Patent Application
20240013507
The present invention relates to an image processing apparatus capable of executing color mapping, an image processing method, and a non-transitory computer-readable storage medium storing a program.
There is known an image processing apparatus that receives a digital original described in a predetermined color space, performs, for each color in the color space, mapping to a color gamut that can be reproduced by a printer, and outputs the original. Japanese Patent Laid-Open No. 2020-27948 describes “perceptual” mapping and “absolute colorimetric” mapping. In addition, Japanese Patent Laid-Open No. 07-203234 describes deciding the presence/absence of color space compression and the compression direction for an input color image signal.
The present invention provides an image processing apparatus for implementing mapping for effectively reducing color degeneration, an image processing method, and a non-transitory computer-readable storage medium storing a program.
The present invention in one aspect provides an image processing apparatus comprising: an input unit configured to input image data; a generation unit configured to generate image data having undergone color gamut conversion from the image data input by the input unit using a conversion unit configured to convert a color gamut of the image data input by the input unit into a color gamut of a device configured to output the image data; and a correction unit configured to correct the conversion unit based on a result of the color gamut conversion, wherein in a case where the correction unit corrects the conversion unit, the generation unit generates image data having undergone color gamut conversion from the image data input by the input unit using the corrected conversion unit, and in the image data having undergone the color gamut conversion by the corrected conversion unit, a color difference in the image data having undergone the color gamut conversion by the conversion unit is expanded.
According to the present invention, it is possible to effectively reduce color degeneration.
Further features of the present invention will become apparent from the following description of exemplary embodiments with reference to the attached drawings.
Hereinafter, embodiments will be described in detail with reference to the attached drawings. Note, the following embodiments are not intended to limit the scope of the claimed invention. Multiple features are described in the embodiments, but limitation is not made to an invention that requires all such features, and multiple such features may be combined as appropriate. Furthermore, in the attached drawings, the same reference numerals are given to the same or similar configurations, and redundant description thereof is omitted.
If mapping to a color gamut that can be reproduced by a device is performed for a plurality of colors outside the color gamut that can be reproduced by the device, the mapping may cause color degeneration. A mechanism for implementing mapping for effectively reducing color degeneration is required.
According to the present disclosure, it is possible to effectively reduce color degeneration.
Terms used in this specification are defined in advance, as follows.
(Color Reproduction Region)
“Color reproduction region” is also called a color reproduction range, a color gamut, or a gamut. Generally, “color reproduction region” indicates the range of colors that can be reproduced in an arbitrary color space. In addition, a gamut volume is an index representing the extent of this color reproduction range. The gamut volume is a three-dimensional volume in an arbitrary color space. Chromaticity points forming the color reproduction range are sometimes discrete. For example, a specific color reproduction range is represented by 729 points on CIE-L*a*b*, and points between them are obtained by using a well-known interpolating operation such as tetrahedral interpolation or cubic interpolation. In this case, as the corresponding gamut volume, it is possible to use a volume obtained by calculating the volumes on CIE-L*a*b* of tetrahedrons or cubes forming the color reproduction range and accumulating the calculated volumes, in accordance with the interpolating operation method. The color reproduction region and the color gamut in this embodiment are not limited to a specific color space. In this embodiment, however, a color reproduction region in the CIE-L*a*b* space will be explained as an example. Furthermore, the numerical value of a color reproduction region in this embodiment indicates a volume obtained by accumulation in the CIE-L*a*b* space on the premise of tetrahedral interpolation.
(Gamut Mapping)
Gamut mapping is processing of performing conversion between different color gamuts, and is, for example, mapping of an input color gamut to an output color gamut of a device such as a printer. Perceptual, Saturation, Colorimetric, and the like of the ICC profile are general. The mapping processing may be implemented by, for example, conversion by a three-dimensional lookup table (3DLUT). Furthermore, the mapping processing may be performed after conversion of a color space into a standard color space. For example, if an input color space is sRGB, conversion into the CIE-L*a*b* color space is performed and then the mapping processing to an output color gamut is performed on the CIE-L*a*b* color space. The mapping processing may be conversion by a 3DLUT, or may be performed using a conversion formula. Conversion between the input color space and the output color space may be performed simultaneously. For example, the input color space may be the sRGB color space, and conversion into RGB values or CMYK values unique to a printer may be performed at the time of output.
(Original Data)
Original data indicates whole input digital data as a processing target. The original data includes one to a plurality of pages. Each single page may be held as image data or may be represented as a drawing command. If a page is represented as a drawing command, the page may be rendered and converted into image data, and then processing may be performed. The image data is formed by a plurality of pixels that are two-dimensionally arranged. Each pixel holds information indicating a color in a color space. Examples of the information indicating a color are, for example, RGB values, CMYK values, a K value, CIE-L*a*b* values, HSV values, and HLS values.
(Color Degeneration)
In this embodiment, the fact that when performing gamut mapping for arbitrary two colors, the distance between the colors after mapping in a predetermined color space is smaller than the distance between the colors before mapping is defined as color degeneration. More specifically, assume that there are a color A and a color B in a digital original, and mapping to the color gamut of a printer is performed to convert the color A into a color C and the color B into a color D. In this case, the fact that the distance between the colors C and D is smaller than the distance between the colors A and B is defined as color degeneration. If color degeneration occurs, colors that are recognized as different colors in the digital original are recognized as identical colors when the original is printed. For example, in a graph, different items have different colors, thereby recognizing the different items. If color degeneration occurs, different colors may be recognized as identical colors, and thus different items of a graph may erroneously be recognized as identical items. The predetermined color space in which the distance between the colors is calculated may be an arbitrary color space. Examples of the color space are the sRGB color space, the Adobe RGB color space, the CIE-L*a*b* color space, the CIE-LUV color space, the XYZ color space, the xyY color space, the HSV color space, and HLS color space.
An 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 a parameter and data necessary for image processing at a predetermined address of the RAM 103. The image processing accelerator 105 loads the above-described parameter and data, and then executes the image processing for the data. Note that the image processing accelerator 105 is not an essential element, and the CPU 102 may execute equivalent processing. More specifically, the image processing accelerator is a GPU or an exclusively designed electric circuit. The above-described parameter can be stored in the storage medium 104 or can be externally acquired via the data transfer I/F 106.
