Image processing apparatus, method, and program

Abstract

To provide an image processing apparatus that can obtain an appropriate color conversion result regardless of an area on an image.SOLUTION: An image processing apparatus comprises: input means that inputs image data; and color conversion means that, when a target pixel having the difference in the pixel value between the target pixel and a peripheral pixel satisfies a condition in the image data input by the input means, executes color conversion on the target pixel by using a first color conversion method, and when the target pixel does not satisfy the condition, executes color conversion on the target pixel by using a second color conversion method different from the first color conversion method. Gradation properties are well maintained in a result of the color conversion executed by using the first color conversion method than in a result of the color conversion executed by using the second color conversion method.SELECTED DRAWING: Figure 3

Description

[Technical Field]

[0001] The present invention relates to an image processing device capable of performing mapping, a method for the image processing device, and a program. [Background technology]

[0002] Printers are known that, based on digital document data described in a specific color space, map each color in the color space to a color gamut that can be reproduced by the printer and output an image. For example, printers are known that identify objects in the document, perform colorimetric mapping for graphic areas, and perform perceptual mapping for photographic areas.

[0003] When colorimetric mapping is performed, color degeneration may occur in multiple colors contained in a digital document. Patent Document 1 describes a method for mapping colors to a color gamut that can be reproduced by a printer, by increasing the distance between colors that cause color degeneration, thereby reducing the degree of color degeneration. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Japanese Patent Application Publication No. 2024-008265 Summary of the Invention [Problem to be solved by the invention]

[0005] When a color conversion method is applied depending on the region on an image, an appropriate color conversion result may not be obtained if the region includes a region where gradation needs to be maintained.

[0006] An object of the present invention is to provide an image processing device, method, and program that can obtain appropriate color conversion results regardless of the area on the image. [Means for solving the problem]

[0007] In order to solve the above problem, the image processing device of the present invention comprises an input means for inputting image data, and a color conversion means for performing color conversion on a target pixel using a first color conversion method if the target pixel has a pixel value difference between the target pixel and surrounding pixels in the image data input by the input means and performing color conversion on the target pixel using a second color conversion method different from the first color conversion method if the target pixel does not satisfy the condition, and is characterized in that the result of color conversion performed using the first color conversion method maintains more gradation than the result of color conversion performed using the second color conversion method. [Effects of the Invention]

[0008] According to the present invention, it is possible to obtain appropriate color conversion results regardless of the area on the image. [Brief explanation of the drawings]

[0009] [Figure 1] FIG. 1 is a block diagram showing a configuration of an image processing device. [Figure 2] FIG. 2 is a diagram illustrating a recording head. [Figure 3] 10 is a flowchart showing processing in the image processing device. [Figure 4] 10 is a flowchart showing processing in the image processing device. [Figure 5] 10 is a flowchart showing the processing of S202. [Figure 6] 1A and 1B are diagrams for explaining color degeneration and its correction. [Figure 7] FIG. 10 is a diagram for explaining a setting process of a first region. [Figure 8] 10 is a flowchart showing processing in the image processing device. [Figure 9] 10 is a flowchart showing processing in the image processing device. [Figure 10] FIG. 10 is a diagram for explaining the determination process in S104. [Figure 11]FIG. 10 is a diagram for explaining a color conversion method. DETAILED DESCRIPTION OF THE INVENTION

[0010] Hereinafter, embodiments will be described in detail with reference to the accompanying drawings. Note that the following embodiments do not limit the scope of the invention claimed. Although multiple features are described in the embodiments, not all of these multiple features are necessarily essential to the invention, and multiple features may be combined arbitrarily. Furthermore, in the accompanying drawings, the same reference numerals are used to designate the same or similar components, and redundant explanations will be omitted.

[0011] [First embodiment] The terms used in this specification are defined as follows.

[0012] (color gamut) The term "color gamut" is also referred to as the color reproduction range, color gamut, or gamut. Generally, the term "color gamut" refers to the range of colors that can be reproduced in an arbitrary color space. The color gamut volume is an index used to express the size of the color gamut. The color gamut volume is a three-dimensional volume in an arbitrary color space. The chromaticity points that make up the color gamut may be discrete. For example, a specific color gamut may be represented by 729 points on the CIE-L*a*b* space, and the points between them may be calculated using known interpolation methods such as tetrahedral interpolation or cubic interpolation. In such cases, the corresponding color gamut volume can be calculated by accumulating the volumes of the tetrahedrons or cubes that make up the color gamut in the CIE-L*a*b* space, depending on the interpolation method used. The color gamut and color gamut in this embodiment are not limited to a specific color space. However, in this embodiment, the color gamut in the CIE-L*a*b* space is used as an example. Furthermore, the numerical values ​​of the color gamut in this embodiment indicate the volume when cumulatively calculated in the CIE-L*a*b* space on the premise of tetrahedral interpolation.

[0013] (gamut mapping) Gamut mapping is the process of converting between different color gamuts, such as mapping an input color gamut to the output color gamut of a device like a printer. Common ICC profiles include Perceptual, Saturation, and Colorimetric. Mapping can be achieved using a 3D lookup table (3DLUT). Alternatively, mapping can be performed after color space conversion to a standard color space. For example, if the input color space is sRGB, it is converted to the CIE-L*a*b* color space, and then mapped to the output color gamut in the CIE-L*a*b* color space. Mapping can be performed using a 3DLUT or a conversion formula. Furthermore, conversions between the input and output color spaces can be performed simultaneously. For example, the input color space can be sRGB, and the output color space can be converted to the printer's native RGB or CMYK values.

[0014] (Manuscript data) Original data refers to the entire input digital data to be processed. Original data can include one or more pages. Each individual page can be held as image data or expressed as drawing commands. If expressed as drawing commands, the data can be rendered and converted into image data before processing. Image data is made up of multiple pixels arranged two-dimensionally. The pixels hold information representing colors in a color space. Examples of information representing colors include RGB values, CMYK values, K values, CIE-L*a*b* values, HSV values, and HLS values. Note that this embodiment can be applied to both one page and multiple pages. In this embodiment, as an example, original data of one page will be described as image data.

[0015] (Color degeneration) In this embodiment, when gamut mapping is performed on any two colors, color degeneration is defined as the phenomenon in which the distance between the colors after mapping in a specified color space is smaller than the distance between the colors before mapping. Specifically, assume that a digital document contains colors A and B, and mapping to the printer's color gamut converts color A to color C and color B to color D. In this case, color degeneration is defined as the distance between colors C and D being smaller than the distance between colors A and B. When color degeneration occurs, colors that are perceived as different in the digital document are perceived as the same color when printed. For example, in a graph, different items are colored differently to make them appear different. When color degeneration occurs, different colors may be perceived as the same color, leading to the possibility that different items on the graph are mistakenly perceived as the same item. The specified color space used to calculate the distance between colors here may be any color space. For example, these include the sRGB color space, Adobe RGB color space, CIE-L*a*b* color space, CIE-LUV color space, XYZ color space, xyY color space, HSV color space, and HLS color space.

