Image processing unit, image processing method, and medium recording image processing program

[0001] The present invention relates to an image processing apparatus, an image processing method, and a medium on which an image processing program is recorded, and more particularly, an image processing apparatus for processing saturation enhancement of image data, image processing, and the like. The method and the medium on which the image processing program is recorded.

[0002] When a photograph is read by a scanner or the like and used as electronic image data, it may be desired to further emphasize the vividness of the original photograph. Conventionally, as a device for performing such emphasis, for example, when the color component of image data is represented by gradation data of red (= R), green (= G), and blue (= B), a desired color is used. Those that increase the value of the component are known.

[0003] That is, when the gradation data is in the range of "0 to 255", "20" is uniformly added to the red gradation data or the blue color is added in order to make the red color more vivid. In order to make it more vivid, "20" was uniformly added to the blue gradation data.

[0004] [Problems to be Solved by the Invention] In the above-mentioned conventional image processing apparatus, a human must determine for each image data how much emphasis is to be performed, and the most suitable one is automatically selected. Could not be applied to.

[0005] The present invention has been made in view of the above problems, and is an image processing device, an image processing method, and an image processing program capable of automatically converting saturation according to different vividness for each image. The purpose is to provide a medium on which images are recorded.

[Means for Solving the Problems] In order to achieve the above object, the invention according to claim 1 is<u style="single">Dot matrix</u>An image processing device that performs predetermined image processing on image data representing information of each pixel, a saturation distribution aggregation means that aggregates the saturation distribution of each pixel in the image data, and this saturation distribution aggregation. Saturation conversion determination means for determining the degree of conversion of the saturation of image data from the saturation distribution status aggregated by the means, and information representing the saturation in the image data based on the determined degree of conversion.<u style="single">New</u>It is configured to include an image data conversion means for converting the image data.

[0007] In the invention according to claim 1, which is configured as described above, the image data is<u style="single">Dot matrix</u>When the saturation distribution aggregation means aggregates the saturation distribution of each pixel in the image data when the information of each pixel is represented, the saturation conversion degree determination means is aggregated by this saturation distribution aggregation means. The degree of saturation conversion of image data is determined from the saturation distribution status, and the image data conversion means is information representing the saturation of the image data based on the determined degree of conversion.<u style="single">New</u>Convert to new image data. That is, the optimum conversion degree is determined from the saturation distribution of the image data for each image and converted.

[0008] In totaling the saturation distribution of each pixel in the image data, if the image data has a saturation parameter, the same parameter may be totaled. Further, even when the same parameter is not provided, for example, a color space having the same saturation parameter can be color-converted from another color space and aggregated based on the converted saturation parameter. It is possible. However, even if it does not rely on such color conversion, as an example, the saturation of each pixel may be determined according to the saturation of the hue of a warm color system in the color component.

[0009] Although it is difficult to determine the saturation without changing the color space, there is a tendency to recognize the difference between warm hues and non-warm hues as vividness from the characteristics of human vision. Yes, it is relatively good to determine the saturation based on this difference.

[0010] Although overlapping with such a tendency, as an example, in the invention according to claim 2, in the image processing apparatus according to claim 1, the color components of the image data are red (= R) and green (=). When it can be expressed by G) and blue (= B), the saturation (= X) is expressed by the following equation.

[0011] X = | G + B-2 × R | ... (1) In the RGB color space often used in computers, etc., when each component matches, it becomes desaturated, and in other cases, it becomes saturated. Occurs. In this case, it is possible to determine the saturation by determining the degree of difference from the desaturation, but if the relational expression is uniformly | G + B-2 × R |, when each component matches. It is preferable because it has the lowest value regardless of the component value, the maximum value in a single color of red or yellow, and a reasonably large saturation in the case of blue or green.

[0012] Of course, the same idea applies to X'= | R + B-2 × G | ... (2) X = | G + R-2 × B | ... (3). Simplicity is obtained. However, as judged by experiment, the best result is obtained in the relational expression of X = | G + B-2 × R | ... (1), which is human as described above. It can be said that there is evidence of recognizing vividness according to the saturation of warm hues as a characteristic of.

[0013] Assuming that the saturation for each pixel is required in this way, the distribution of saturation as an image does not necessarily have to be obtained for all pixels of the image data. For example, the invention according to claim 3 is the above-mentioned claim. In the image processing apparatus according to any one of Item 1 or 2, the image data is thinned out corresponding to a predetermined extraction rate to obtain the saturation distribution state.

[0014] For the purpose of obtaining the distribution, the distribution of the saturation with a certain degree of certainty according to the extraction rate even if the thinning is performed at a predetermined extraction rate without obtaining the saturation for all the pixels. Can be obtained.

[0015] Although there are various thinning methods, the invention according to claim 4 is the image processing apparatus according to claim 3, wherein the predetermined extraction rate is a pixel in the first direction in the image data. Of the number of images and the number of pixels in the second direction orthogonal to the first direction, the extraction rate is such that a predetermined number of extracted pixels can be extracted in the direction in which the number of pixels is small. In this case, a predetermined number of extracts is secured on the short side in the vertical and horizontal directions.

[0016] Since the image is flat, the image data is naturally distributed in the vertical direction and the horizontal direction, but in determining a certain extraction rate, at least a certain number of extractions is secured on the short side. It will maintain the certainty according to the extraction rate.

[0017] Of course, it suffices if the saturation distribution of each pixel in the image data can be aggregated, and the specific method thereof need not be limited to these.