In the printing apparatus 108, a CPU 111 reads out a program stored in a storage medium 113 to a RAM 112 as a work area and executes the readout program, thereby comprehensively controlling the printing apparatus 108. An 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 a parameter and data necessary for image processing at a predetermined address of the RAM 112. The image processing accelerator 109 loads the above-described parameter and data, and then executes the image processing for the data. Note that the image processing accelerator 109 is not an essential element, and the CPU 111 may execute equivalent processing. The above-described parameter can be stored in the storage medium 113, or can be stored in a storage (not shown) such as a flash memory or an HDD.
The image processing to be performed by the CPU 111 or the image processing accelerator 109 will now be explained. This image processing is, for example, processing of generating, based on acquired print data, data indicating the dot formation position of ink in each scan by a printhead 115. The CPU 111 or the image processing accelerator 109 performs color conversion processing and quantization processing for the acquired print data.
The color conversion processing is processing of performing color separation to ink concentrations to be used in the printing apparatus 108. For example, the acquired print data contains image data indicating an image. In a case where the image data is data indicating an image in a color space coordinate system such as sRGB as the expression colors of a monitor, data indicating an image by color coordinates (R, G, B) of the sRGB is converted into ink data (CMYK) to be handled by the printing apparatus 108. The color conversion method is implemented by, for example, matrix operation processing or processing using a 3DLUT or 4DLUT.
In this embodiment, as an example, the printing apparatus 108 uses inks of black (K), cyan (C), magenta (M), and yellow (Y) for printing. Therefore, image data of RGB signals is converted into image data formed by 8-bit color signals of K, C, M, and Y. The color signal of each color corresponds to the application amount of each ink. Furthermore, the ink colors are four colors of K, C, M, and Y, as examples. However, to improve image quality, it is also possible to use other ink colors such as inks of fluorescence ink (F) and light cyan (Lc), light magenta (Lm), and gray (Gy) having low concentrations. In this case, color signals corresponding to the inks are generated.
After the color conversion processing, quantization processing is performed for the ink data. This quantization processing is processing of decreasing the number of tone levels of the ink data. In this embodiment, quantization is performed by using a dither matrix in which thresholds to be compared with the values of the ink data are arrayed in individual pixels. After the quantization processing, binary data indicating whether to form a dot in each dot formation position is finally generated.
After the image processing is performed, a 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 so as to operate a carriage motor (not shown) for operating the printhead 115, and to operate a conveyance motor for conveying a print medium. The printhead 115 scans the print medium and also discharges ink droplets onto the print medium, thereby forming an image.
The image processing apparatus 101 and the printing apparatus 108 are connected to each other via a communication line 107. In this embodiment, a Local Area Network (LAN) will be explained as an example of the communication line 107. However, the connection may also be obtained by using, for example, a USB hub, a wireless communication network using a wireless access point, or a Wifi direct communication function.
A description will be provided below by assuming that the printhead 115 has nozzle arrays for four color inks of cyan (C), magenta (M), yellow (Y), and black (K).
In step S101, the CPU 102 receives original data. For example, the CPU 102 acquires original data stored in the storage medium 104. Alternatively, the CPU 102 may acquire original data via the data transfer I/F 106. The CPU 102 acquires image data including color information from the received original data (acquisition of color information). The image data includes values representing a color expressed in a predetermined color space. In acquisition of the color information, the values representing a color are acquired. Examples of the values representing a color are sRGB data, Adobe RGB data, CIE-L*a*b* data, CIE-LUV data, XYZ color system data, xyY color system data, HSV data, and HLS data.
In step S102, the CPU 102 performs color conversion for the image data using color conversion information stored in advance in the storage medium 104. In this embodiment, the color conversion information is a gamut mapping table, and gamut mapping is performed for the color information of each pixel of the image data. The image data obtained after gamut mapping is stored in the RAM 103 or the storage medium 104. More specifically, the gamut mapping table is a 3DLUT. By the 3DLUT, a combination of output pixel values (Rout, Gout, Bout) can be calculated with respect to a combination of input pixel values (Rin, Gin, Bin). If each of the input values Rin, Gin, and Bin has 256 tones, a table Table1[256][256][256][3] having 256×256×256=16,777,216 sets of output values in total is preferably used. The CPU 102 performs color conversion using the gamut mapping table. More specifically, color conversion is implemented by performing, for each pixel of the image formed by the RGB pixel values of the image data received in step S101, the following processing given by:
Rout=Table1[Rin][Gin][Bin][0] (1)
Gout=Table1[Rin][Gin][Bin][1] (2)
Bout=Table1[Rin][Gin][Bin][2] (3)
The table size may be reduced by decreasing the number of grids of the LUT from 256 grids to, for example, 16 grids and deciding output values by interpolating table values of a plurality of grids.
In step S103, using the image data received in step S101, the image data obtained after the gamut mapping in step S102, and the gamut mapping table, the CPU 102 creates a color degeneration-corrected table. The form of the color degeneration-corrected table is the similar to the form of the gamut mapping table. Step S103 will be described later.
In step S104, the CPU 102 generates corrected image data having undergone color degeneration correction by applying (performing an operation) the color degeneration-corrected table created in step S103 to the image data received in step S101. The generated color degeneration-corrected image data is stored in the RAM 103 or the storage medium 104.
In step S105, the CPU 102 outputs, via the data transfer I/F 106, the color degeneration-corrected image data generated in step S104. The gamut mapping may be mapping from the sRGB color space to the color reproduction gamut of the printing apparatus 108. In this case, it is possible to suppress color degeneration caused by the gamut mapping to the color reproduction gamut of the printing apparatus 108.
The color degeneration-corrected table creation processing in step S103 will be described in detail with reference to
In step S201, the CPU 102 detects unique colors of the image data received in step S101. In this embodiment, the term “unique color” indicates a color used in image data. For example, in a case of black text data with a white background, unique colors are white and black. Furthermore, for example, in a case of an image such as a photograph, unique colors are colors used in the photograph. The CPU 102 stores the detection result as a unique color list in the RAM 103 or the storage medium 104. The unique color list is initialized at the start of step S201. The CPU 102 repeats the detection processing for each pixel of the image data, and determines, for all the pixels included in the image data, whether the color of each pixel is different from unique colors detected until now. If it is determined that the color of the pixel is determined as a unique color, this color is stored as a unique color in the unique color list.