[0016] FIG. 1 is a block diagram showing an example of the configuration of an image processing device according to this embodiment. The image processing device 101 may be, for example, a PC, a tablet, a server, or a recording device. FIG. 1 shows an example in which the image processing device 101 is configured separately from a recording device 108. The CPU 102 reads programs stored in a storage medium 104, such as a hard disk drive (HDD) or a read-only memory (ROM), into a RAM 103 serving as a work area, and executes the programs to perform various image processing. For example, the CPU 102 receives commands from a user via a human interface device (HID) I / F (not shown). The CPU 102 then performs various image processing operations in accordance with the received commands and the programs stored in the storage medium 104. The CPU 102 also performs predetermined processing on manuscript data received via a data transfer I / F 106 in accordance with the programs stored in the storage medium 104. The CPU 102 then displays the results and various information on a display (not shown) and transmits the results and various information as recording data via the data transfer I / F 106.

[0017] The image processing accelerator 105 is hardware capable of executing image processing faster than the CPU 102. The image processing accelerator 105 is activated when the CPU 102 writes parameters and data required for image processing to a predetermined address in the RAM 103. The image processing accelerator 105 reads the parameters and data and then executes image processing on the data. However, the image processing accelerator 105 is not an essential element, and equivalent processing may be executed by the CPU 102. Specifically, the image processing accelerator is a GPU or a specially designed electrical circuit. The parameters may be stored in the storage medium 104 or may be obtained from an external device via the data transfer I / F 106. The image processing device 101 is not limited to the configuration shown in FIG. 1 and may have a configuration appropriate for the functions executable by the device used as the image processing device 101.

[0018] In the recording device 108, the CPU 111 reads out a program stored in the storage medium 113 into the RAM 112, which serves as a work area, and executes the program, thereby providing overall control over the recording device 108. The image processing accelerator 109 is hardware capable of executing image processing faster than the CPU 111. The image processing accelerator 109 is activated when the CPU 111 writes parameters and data required for image processing to a predetermined address in the RAM 112. After reading the parameters and data, the image processing accelerator 109 executes image processing on the data. However, the image processing accelerator 109 is not an essential element, and equivalent processing may be executed by the CPU 111. The parameters may be stored in the storage medium 113, or may be stored in storage (not shown), such as a flash memory or a HDD.

[0019] Here, we will explain the image processing performed by the CPU 111 or the image processing accelerator 109. Image processing is, for example, processing that generates data indicating the ink dot formation positions during each scan by the print head 115 based on the acquired print data. The CPU 111 or the image processing accelerator 109 performs color conversion processing and quantization processing on the acquired print data.

[0020] The color conversion process is a process for separating colors into ink densities that can be handled by the printing device 108. For example, the acquired printing data includes image data that represents an image. If the image data represents an image in color space coordinates such as sRGB, which are the representation colors of a monitor, the data that represents the image in the sRGB color coordinates (R, G, B) is converted into ink data (CMYK) that can be handled by the printing device 108. The color conversion method is realized by matrix calculation processing or processing using 3DLUT or 4DLUT, etc.

[0021] In this embodiment, the recording device 108 uses, as an example, black (K), cyan (C), magenta (M), and yellow (Y) inks for recording. Therefore, image data of RGB signals is converted into image data consisting of 8-bit color signals for K, C, M, and Y, respectively. Each color signal corresponds to the amount of ink applied. Although four ink colors (K, C, M, and Y) have been used as an example, other ink colors, such as fluorescent ink (F), light-density light cyan (Lc), light magenta (Lm), or gray (Gy) ink, may be used to improve image quality. In this case, color signals corresponding to those inks are generated.

[0022] After the color conversion process, the ink data is subjected to a quantization process. The quantization process reduces the number of gradation levels in the ink data. In this embodiment, quantization is performed using a dither matrix that arranges threshold values ​​for comparison with the ink data value for each pixel. After the quantization process, binary data is ultimately generated that indicates whether or not a dot will be formed at each dot formation position.

[0023] After image processing, the printhead controller 114 transfers the binary data to the printhead 115. At the same time, the CPU 111 performs printing control via the printhead controller 114 to operate a carriage motor (not shown) that drives the printhead 115, and also to operate a transport motor that transports the print medium. The printhead 115 scans over the print medium, and at the same time, the printhead 115 ejects ink droplets onto the print medium, thereby forming an image.

[0024] The image processing device 101 and the recording device 108 are connected via a communication line 107. In this embodiment, a local area network (LAN) is described as an example of the communication line 107, but it may also be a USB hub, a wireless communication network using a wireless access point, or a connection using a Wi-Fi Direct communication function. Furthermore, devices other than the image processing device 101 and the recording device 108 may be connected to the communication line 107. For example, a host PC may be connected to the communication line 107, and the image processing device 101 and the recording device 108 may each be configured to be able to communicate with the host PC.

[0025] The following description will be given assuming that the recording head 115 has nozzle rows for four color inks: cyan (C), magenta (M), yellow (Y), and black (K).

[0026] FIG. 2 is a diagram illustrating a print head 115 according to this embodiment. In this embodiment, an image is printed in a unit area corresponding to one nozzle array through multiple scans N times. The print head 115 includes a carriage 116, nozzle arrays 117(K), 118(C), 119(M), and 120(Y), and an optical sensor 122. The carriage 116, which carries the five nozzle arrays 117, 118, 119, and 120 and the optical sensor 122, can move back and forth along the X direction (main scanning direction) in the figure by the driving force of a carriage motor transmitted via a belt 121. As the carriage 116 moves in the X direction relative to the print medium, ink droplets are ejected from each nozzle of the nozzle array in the direction of gravity (-z direction in the figure) based on image data. This prints an image corresponding to 1 / N main scanning passes on the print medium placed on a platen 123. When one main scan is completed, the recording medium is transported in a transport direction that intersects with the main scan direction (the -y direction in the figure) by a distance corresponding to the width of 1 / N of the main scan. Through these operations, an image with a width equivalent to one nozzle row is recorded over N scans. By alternately repeating such main scans and transport operations, an image is gradually formed on the recording medium. In this way, control is performed to complete image recording on a specified area.

[0027] As an example, in this embodiment, it is assumed that a graphic area has a gradation area (gradation area), such as a bar graph with a gradation representation, and the rest of the graphic area is solid. When a color conversion method is applied based on the results of area detection, the color conversion method applied to the detected graphic area will also be applied to the gradation area. This becomes more likely the greater the proportion of solid areas compared to gradation areas. As a result, the gradation will be lost, resulting in unnatural gradation steps and color shifts. Therefore, in this embodiment, by determining the color conversion method on a pixel-by-pixel basis, an appropriate color conversion method can be applied even in cases such as those described above.

[0028] Fig. 3 is a flowchart showing the processing in the image processing device 101. The processing in Fig. 3 is realized, for example, by the CPU 102 reading and executing a program stored in the RAM 103. In this embodiment, the processing in Fig. 3 is described as being executed by the image processing device 101, but it may also be executed by the recording device 108, or may be configured so that the processing is shared between the image processing device 101 and the recording device 108.

[0029] In S101, the CPU 102 acquires manuscript data to be recorded in the recording device 108. Specifically, for example, the CPU 102 receives the manuscript data from an external host PC via the data transfer I / F 106 of the image processing device 101. Note that the manuscript data is assumed to be image data configured by arranging multiple pixels two-dimensionally. Also, the manuscript data is assumed to be RGB data defined in the RGB color space.