[0018] In determining the degree of saturation conversion from the saturation distribution status, various methods can be adopted for the analysis of the specific distribution status, but as an example, the invention according to claim 5 is In the image processing apparatus according to any one of claims 1 to 4, the saturation conversion determination means has a small saturation, which is a predetermined ratio from the upper end in the aggregated saturation distribution. In some cases, the saturation is emphasized, and when the same saturation is large, the degree of saturation enhancement is weakened to determine the degree of saturation conversion. That is, in the saturation conversion determination means, the saturation is such that the saturation, which is a predetermined ratio from the upper end in the aggregated saturation distribution, is lower than the numerical range that the saturation can take. The degree of saturation conversion is determined by increasing the degree of emphasis.

[0019] That is, as a determination of the state of the saturation distribution, the overall saturation is recognized depending on whether the saturation is high or low when only a certain distribution is taken out from the side with high saturation, and this saturation is determined. If it is small, the saturation is emphasized, and if it is large enough, it is judged that it is not enough to emphasize. Of course, in addition to this, the tendency of the saturation of the entire image data may be determined by adopting another statistical method. Further, the image data may be partially captured instead of being captured as a whole.

[0020] Even if the degree of saturation conversion is determined by the above-mentioned method, good results cannot be obtained if the saturation is weakly emphasized too much, and the saturation and brightness are not obtained. The invention according to claim 6 relates to the above-mentioned image processing apparatus according to any one of claims 1 to 5, wherein the degree of saturation conversion is weakened when the brightness is low. That is, the lower the brightness of the image data in the numerical range that the brightness can take, the weaker the degree of saturation conversion.

[0021] Regarding the brightness or the brightness and the saturation, there is a relationship that the color space has an inverted conical shape up to a certain range, and it can be said that the component value of the hue is not large even if the brightness is low. In such a case, if an attempt is made to apply a conversion degree according to a small saturation, the conical color space may be breached. Therefore, when the brightness is low, the degree of saturation conversion is weakened to prevent this from happening.

[0022] Further, the conversion of saturation may not be appropriate depending on the image data, and as an example, the invention according to claim 7 is in the image processing apparatus according to any one of claims 1 to 6. , The binary image data is determined based on the saturation distribution, and if the binary image data is used, the saturation is not converted.

[0023] If it is binary image data, there are only two types of saturation, and there is no possibility that the saturation can be converted and emphasized. Therefore, it is determined that the data is binary image data from the saturation distribution, and the saturation is not converted.

[0024] As an example of binary image data, there is black-and-white monochrome image data, and in the invention according to claim 8, the saturation distribution is concentrated in achromaticity in the image processing apparatus according to claim 7. It is configured to sometimes judge that it is black and white binary image data.

[0025] There is no saturation whether it is white or black. Therefore, the saturation distribution is also concentrated in desaturation. In such a case, it is sufficient to judge that the data is binary image data and not perform the saturation conversion.

[0026] Further, the invention according to claim 1 determines the frame portion of the image data based on the protruding saturation distribution, and if there is a frame portion, the data in the frame portion is not used for detecting the saturation distribution. There is.

[0027] What can occur frequently when processing an image is to have a frame, and if it exists as a single-color frame, the saturation distribution corresponding to that color naturally protrudes. Therefore, if such a prominent saturation distribution is used as a reference for determining the saturation of an image, it may not be possible to make an effective determination. Therefore, it is determined to be a frame portion and is not used for detecting the saturation distribution.

[0028] Further, the invention according to claim 1 has a configuration in which it is determined that the saturation distribution concentrated in the achromaticity is the frame portion.

[0029] A white frame or a black frame is frequently adopted and can occur depending on the result of trimming, and concentrates on achromaticity. Therefore, the saturation distribution concentrated on this achromaticity is determined as the frame portion.

[0030] By the way, the invention according to claim 9 has a configuration in which the saturation conversion is not performed when the image data is not a natural image in the image processing apparatus according to any one of claims 1 to 8. ..

[0031] It can be said that it is almost unnecessary for a business graph or the like, because it is a natural picture such as a photograph that tends to have a problem such as weak saturation. On the contrary, modifying something like a business graph can result in something different from the creator's image. Therefore, the saturation conversion is performed only in the case of such a natural image.

[0032] As an example of determining whether or not the image is a natural image, in the invention according to claim 10, the image data is natural when the saturation distribution exists in a spectral pattern in the image processing apparatus according to claim 9. The configuration is provided with a natural image determining means for determining that the image is not an image.

[0033] It can be said that the saturation distribution has a smooth width as a feature of natural paintings. Therefore, if the saturation distribution appears in a line spectrum, it can be generally judged that the image is not a natural image. In the invention according to claim 10 configured as described above, the natural image determining means determines the state of the saturation distribution, and if it exists in the line spectrum, it is determined that the image data is not a natural image, thereby coloring. Degree conversion is no longer performed.

[0034] As described above, various methods can be adopted, and are not limited to these, in determining the degree of conversion of the saturation of image data from the distribution of saturation.

[0035] By the way, as a more specific method for converting the saturation of image data, the invention according to claim 11 is described in the image processing apparatus according to any one of claims 1 to 10 above. Degree conversion is performed by shifting in the radial direction according to the above conversion degree in the Luv space, which is a standard chromaticity system.

[0036] That is, if the image data has a saturation parameter, the same parameter may be converted, but the parameter for brightness or lightness and the hue parameter in the plane coordinate system for each brightness are used. In the Luv space, which is the standard color system, the radial direction corresponds to the saturation. Therefore, in the same Luv space, the saturation is converted by the radial shift.

