added value of the right edge width RW of the target block and the left edge width LW of the adjacent block is It is determined whether it is within a predetermined connection error threshold Th4 (step S580). Prior to this determination, the CPU 160 obtains the left edge width LW with reference to the edge pattern table 181 from the pattern number of the adjacent block.

  FIG. 14 is an explanatory diagram showing a state of comparison between the added value of the right edge width RW of the target block and the left edge width LW of the adjacent block and the connection error threshold Th4. As shown in the drawing, the sum of the flat portion (right edge width RW) present on the right side of the basic edge pattern of the target block and the flat portion (left edge width LW) present on the left side of the basic edge pattern of the adjacent block is large. When the connection error threshold Th4 is exceeded, it can be determined that the gradient is not continuous between the block of interest and the adjacent block, and separate blurred portions are formed. Therefore, in step S580 described above, “No” is determined. On the other hand, when the above-mentioned sum is small and becomes equal to or less than the connection error threshold Th4, it can be considered that the edge pattern of the block of interest and the edge pattern of the adjacent block have a continuous gradient. Then, it is determined as “Yes”.

  If it is determined in step S580 that the added value of the right edge width RW of the target block and the left edge width LW of the adjacent block is within a predetermined connection error threshold Th4 (step S580: Yes), the CPU 160 The accumulated blur width CW accumulated in the left side block so far (upper in the case of vertical edge connection processing) is stored in the horizontal accumulation buffer 173 (in the vertical edge connection processing, in the vertical accumulation buffer 172). Read from. Then, a process of adding the left edge width LW and the central edge width MW of the block of interest to the accumulated blur width CW is performed (step S590). When the new cumulative blur width CW is obtained in this way, the CPU 160 updates the new value by overwriting the horizontal cumulative buffer 173 (vertical cumulative buffer 172 in the case of vertical edge connection processing). In this way, when the gradient of the edge pattern between adjacent blocks continues in one direction, the blur width of each block can be accumulated sequentially. In this embodiment, in step S590, the CPU 160 determines the determined blur width DW of the block of interest as “0” for convenience. That is, when the blur width is accumulated, the block in the middle of accumulation is regarded as a flat block.

  FIG. 15 is an explanatory diagram showing the concept of calculating the cumulative blur width CW in step S590. As shown in the drawing, in step S590, a value obtained by adding the left edge width LW and the central edge width MW of the block of interest to the cumulative blur width CW so far is set as a new cumulative blur width CW. Prior to this calculation, the CPU 160 refers to the edge pattern table 181 to obtain the left edge width LW and the central edge width MW corresponding to the pattern number of the block of interest.

  When it is determined in step S570 that the inclination direction of the target block does not match the inclination direction of the adjacent block (step S570: No), or in step S580, the right edge width RW of the target block and the adjacent block When it is determined that the sum with the left edge width LW exceeds the connection error threshold Th4 (step S580: No), the CPU 160 determines the horizontal accumulation buffer 173 (in the vertical edge connection process, the vertical accumulation buffer). It is determined whether the accumulated blur width CW read from 172) is “0” (step S600). If it is determined by this determination that the cumulative blur width CW is “0” (step S600: Yes), the block of interest is the starting point at which blurring occurs, and further, according to the determination results in steps S570 and S580, Since the edge pattern is not continuous with the adjacent block on the right side (the lower side in the vertical edge connection process), it can be determined that the target block has a single edge pattern. Accordingly, the CPU 160 determines the center edge width MW of the block of interest as the determined blur width DW (step S610). Prior to this process, the CPU 160 refers to the edge pattern table 181 and acquires the center edge width MW corresponding to the pattern number of the block of interest.