[0030] In S102, the CPU 102 focuses on a pixel on the image represented by the image data. Then, the CPU 102 determines whether the difference in pixel value between the pixel of interest and its surrounding pixels is within a predetermined range. The pixel of interest may be selected individually, or multiple pixels, such as four or sixteen pixels, may be analyzed as a group by lowering the resolution. In the case of multiple pixels, the analyzed pixel value may be the average of the pixel values ​​of the multiple pixels, or the pixel value of a specific pixel among the multiple pixels. Lowering the resolution in this way reduces the number of pixels to be calculated, thereby speeding up processing. Averaging also reduces the effects of noise. Converting the resolution by thinning out pixels simplifies processing while avoiding the effects of blurring due to interpolation.

[0031] When the RGB values ​​of the coordinates (x, y) are R(x, y), G(x, y), and B(x, y), respectively, the difference ΔRGB(x, y) in pixel values ​​between the pixel of interest and the surrounding pixels adjacent to the pixel of interest is calculated as shown in equations (1) to (4).

[0032] ΔR(x,y)=MAX(ABS(R(x-1,y)-R(x,y)),ABS(R(x,y-1)-R(x,y))) ···(1) ΔG(x,y)=MAX(ABS(G(x-1,y)-G(x,y)),ABS(G(x,y-1)-G(x,y))) ···(2) ΔB(x,y)=MAX(ABS(B(x-1,y)-B(x,y)),ABS(B(x,y-1)-B(x,y))) ···(3) ΔRGB(x,y)=MAX(ΔR(x,y),ΔG(x,y),ΔB(x,y)) ···(4) Here, MAX(a, b, c) is a function that calculates the maximum value of a, b, and c, and ABS(d) is a function that calculates the absolute value of d.

[0033] In this embodiment, an example of the predetermined range is 3 or more and 15 or less. Here, "3" is an example of a difference value representing a noise level, and "15" is an example of a difference value representing an edge level. In other words, if the difference in pixel value between the target pixel and a peripheral pixel exceeds 15, the target pixel can be determined to be an edge pixel. Here, an edge pixel is, for example, a pixel at the end (edge ​​portion) of a gradation area. In this case, in S101, it is determined that the pixel is not within the predetermined range, and the process proceeds to S103.

[0034] Furthermore, the pixel of interest can be determined to be a pixel in a photographic area if the difference in pixel value between the pixel of interest and its surrounding pixels is greater than or equal to 3 and less than or equal to 15. In this case, in S101, it is determined that the difference is within the predetermined range, and the process proceeds to S106.

[0035] In S106, the CPU 102 determines that the pixel of interest is a target for color conversion for a photographic area, and performs color conversion on the pixel of interest using a color conversion method for a photographic area that has been prepared in advance. After S106, the process proceeds to S107.

[0036] Furthermore, if the difference in pixel value between the target pixel and its surrounding pixels is less than 3, i.e., if ΔRGB(x,y) is extremely small, it is determined in S101 that the difference is outside the predetermined range, and the process proceeds to S103. For solid areas in a graphic area, the difference in pixel value between the target pixel and its surrounding pixels is ideally 0, but as a result of the inclusion of noise, an extremely small value may be calculated as ΔRGB(x,y). For this reason, in this embodiment, in order to temporarily suspend the determination of whether a target pixel for which such a value is calculated belongs to a photographic area or a graphic area at the timing of S102, the predetermined range that is the determination criterion in S102 is set to be equal to or higher than the noise level and equal to or lower than the edge level.

[0037] In S103, CPU 102 determines whether the difference in pixel values ​​is less than a predetermined value. Here, the predetermined value is a difference value of 15, which represents an edge level. If it is determined that the difference is not less than the predetermined value, the pixel is treated as an edge pixel and the process proceeds to S105. For example, if the difference in pixel values ​​is 16, the process proceeds from S102 to S103 to S105.

[0038] In S105, the CPU 102 determines that the pixel of interest is to be subjected to color conversion for a graphics area, and performs color conversion on the pixel of interest using a color conversion method for a graphics area that has been prepared in advance. After S105, the process proceeds to S107. In other words, the color conversion method for a graphics area is applied to the edge pixel.

[0039] 11(a) to 11(d) are diagrams for explaining the color conversion methods set in S105 and S106. FIG. 11(a) is a diagram showing the relationship between the color space of a standard display and the color space of the recording device 108. That is, the solid line 1101 in FIG. 11(a) is the sRGB color space specified as IEC 61966-2-1:1999 in the L*a*b* uniform color space, which is a color space that can be used for original data from the host PC. The dashed line 1102 in FIG. 11(a) is the color reproduction color gamut of the recording device 108.

[0040] When colors displayed on a standard display are output by a printing device, colors outside the color gamut of the printing device must be mapped to appropriate colors within the gamut. This is generally called color space compression (color mapping). Generally, there are multiple methods of color space compression, and they are used depending on the purpose. In Figure 11(a), WP1111 and WP1121 are the brightest colors (white points) within the gamut of the standard display and recording device 108, respectively. BP1112 and BP1122 are the darkest colors (black points) within the gamut of the standard display and recording device 108, respectively.

[0041] FIG. 11(b) is a diagram illustrating the color conversion method applied in S106. As indicated by the bold solid line 1111 in FIG. 11(b), the white point and black point of the standard display are mapped to the white point and black point of the color reproduction color gamut of the recording device 108, respectively. Other colors are converted so that their correlation with the white point and black point is maintained. The conversion is then performed by compressing saturation in the color direction so that the entire color space 1101 of the standard display fits within the color reproduction color gamut 1102 of the recording device 108. Therefore, colors in the color space 1101 of the standard display are converted to the bold line 1111, and colors in the original color reproduction color gamut 1102 are converted to the bold dashed line 1112. The color conversion method of FIG. 11(b) is suitable for processing image data with a large number of colors, such as photographs. In FIG. 11(b), both lightness and saturation are compressed across almost the entire color gamut of the standard display.

[0042] Figure 11(c) is a diagram illustrating the color conversion method applied in S105. As shown in Figure 11(c), this method does not compress colors within the color reproduction color gamut of the recording device 108, but compresses colors outside the color reproduction color gamut in both lightness and saturation. The thick arrows in Figure 11(c) represent the compression process. The multiple colors included in the thick arrows are displayed as different colors on a standard display, but after mapping, they become the same color at the end of the arrows.

[0043] In S105, the color conversion method shown in FIG. 11(d) may be applied. As shown in FIG. 11(d), this method involves mapping only the white point of the standard display to the white point of the recording device 108, and then compressing colors within the color reproduction color gamut of the recording device 108 without compressing them in both lightness and saturation for colors outside the print color gamut. This method aims to reproduce the relative color difference between each color on the standard display and white as the relative color difference between each color and paper white when printed, and is called "relative colorimetric." Even with this color conversion method, the multiple colors included in the bold arrows are represented as different colors on the standard display, but after color conversion, they become the same color at the end of the arrow.