[0037] The Luv space is adopted here because the luminance is independent and the change in saturation does not affect the luminance. However, when using such a Luv space, conversion is required if the original image data does not correspond.

[0038] On the other hand, as an example corresponding to the case where the image data is represented by equal hue components so as to be frequently used in the image data, the invention according to claim 12 has the above claims 1 to 1. In the image processing apparatus according to any one of 10, when the image data is represented by the component values of a plurality of substantially equal hue components, the component values excluding the achromatic component are changed according to the degree of conversion and the saturation. It is configured to perform the conversion of.

[0039] When the image data is represented by the component values of a plurality of substantially equal hue components as in RGB, it can be said that there is an achromatic component. Therefore, the component value excluding the desaturation component has an influence on the saturation, and this component value is changed to convert the saturation. As an example of dealing with this achromaticity component, the invention according to claim 13 is the image processing apparatus according to claim 12, wherein the difference value obtained by subtracting the minimum component value in a plurality of hue components from other component values is obtained. The saturation is converted by increasing or decreasing according to the degree of conversion.

[0040] Of the plurality of color components, the minimum component value is also contained in other color components, and they are merely combined to form an achromatic gray. Therefore, the difference value of other colors exceeding this minimum component value affects the saturation, and the saturation is converted by increasing or decreasing this difference value.

[0041] As another example, in the invention according to claim 14, in the image processing apparatus according to claim 12, a difference value obtained by subtracting an equivalent value of luminance from each component value is obtained according to the degree of conversion. It is configured to increase or decrease the saturation to convert the saturation.

[0042] If the component values excluding the achromatic component are simply changed, the brightness is changed. Therefore, the brightness can be saved by subtracting the equivalent value of the brightness from each component value in advance and performing the saturation conversion so as to increase or decrease the difference value.

[0043] As described above, the method of aggregating the saturation distribution in the image data and converting the image data does not have to be limited to a physical device, and it is easy to function as the method as well. It can be understood. For this reason,<u style="single">Dot matrix</u>It is an image processing method that performs predetermined image processing on image data representing the information of each pixel, and aggregates the distribution of saturation as a whole based on the saturation of each pixel, and the aggregated saturation. Information that indicates the degree of saturation in the image data to be converted based on the degree of conversion of the image data, and based on the determined degree of conversion.<u style="single">New</u>When converting to data image data and aggregating the distribution of the above saturation, the distribution that concentrates on the achromaticity and protrudes is determined as the frame part, and if there is the frame part, the data of the frame part May not be used for detecting the saturation distribution.

[0044] That is, there is no difference in that it is not necessarily limited to a physical device and is effective as a method thereof.

[0045] By the way, such an image processing device may exist alone or may be used in a state of being incorporated in a certain device. The idea of the invention is not limited to this, and various aspects thereof. Is included. Therefore, it can be changed as appropriate, such as software or hardware.

[0046] As an example thereof, even in a printer driver that converts the input image data into image data corresponding to the printing ink and prints the image data on a predetermined color printer, the distribution of saturation in the image data is aggregated. The degree of conversion of the saturation of the image data can be determined from this distribution state, and the image data can be converted based on the same degree of conversion.

That is, the printer driver converts the input image data corresponding to the printing ink, but at this time, the saturation distribution of the input image data is obtained, and the image data is more vivid in the optimum range. The input image data is converted and printed so that various images can be reproduced.

[0048] When the software of an image processing device is used as an example of embodying the idea of the invention, it must be said that the software naturally exists and is used on the recording medium on which the software is recorded. As an example,<u style="single">Dot matrix</u>A computer-readable recording medium that records an image processing program that causes a computer to execute predetermined image processing on image data representing information of each pixel. The above image data is input and the color of each pixel is colored. Based on the step of summarizing the distribution of saturation as a whole based on the degree, the step of determining the degree of conversion of the saturation of the image data from the aggregated saturation distribution status, and the step of determining the degree of conversion determined. Information representing saturation in image data<u style="single">New</u>It is provided with a step of converting to the image data, and when the above-mentioned saturation distribution is aggregated, the distribution that concentrates on the achromaticity and protrudes is determined as the frame portion, and if there is the frame portion, the frame portion is provided. The data of the above may be configured not to be used for detecting the distribution of saturation.

Of course, the recording medium may be a magnetic recording medium or an optical magnetic recording medium, and any recording medium to be developed in the future can be considered in exactly the same manner. In addition, there is no doubt that the duplication stages of the primary and secondary replicas are the same. In addition, the present invention is still used even when a communication line is used as the supply method.

[0050] Further, even when a part is software and a part is realized by hardware, there is no difference in the idea of the invention, and it is necessary to store a part on a recording medium. It may be in a form that is appropriately read according to the situation. Furthermore, it goes without saying that it can also be applied to an image processing device built in a color facsimile machine, a color copier, a color scanner, a digital camera, a digital video, or the like.

[Effect of the Invention] As described above, the present invention provides an image processing apparatus capable of performing optimum saturation conversion according to each image by aggregating the distribution state of saturation. can do.

[0052] Further, the saturation can be aggregated according to the characteristics of human visual recognition.

[0053] Further, according to the second aspect of the present invention, when the RGB color space is adopted, the amount of calculation can be reduced and the saturation can be easily totaled.

[0054] Further, according to the invention of claim 3, the processing amount can be reduced.