As a determination method, it is determined whether the color of the target pixel is a color included in the created unique color list. In a case where it is determined that the color is not included in the list, color information is newly added to the unique color list. In this way, the unique color list included in the image data can be detected. For example, if the input image data is sRGB data, each of the input values has 256 tones, and thus 256×256×256=16,777,216 unique colors in total are detected. In this case, the number of colors is enormous, thereby decreasing the processing speed. Therefore, the unique colors may be detected discretely. For example, the 256 tones may be reduced to 16 tones, and then unique colors may be detected. If the number of colors is reduced, colors may be reduced to the colors of the closest grids. In this way, it is possible to detect 16×16×16=4,096 unique colors in total, thereby improving the processing speed.
In step S202, based on the unique color list detected in step S201, the CPU 102 detects the number of combinations of colors subjected to color degeneration, among the combinations of the unique colors included in the image data.
ΔE407=√{square root over ((L403−L404)2+(a403−a404)2+(b403−b404)2)} (4)
ΔE408=√{square root over ((L405−L406)2+(a405−a406)2+(b405−b406)2)} (5)
In a case where the color difference ΔE 408 is smaller than the color difference ΔE 407, the CPU 102 determines that color degeneration has occurred. Furthermore, in a case where the color difference ΔE 408 does not have such magnitude that a color difference can be identified, the CPU 102 determines that color degeneration has occurred. This is because if there is such color difference between the colors 405 and 406 that the colors can be identified as different colors based on the human visual characteristic, it is unnecessary to correct the color difference. In terms of the visual characteristic, for example, a predetermined value of 2.0 may be used as the color difference ΔE with which the colors can be identified as different colors. That is, in a case where the color difference ΔE 408 is smaller than the color difference ΔE 407 and is smaller than 2.0, it may be determined that color degeneration has occurred.
In step S203, the CPU 102 determines whether the number of combinations of colors that have been determined in step S202 to be subjected to color degeneration is zero. If it is determined that the number of combinations of colors that have been determined to be subjected to color degeneration is zero, the process advances to step S204, and the CPU 102 determines that the image data requires no color degeneration correction, thereby ending the processing shown in
Since color degeneration correction changes the colors, the combinations of colors not subjected to color degeneration are also changed, which is unnecessary. Therefore, based on, for example, a ratio between the total number of combinations of the unique colors and the number of combinations of the colors subjected to color degeneration, it may be determined whether color degeneration correction is necessary. More specifically, in a case where the majority of all the combinations of the unique colors are combinations of the colors subjected to color degeneration, it may be determined that color degeneration correction is necessary. This can suppress a color change caused by excessive color degeneration correction.
In step S205, based on the input image data, the image data having undergone the gamut mapping, and the gamut mapping table, the CPU 102 performs color degeneration correction for the combinations of the colors subjected to color degeneration.
Color degeneration correction will be described in detail with reference to
Next, the color degeneration correction processing will be described in detail. A color difference correction amount 409 that increases the color difference ΔE is obtained from the color difference ΔE 408. In terms of the visual characteristic, the difference between the color difference ΔE 408 and 2.0 which is the color difference ΔE with which the colors can be recognized as different colors is the color difference correction amount 409. More preferably, the difference between the color difference ΔE 407 and the color difference ΔE 408 is the color difference correction amount 409. As a result of correcting the color 405 by the color difference correction amount 409 on an extension from the color 406 to the color 405 in the CIE-L*a*b color space, a color 410 is obtained. The color 410 is separated from the color 406 by a color difference obtained by adding the color difference ΔE 408 and the color difference correction amount 409. The color 410 is on the extension from the color 406 to the color 405 but this embodiment is not limited to this. As long as the color difference ΔE between the colors 406 and 410 is equal to the color difference obtained by adding the color difference ΔE 408 and the color difference correction amount 409, the direction can be any of the lightness direction, the chroma direction, and the hue angle direction in the CIE-L*a*b* color space. Not only one direction but also any combination of the lightness direction, the chroma direction, and the hue angle direction may be used. Furthermore, in the above example, color degeneration is corrected by changing the color 405 but the color 406 may be changed. Alternatively, both the colors 405 and 406 may be changed. If the color 406 is changed, the color 406 cannot be changed outside the color gamut 402, and thus the color 406 is moved and changed on the boundary surface of the color gamut 402. In this case, with respect to the shortage of the color difference ΔE, color degeneration correction may be performed by changing the color 405.
In step S206, the CPU 102 changes the gamut mapping table using the result of the color degeneration correction processing in step S205. The gamut mapping table before the change is a table for converting the color 403 as an input color into the color 405 as an output color. In accordance with the result of step S205, the table is changed to a table for converting the color 403 as an input color into the color 410 as an output color. In this way, the color degeneration-corrected table can be created. The CPU 102 repeats the processing of changing the gamut mapping table the number of times that is equal to the number of combinations of the colors subjected to color degeneration. The gamut mapping table in this embodiment is a table for calculating a combination of output pixel values (Rout, Gout, Bout) for a combination of input pixel values (Rin, Gin, Bin). Therefore, the output color of the gamut mapping table should be changed so that the color 405 of the output color becomes the output pixel value for the combination of the color 403 which is the input color. However, the output color 405 is expressed in the CIE-L*a*b* color space, and is not the output value (R, G, B) of the gamut mapping table. Therefore, it is necessary to convert from the CIE-L*a*b* color space to the output values of the gamut mapping table. In this embodiment, colorimetry is performed by printing the output pixel values of the gamut mapping table in advance. Then, a table is created in which the L*a*b* values and the output pixel values are associated with each other. The created correspondence table between the L*a*b* values and the output pixel values is held in the RAM 103 or the storage medium 104 in advance. When changing the gamut mapping table in S206, the CPU 102 uses a prestored table in which the L*a*b* values and the output pixel values are associated with each other to obtain the L*a values of the color 405 of the output color. Convert *b* values to output pixel values in the gamut mapping table. Then, the converted output pixel value is changed to become the output pixel value of the gamut mapping table. By doing so, the color 405 of the output color can be changed as the output pixel value of the gamut mapping table. The color 410 of the output color performs similar processing.