[0044] In S105, another color conversion method may be used in which the white point and black point of the standard display are mapped to the white point and black point of the recording device 108, respectively, and then colors within the color reproduction gamut of the recording device 108 are not compressed, but saturation compression is performed on colors outside the color reproduction gamut.

[0045] If it is determined in S103 that the difference is less than the predetermined value, the process proceeds to S104. For example, if the difference in pixel value is 2, the process proceeds in the order of S102 to S103 to S104.

[0046] In S104, the CPU 102 determines whether the pixel of interest is located between edge pixels. In this embodiment, if it is determined in S102 that the difference in pixel values ​​is an extremely small value that is not within a predetermined range, the CPU 102 determines in S104 whether to apply the color conversion method for a graphic area or a color conversion method for a photographic area to the pixel of interest.

[0047] FIG. 10 is a diagram illustrating the determination process of S104. Pixels 1001 and 1002 in FIG. 10 are edge pixels (denoted by "E") whose differences with adjacent surrounding pixels are determined to exceed the edge level. For the sake of explanation, it is assumed here that the differences between adjacent surrounding pixel values ​​are calculated in the left-right direction in FIG. 10. Pixels 1003 and 1004 are pixels (denoted by "K") whose differences with their surrounding pixels are determined to be less than a predetermined value in S103, and for which the determination of whether to perform color conversion for a graphic area or color conversion for a photographic area has been temporarily suspended.

[0048] As shown in Figure 10, pixel 1003 is located between edge pixels 1001 and 1002. Therefore, in S104, it is determined that the pixel of interest is located between edge pixels, and the process proceeds to S106, where color conversion is performed on the pixel of interest using a color conversion method for photographic areas that has been prepared in advance. On the other hand, pixel 1004 is not located between edge pixels. Therefore, in S104, it is determined that the pixel of interest is not located between edge pixels, and the process proceeds to S105, where color conversion is performed on the pixel of interest using a color conversion method for graphic areas that has been prepared in advance.

[0049] In this way, in this embodiment, when the difference between the pixel of interest and its neighboring pixels is extremely small, the color conversion method applied varies depending on whether the pixel of interest is located between edge pixels. As a result, even if there is a gradation area within a graphic area, such as a bar graph with a gradation, the color conversion method for photographic areas is applied to the gradation area for color conversion. This makes it possible to maintain the gradation of the gradation area.

[0050] Here, we will explain the differences between color conversion methods for graphic regions and photographic regions. When two different input values ​​are input into a color conversion method, two output values ​​are output. If the difference between the two output values ​​is equal to or greater than a threshold, the gradation of the two output values ​​is said to be maintained. For all input values, when two combinations are input into the color conversion method, the number of output values ​​for which the difference is equal to or less than the threshold is defined as the number of non-gradation combinations. The difference in output values ​​being equal to or less than the threshold corresponds to the occurrence of color degeneration. When comparing color conversion methods for graphic regions and photographic regions, the number of non-gradation combinations when the above-described method is implemented using the color conversion method for graphic regions is greater than the number of non-gradation combinations when the above-described method is implemented using the color conversion method for photographic regions. This is because the color conversion method for photographic regions emphasizes the preservation of gradation, while the color conversion method for graphic regions emphasizes eye-catching features over the preservation of gradation.

[0051] In S107, the CPU 102 determines whether color conversion has been completed for all pixels of the original data. If it is determined that color conversion has been completed for all pixels, the process proceeds to S108. If it is determined that color conversion has not been completed, the CPU 102 focuses on the next pixel and repeats the process from S102.

[0052] In S108, the CPU 102 executes print processing on the document data. Specifically, for example, the CPU 102 executes four processes, namely, ink color separation processing, output characteristic conversion processing, quantization processing, and recording processing, on each pixel that has undergone color conversion in S105 and S106.

[0053] The ink color separation process converts the output values ​​Rout, Gout, and Bout of the color conversion process into output values ​​for each ink color to be printed using an inkjet printing method. In this embodiment, printing is assumed to use four ink colors: cyan, magenta, yellow, and black. There are various methods for this conversion. For example, as with the color conversion process, a three-dimensional LUT is used to calculate an optimal combination of ink color pixel values ​​(C, M, Y, K) for a combination of output pixel values ​​(Rout, Gout, Bout). For example, a three-dimensional LUT2

[0256]

[0256]

[0256] [4] as shown in the following equations (5) to (8) is used.

[0054] C=LUT2[Rout][Gout][Bout][0] ···(5) M=LUT2[Rout][Gout][Bout][1] ···(6) Y=LUT2[Rout][Gout][Bout][2] ···(7) K=LUT2[Rout][Gout][Bout][3] ···(8) Alternatively, the number of grids in the LUT may be reduced from 256 to, for example, 16, and the table size may be reduced by determining the output value by interpolating table values ​​of a plurality of grids.

[0055] Next, the output characteristic conversion process converts the density of each ink color into a printing dot count rate. Specifically, for example, the density of each color with 256 gradations is converted into the dot count rates Cout, Mout, Yout, and Kout with 1024 gradations. For this purpose, a one-dimensional LUT3[4]

[0256] is used, which sets the appropriate printing dot count rate for each ink color density, as shown in the following equations (9) to (12).

[0056] Cout = LUT3[0][C] (9) Mout = LUT3[1][M] (10) Yout=LUT3[2][Y] (11) Kout=LUT3[3][K] (12) Alternatively, the number of grids in the LUT may be reduced from 256 to, for example, 16, and the table size may be reduced by determining the output value by interpolating table values ​​of a plurality of grids.

[0057] Next, the quantization process converts the print dot count rates Cout, Mout, Yout, and Kout of each ink color into the actual On / Off state of print dots for each pixel. Various methods can be used for quantization, such as error diffusion and dithering. For example, the quantization is realized by the dithering method as shown in the following equations (13) to (16).

[0058] Cdot=Halftone[Cout][x][y]···(13) Mdot=Halftone[Mout][x][y]···(14) Ydot=Halftone[Yout][x][y]···(15) Kdot=Halftone[Kout][x][y]···(16) Then, by comparing with a threshold value corresponding to each pixel position (x, y), the recording dot of each ink color is turned on / off. Here, Cout, Mout, Yout, and Kout are each expressed in 10 bits and take a value range of 0 to 1023. Therefore, the occurrence probability of each recording dot is Cout / 1023, Mout / 1023, Yout / 1023, and Kout / 1023.

[0059] Finally, the recording head 115 records the image on the recording medium.

[0060] As described above, according to this embodiment, the presence or absence of gradation is determined based on the conditions in S102 and S104, and a color conversion method for photographic areas that emphasizes gradation is selected for areas where gradation reproduction is required, while a color conversion method for graphic areas that emphasizes attention to detail is selected for other areas. This makes it possible to achieve both attention to detail and gradation.

[0061] In Figure 3, only edge pixel detection may be performed on the image data in advance. In other words, detection of edge pixels where the difference in pixel value between the pixel of interest and its surrounding pixels is greater than the edge level may be performed in an earlier stage of the processing in Figure 3. Note that when white is the starting point or ending point of a gradation, if no edge pixel exists up to the white pixel, the white pixel may be regarded as the edge pixel.