[0055] Further, according to the fourth aspect of the present invention, it becomes easy to eliminate the bias of the extraction points of the image and make the saturation distribution accurate.

[0056] Further, according to the fifth aspect of the present invention, the determination is facilitated by determining the saturation at a predetermined ratio portion from the upper end in the saturation distribution state.

[0057] Further, according to the invention of claim 6, it is possible to prevent a defect due to saturation conversion that occurs when the luminance is low.

[0058] Further, according to the invention of claim 7, it is possible to prevent the saturation conversion from being performed in the case of a binary image that does not require the saturation conversion, and the saturation that has already been obtained can be obtained. This can be easily detected from the distribution. Further, according to the invention of claim 8, it is possible to prevent the saturation conversion from being performed in a frequently used black and white image.

[0059] Further, according to the first aspect of the present invention, it is possible to prevent the frame portion unrelated to the image data from being used for detecting the saturation distribution, and from the already obtained saturation distribution, this can be achieved. Can be easily detected. Further, according to the invention of claim 1, a white or black frame portion that tends to occur as a frame portion can be easily detected.

[0060] Further, according to the invention of claim 9, it is possible to prevent the saturation conversion from being performed when the natural image does not require the saturation conversion, and according to the invention of claim 10. For example, images other than natural images can be easily detected from the spectral saturation distribution.

[0061] Further, according to the invention of claim 11, only the saturation can be converted relatively easily.

[0062] Further, according to the invention of claim 12, the saturation can be converted relatively easily even in the case of a color component value frequently used in image data, and according to the invention of claim 13. , The achromatic component can be easily detected by using the minimum component value.

[0063] Further, according to the invention of claim 14, it is possible to convert the saturation while preserving the brightness.

[0064] Further, according to the invention of claim 15, it is possible to provide an image processing method capable of performing optimum saturation conversion according to each image by aggregating the distribution state of saturation. According to the invention of claim 16, it is possible to provide a medium on which an image processing program capable of performing optimum saturation conversion according to each image is recorded in the same manner.

BEST MODE FOR CARRYING OUT THE INVENTION Hereinafter, embodiments of the present invention will be described with reference to the drawings.

[0066] FIG. 1 shows a block diagram of an image processing system according to an embodiment of the present invention, and FIG. 2 shows a specific hardware configuration example in a block diagram.

[0067] In the figure, the image input device 10 outputs image data to the image processing device 20 by capturing an image or the like, and the image processing device 20 performs image processing such as predetermined saturation enhancement and outputs the image. It outputs to the device 30, and the image output device 30 displays the image-processed image.

[0068] Here, a specific example of the image input device 10 corresponds to a scanner 11 or a digital still camera 12, and a specific example of the image processing device 20 corresponds to a computer system including a computer 21 and a hard disk 22. Specific examples of the device 30 include a printer 31 and a display 32.

[0069] In this image processing system, since an attempt is made to adjust an image having low saturation to an optimum level, it may be image data obtained by capturing a photograph with a scanner 11 as an image input device 10. Image data with low saturation taken by the digital still camera 12 is processed and input to the computer system as the image processing device 20.

[0070] The image processing device 20 has at least a saturation distribution totaling means for totaling the saturation distribution and a saturation for determining the degree of conversion of the saturation of the image data from the aggregated saturation distribution status. A conversion degree determining means and an image data converting means for converting image data based on the determined degree of conversion are configured. Of course, the image processing device 20 may also be configured as a color conversion means for correcting the difference in color depending on the model, a resolution conversion means for converting the resolution corresponding to each model, and the like. .. In this example, the computer 21 executes each image processing program stored in the internal ROM or the hard disk 22 while using RAM or the like. In addition to being supplied via various recording media such as CD-ROMs, floppy disks, and MOs, such image processing programs are connected to an external network via a public communication line using a modem or the like, and are software. And data is also downloaded and introduced.

The execution result of this image processing program is obtained as image data whose saturation is adjusted as described later, and the same image can be printed by the printer 31 which is the image output device 30 based on the obtained image data. It is displayed on the display 32 which is the output device 30. More specifically, this image data is RGB (green, blue, red) gradation data, and the image is dots arranged in a grid pattern in the vertical direction (height) and the horizontal direction (width). It is configured as matrix data. That is, the image data represents the information of each pixel by decomposing the image into dots matrix-shaped pixels.

[0072] In the present embodiment, a computer system is incorporated between the image input / output devices to perform image processing, but such a computer system is not always required, and as shown in FIG. A system may be used in which an image processing device for adjusting saturation is incorporated in the digital still camera 12a, and the converted image data is used to be displayed on the display 32a or printed on the printer 31a. Further, as shown in FIG. 4, in the printer 31b that inputs and prints the image data without going through the computer system, the image data input via the scanner 11b, the digital still camera 12b, the modem 13b, or the like is automatically output. It can also be configured to adjust the saturation.

[0073] The flowchart of FIG. 5 schematically shows the present image conversion process, and the saturation distribution aggregation means, the saturation conversion degree determination means, and the image data conversion means are roughly shown for each block indicated by the alternate long and short dash line. And correspond to.

[0074] First, the aggregation processing of the saturation distribution will be described.