As described above, by applying the color degeneration-corrected gamut mapping table to the input image data, it is possible to perform correction of increasing the distance between the colors for each of the combinations of the colors subjected to color degeneration, among the combinations of the unique colors included in the input image data. As a result, it is possible to efficiently reduce color degeneration with respect to the combinations of the colors subjected to color degeneration. For example, assume that if the input image data is sRGB data, the gamut mapping table is created on the premise that the input image data has 16,777,216 colors. The gamut mapping table created on this premise is created in consideration of color degeneration and chroma even for colors not actually included in the input image data. In this embodiment, it is possible to adaptively correct the gamut mapping table with respect to the input image data by detecting the colors of the input image data. Then, it is possible to create the gamut mapping table for the colors of the input image data. As a result, it is possible to perform preferred adaptive gamut mapping for the input image data, thereby efficiently reducing color degeneration.
In this embodiment, the processing in a case where the input image data includes one page has been explained. The input image data may include a plurality of pages. If the input image data includes a plurality of pages, the processing procedure shown in
In this embodiment, the color degeneration-corrected gamut mapping table is applied to the input image data but a correction table for performing color degeneration correction for the image data having undergone gamut mapping may be created. In this case, based on the result of the color degeneration correction processing in step S205, a correction table for converting color information before correction into color information after correction may be generated. The generated correction table is a table for converting the color 405 into the color 410 in
In this embodiment, the user may be able to input an instruction indicating whether to execute the color degeneration correction processing. In this case, a UI screen shown in
The second embodiment will be described below concerning points different from the first embodiment. The first embodiment has explained that color degeneration correction is performed for a single color. Therefore, depending on combinations of colors of the input image data, a tint may change while reducing the degree of color degeneration. More specifically, if color degeneration correction is performed for two colors having different hue angles, and the color is changed by changing the hue angle, a tint is different from the tint of the color in the input image data. For example, if color degeneration correction is performed for blue and purple by changing a hue angle, purple is changed into red. If a tint changes, this may cause the user to recall a failure of an apparatus such as an ink discharge failure.
Furthermore, in the first embodiment, color degeneration correction is repeated the number of times that is equal to the number of combinations of the unique colors of the input image data. Therefore, the distance between the colors can be increased reliably. However, if the number of unique colors of the input image data increases, as a result of changing the color to increase the distance between the colors, the distance between the changed color and another unique color may be decreased. To cope with this, the CPU 102 needs to repeatedly execute color degeneration correction in step S205 so as to have expected distances between colors with respect to all the combinations of the unique colors of the input image data. Since the amount of processing of increasing the distance between colors is enormous, the processing time increases.
To cope with this, in this embodiment, color degeneration correction is performed in the same direction for every predetermined hue angle by setting a plurality of unique colors as one color group. To perform correction by setting a plurality of unique colors as one color group, in this embodiment, a unique color (to be described later) as a reference is selected from the color group. Furthermore, by limiting the correction direction to the lightness direction, it is possible to suppress a change of a tint. By performing correction in the lightness direction by setting the plurality of unique colors as one color group, it is unnecessary to perform processing for all the combinations of the colors of input image data, thereby reducing the processing time.
A CPU 102 detects the number of combinations of colors subjected to color degeneration, similar to the first embodiment, with respect to the combinations of the unique colors of the input image data within the hue range 501. Referring to
In
First, the CPU 102 decides a unique color (reference color) as the reference of the color degeneration correction processing for each hue range. In this embodiment, the maximum lightness color, the minimum lightness color, and the maximum chroma color are decided as reference colors. In
Next, the CPU 102 calculates, for each hue range, a correction ratio R from the number of combinations of the unique colors and the number of combinations of the colors subjected to color degeneration within the target hue range. A preferred calculation formula is given by:
correction ratio R=number of combinations of colors subjected to color degeneration/number of combinations of unique colors
The correction ratio R is lower as the number of combinations of the colors subjected to color degeneration is smaller, and is higher as the number of combinations of the colors subjected to color degeneration is larger. As described above, as the number of combinations of the colors subjected to color degeneration is larger, color degeneration correction can be performed more strongly.