  If it is determined in step S600 that the accumulated blur width CW is not “0”, the target block is not continuous with the adjacent block on the right side (lower side in the edge connection processing in the vertical direction). The adjacent block on the left side (upper side in the case of edge connection processing in the vertical direction) is continuous. That is, the target block corresponds to the end of the blurred portion. Therefore, the CPU 160 adds the left edge width LW and the central edge width MW of the block of interest to the accumulated blur width CW read from the horizontal accumulation buffer 173 (vertical accumulation buffer 172 in the case of vertical edge linking processing). This value is determined as the determined blur width DW (step S620). Prior to this processing, the CPU 160 refers to the edge pattern table 181 and acquires the left edge width LW and the central edge width MW corresponding to the pattern number of the block of interest.

  Subsequently, the CPU 160 determines whether the current block of interest is the final block of the window of interest (step S630). As a result, if it is determined that the current block of interest is the final block (step S630: Yes), the above-described series of edge connection processing ends, and the process returns to the blur determination processing shown in FIG. On the other hand, if the current block of interest is not the final block (step S630: No), the CPU 160 returns the process to step S500, and repeatedly executes the various processes described above for the block at the next position.

  In the edge linking process having such a configuration, the edge number is linked using the pattern number associated with the basic edge pattern and the edge width (LW, MW, RW) associated with the basic edge pattern. The verification process can be speeded up.

C-5. Block blur judgment processing:
FIG. 16 is a flowchart of the block blur determination process executed in step S140 of the blur determination process shown in FIG. This process is a process executed subsequent to the above-described edge connection process, and is a process for determining whether or not the block of interest is in focus. The CPU 160 uses the block blur determination unit 165 to execute block blur determination processing according to the following procedure.

  When this process is started, first, the CPU 160 moves a block of interest to be subjected to the block blur determination process along the series (step S700). The first destination is the block that exists at the top left of the window of interest. Each time the process of step S700 is executed, the block of interest moves to the right, and when it reaches the right edge of the window of interest, it moves back to the left and moves downward by the height of the block, and again moves to the right. To do.

  Next, the CPU 160 acquires the determined blur width DW in the horizontal and vertical directions of the block of interest determined by the edge connection process in step S130 (step S710), and the larger value of the two determined blur widths DW. Is determined as the maximum defined blur width MDW (step S720).

  When the maximum determined blur width MDW is determined, the CPU 160 determines whether or not the maximum determined blur width MDW is “0” (step S730). As a result, if the maximum definite blur width MDW is “0” (step S730: Yes), there is no large luminance change in both the horizontal and vertical directions (in this embodiment, the basic edge pattern is unknown, CPU 160 determines that the block of interest is a “flat block” (step S740).

  If the maximum determined blur width MDW is not “0” in step S730 (step S730: No), the CPU 160 determines whether the maximum determined blur width MDW is equal to or smaller than a predetermined blur width threshold Th5 (for example, “16”). It is determined whether or not (step S750). If the maximum definite blur width MDW satisfies this condition as a result of this determination (step S750: Yes), it is determined that the block of interest is a “focus block” (step S770). On the other hand, if the maximum determined blur width MDW is larger than the predetermined blur width threshold Th5 (step S750: No), it is determined that the block of interest is a “blurred block” (step S760).

  When the determination of whether the current block of interest is the “focus block”, the “blurred block”, or the “flat block” is completed by the above processing, the CPU 160 then determines that the current block of interest is the last of the window region of interest. It is determined whether it is a block (step S780). As a result, if it is determined that the current block of interest is the final block (step S780: Yes), the series of block blur determination processes described above is terminated, and the process returns to the blur determination process illustrated in FIG. On the other hand, if the current block of interest is not the final block (step S780: No), the CPU 160 returns the process to step S700, and repeatedly executes the various processes described above for the block at the next position.

  According to the block blur determination process described above, the determined blur width DW having the larger value of the horizontal determined blur width DW and the vertical determined blur width DW is determined as the maximum determined blur width MDW. If the maximum defined blur width MDW is equal to or greater than the predetermined blur width threshold Th5, it is determined that the block of interest is blurred. Therefore, the blur determination can be performed with higher accuracy than in the case where the determination is made in one direction.