[0062] In this embodiment, the difference in the RGB color space is used to determine the gradation, but this is not limiting and the color difference ΔE in the L*a*b* color space may also be used. In this case, a determination closer to perception is possible. In addition, in S102 of this embodiment, an example has been described in which the determination is made using pixels above and below and to the left and right of the pixel of interest. However, this is not limited to the above, and the determination may be made using, for example, the sum of the differences between the pixel of interest and eight pixels, including four pixels above and below and to the left and right, and four pixels at the top left, top right, bottom left, and bottom right. This allows for more rigorous determination. Similarly, the determination may be made using the sum of the differences between the pixel of interest and pixels two pixels away from it in the vertical and horizontal directions. In this case, a wider range of determination can be made.

[0063] [Second embodiment] The second embodiment will be described below, focusing on the differences from the first embodiment. In the first embodiment, two color conversion methods are prepared in advance, and a configuration is described in which one of the color conversion methods is to be used. In this embodiment, a color conversion method is created within the image processing device, and the created color conversion method is used as one of the candidate color conversion methods that can be applied. This makes it possible to apply a color conversion method that is more appropriate for the input document data.

[0064] Fig. 4 is a flowchart showing the processing in the image processing device 101 in this embodiment. The processing in Fig. 4 is realized, for example, by the CPU 102 reading and executing a program stored in the RAM 103. In this embodiment, the processing in Fig. 4 is described as being executed by the image processing device 101, but it may also be executed by the recording device 108, or may be configured so that the image processing device 101 and the recording device 108 share the execution of the processing.

[0065] S201 is the same as S101 in FIG. 3, so its description will be omitted.

[0066] In S202, the CPU 102 creates a color conversion table that corrects color degeneration by increasing the distance between colors in the color space.

[0067] FIG. 5 is a flowchart showing the process of creating a color conversion table for correcting color degeneration, which is performed in S202.

[0068] In S301, the CPU 102 detects color information of the document data acquired in S201. The detection process is repeated for each pixel of the document data, and is executed for all pixels included in the document data, to create a color information list.

[0069] For example, if the text data is black text on a white background, the detected color information is white and black. Also, if the image is a photograph or the like, the detected color information is the colors used in the photograph. CPU 102 stores the detection results as a color information list in RAM 103 or storage medium 104. Note that the color information list is initialized at the start of S301. CPU 102 repeats the detection process for each pixel of the image data, and determines for all pixels included in the image data whether the color of each pixel is different from the color information detected up to that point. If it is determined that the color is different from the color information detected up to that point, the color is added to the color information list and stored.

[0070] One method of determination is to determine whether the color of the target pixel is included in a previously created color information list. If it is determined that the color is not included, new color information can be added to the color information list. This allows the color information list contained in the original data to be detected. For example, if the original data is sRGB data, each pixel has 256 gradations, so color information of 256 x 256 x 256, for a total of 16,777,216 colors, is detected. In this case, the number of colors becomes enormous, slowing down the processing speed. Therefore, color information can be detected discretely. For example, the 256 gradations can be reduced to 16 gradations before color information detection. When reducing the number of colors, the color can be reduced to the color of the nearest grid. In this way, only 4,096 colors (16 x 16 x 16) are detected, improving processing speed.

[0071] Next, in S302, the CPU 102 detects, based on the color information list detected in S301, the number of color combinations in the color information list that will cause color degeneration when a pre-prepared color conversion table is used.

[0072] FIG. 6 is a diagram illustrating color degeneration and its correction. A color gamut 601 is the color gamut of the original data, and in this embodiment, it is the sRGB color gamut. A color gamut 602 is the color gamut after color conversion processing, and corresponds to the color gamut of the recording device 108. In FIG. 6, a color 605 is the color after color 603 has been converted by gamut mapping. A color 606 is the color after color 604 has been converted by gamut mapping. Color degeneration is determined when a color difference ΔE 607 between colors 605 and 606 is smaller than a color difference ΔE 608 between colors 603 and 604. The color difference ΔE can be calculated, for example, using the Euclidean distance in a color space. In this embodiment, the Euclidean distance in the CIE-L*a*b* color space (hereinafter referred to as color difference ΔE) is used as an example. The color difference ΔE between the color (L1, a1, b1) and the color (L2, a2, b2) is calculated by the following formula (17).

[0073] TIFF2025187827000002.tif12106...(17) Next, in S303, CPU 102 determines whether the number of color combinations that have undergone color degeneration in S302 is zero. If it is determined that the number of color combinations that have undergone color degeneration is zero, it determines that color degeneration correction is unnecessary, and proceeds to S304. In S304, CPU 102 sets a color conversion method (color conversion table) for the graphics area that has been prepared in advance as the color conversion method to be used in S206. Hereinafter, the color conversion method will be referred to as the color conversion table. Then, the processing of FIG. 5 ends. On the other hand, if it is determined that the number of color combinations that have undergone color degeneration is not zero, proceeds to S305.

[0074] In S305, the CPU 102 performs color reduction correction on the color combinations that are subject to color reduction, based on the original data, the color-converted original data, and the color conversion table. Color reduction correction is a process for correcting the color difference ΔE607 between colors 605 and 606 so that the color difference ΔE611 between colors 609 and 610 is approximately equal to the color difference ΔE608. The color reduction correction is repeated the number of times the number of color combinations that are subject to color reduction is equal. The results of the color reduction correction are stored in a table, including color information before and after the correction. For example, in FIG. 6, the color information is in the CIE-L*a*b* color space. Therefore, the image data at the time of input and the color space of the image data at the time of output may be converted to the color space of the image data at the time of output. In this case, the color information before the color reduction correction in the color space of the image data at the time of input and the color information after the color reduction correction in the color space of the image data at the time of output are stored in a table. In FIG. 6 , colors 609 and 610 after color degeneration correction are on an extension line from color 605 to color 606, but this is not limited to this in the present embodiment. As long as the color difference ΔE 611 between colors 609 and 610 is a color difference ΔE 608, it may be in any direction of the lightness direction, saturation direction, or hue angle direction in the CIE-L*a*b* color space. Furthermore, it may be in one direction or a combination of the lightness direction, saturation direction, and hue angle direction. Furthermore, while FIG. 6 shows an example in which both colors 603 and 604 are corrected, it is also possible to correct one of the colors so that the color difference ΔE 608 is achieved.

[0075] In S306, CPU 102 creates a color conversion table using the result of the color degeneration correction in S305. The color conversion table prepared in advance is a table that converts color 603 to color 605 and color 604 to color 606. As a result of the color degeneration correction in S305, the color conversion table is changed to one that converts color 603 to color 609 and color 604 to color 610. The change of the color conversion table is repeated the number of times the number of color combinations that are color degenerate. As described above, a color conversion table after color degeneration correction is created in S202. After S306, the processing in FIG. 5 ends.

[0076] S203 to S205 and S207 are the same as the explanations for S102 to S104 and S106 in FIG. 3, and therefore their explanations will be omitted.

[0077] In S206, CPU 102 determines that the pixel of interest is to be subjected to color conversion for a graphics area, and performs color conversion on the pixel of interest using a color conversion table for a graphics area. At this time, if S304 is being executed in Fig. 5, a color conversion table for a graphics area prepared in advance is used in S206. On the other hand, if S306 is being executed in Fig. 5, the color conversion table after color degeneration correction created in S306 is used in S206.