Next, the CPU 102 calculates, for each hue range, a correction amount based on the correction ratio R and pieces of color information of the maximum lightness, the minimum lightness, and the maximum chroma. The CPU 102 calculates, as correction amounts, a correction amount Mh on a side brighter than the maximum chroma color and a correction amount Ml on a side darker than the maximum chroma color. Similar to the first embodiment, the color information in the CIE-L*a*b* color space is represented in a color space with three axes of L*, a*, and b*. The color 601 as the maximum lightness color is represented by L601, a601, and b601. The color 602 as the minimum lightness color is represented by L602, a602, and b602. The color 603 as the maximum chroma color is represented by L603, a603, and b603. The preferred correction amount Mh is a value obtained by multiplying the color difference ΔE between the maximum lightness color and the maximum chroma color by the correction ratio R. The preferred correction amount Ml is a value obtained by multiplying the color difference ΔE between the maximum chroma color and the minimum lightness color by the correction ratio R. The correction amounts Mh and Ml are calculated by:
Mh=√{square root over ((L601−L603)2+(a601−a603)2+(b601−b603)2)}×R (6)
Ml=√{square root over ((L602−L603)2+(a602−a603)2+(b602−b603)2)}×R (7)
As described above, the color difference ΔE to be held after gamut mapping is calculated. The color difference ΔE to be held after gamut mapping is the color difference ΔE before gamut mapping. In
Next, the CPU 102 generates a lightness correction table for each hue range. The lightness correction table is a table for expanding lightness between colors in the lightness direction based on the lightness of the maximum chroma color and the correction amounts Mh and Ml. In
The lightness correction table is a 1DLUT. In the 1DLUT, input lightness is lightness before correction, and output lightness is lightness after correction. The lightness after correction is decided in accordance with a characteristic based on minimum lightness after correction, the lightness of the maximum chroma color after gamut mapping, and maximum lightness after correction. The maximum lightness after correction is lightness obtained by adding the correction amount Mh to the lightness of the maximum chroma color after gamut mapping. The minimum lightness after correction is lightness obtained by subtracting the correction amount Ml from the lightness of the maximum chroma color after gamut mapping. In the lightness correction table, the relationship between the minimum lightness after correction and the lightness of the maximum chroma color after gamut mapping is defined as a characteristic that linearly changes. Furthermore, the relationship between the lightness of the maximum chroma color after gamut mapping and the maximum lightness after correction is defined as a characteristic that linearly changes. In
If the maximum lightness after correction exceeds the maximum lightness of the color gamut after gamut mapping, the CPU 102 performs maximum value clip processing. The maximum value clip processing is processing of subtracting the difference between the maximum lightness after correction and the maximum lightness of the color gamut after gamut mapping in the whole lightness correction table. In other words, the whole lightness correction table is shifted in the low lightness direction until the maximum lightness of the color gamut after gamut mapping becomes equal to the maximum lightness after correction. In this case, the lightness of the maximum chroma color after gamut mapping is also moved to the low lightness side. As described above, if the unique colors of the input image data are localized to the high lightness side, it is possible to improve the color difference ΔE and reduce color degeneration by using the lightness tone range on the low lightness side. On the other hand, if the minimum lightness after correction is lower than the minimum lightness of the color gamut after gamut mapping, the CPU 102 performs minimum value clip processing. The minimum value clip processing adds the difference between the minimum lightness after correction and the minimum lightness of the color gamut after gamut mapping in the whole lightness correction table. In other words, the whole lightness correction table is shifted in the high lightness direction until the minimum lightness of the color gamut after gamut mapping becomes equal to the minimum lightness after correction. As described above, if the unique colors of the input image data are localized to the low lightness side, it is possible to improve the color difference ΔE and reduce color degeneration by using the lightness tone range on the high lightness side.
Next, the CPU 102 applies, to the gamut mapping table, the lightness correction table created for each hue range. First, based on color information held by the output value of the gamut mapping, the CPU 102 decides the lightness correction table of a specific hue angle to be applied. For example, if the hue angle of the output value of the gamut mapping is 25°, the CPU 102 decides to apply the lightness correction table of the hue range 501 shown in
As described above, in this embodiment, the lightness correction table created based on the reference color is also applied to a color other than the reference color within the hue range 501. Then, with reference to the color after the lightness correction, for example, the color 612, mapping to a color gamut 616 is performed not to change the hue, as will be described later. That is, within the hue range 501, the color degeneration correction direction is limited to the lightness direction. With this arrangement, it is possible to suppress a change of a tint. Furthermore, it is unnecessary to perform color degeneration correction processing for all the combinations of the unique colors of the input image data, thereby making it possible to reduce the processing time.
In addition, in accordance with the hue angle of the output value of the gamut mapping, the lightness correction tables of adjacent hue ranges may be combined. For example, if the hue angle of the output value of the gamut mapping is Hn°, the lightness correction table of the hue range 501 and that of a hue range 502 are combined. More specifically, the lightness value of the output value after the gamut mapping is corrected by the lightness correction table of the hue range 501 to obtain a lightness value Lc501. Furthermore, the lightness value of the output value after the gamut mapping is corrected by the lightness correction table of the hue range 502 to obtain a lightness value Lc502. At this time, the intermediate hue angle of the hue range 501 is a hue angle H501, and the intermediate hue angle of the hue range 502 is a hue angle H502. In this case, the corrected lightness value Lc501 and the corrected lightness value Lc502 are complemented, thereby calculating a corrected lightness value Lc. The corrected lightness value Lc is calculated by:
As described above, by combining the lightness correction tables to be applied, in accordance with the hue angle, it is possible to suppress a sudden change of correction intensity caused by a change of the hue angle.
If the color space of the color information after correction is different from the color space of the output value after gamut mapping, the color space is converted and set as the output value after gamut mapping. For example, if the color space of the color information after correction is the CIE-L*a*b* color space, the following search is performed to obtain an output value after gamut mapping.
If the value after lightness correction exceeds the color gamut after gamut mapping, mapping to the color gamut after gamut mapping is performed. For example, the color 612 shown in
ΔE=√{square root over ((Ls−Lt)2+(as−at)2+(bs−bt)2)} (9)
ΔL=√{square root over ((Ls−Lt)2)} (10)
ΔC=√{square root over ((as−at)2+(bs−bt)2)} (11)
ΔH=ΔE−(ΔL+ΔC) (12)
ΔEw=Wl×ΔL+Wc×ΔC+Wh×ΔH (13)
Since the color difference ΔE is converted and expanded in the lightness direction, mapping is performed by focusing on lightness more than chroma. That is, the weight Wl of lightness is larger than the weight Wc of chroma. Furthermore, since hue largely influences a tint, it is possible to minimize a change of the tint before and after correction by performing mapping by focusing on hue more than lightness and chroma. That is, the weight Wh of hue is equal to or larger than the weight Wl of lightness, and is larger than the weight Wc of chroma. As described above, according to this embodiment, it is possible to correct the color difference ΔE while maintaining a tint.
Furthermore, the color space may be converted at the time of performing color difference minimum mapping. It is known that in the CIE-L*a*b* color space, a color change in the chroma direction does not obtain the same hue. Therefore, if a change of the hue angle is suppressed by increasing the weight of hue, mapping to a color of the same hue is not performed. Thus, the color space may be converted into a color space in which the hue angle is bent so that the color change in the chroma direction obtains the same hue. As described above, by performing color difference minimum mapping by weighting, it is possible to suppress a change of a tint.