C-6. Window blur determination processing:
FIG. 17 is a flowchart of the window blur determination process executed in step S150 of the blur determination process shown in FIG. While the above-described block blur determination process is a process for determining the presence or absence of blur for each block, this window blur determination process is based on the result of the block blur determination process and the window region shown in FIG. This is processing for determining the presence or absence of blur for each time. The CPU 160 uses the window blur determination unit 166 to execute window blur determination processing according to the following procedure.

  When this process is executed, the CPU 160 uses the determination result of the block blur determination process in step 140 to count the number of in-focus blocks and the number of blur blocks for all the blocks in the window of interest (step S800). .

  When the number of blocks is totaled in step S800, the CPU 160 obtains a result obtained by dividing the number of focused blocks Nf by the sum of the number of focused blocks Nf and the number of blurred blocks Nn, as shown in the following equation (11). The focal block rate Rf is calculated, and it is determined whether the focused block rate Rf is equal to or greater than a predetermined focusing threshold Th6 (step S810).

Rf = Nf / (Nf + Nn) (11)

  If the result of this determination is that the total number of in-focus blocks is equal to or greater than the in-focus threshold Th6 (step S810: Yes), the current window of interest is determined as the “in-focus window” (step S830), and the in-focus density threshold is satisfied. If not (No at Step S810), it is determined as a “blurred window” and the process returns to the blur determination process shown in FIG. 4 (Step S820).

  In this embodiment, the focused block rate Rf obtained by the above equation (10) is used for the window blur determination, but the present invention is not limited to this. For example, another method may be used, such as determining window blur depending on whether the number of focused blocks is equal to or greater than a predetermined threshold. In addition, a method that takes into account the number of flat blocks, for example, when the number of flat blocks occupies a predetermined ratio or more with respect to the total number of blocks in the window of interest, a “blurred window” is used regardless of the value of the focused block rate Rf A method such as handling may be used.

  As mentioned above, although several Example and modification of this invention were described, this invention is not limited to such Example, Of course, in the range which does not deviate from the summary of this invention, it can implement in a various aspect. is there. For example, the image processing apparatus according to the present invention is not limited to the printer shown in the embodiments, but can be mounted on a digital still camera, a mobile phone with a camera, a photo viewer for enlarging a photographic image, or the like. Of course, the present invention can also be employed when the photographic image is selected on a computer monitor by being mounted on a computer. The present invention can also be realized in the form of a blur detection method, a blur detection program, or a computer-readable recording medium that records the program.

1 is an explanatory diagram illustrating an appearance of a printer 100 as an example of an image processing apparatus. FIG. 2 is an explanatory diagram illustrating an internal configuration of a printer 100. FIG. 3 is a flowchart of print processing executed by a CPU 160 of the printer 100. 6 is a flowchart of blur determination processing executed in printing processing. It is explanatory drawing which shows the concept of a block and a window. It is a flowchart of the block pattern calculation performed by the blurring determination process. It is explanatory drawing which shows the calculation method of the normalization brightness | luminance pattern showing the brightness | luminance change of the horizontal direction or the vertical direction in the said block from the luminance value of each pixel in a block. It is a flowchart of the edge pattern collation process performed by a blurring determination process. 5 is an explanatory diagram illustrating an example of an edge pattern table 181. FIG. It is explanatory drawing which shows the pattern table of the edge pattern table. It is a flowchart of the edge connection process performed by a blurring determination process. It is explanatory drawing which shows the example in which the inclination direction of a basic edge pattern corresponds with an attention block and an adjacent block. It is explanatory drawing which shows the example from which the inclination direction of a basic edge pattern does not correspond with an attention block and an adjacent block. It is explanatory drawing which shows the mode of comparison with the addition value of the right edge width | variety RW of an attention block, and the left edge width | variety LW of an adjacent block, and connection error threshold value Th4. It is explanatory drawing which shows the calculation concept of the accumulation blur width CW. It is a flowchart of the block blur determination process performed by the blur determination process. It is a flowchart of the window blur determination process performed by the blur determination process.