[0078] S208 and S209 are the same as those in S107 and S108, and therefore their explanation will be omitted.

[0079] As described above, according to this embodiment, a color conversion method is created within the image processing device, and the created color conversion method is selected as one of the candidate color conversion methods that can be applied, thereby making it possible to apply a color conversion method that is more appropriate for the input document data.

[0080] (Variation) If the area for setting the color conversion method and the area to which the set color conversion method is applied match, the color conversion method may be set taking into account unnecessary colors, resulting in inappropriate color conversion results. In S301, color information is extracted for all pixels of the original data. However, the greater the number of colors, the more difficult it becomes to reduce color degeneration in all color combinations. Furthermore, it is difficult to recognize color degeneration in isolated points consisting of only one pixel or thin lines consisting of one pixel, and the need to reduce color degeneration is not particularly strong. Furthermore, taking the color of such isolated points or thin lines into account reduces the effect of correcting color degeneration for other color information.

[0081] Therefore, in this modified example, instead of setting the color conversion method using the color information of all pixels in the original data, a first region used to set the color conversion method and a second region not used to set the color conversion method are set, and the color conversion method is set using the color information of the first region. In this modified example, a region required for color discrimination is set from the original data, and a color conversion table is created using only the color information of that region.

[0082] In this modification, the process of Fig. 4 is executed, and a color conversion table is created in S202. However, in S301 of Fig. 5, color information is detected as follows.

[0083] In this modification, color information that is identifiable by humans and that is discriminable in the output of the recording device 108 is color information in an area having a predetermined or greater area in a plane, and this area is set as the first area. Specifically, for example, an area in the original data where pixels having the same color information are consecutive in two or more pixels vertically and two or more pixels horizontally is set as the first area.

[0084] FIG. 7 is a diagram illustrating the process of setting the first region in this modified example. As indicated by the arrows in FIG. 7(a), line processing is performed sequentially on the document data, pixel by pixel. In pixel-by-pixel processing, CPU 102 determines whether the color information of each of the three pixels (pixels 701, 702, and 703) surrounding a target pixel (target pixel) 700 shown in FIG. 7(b) is the same as the color information of the target pixel. If it is determined that the color information is the same, the four pixels including the target pixel are set as the first region. Note that pixels already set as the first region may be reset as the first region in pixel-by-pixel processing. Setting the first region is not limited to the above method, as long as it is possible to extract a region with the same color information that has a predetermined or greater planar area. In this modified example, regions with the same color information are extracted. However, although the original image data had the same color information, the color information may vary within a predetermined range in image data after lossy compression, such as JPEG. Therefore, areas with the same color information, for example, areas where the color difference ΔE is within 1.0 or the difference in RGB values ​​is within a predetermined value, may be extracted as areas with the same color information. Alternatively, a range of allowable variations may be set.

[0085] Steps from S302 onward are performed in the same manner as described in the second embodiment. In the process described in this modification, the area to which the generated color conversion method is actually applied is determined in steps S203 to S205. In this case, isolated pixels, thin lines, etc. that have an area equal to or greater than a predetermined value and are not extracted as areas having the same color information become areas to which the color conversion method is applied. Therefore, isolated pixels, thin lines, etc. that are made up of colors close to the color that is the target of color degeneration correction can also be corrected to a suitable color.

[0086] According to this modification, a first region used to set a color conversion method and a second region not used to set a color conversion method are set in the document data. This configuration allows an appropriate color conversion method to be set based only on the information in the region (i.e., the first region) necessary for color degeneration correction. As a result, color conversion results suitable for the recording device 108 can be obtained without performing unnecessary color degeneration correction.

[0087] [Third embodiment] The third embodiment will be described below, focusing on the differences from the first and second embodiments. In this embodiment, a process for detecting a gradation area with higher accuracy will be described.

[0088] Fig. 8 is a flowchart showing the processing in the image processing device 101 in this embodiment. The processing in Fig. 8 is realized, for example, by the CPU 102 reading and executing a program stored in the RAM 103. In this embodiment, the processing in Fig. 8 is described as being executed by the image processing device 101, but it may also be executed by the recording device 108, or may be configured so that the processing is shared between the image processing device 101 and the recording device 108.

[0089] S401 to S404 are the same as S101 to S104 in Fig. 3, and therefore their explanation will be omitted. If it is determined in S404 that the pixel of interest is located between edge pixels, the process proceeds to S405.

[0090] In this embodiment, a process of determining whether a pixel of interest that is determined to be located between edge pixels is included in the gradation area is further performed in S405.

[0091] For simplicity, the process of determining whether a pixel is in a horizontal gradation area will be described. The coordinates of two horizontal edge pixels are (x1, y1) and (x2, y1). All adjacent pixel differences between the two edge pixels are below the noise level. The edge difference ΔEDGE in this case is calculated using equations (18) to (21).

[0092] ΔRe=ABS(R(x1,y1)-R(x2,y1)) ···(18) ΔGe=ABS(G(x1,y1)-G(x2,y1)) ···(19) ΔBe=ABS(B(x1,y1)-B(x2,y1)) ···(20) ΔEDGE=MAX(ΔRe,ΔGe,ΔBe) ···(21) In S405, the CPU 102 determines whether ΔEDGE is greater than a predetermined value. Because differences in pixel values ​​between neighboring pixels accumulate in either horizontal direction in a gradation area, the difference in pixel values ​​between edge pixels is considered to be greater than the predetermined value. Therefore, if ΔEDGE is determined to be greater than the predetermined value, the target pixel is determined to be a pixel in a gradation area. The predetermined value may be, for example, an edge level. It may also be any value desired to detect a gradation area, such as the difference in pixel values ​​between the start and end points of the gradation.

[0093] The process of determining whether a pixel is in a vertical gradation area will be described. The coordinates of two vertical edge pixels are (x3, y3) and (x3, y4). All adjacent pixel differences between the two edge pixels are below the noise level. The edge difference ΔEDGE in this case is calculated using equations (22) to (25).

[0094] ΔRe=ABS(R(x3,y3)-R(x3,y4)) ···(22) ΔGe=ABS(G(x3,y3)-G(x3,y4)) ···(23) ΔBe=ABS(B(x3,y3)-B(x3,y4)) ···(24) ΔEDGE=MAX(ΔRe,ΔGe,ΔBe) ···(25) In S405, the CPU 102 determines whether ΔEDGE is greater than a predetermined value. Because differences in pixel values ​​between neighboring pixels accumulate in either vertical direction in a gradation area, the difference in pixel values ​​between edge pixels is considered to be greater than the predetermined value. Therefore, if ΔEDGE is determined to be greater than the predetermined value, the target pixel is determined to be a pixel in a gradation area. The predetermined value may be, for example, an edge level. It may also be any value desired to detect a gradation area, such as the difference in pixel values ​​between the start and end points of the gradation.