Referring to
This embodiment has explained the example in which the lightness correction table is created for each hue range. However, the lightness correction table may be created by combining with the lightness correction table of the adjacent hue range. More specifically, within a hue range obtained by combining the hue ranges 501 and 502 in
This embodiment has explained the example in which the color difference ΔE is corrected in the lightness direction by setting a plurality of unique colors as one group. As the visual characteristic, it is known that sensitivity to the lightness difference varies depending on chroma, and sensitivity to the lightness difference of low chroma is higher than sensitivity to the lightness difference of high chroma. Therefore, the correction amount in the lightness direction may be controlled by a chroma value. That is, the correction amount in the lightness direction is controlled to be small for low chroma, and correction is performed, for high chroma, by the above-described correction value in the lightness direction. More specifically, if correction of lightness is performed by the lightness correction table, the lightness value Ln before correction and the lightness value Lc after correction are divided by a chroma correction ratio S. Based on the chroma value Sn of the output value after gamut mapping and the maximum chroma value Sm of the color gamut after gamut mapping at the hue angle of the output value after gamut mapping, the chroma correction ratio S is calculated by:
S=Sn/Sm (14)
Lc′=S×Lc+(1−S)×Ln (15)
That is, as the maximum chroma value Sm of the color gamut after gamut mapping is closer, the chroma correction ratio S is closer to 1, and Lc′ is closer to the lightness value Lc after correction, which is obtained by the lightness correction table. On the other hand, as the chroma value Sn of the output value after gamut mapping is lower, the chroma correction ratio S is closer to 0, and Lc′ is closer to the lightness value Ln before correction. In other words, as the chroma value Sn of the output value after gamut mapping is lower, the correction value of lightness is smaller. Furthermore, the correction amount may be set to zero in a low-chroma color gamut. With this arrangement, it is possible to suppress a color change around a gray axis. Furthermore, since color degeneration correction can be performed in accordance with the visual sensitivity, it is possible to suppress excessive correction.
The third embodiment will be described below concerning points different from the first and second embodiments. If colors of input image data have different hue angles, identifiability may degrade after gamut mapping. For example, like high-chroma colors having a complementary color relationship, even if a sufficient distance between colors is kept by having sufficiently different hue angles, a lightness difference may decrease after gamut mapping. If mapping to the low chroma side is performed, it is assumed that degradation of identifiability caused by a decrease in lightness difference is conspicuous. In this embodiment, if the lightness difference after gamut mapping decreases to a predetermined color difference ΔE or smaller, correction is performed to increase the lightness difference. This arrangement can suppress degradation of identifiability.
Color degeneration determination processing in step S202 according to this embodiment will be described. In step S202, based on a unique color list detected in step S201, a CPU 102 detects the number of combinations of colors subjected to lightness degeneration from combinations of unique colors included in image data. A description will be provided with reference to a schematic view shown in
The ordinate in
ΔL807=√{square root over ((L803−L804)2)} (16)
ΔL808=√{square root over ((L805−L806)2)} (17)
If the lightness difference ΔL 808 is smaller than the lightness difference ΔL 807, the CPU 102 determines that the lightness difference has decreased. Furthermore, in a case where the lightness difference ΔL 808 does not have such magnitude that a color difference can be identified, the CPU 102 determines that color degeneration has occurred. If the lightness difference between the colors 805 and 806 is such lightness difference that the colors can be identified as different colors based on the human visual characteristic, it is unnecessary to perform processing of correcting the lightness difference. In terms of the visual characteristic, 2.0 is set as the lightness difference ΔL with which the colors can be identified as different colors. That is, in a case where the lightness difference ΔL 808 is smaller than the lightness difference ΔL 807 and is smaller than 2.0, the CPU 102 may determine that lightness difference has decreased.
Next, color degeneration correction processing in step S205 according to this embodiment will be described with reference to
correction ratio T=number of combinations of colors with decreased lightness difference/number of combinations of unique colors
The correction ratio T is lower as the number of combinations of the colors with the decreased lightness difference is smaller, and is higher as the number of combinations of the colors with the decreased lightness difference is larger. As described above, as the number of combinations of the colors with the decreased lightness difference is larger, color degeneration correction can be performed more strongly.
Next, lightness difference correction is performed based on the correction ratio T and lightness before gamut mapping. Lightness Lc after lightness difference correction is obtained by dividing lightness Lm before gamut mapping and lightness Ln after gamut mapping by the correction ratio T. That is, the lightness Lm is the lightness of the color 804, and the lightness Ln is the lightness of the color 806. A calculation formula is given by:
Lc=T×Lm+(1−T)×Ln
The CPU 102 repeats the above lightness difference correction processing the number of times that is equal to the number of combinations of the unique colors of the input image data. Referring to
As described above, according to this embodiment, it is possible to perform, for a color included in the image data, gamut mapping that is corrected to increase the lightness difference, thereby reducing the degree of color degeneration caused by gamut mapping.
This embodiment has explained the colors 803 and 804. The lightness difference correction processing for the colors 803 and 804 may be applied to another color. For example, the lightness difference correction processing of this embodiment may be performed for a reference color of color degeneration correction processing, and may also be applied to another color. For example, the lightness difference correction processing for the colors 803 and 804 may be applied to a color within a predetermined hue range including the color 803 and a color within a predetermined hue range including the color 804. As described above, it is possible to reduce color degeneration and a decrease in the lightness difference caused by gamut mapping, and also to reduce a change of a tint.
The fourth embodiment will be described below concerning points different from the first to third embodiments. Among colors included in input image data, there are colors that are identical colors but have different meanings. For example, a color used in a graph and a color used as part of gradation have different meanings in identification. For a color used in a graph, it is important to distinguish the color from another color in the graph. Therefore, it is necessary to perform color degeneration correction strongly. On the other hand, for a color used as part of gradation, tonality with colors of surrounding pixels is important. It is thus necessary to perform color degeneration correction weakly. Assume that the two colors are identical colors and undergo color degeneration correction at the same time. In this case, if color degeneration correction is uniformly performed for the input image data by focusing on color degeneration correction of the color in the graph, color degeneration correction is performed strongly for gradation, and tonality in gradation degrades. On the other hand, if color degeneration correction for gradation is uniformly performed for the input image data by focusing on tonality in gradation, color degeneration correction is performed weakly for the graph, and identifiability of the color in the graph degrades. In addition, the number of combinations of unique colors becomes large, and the effect of reducing color degeneration lowers. The same applies to a case where the input image data includes a plurality of pages and color degeneration correction processing is uniformly performed for the plurality of pages and a case where the input image data includes one page and color degeneration correction processing is uniformly performed for the entire page.