Explanation of symbols

DESCRIPTION OF SYMBOLS 100 ... Printer 110 ... Scanner 120 ... Memory card slot 130 ... USB interface 140 ... Operation panel 145 ... Liquid crystal display 150 ... Control unit 160 ... CPU
161 ... Image data input unit 162 ... Block pattern calculation unit 163 ... Pattern collation unit 164 ... Edge connection unit 165 ... Block blur determination unit 166 ... Window blur determination unit 170 ... RAM
171 ... Pattern number buffer 172 ... Vertical accumulation buffer 173 ... Horizontal accumulation buffer 180 ... ROM
181 ... Edge pattern table 210 ... Carriage 211 ... Ink head 212 ... Ink cartridge 220 ... Carriage motor 230 ... Motor 260 ... Drive belt 270 ... Platen 280 ... Slide axis P ... Printing paper MC ... Memory card

Claims (17)

An image processing device for detecting blur of a bitmap image configured as a collection of pixels,
An image data input unit for inputting image data for each pixel constituting the image;
At least a part of the image is divided into a plurality of blocks each having a predetermined number of pixels, and the block of the image data is determined based on the input image data of pixels belonging to the block for each block. A block pattern calculation unit for calculating a coefficient group composed of a plurality of coefficients representing a change in a predetermined direction at
A pattern storage unit that classifies and stores a plurality of types of patterns in which representative changes in at least one direction in the block of image data of pixels in the block are represented by the coefficient group as basic patterns;
A pattern matching unit that matches the calculated pattern of change of the coefficient group with the plurality of stored basic patterns and selects a basic pattern to be approximated;
An image processing apparatus comprising: a blur determination unit that determines blur of an image represented by the image data based on an arrangement in the image of each basic pattern selected for the block. The image processing apparatus according to claim 1,
The blur determination unit
A first gradient formed by a change in each coefficient of the coefficient group of the selected basic pattern of the block from which the basic pattern has been selected, and each coefficient group of the basic pattern selected by a block adjacent to the block Continuity testing means for testing continuity with the second slope formed by the coefficient change;
According to the test result of the continuity detecting means, when the gradients are not continuous, the gradient width of the block from which the basic pattern is selected is detected as a blur width indicating the blur level of the block, and the gradients are detected. A blur width detecting means for detecting, as the blur width, a range in which the gradients continue along the series of the blocks in the predetermined direction in the image divided into the blocks,
An image processing apparatus comprising: a blur determination unit that determines blur of an image represented by the image data based on the detected blur width. The image processing apparatus according to claim 2,
The pattern storage unit includes means for storing the basic pattern in association with the pattern number uniquely assigned to each basic pattern,
The pattern matching unit includes means for selecting the pattern number from the pattern storage unit as a basic pattern that approximates the calculated change shape of the coefficient group,
The continuity test means is an image processing apparatus comprising means for testing the continuity between the first gradient and the second gradient with reference to the selected pattern number. The image processing apparatus according to claim 3,
The pattern storage unit further includes means for storing the gradient width of each basic pattern in association with the pattern number,
The blur width detecting unit reads out the width of the gradient associated with the pattern number of the block from which the pattern number is selected from the pattern storage unit when the gradients are not continuous, and calculates the blur width. When the gradients are detected, the width of the gradient associated with the pattern number along the sequence in the predetermined direction of the blocks in the image divided into the blocks is detected by the pattern storage unit. An image processing apparatus comprising means for detecting the blur width as an accumulated value of the gradient width. An image processing apparatus according to any one of claims 2 to 4,
The blur determination means includes
Block blur determination means for determining a blur situation for each block based on the length of the detected blur width;
An image processing apparatus comprising: an image blur determination unit that aggregates the determination results and performs blur determination on the entire image. An image processing apparatus according to any one of claims 2 to 4,
The blur determination means includes
Block blur determination means for determining a blur situation for each block based on the length of the detected blur width;
The blur conditions determined by the block blur determination unit are totaled for all blocks included in the window area composed of the predetermined number of blocks in the image data, and the window area is based on the total result. Window blur determining means for determining whether or not the image is blurred,
As a result of moving the window area in the image data, if there is at least one window area that is determined not to be blurred, it is determined that the entire image represented by the image data is not blurred. An image processing apparatus comprising: The image processing apparatus according to claim 2, wherein:
The predetermined directions are a horizontal direction and a vertical direction,
The block pattern calculation unit includes, as the coefficient group, a first coefficient group representing a horizontal change in the block of the image data, and a first coefficient representing a vertical change in the block of the image data. Means for calculating a coefficient group of 2;
The pattern matching unit includes means for selecting the approximate basic pattern for each of the first coefficient group and the second coefficient group,
The blur detection means includes horizontal / vertical blur width detection means for detecting the blur width in the horizontal direction and the vertical direction, respectively.
The blur determination unit determines a blur of an image represented by the image data based on a larger blur width of the blur widths calculated in the horizontal direction and the vertical direction, respectively. An image processing apparatus including a determination unit. An image processing apparatus according to any one of claims 1 to 7,
The image processing apparatus, wherein the pattern matching unit does not select the basic pattern when satisfying a condition such that the calculated coefficient group falls within a predetermined dispersion range. An image processing apparatus according to any one of claims 1 to 8,
The image processing apparatus, wherein the pattern matching unit selects, from the pattern storage unit, a basic pattern that minimizes an average error with the calculated coefficient group from the plurality of types of basic patterns. An image processing apparatus according to any one of claims 1 to 8,
The pattern matching unit executes error calculation processing for calculating an average error between the plurality of types of basic edge patterns and the calculated coefficient group in the order of the arrangement of the plurality of types of basic patterns, and the calculated error is calculated. An image processing apparatus that terminates the error calculation process and selects a basic pattern having the error equal to or less than a predetermined threshold from the pattern storage unit when the error is equal to or less than a predetermined threshold. An image processing apparatus according to any one of claims 1 to 10,
The block is an image processing apparatus configured with 8 pixels in each of a horizontal direction and a vertical direction. An image processing apparatus according to any one of claims 1 to 11,
The image data is each gradation value in the RGB color space,
The image processing apparatus, wherein the coefficient group is calculated based on a luminance value or brightness for each pixel calculated from each gradation value in the RGB color space. An image processing apparatus according to any one of claims 1 to 12,
Furthermore, an image processing apparatus provided with the presentation part which shows a user the image determined not to be blurred by the blur determination part. The image processing apparatus according to claim 13,
An image processing apparatus further comprising a printing unit that prints an image selected by the user from the presented images. A blur detection method for detecting blur in a bitmap image configured as a collection of pixels,
Input image data for each pixel constituting the image,
At least a part of the image is divided into a plurality of blocks each having a predetermined number of pixels, and the block of the image data is determined based on the input image data of pixels belonging to the block for each block. A coefficient group consisting of a plurality of coefficients representing a change in a predetermined direction at
A pattern storage unit that stores a plurality of types of patterns in which representative changes in at least one direction in the block of image data of pixels in the block are represented by the coefficient group and stored as basic patterns; and the calculation The basic pattern to be approximated is selected by comparing with the change pattern of the coefficient group
A blur detection method for determining blur of an image represented by the image data based on an arrangement of the basic patterns selected for the block in the image. A computer program for detecting blur in a bitmap image configured as a collection of pixels,
A function of inputting image data for each pixel constituting the image;
At least a part of the image is divided into a plurality of blocks each having a predetermined number of pixels, and the block of the image data is determined based on the input image data of pixels belonging to the block for each block. A function of calculating a coefficient group composed of a plurality of coefficients representing a change in a predetermined direction at
A pattern storage unit that stores a plurality of types of patterns in which representative changes in at least one direction in the block of image data of pixels in the block are represented by the coefficient group and stored as basic patterns; and the calculation A function for selecting a basic pattern to be approximated by comparing with a pattern of changes in the coefficient group,
A computer program that causes a computer to realize a function of determining blur of an image represented by the image data based on an arrangement of each basic pattern selected for the block in the image.   The computer-readable recording medium which recorded the computer program of Claim 16.

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