[0095] If it is determined in S405 that ΔEDGE is greater than the predetermined value, the process proceeds to S407. In S407, the CPU 102 determines that the pixel of interest is to be subjected to color conversion for a photographic area, and performs color conversion on the pixel of interest using a color conversion method for photographic areas that is prepared in advance. This makes it possible to detect gradation areas with higher accuracy. After S407, the process proceeds to S408. On the other hand, if it is determined in S405 that ΔEDGE is not greater than the predetermined value, the process proceeds to S406. In S406, the CPU 102 determines that the pixel of interest is to be subjected to color conversion for a graphic area, and performs color conversion on the pixel of interest using a color conversion method for graphic areas that is prepared in advance. After S406, the process proceeds to S408.

[0096] The process of S405 is executed for each pixel, but because the determination is made on the difference between edge pixels, the same determination result is obtained for all pixels sandwiched between the edge pixels. In other words, if it is determined that any pixel between the edge pixels is greater than the predetermined value, it is determined that all pixels between the edge pixels are greater than the predetermined value, and the process proceeds to S407.

[0097] S408 to S409 are the same as S107 to S108 in FIG. 3, so a description thereof will be omitted.

[0098] As described above, this embodiment makes it possible to detect gradation areas with higher accuracy. Then, a color conversion method that emphasizes gradation is applied to all pixels within an area that is determined to be a gradation area and is sandwiched between edge pixels. This allows the color conversion method that emphasizes gradation to be applied not only to photographic areas but also to areas where gradation is important, such as gradation areas within graphic areas. This prevents the loss of gradation and the occurrence of unnatural gradation steps and color shifts, thereby producing a good output image.

[0099] In this embodiment, the difference in the RGB color space is used to determine ΔEDGE, but this is not limiting, and the color difference ΔE in the L*a*b* color space may also be used, which allows for a determination that is closer to perception.

[0100] [Fourth embodiment] The fourth embodiment will be described below, focusing on the differences from the first to third embodiments. In the third embodiment, a gradation area was detected using a feature in which differences in pixel values ​​between edge pixels are continuously accumulated. In this embodiment, a process for detecting a gradation area will be described using other features specific to gradation areas.

[0101] 9 is a flowchart showing the processing in the image processing device 101 in this embodiment. The processing in FIG. 9 is realized, for example, by the CPU 102 reading and executing a program stored in the RAM 103. In this embodiment, the processing in FIG. 9 is described as being executed by the image processing device 101, but it may also be executed by the recording device 108, or may be configured so that the processing is shared between the image processing device 101 and the recording device 108.

[0102] S901 to S903 are the same as S101 to S103 in Fig. 3, and therefore a description thereof will be omitted. If it is determined in S903 that the difference in pixel value is less than the predetermined value, the process proceeds to S904.

[0103] In this embodiment, if a pixel of interest is determined to be sandwiched between edge pixels, a process is further performed in S904 to determine whether it is a pixel in a gradation area. A characteristic of consecutive pixels in a gradation area is that the density value changes continuously. In other words, consecutive pixels in a gradation area have a monotonically increasing or decreasing density value. Therefore, in this embodiment, a gradation area is detected by determining whether the pixel values ​​of three positionally consecutive pixels monotonically increase or decrease.

[0104] Here, let three colors that are continuous in the vertical or horizontal direction be color C1 = (R1, G1, B1), color C2 = (R2, G2, B2), and color C3 = (R3, G3, B3).

[0105] In S904, the CPU 102 determines whether or not the condition that the pixel values monotonically increase or decrease in the arrangement of the target pixel and the surrounding pixels is satisfied. Specifically, for example, the CPU 102 determines whether or not all of the colors of color C1, color C2, and color C3 are different and the conditions of the following formulas (26) to (31) are simultaneously satisfied.

[0106] MIN(R3, MIN(R2, R1)) ≠ R2 ···(26) MAX(MAX(R2, R1), R3) ≠ R2 ···(27) MIN(G3, MIN(G2, G1)) ≠ G2 ···(28) MAX(MAX(G2, G1), G3) ≠ G2 ···(29) MIN(B3, MIN(B2, B1)) ≠ B2 ···(30) MAX(MAX(B2, B1), B3) ≠ B2 ···(31) Here, MIN(a, b) is a function that outputs a when a < b, and MAX(a, b) is a function that outputs a when a > b.

[0107] When the RGB values ​​of three vertically or horizontally consecutive pixels monotonically increase or decrease (i.e., the condition is satisfied), Equations (25) to (30) simultaneously hold, including when the pixel values ​​are equal. In other words, the pixel value of the middle pixel among three vertically or horizontally consecutive pixels is never maximum or minimum. When the above relationship holds between multiple horizontally or vertically consecutive pixels, the area of ​​those multiple pixels can be determined to be a gradation area. In this case, it is determined in S904 that the condition is satisfied, and the process proceeds to S906. In S906, the CPU 102 determines that the pixel of interest is a target for color conversion for a photographic area and performs color conversion on the pixel of interest using a color conversion method for a photographic area that has been prepared in advance. This enables more accurate detection of a gradation area. After S906, the process proceeds to S907. On the other hand, if it is determined in S904 that the condition is not satisfied, the process proceeds to S905. In S905, the CPU 102 determines that the pixel of interest is a target for color conversion for a graphic area, and performs color conversion on the pixel of interest using a color conversion method for a graphic area that has been prepared in advance. After S905, the process proceeds to S907.

[0108] S907 to S908 are the same as S107 to S108 in FIG. 3, so a description thereof will be omitted.

[0109] As described above, according to this embodiment, a gradation area is determined when the values ​​of three consecutive colors monotonically increase or decrease, so that the gradation area can be correctly detected regardless of the gradient of the gradation (rate of density increase). In particular, in gradation areas where the hue changes significantly, the adjacent difference may be large, but even in such cases, the gradation area can be correctly detected. Furthermore, since a threshold is not required, there is no need to adjust the threshold.

[0110] In this embodiment, the difference in the RGB color space is used to determine ΔEDGE, but this is not limiting, and the color difference ΔE in the L*a*b* color space may also be used, which allows for a determination that is closer to perception.

[0111] The present invention can also be realized by supplying a program that realizes one or more functions of the above-described embodiments to a system or device via a network or a storage medium, and having one or more processors in the computer of the system or device read and execute the program. It can also be realized by a circuit (e.g., ASIC) that realizes one or more functions.