In this embodiment, in either of the case where the input image data includes a plurality of pages and the case where the input image data includes one page, a plurality of areas are set and color degeneration correction processing is performed individually for each area. As a result, the color degeneration correction processing can be performed for each area with appropriate correction intensity in accordance with colors on the periphery. For example, a color in a graph can be corrected by focusing on identifiability, and a color in gradation can be corrected by focusing on tonality.
Steps S301, S302, and S307 are the same as steps S101, S102, and S105 of
In step S303, a CPU 102 sets areas in the input image data. In step S304, the CPU 102 performs processing of creating the above-described color degeneration-corrected gamut mapping table for each area set in step S303. That is, since the number of use unique colors is different for each area, the color degeneration-corrected gamut mapping table which is created by the processing of
In step S305, the CPU 102 applies, to each area, the color degeneration-corrected gamut mapping table which has been created in step S304. In step S306, the CPU 102 determines whether the processes in steps S304 and S305 have been performed for the areas set in step S303. If it is not determined that the processes have been performed for all the areas, the processes from step S304 are performed by focusing on an area for which the processes in steps S304 and S305 have not been performed. If it is determined that the processes have been performed for all the areas, the process advances to step S307.
The area setting processing in step S303 will be described in detail. FIG. 10 is a view for explaining an example of a page of the image data (to be referred to as original data hereinafter) input in step S301 of
Instruction 1) TEXT drawing instruction (X1, Y1, color, font information, character string information)
Instruction 2) BOX drawing instruction (X1, Y1, X2, Y2, color, paint shape)
Instruction 3) IMAGE drawing instruction (X1, Y1, X2, Y2, image file information)
In some cases, drawing instructions such as a DOT drawing instruction for drawing a dot, a LINE drawing instruction for drawing a line, and a CIRCLE drawing instruction for drawing a circle are used as needed in accordance with the application purpose. For example, a general PDL such as Portable Document Format (PDF) proposed by Adobe, XPS proposed by Microsoft, or HP-GL/2 proposed by HP may be used.
An original page 1000 in
<TEXT>50,100,550,150,BLACK,STD-18, “abcdefghijklmnopqrstuv”</TEXT>
<IMAGE>250,300,580,700,“PORTRAIT.jpg”</IMAGE>
<PAGE=001> of the first row is a tag representing the number of pages in this embodiment. Normally, since the PDL is designed to be able to describe a plurality of pages, a tag representing a page break is described in the PDL. In this example, the section up to </PAGE> represents the first page. In this embodiment, this corresponds to the original page 1000 in
The section from <TEXT> of the second row to </TEXT> of the third row is drawing instruction 1, and this corresponds to the first row of an area 1001 in
The section from <TEXT> of the fourth row to </TEXT> of the fifth row is drawing instruction 2, and this corresponds to the second row of the area 1001 in
The section from <TEXT> of the sixth row to </TEXT> of the seventh row is drawing instruction 3, and this corresponds to the third row of the area 1001 in
The section from <BOX> to </BOX> of the eighth row is drawing instruction 4, and this corresponds to an area 1002 in
Next, the IMAGE instruction of the ninth and 10th rows corresponds to an area 1003 in
There is a case where an actual PDL file integrates “STD” font data and a “PORTRAIT.jpg” image file in addition to the above-described drawing instruction group. This is because if the font data and the image file are separately managed, the character portion and the image portion cannot be formed only by the drawing instructions, and information needed to form the image shown in
In an original page described in PDL, like the original page 1000 shown in
In addition, it is found that both the BOX instruction and the IMAGE instruction are apart from the TEXT instructions by 100 pixels in the Y direction.
Next, in the BOX instruction and the IMAGE instruction, the start points and the end points of the drawing x-coordinates are as follows, and it is found that these are apart by 50 pixels in the X direction.
Thus, three areas can be set as follows.
Not only the configuration for thus analyzing PDL and performing area setting but also a configuration for performing area setting using a drawing result may be employed. The configuration will be described below.
In step S402, the CPU 102 determines, for each tile, whether it is a blank tile. This determination may be done based on the start point and the end point of the x- and y-coordinates in a drawing instruction, as described above, or may be done by detecting tiles in which all pixel values in the actual unit tiles are R=G=B=255. Whether to determine based on the drawing instructions or determine based on the pixel values may be decided based on the processing speed and the detection accuracy.
In step S403, the CPU 102 sets the initial values of the values as follows.
More specifically, the setting is done in the following way.
That is, at the time of completion of the processing of step S403, all tiles are set with “0” or “−1”.
In step S404, the CPU 102 searches for a tile whose area number is “−1”. More specifically, determination is performed for the ranges of x=0 to 19 and y=0 to 26 in the following way.
If an area with the area number “−1” is detected for the first time, the process advances to step S405. At this time, in step S405, the CPU 102 determines that a tile with the area number “−1” exists, and advances to step S406. If the area numbers of all areas are not “−1”, the CPU 102 determines, in step S405, that there exists no tile with the area number “−1”. In this case, the process advances to step S410.
In step S406, the CPU 102 increments the area number maximum value by +1, and sets the area number of the tile to the updated area number maximum value. More specifically, the detected area (x3, y3) is processed in the following way.
For example, here, since the area is an area detected for the first time after the processing of step S406 is executed for the first time, the area number maximum value is “1”, and the area number of the tile is set to “1”. From then on, every time the processing of step S406 is executed, the number of areas increases by one. After this, in steps S407 to S409, processing of expanding continuous non-blank areas as the same area is performed.
In step S407, the CPU 102 searches for a tile that is a tile adjacent to the tile whose area number is the area number maximum value and has the area number “−1”. More specifically, the following determination is performed for the ranges of x=0 to 19 and y=0 to 26.
If an adjacent area with the area number “−1” is detected for the first time, the CPU 102 determines, in step S408, that an adjacent area with the area number “−1” is detected, and advances to step S409. On the other hand, if the area numbers of all areas are not “−1”, the CPU 102 determines, in step S408, that an adjacent area with the area number “−1” is not detected, and advances to step S405.