[0075] Before explaining how to express the saturation, the pixels to be distributed will be described. As shown in step S102 of FIG. 5, the thinning process for thinning out the target pixels is executed. As shown in FIG. 6, in the case of a bitmap image, it is formed as a two-dimensional dot matrix consisting of predetermined dots in the vertical direction and predetermined dots in the horizontal direction. It is necessary to check the saturation of the pixel. However, this distribution aggregation process aims to obtain the tendency of saturation of the entire image, and does not necessarily have to be accurate. Therefore, it is possible to thin out to the extent that it is within a certain error range. According to the statistical error, the error with respect to the number of samples N can be expressed as approximately 1 / (N ** (1/2)). However, ** represents a power. Therefore, N = 10000 in order to perform processing with an error of about 1%.

[0076] Here, the bitmap screen shown in FIG. 6 has the number of pixels of (width) × (height), and the sampling period ratio is ratio = min (width, height) / A + 1 ... (4). To do. Here, min (width, height) is the smaller of width and height, and A is a constant. Further, the sampling period ratio referred to here indicates the number of pixels to be sampled, and the pixels marked with a circle in FIG. 7 indicate the case where the sampling period ratio = 2. That is, one pixel is sampled every two pixels in the vertical direction and the horizontal direction, and every other pixel is sampled. The number of sampling pixels in one line when A = 200 is shown in FIG.

As is clear from the figure, it can be seen that the number of samples is at least 100 pixels or more when there is a width of 200 pixels or more, except when the sampling period ratio = 1 in which sampling is not performed. Therefore, in the case of 200 pixels or more in the vertical direction and the horizontal direction, (100 pixels) × (100 pixels) = (10000 pixels) is secured, and the error can be reduced to 1% or less.

[0078] Here, min (width, height) is used as a reference for the following reasons. For example, assuming that width &gt;&gt; height as in the bitmap image shown in Fig. 9 (a), if the sampling period ratio is determined by the longer width, as shown in Fig. 9 (b). In addition, it may happen that only two lines, the upper end and the lower end, are extracted in the vertical direction. However, if the sampling period ratio is determined based on the smaller min (width, height), thinning is performed so that the intermediate portion is included even in the smaller vertical direction as shown in Fig. (C). Will be able to. That is, sampling with a predetermined number of extractions is possible.

[0079] In this example, the pixels in the vertical direction and the pixels in the horizontal direction are thinned out at an accurate sampling cycle. This is suitable for processing while thinning out the pixels that are sequentially input. However, when all the pixels are input, the pixels may be selected by randomly specifying the coordinates in the vertical direction and the horizontal direction. In this way, when the minimum required number of pixels such as 10000 pixels is determined, the process of randomly extracting until the number of pixels reaches 10000 is repeated, and the extraction can be stopped when the number of pixels reaches 10000.

[0080] If the pixel data for the pixel selected in this way has saturation as its component element, it is possible to obtain the distribution using the value of the saturation. On the other hand, even in the case of image data in which saturation is not a direct component element, it has a component value that indirectly represents saturation. Therefore, the saturation value can be obtained by converting from a color space in which the saturation is not a direct component element to a color space in which the saturation value is a direct component value. For example, in the Luv space as a standard color system, the L-axis represents brightness (brightness), and the U-axis and V-axis represent hue. Here, in the U-axis and the V-axis, the distance from the intersection of both axes represents the saturation, so (U + V) ** (1/2) is substantially the saturation.

[0081] The color conversion between such different color spaces is not uniquely determined by the conversion formula, and the mutual correspondence is obtained for the color spaces whose coordinates are the respective component values, and this correspondence is obtained. It is necessary to refer to the color conversion table that stores the relationships and perform sequential conversion. Since it is a table, the component values are expressed as gradation values, and in the case of 256 gradations with three-dimensional coordinate axes, a color conversion table of about 16.7 million (256 x 256 x 256) elements is displayed. Must have. In such a case, as a result of considering the efficient use of storage resources, instead of preparing the correspondence for all the coordinate values, usually prepare the correspondence for the appropriate discrete grid points. , Interpolation operation is used together. Since this interpolation operation can be performed through some multiplication and addition, the amount of operation processing becomes enormous.

[0082] That is, if a full-size color conversion table is used, the amount of processing is small, but the table size becomes unrealistic, and if the table size is made a realistic size, the amount of arithmetic processing is unrealistic. Often becomes.

[0083] In view of such a situation, in the present embodiment, the alternative value X of the saturation is obtained as follows by directly using the standard RGB gradation data as the image data.

[0084] X = | G + B-2 × R | ... (1) Originally, the saturation is 0 when R = G = B, and either one RGB color or two colors. It becomes the maximum value when mixed by a predetermined ratio. Although it is possible to express the saturation directly from this property, even with the simple equation (1), if it is a single color of red or cyan, which is a mixed color of green and blue, the saturation will be the maximum value, and each It is "0" when the components are uniform. In addition, the single colors of green and blue have reached about half of the maximum value.

[0085] In a situation such as an RGB color space in which each component independently represents a component of each color, which is indicated by roughly equal hue component component values, X'= | R as described above. + B-2 × G | ... (2) X = | G + R-2 × B | ... (3) can also be substituted.

[0086] In the thinning process, the saturation distribution of the pixels thinned out in the above-mentioned sampling cycle is taken from the RGB image data based on the equation (1). In Eq. (1), the saturation is distributed in the range of the minimum value "0" to the maximum value "511", and the distribution is roughly as shown in FIG. Finally, in step S112, the saturation index of the entire image is obtained from this distribution, but there are some items to consider before that.

[0087] The first is the case where the image is a binary image such as a black-and-white image. The concept of saturation conversion is inappropriate for binary images including black and white images. If there is a black-and-white image as shown in FIG. 11, the saturation distribution based on Eq. (1) for this image is concentrated on "0" as shown in FIG.