[0112] The disclosure of the present embodiment includes the following image processing device, method, and program. (Item 1) an input means for inputting image data; color conversion means for executing color conversion on a pixel of interest using a first color conversion method when the pixel of interest, which has a difference in pixel value between the pixel of interest and surrounding pixels, satisfies a condition in the image data input by the input means, and for executing color conversion on the pixel of interest using a second color conversion method different from the first color conversion method when the pixel of interest does not satisfy the condition; Equipped with The result of color conversion performed by the first color conversion method maintains gradation better than the result of color conversion performed by the second color conversion method. 1. An image processing device comprising: (Item 2) 2. The image processing device according to item 1, further comprising a correction unit that corrects the second color conversion method based on the image data input by the input unit. (Item 3) The correction means inputting the first color and the second color represented by the image data into the second color conversion method to obtain a third color and a fourth color; correcting the second color conversion method so that color degeneration does not occur when color degeneration occurs as a result of comparison between the first color and the second color and between the third color and the fourth color; When the target pixel does not satisfy the condition, the color conversion means performs color conversion on the target pixel using the second color conversion method corrected by the correction means, instead of the second color conversion method. 3. The image processing device according to item 2, (Item 4) 4. The image processing device according to item 3, wherein the correction means corrects the second color conversion method so as to separate the third color and the fourth color. (Item 5) Item 4. The image processing device according to item 4, wherein the correction means corrects the second color conversion method so as to separate the third color and the fourth color in at least one of the directions of brightness, saturation, and hue angle. (Item 6) The correction by the correction means is not performed if the color degeneration does not occur, When the target pixel does not satisfy the condition, the color conversion means performs color conversion on the target pixel using the second color conversion method. 6. The image processing device according to any one of items 3 to 5, (Item 7) Further, a setting means for setting an area on an image represented by the image data is provided, the first color and the second color are colors obtained from the area set by the setting means; 7. The image processing device according to any one of items 3 to 6, (Item 8) 8. The image processing device according to item 7, wherein the region has a predetermined area. (Item 9) 9. The image processing device according to item 7 or 8, wherein the region is a region having color information within a predetermined range. (Item 10) 10. The image processing device according to any one of items 7 to 9, characterized in that the area set by the setting means is different from the area to which the second color conversion method or the second color conversion method corrected by the correction means is applied. (Item 11) 11. The image processing device according to any one of items 1 to 10, wherein the conditions include that the target pixel is located between edge pixels in a predetermined region. (Item 12) Item 12. The image processing device according to item 11, wherein the difference in pixel value between the edge pixels is greater than a predetermined value. (Item 13) 13. The image processing device according to item 11 or 12, wherein in the predetermined region, pixel values ​​increase or decrease in a sequence of pixels including the pixel of interest and pixels adjacent to the pixel of interest. (Item 14) 14. The image processing device according to any one of items 11 to 13, wherein the predetermined area is a gradation area. (Item 15) 15. The image processing device according to any one of items 1 to 14, further comprising a recording control means for executing processing for recording an image on a recording medium based on the image data on which color conversion has been performed by the color conversion means. (Item 16) Item 16. The image processing device according to item 15, wherein the processing for recording the image includes ink color separation processing, output characteristic conversion processing, quantization processing, and recording processing. (Item 17) 17. The image processing device according to any one of items 1 to 16, wherein the first color conversion method and the second color conversion method are methods for mapping an input color gamut to an output color gamut of a recording device. (Item 18) 1. A method performed in an image processing device, comprising: an input step of inputting image data; a color conversion step of performing color conversion on the pixel of interest using a first color conversion method when the pixel of interest, which has a difference in pixel value between the pixel of interest and surrounding pixels, satisfies a condition in the image data input in the input step, and performing color conversion on the pixel of interest using a second color conversion method different from the first color conversion method when the pixel of interest does not satisfy the condition; and The result of color conversion performed by the first color conversion method maintains gradation better than the result of color conversion performed by the second color conversion method. A method characterized by: (Item 19) 18. A program for causing a computer to function as each means of the image processing device according to any one of items 1 to 17.

[0113] The invention is not limited to the above-described embodiments, and various changes and modifications can be made without departing from the spirit and scope of the invention. Accordingly, the following claims are appended to apprise the public of the scope of the invention. [Explanation of symbols]

[0114] 101 Image processing device: 108 Recording device: 102, 111 CPU: 103, 112 RAM: 104, 113 Storage medium

Claims

1. an input means for inputting image data; color conversion means for executing color conversion on a pixel of interest using a first color conversion method when the pixel of interest, which has a difference in pixel value between the pixel of interest and surrounding pixels, satisfies a condition in the image data input by the input means, and for executing color conversion on the pixel of interest using a second color conversion method different from the first color conversion method when the pixel of interest does not satisfy the condition; Equipped with a result of color conversion performed by the first color conversion method that maintains gradation better than a result of color conversion performed by the second color conversion method; 1. An image processing device comprising:

2. 2. The image processing apparatus according to claim 1, further comprising a correction unit that corrects the second color conversion method based on the image data input by the input unit.

3. The correction means inputting the first color and the second color represented by the image data into the second color conversion method to obtain a third color and a fourth color; correcting the second color conversion method so that color degeneration does not occur when color degeneration occurs as a result of comparison between the first color and the second color and between the third color and the fourth color; when the target pixel does not satisfy the condition, the color conversion means performs color conversion on the target pixel using the second color conversion method corrected by the correction means, instead of the second color conversion method; 3. The image processing device according to claim 2.

4. 4. The image processing apparatus according to claim 3, wherein the correcting means corrects the second color conversion method so as to separate the third color and the fourth color.

5. 5. The image processing device according to claim 4, wherein the correction means corrects the second color conversion method so as to separate the third color and the fourth color in at least one direction of lightness, saturation, or hue angle.

6. The correction by the correction means is not performed if the color degeneration does not occur, the color conversion means, when the target pixel does not satisfy the condition, performs color conversion on the target pixel using the second color conversion method; 4. The image processing device according to claim 3.

7. Further, a setting means for setting an area on an image represented by the image data is provided, the first color and the second color are colors obtained from the area set by the setting means; 4. The image processing device according to claim 3.

8. 8. The image processing apparatus according to claim 7, wherein the region has a predetermined area.

9. 8. The image processing apparatus according to claim 7, wherein the area has color information within a predetermined range.

10. 8. The image processing device according to claim 7, wherein the area set by the setting means is different from the area to which the second color conversion method or the second color conversion method corrected by the correction means is applied.

11. 2. The image processing apparatus according to claim 1, wherein the condition includes that the pixel of interest is located between edge pixels in a predetermined area.

12. 12. The image processing apparatus according to claim 11, wherein the difference in pixel value between the edge pixels is greater than a predetermined value.

13. 12. The image processing apparatus according to claim 11, wherein in the predetermined region, pixel values ​​increase or decrease in a pixel sequence including the pixel of interest and pixels adjacent to the pixel of interest.

14. 12. The image processing device according to claim 11, wherein the predetermined area is a gradation area.

15. 2. The image processing apparatus according to claim 1, further comprising a recording control unit that executes a process for recording an image on a recording medium based on the image data that has been color converted by the color conversion unit.

16. 16. The image processing apparatus according to claim 15, wherein the processing for recording the image includes ink color separation processing, output characteristic conversion processing, quantization processing, and recording processing.

17. 2. The image processing apparatus according to claim 1, wherein the first color conversion method and the second color conversion method are methods for mapping an input color gamut to an output color gamut of a recording device.

18. 1. A method performed in an image processing device, comprising: an input step of inputting image data; a color conversion step of performing color conversion on the pixel of interest using a first color conversion method when the pixel of interest, which has a difference in pixel value between the pixel of interest and surrounding pixels, satisfies a condition in the image data input in the input step, and performing color conversion on the pixel of interest using a second color conversion method different from the first color conversion method when the pixel of interest does not satisfy the condition; and a result of color conversion performed by the first color conversion method that maintains gradation better than a result of color conversion performed by the second color conversion method; A method characterized by:

19. A program for causing a computer to function as each of the means of the image processing apparatus according to any one of claims 1 to 17.

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