In step S409, the CPU 102 sets the area number of the tile that is the adjacent tile and has the area number “−1” to the area number maximum value. More specifically, this is implemented by setting, for the detected adjacent tile, the tile position of interest to (x4, y4) and performing processing in the following way.
If the area number of the adjacent tile is updated in step S409, the process returns to step S407 to continue the search to check whether another adjacent non-blank tile exists. In a situation in which no adjacent non-blank tile exists, that is, if a tile to which the area number maximum value should be added does not exist, the process returns to step S404.
In a state in which the area numbers of all areas are not “−1”, that is, if all areas are blank areas, or any area number is set, it is determined that there exists no tile with the area number “−1”. If the CPU 102 determines, in step S405, that there exists no tile with the area number “−1”, the process advances to step S410.
In step S410, the CPU 102 sets the area number maximum value as the number of areas. That is, the area number maximum value set so far is the number of areas existing in the original page. The area setting processing in the original page is thus ended.
As shown in
A human visual sense has a characteristic that the difference between two colors that are spatially adjacent or exist in very close places can easily be relatively perceived, but the difference between two colors that exist in places spatially far apart can hardly be relatively perceived. That is, the result of “output as different colors” can readily be perceived if the processing is performed for identical colors that are spatially adjacent or exist in very close places, but can hardly be perceived if the processing is performed for identical colors that exist in places spatially far apart.
In this embodiment, areas considered as different areas are separated by a predetermined distance or more on a paper surface. In other words, pixels separated via a background color by a distance smaller than a predetermined distance on a paper surface are considered to be in the same area. Examples of the background color are white, black, and gray. The background color may be a background color defined in the original data. If printing is executed on an A4 paper, a preferred distance is, for example, 0.7 mm or more. The preferred distance may be changed in accordance with a printed paper size. Alternatively, the preferred distance may be changed in accordance with an assumed observation distance. Furthermore, even if the areas are not separated by the predetermined distance on the paper surface, different objects may be considered as different areas. For example, even if an image area and a box area are not separated by the predetermined distance, the objects types are different, and thus these areas may be set as different areas.
In this embodiment, by performing area division as described above, it is possible to detect, for each area, the number of combinations of colors to undergo color degeneration correction processing. By detecting the number of combinations of colors for each area, color degeneration correction corresponding to each color distribution is performed for each of the different areas. On the other hand, by detecting the number of combinations of colors for each area, the same color degeneration correction is performed even for different areas which have identical color distributions. As a result, for example, the results of color degeneration correction processes for graphs that are separated as areas but have identical color distributions can be identical correction results.
Furthermore, by detecting, for each area, the number of combinations of colors to undergo color degeneration correction processing, it is possible to prevent the effect of reducing color degeneration from lowering due to an increase in number of combinations of unique colors.
As described above, in this embodiment, even in the same original page, portions that are spatially far apart are set as different areas and gamut mapping suitable for each area is performed, thereby making it possible to prevent both degradation of tonality and degradation of color degeneration correction.
This embodiment has explained an example of setting a plurality of areas in one page of original data but the operation of this embodiment may be applied by setting a page group included in a plurality of pages of original data as “areas” described in this embodiment. That is, the “areas” in step S303 may be set as a page group among the plurality of pages. Note that the page group includes not only a plurality of pages but also a single page.
Assume that original data to be printed is document data formed from a plurality of pages. Consider a specific page group, among the plurality of pages, to be set as a creation target of the above-described color degeneration-corrected gamut mapping table. For example, the document data is formed from the first to third pages. If each page is set as a creation target of the color degeneration-corrected gamut mapping table, each of the first, second, and third pages is set as a creation target. A group of the first and second pages may be set as a creation target, and the third page may be set as another creation target. The creation target is not limited to a group of pages included in the document data. For example, an area of a portion of the first page may be set as a creation target. In step S303, in accordance with a predetermined group, a plurality of creation targets may be set for the original data. Note that the user may be able to designate a group to be set as a creation target.
As described above, in this embodiment, even in a plurality of pages, a page group is set as a creation target, and a color degeneration-corrected gamut mapping table is applied to each creation target, thereby making it possible to prevent both degradation of tonality and degradation of color degeneration correction.
Embodiment(s) of the present invention can also be realized by a computer of a system or apparatus that reads out and executes computer executable instructions (e.g., one or more programs) recorded on a storage medium (which may also be referred to more fully as a ‘non-transitory computer-readable storage medium’) to perform the functions of one or more of the above-described embodiment(s) and/or that includes one or more circuits (e.g., application specific integrated circuit (ASIC)) for performing the functions of one or more of the above-described embodiment(s), and by a method performed by the computer of the system or apparatus by, for example, reading out and executing the computer executable instructions from the storage medium to perform the functions of one or more of the above-described embodiment(s) and/or controlling the one or more circuits to perform the functions of one or more of the above-described embodiment(s). The computer may comprise one or more processors (e.g., central processing unit (CPU), micro processing unit (MPU)) and may include a network of separate computers or separate processors to read out and execute the computer executable instructions. The computer executable instructions may be provided to the computer, for example, from a network or the storage medium. The storage medium may include, for example, one or more of a hard disk, a random-access memory (RAM), a read only memory (ROM), a storage of distributed computing systems, an optical disk (such as a compact disc (CD), digital versatile disc (DVD), or Blu-ray Disc (BD)™), a flash memory device, a memory card, and the like.
In summary, the disclosure of the above embodiments include the following image processing apparatus, the image processing method, and the non-transitory computer-readable storage medium.
(Item 1). An image processing apparatus including:
The disclosure of the above embodiments further include the following image processing apparatus, the image processing method, and the non-transitory computer-readable storage medium.
(Item 1). An image processing apparatus including:
While the present invention has been described with reference to exemplary embodiments, it is to be understood that the invention is not limited to the disclosed exemplary embodiments. The scope of the following claims is to be accorded the broadest interpretation so as to encompass all such modifications and equivalent structures and functions.
This application claims the benefit of Japanese Patent Application No. 2022-109986, filed Jul. 7, 2022, which is hereby incorporated by reference herein in their entirety.
| Number | Date | Country | Kind |
|---|---|---|---|
| 2022-109986 | Jul 2022 | JP | national |