[0088] Therefore, when the black-and-white check is performed in step S104, it can be determined whether or not the number of pixels having the saturation "0" matches the number of pixels selected by thinning out. Then, in the case of a black-and-white image, the present image conversion process is terminated without executing the following process.

[0089] The binary data is not limited to black and white, and may be colored binary data. In such a case as well, the process of converting the saturation is not necessary, and if the distribution is examined and the distribution is concentrated on only two or one saturation, it is judged as binary data and the process is interrupted. Should be planned.

The second considers whether the image is a business graph-like or photographic-like natural image. In natural paintings, the process of enhancing saturation may be required, but in business graphs and drawing-type drawings, it is often preferable not to emphasize contrast. Therefore, in step S106, it is checked whether or not the image is a natural image.

[0091] In natural paintings, the number of colors including shadows is extremely large, but in business graphs and draw-type drawings, the number of colors is often limited. Therefore, if the number of colors is small, it can be determined that the image is not a natural image. In order to accurately determine the number of colors, it is necessary to determine how many of the 16.7 million colors are used as described above, but this is not realistic. On the other hand, when the number of colors is extremely small as in a business graph, there is a low probability that different colors will have the same saturation. That is, the approximate number of colors can be determined from the saturation. If the number of colors is small, the distribution of saturation is sparse, and in a business graph or the like, it appears in a line spectrum as shown in FIG. Therefore, in step S106, it is counted how many saturations whose distribution number is not "0" appear among the 512 levels of saturation. Then, if it is "128" or less, it is determined that the image is not a natural image, and the present image conversion process is terminated without executing the following process as in the case of binary data. Of course, it is possible to change whether or not the threshold value is "128" or less.

[0092] Further, whether or not the distribution is in the form of a line spectrum can be determined by the adjacent ratio of the luminance values whose number of distributions is not "0". That is, it is determined whether or not the number of distributions is a saturation other than "0" and the number of distributions is in the adjacent saturation. If at least one of the two adjacent saturations is adjacent, do nothing, and if both are not adjacent, count, and as a result, the ratio of the number of non-zero saturations to the count value. You just have to judge. For example, if the number of saturations other than "0" is "20" and the number of non-adjacent ones is "20", it can be seen that they are distributed in a line spectrum.

[0093] Further, when the image processing program is executed via the operating system, it is possible to judge by the extension of the image file. Of the bitmap files, especially photographic images, the files are compressed, and an implicit extension is often used to indicate the compression method. For example, if the extension is "JPG", it can be seen that it is compressed in JPEG format. Since the operating system manages the file name, if you make an inquiry to the operating system from the printer driver etc., the extension of the file will be answered, so in a natural picture based on that extension It is sufficient to judge that there is, and to emphasize the contrast. It can also be determined that saturation enhancement is not performed if the extension is peculiar to a business graph such as "XLS".

[0094] The third consideration is whether or not there is a frame around the image as shown in FIG. If such a frame is white or black, the saturation distribution is as shown in FIG. 15, the distribution of the number of pixels with saturation "0" is prominent, and it is smooth corresponding to the natural image inside. It also appears as a saturation distribution.

[0095] Of course, it is appropriate not to take the frame portion into consideration of the saturation distribution. Therefore, in the check of the frame portion in step S108, the number of pixels with the saturation "0" is sufficiently large, and the frame portion is selected by thinning out. It is determined whether or not the number of pixels matches, and if it is affirmative, it is determined that there is a frame portion, and the frame portion processing is performed in step S110. In this frame portion processing, in order to ignore the frame portion, the number of pixels with saturation "0" is made the same as the number of pixels with adjacent saturation "1", and this difference value is subtracted from the total number of pixels. .. As a result, in the following processing, it can be treated as if there is no frame portion.

[0096] In this example, a white or black frame portion is targeted, but there may be a case where there is a frame of a specific color. In such a case, a line spectrum shape that protrudes in the original smooth curve drawn by the saturation distribution appears. Therefore, a line spectrum-like object in which there is a large difference between adjacent saturations may be considered as a frame and not included in the saturation distribution. In this case, since the color may be used in other than the frame portion, the average of the saturation values on both sides may be assigned.

[0097] After taking the above considerations into consideration, the saturation index for this image is determined in step S112 based on the aggregated saturation distribution. After taking the above considerations into consideration, it is assumed that the aggregated saturation distribution is as shown in FIG. In the present embodiment, the range occupied by the upper "16%" as the number of distributions is obtained in the range of the effective number of pixels obtained by subtracting the number of pixels that should not be considered as described above. Then, the saturation index S is determined based on the following equation assuming that the lowest saturation "A" in this range represents the saturation of this image.

That is, if A &lt;92, then S = -A × (10/92) +50 ... (5) If 92 A &lt;184, then S = -A × (10/46) +60 ... (6) ) If 184 A &lt;230, then S = -A × (10/23) +100 ... (7) If 230 A, then S = 0 ... (8). FIG. 16 shows the relationship between the saturation A and the saturation index S. As shown in the figure, the saturation index S is large when the saturation "A" is small and decreases when the saturation "A" is large in the range from the maximum value "50" to the minimum value "0". It will change gradually.

[0099] In this embodiment, the saturation occupied by a certain percentage of the top in the range of the aggregated saturation distribution is used, but the saturation is not limited to this, and for example, an average value is calculated or a median is obtained. It may be used as a basis for calculating the saturation index. However, when a certain percentage of the top in the saturation distribution is taken, the influence of the sudden error is weakened, so that good results can be obtained as a whole.

[0100] Even if the saturation is converted, it is mostly emphasized and often not weakened. Of course, it is possible to consider not only the case of emphasizing as necessary but also the case of weakening, but the following explanation is based on the premise of emphasizing. In this regard, the saturation index S should be read as the saturation enhancement index S.

[0101] In emphasizing the saturation based on the saturation enhancement index S, if the image data has a saturation parameter as described above, the same parameter may be converted, but as in this case. When the RGB chromaticity space is adopted, it can be performed by once converting to the Luv space which is a standard chromaticity system and shifting in the radial direction in the Luv space as follows.

[0102] First, the RGB gradation data (Rx, Gx, Bx) is converted into the Luv gradation data (L, u, v) with reference to the color conversion table, and then the same gradation data is obtained based on the following equation. Convert (L, u, v).

[0103] u'= (S + 100) / 100 × u ... (9) v'= (S + 100) / 100 × v ... (10) As described above, the saturation enhancement index S is Since the range is from the maximum value "50" to the minimum value "0", (u, v) is multiplied by a maximum of 1.5 to become (u', v'). If the image data is RGB, the image conversion process is terminated by reconverting the Luv gradation data (L, u', v') to RGB.

[0104] In the conversion of Eqs. (9) and (10), emphasis is given regardless of the parameter L of brightness (brightness), but such conversion may not be preferable. FIG. 17 shows a state in which the Luv space is cut in a pseudo-vertical manner. As shown in the figure, this color space has a shape in which the bottom surfaces of two cones having vertices at L = 0 and L = 100 face each other. Therefore, when the coordinate values of (u, v) are shifted outward in the radial direction based on Eqs. (9) and (10), the B1 point becomes the B1'point as long as the brightness L is not so small. Although it can be changed, if the brightness L is changed from the B2 point to the B2'point in the range where the brightness L is very small, it breaks through the inverted conical space. Such a conversion is not possible in reality and appears as a result of hue shift.

[0105] Therefore, as a countermeasure, the saturation enhancement index S is changed according to the luminance L. That is, if L &lt;30, then S'= S × 0 = 0 ... (11) 30 L &lt;50, then S'= S × 0.8 ... (12) If 50 L, then S'= S ... ( 13). Originally, even a portion having a large brightness L will break through from the conical space, and it is conceivable to apply the same correction. However, the image data in the portion having a large brightness L is close to white when printed by a printer, and the influence of color shift is hardly felt. Therefore, it was obtained that such correction does not have to be performed in order to speed up the processing.

[0106] Until now, since RGB image data has been once converted into image data in Luv space and then returned to RGB after saturation enhancement, the amount of calculation has to be increased. .. Therefore, a modified example of emphasizing saturation by using RGB gradation data as it is will be described below.

[0107] When each component is a component value of a hue component having a substantially equal relationship as in the RGB color space, if R = G = B, it is gray and becomes achromatic. Therefore, considering that the minimum value of each component of RGB is merely reduced in saturation without affecting the hue of each pixel, the minimum value of each component is set from all the component values. It can be said that the saturation can be emphasized by subtracting and enlarging the difference value.

[0108] First, the saturation enhancement parameter Sratio, which is advantageous for calculation, is obtained from the saturation enhancement index S described above as Sratio = (S + 100) / 100 ... (14). In this case, when the saturation enhancement index S = 0, the saturation enhancement parameter Sratio = 1 is set and the saturation is not emphasized. Next, assuming that the component value of blue (B) in each component (R, G, B) of the RGB gradation data is the minimum value, this saturation enhancement parameter Sratio is used to convert as follows.

[0109] R'= B + (RB) × Sratio ... (15) G'= B + (GB) × Sratio ... (16) B'= B ... (17) As a result, the RGB color space Since it is not necessary to perform two color conversions that make one round trip between the and Luv space, the calculation time can be reduced. In this embodiment, a method of simply subtracting the minimum value component from the other component values is adopted for the achromaticity component, but another conversion formula is adopted when subtracting the achromaticity component. It doesn't matter what you do. However, in the case of only subtracting the minimum value as in equations (15) to (17), there is an effect that the amount of calculation becomes easy because multiplication and division are not involved.

[0110] Even when the equations (15) to (17) are adopted, good conversion is possible, but in this case, when the saturation is emphasized, the brightness tends to be improved and the whole becomes brighter. Therefore, in the next modification, the conversion is performed for the difference value obtained by subtracting the equivalent value of the luminance from each component value.

[0111] First, in order to obtain the brightness, if the color is converted to the Luv space described above, the amount of calculation becomes large. Therefore, the following equation for directly obtaining the brightness from RGB used in the case of television or the like. Use the conversion formula of.

[0112] Y = 0.30R + 0.59G + 0.11B ... (18) On the other hand, saturation enhancement is R'= R + R ... (19) G'= G + G ... (20) Let B'= B + B ... (21). The addition / subtraction values ΔR, ΔG, ΔB are calculated by the following equations based on the difference value from the brightness. That is, R = (RY) × Sratio ... (22) G = (GY) × Sratio ... (23) B = (BY) × Sratio ... (24), and as a result, R '= R + (RY) × Sratio ... (25) G'= G + (GY) × Sratio ... (26) B'= B + (BY) × Sratio It can be converted as ... (27). The preservation of brightness is clear from the following equation.

【0113】<img file="JP4126509B2_D0001.tif" />Also, when the input is gray (R = G = B), the brightness Y = R = G = B, so the addition / subtraction value R = G = B = 0, and the achromatic color is not colored. .. If equations (25) to (27) are used, the brightness is preserved, and even if the saturation is emphasized, it does not become bright as a whole.

[0114] In order to convert the image data in step S114, the converted RGB gradation data (R', G', B') is obtained from the RGB gradation data of each pixel by one of these methods. Such work will be performed for all pixels.

[0115] Next, the operation of the present embodiment having the above configuration will be described step by step.

[0116] Assuming that a photograph is taken by a scanner 11 or the like, the image data representing the photograph as RGB gradation data is taken into the computer 21, and the CPU executes the image processing program shown in FIG. 5 to obtain the image data. Executes the process of emphasizing the saturation of.

[0117] First, in step S102, the image data is thinned out within a range within a predetermined error, the saturation X of the selected pixel is obtained, and the distribution is aggregated. Since the distribution as it is cannot be used, first, it is determined in step S104 whether the image is a binary image such as black and white, and in step S106 it is determined whether it is a natural image. Except when it is a binary image or when it is not a natural image, in step S108, it is determined whether or not there is a frame part in the image data, and if there is a frame part, the saturation distribution obtained by removing the frame part is about the upper predetermined distribution range. Find the minimum saturation A.

[0118] Once this saturation A is obtained, the saturation index (saturation enhancement index) S is determined from the belonging range of the saturation "A" based on the following equation.

[0119] If A &lt;92, then S = -A × (10/92) +50 ... (5) If 92 A &lt;184, then S = -A × (10/46) +60 ... (6) 184 If A &lt;230, then S = -A x (10/23) +100 ... (7) If 230A, then S = 0 ... (8) Based on the saturation enhancement index S obtained in this way. , Image data is converted in step S114. As an example, if RGB gradation data is used directly while preserving brightness, R'= R + (RY) × Sratio ... (25) G' = G + (GY) × Sratio ... (26) B'= B + (BY) × Sratio The image data for all pixels is converted based on each equation (27). As a result, even in the case of a photograph in which the saturation is weak, the saturation changing in a narrow range can be emphasized and a vivid image can be obtained. After that, if it is output to the display 32, if it is output as RGB, a colorful image will be reproduced on the screen, and if it is output to the printer 31, it will be converted to the CMYK color space of color ink. After that, a colorful image is reproduced on the paper surface by gradation conversion and printing output.

[0120] Of course, as described above, such image processing is not performed when the image is not a binary image or a natural image. Further, in the above-described embodiment, the selection conditions of the saturation enhancement index and the like are fixed, but the user may be able to select the saturation enhancement index via a predetermined GUI on the computer 21. In this way, it becomes possible to automatically convert to the optimum range based on the value set by the user. In particular, if it is not converted to Luv space, it may not always be possible to say that the hue is preserved. In such a case, the saturation enhancement index S is weakened to prevent the rotation of the dye. It suffices to make it possible to ignore the hue shift. Further, it is sufficient to make it possible to select a plurality of conversion methods described above, or to prepare an optimum saturation enhancement index setting for each of them. It is also possible for the user to specify a part of the image data and execute the saturation enhancement process only within the range.

[0121] In this way, after obtaining the saturation distribution for the pixels of the image data while thinning out in step S102, the saturation of the image is used by using the lowest saturation in the range occupied by the upper predetermined distribution ratio. Since the saturation enhancement index S is calculated by the saturation conversion degree as well as the saturation (step S112), it becomes possible to automatically determine the saturation enhancement degree different for each image. After that, by converting the image data by a predetermined saturation enhancement conversion formula (step S114), the saturation of the image can be made vivid.

BRIEF DESCRIPTION OF THE DRAWINGS FIG. 1 is a block diagram of an image processing system to which an image processing apparatus according to an embodiment of the present invention is applied.

FIG. 2 is a block diagram showing a specific hardware configuration example of the image processing device.

FIG. 3 is a schematic block diagram showing another application example of the image processing apparatus of the present invention.

FIG. 4 is a schematic block diagram showing another application example of the image processing apparatus of the present invention.

FIG. 5 is a flowchart showing a saturation conversion process in the image processing apparatus of the present invention.

FIG. 6 is a diagram showing coordinates in a conversion source image.

FIG. 7 is a diagram showing a sampling period.

FIG. 8 is a diagram showing the number of sampling pixels.

FIG. 9 is a diagram showing a relationship between a conversion source image and sampled pixels.

FIG. 10 is a schematic diagram of an aggregated state of saturation distribution.

FIG. 11 is a diagram showing a black and white image.

FIG. 12 is a diagram showing a saturation distribution of a black and white image.

FIG. 13 is a diagram showing a saturation distribution when the image is not a natural image.

FIG. 14 is a diagram showing an image with a frame portion.

FIG. 15 is a diagram showing a saturation distribution of an image having a frame portion.

FIG. 16 is a diagram showing the relationship between saturation A and saturation enhancement index S.

FIG. 17 is a diagram showing the enhancement limit of saturation in Luv space.

[Code description] 10 ... Image input device 11 ... Scanner 11b ..., Scanner 12 ... Digital still camera 12a ... Digital still camera 12b ... Digital still camera 13b ... Moderator 20 ... image processing device 21 ... computer 22 ... hard disk 30 ... image output device 31 ... printer 31a ... printer 31b ... printer 32 ... display 32a ... display