Image processing device and image processing program

Abstract

To provide an image processing device and an image processing program, which improve visibility of an image output by a second device in comparison to the case where reproduction parameters applied to an image output by a first device is applied to the second device as they are.SOLUTION: A reproduction parameter is adjusted by changing the degree of change in the reproduction parameter for correcting visibility of an image with respect to a standard value according to a ratio of a second color gamut representing a range in which the image can be shown on a second device to a first color gamut representing a range in which the image can be shown on a first device.SELECTED DRAWING: Figure 4

Description

[Technical field]

[0001] The present invention relates to an image processing device and an image processing program. [Background technology]

[0002] Patent Document 1 discloses an image processing method characterized by calculating an average value of pixel values ​​in an image, calculating a maximum value of pixel values ​​in the image, calculating a minimum value of pixel values ​​in the image, and for pixels having pixel values ​​in an interval between the minimum value and a value obtained by subtracting the average value from the maximum value, replacing the pixel values ​​in the image with a tone conversion characteristic that increases the brightness of pixels in the image more than pixels outside the interval.

[0003] Patent Document 2 discloses an image display device that generates output image data by correcting an input image based on the Retinex theory, and includes a separation means that separates the input image into an illumination light component and a reflected light component using a filter, a corrected illumination light component calculation means that calculates a corrected illumination light component by adjusting the separated illumination light component with an illumination light component adjustment parameter that adjusts its brightness, and a generation means that generates the output image data based on the corrected illumination light component, wherein the corrected illumination light component calculation means calculates the corrected illumination light component by determining, based on the illumination light component adjustment parameter, a degree of mixing and adding the illumination light component separated by the separation means and an adjusted illumination light component in which a degree of emphasis of a low gradation component of this illumination light component has been adjusted.

[0004] Patent Document 3 discloses an image processing device including a characteristic information acquisition means for acquiring characteristic information indicating characteristics related to at least one of the distribution of specific brightness regions in a luminance image and the size of dark flat regions, an emphasis degree information generation means for generating emphasis degree information indicating a degree of emphasis of a specific component that affects the image quality of the luminance image based on the characteristic information, and a luminance reproduction image generation means for generating a luminance reproduction image in which the specific component of the luminance image is reproduced so as to be emphasized at the emphasis degree indicated by the emphasis degree information, wherein the characteristic information acquisition means acquires aggregation degree information indicating a degree of aggregation of regions of each of a plurality of brightness levels in the luminance image as the characteristic information indicating characteristics related to the distribution of specific brightness regions in the luminance image, and the emphasis degree information generation means generates the emphasis degree information based on the aggregation degree information and a weight of each of the plurality of brightness levels. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] JP 2008-271316 A [Patent Document 2] Patent No. 5247910 [Patent Document 3] Patent No. 6357881 Summary of the Invention [Problem to be solved by the invention]

[0006] For example, even if one tries to apply reproduction parameters applied to an image output by a first device such as an LCD display as they are when output by a second device such as a printer, the color gamut that can be output differs between devices, and the reproduction parameters used by the first device may not be appropriate for use by the second device.

[0007] The present invention aims to provide an image processing device and an image processing program that can improve the visibility of an image output on a second device compared to when reproduction parameters applied to an image output on a first device are directly applied to the second device. [Means for solving the problem]

[0008] An image processing device according to a first aspect includes a processor, and the processor adjusts a reproduction parameter for correcting visibility of an image by changing a degree to which the reproduction parameter is changed from a reference value in accordance with a ratio of a second color gamut representing a range in which an image can be represented on a second device to a first color gamut representing a range in which an image can be represented on a first device.

[0009] In the image processing device according to the second aspect, the reproduction parameters are derived using an average luminance of a dark area in which pixel values ​​satisfy a predetermined criterion.

[0010] In the image processing device according to the third aspect, the reproduction parameters are derived using an average luminance for each of a plurality of brightness hierarchies obtained by dividing the dark region into a plurality of brightness hierarchies.

[0011] In the image processing device according to a fourth aspect, the reproduction parameters are derived from the average luminance and the size of the dark area.

[0012] In the image processing device according to the fifth aspect, the reproduction parameter is higher as the size of the dark area is larger.

[0013] In the image processing device according to a sixth aspect, the degree of change from the reference value increases as the compression ratio, which is the ratio of the second color gamut to the first color gamut, increases.

[0014] In an image processing device according to a seventh aspect, the processor adjusts the reproduction parameters by changing the degree to which the reproduction parameters are changed from the reference value in accordance with a ratio of a second color gamut representing the range in which the pixels of an image can be represented on the second device to a first color gamut representing the range in which the pixels of an image can be represented on the first device.

[0015] An image processing program according to an eighth aspect causes a computer to function as the image processing device according to any one of the first to seventh aspects. Effect of the Invention

[0016] According to the first aspect, there is an advantage that the visibility of the image output by the second device can be improved compared to a case where the reproduction parameters applied to the image output by the first device are applied directly to the second device.

[0017] According to the second aspect, there is an effect that visibility can be corrected with reference to the dark area.

[0018] According to the third aspect, there is an effect that it is possible to correct the visibility more optimally compared to the case where the average luminance of the entire dark area is used.

[0019] According to the fourth aspect, there is an effect that it is possible to correct the visibility more optimally compared to the case where only the average luminance of the entire dark area is used.

[0020] According to the fifth aspect, there is an effect that it is possible to derive optimal reproduction parameters that reduce the degree of change from the reference value as the size of the dark region increases.

[0021] According to the sixth aspect, it is possible to derive an optimal reproduction parameter that is changed from the reference value by a larger degree as the compression ratio increases.

[0022] According to the seventh aspect, it is possible to adjust the reproduction parameters based on pixel information of an image, and it is possible to determine more appropriate reproduction parameters compared to the case where the reproduction parameters are adjusted depending on the device.

[0023] According to the eighth aspect, it is possible to provide an image processing program that can improve the visibility of an image output on a second device compared to when reproduction parameters applied to an image output on a first device are applied directly to the second device. [Brief description of the drawings]

[0024] [Figure 1] 1 is a schematic configuration diagram of an image processing system according to an embodiment of the present invention. [Diagram 2] 1 is a schematic block diagram of an image processing apparatus according to a first embodiment of the present invention. [Diagram 3] 1 is a schematic block diagram of an image forming apparatus according to a first embodiment of the present invention. [Figure 4] 1 is a block diagram showing an example of a functional configuration of an image processing apparatus according to a first embodiment of the present invention. [Diagram 5] FIG. 2 is a diagram showing an example of a landscape image for explaining a dark region according to the first embodiment of the present invention. [Figure 6] FIG. 2 is a diagram showing an example of a landscape image for explaining a dark region according to the first embodiment of the present invention. [Figure 7] FIG. 4 is a diagram showing an example of a coefficient a associated with the ratio of the number of pixels in a dark area according to the first embodiment of the present invention. [Figure 8] 1 is a diagram showing examples of color gamuts of an input device and an output device on the a*, b* plane. [Figure 9] 1 is a diagram showing examples of color gamuts of an input device and an output device on the L*, a* plane. [Figure 10] FIG. 13 is a diagram showing an example of a function that associates the compression ratio with the degree of reduction of the reproduction parameter. [Figure 11]5 is a flowchart showing an example of an operation of the information processing device according to the first embodiment of the present invention. [Figure 12] FIG. 11 is a diagram showing an example of the coefficient a(r) associated with the ratio of the number of pixels in a dark area when the coefficient a(r) according to the second embodiment of the present invention is a linear function. [Figure 13] FIG. 11 is a diagram showing an example of coefficient a(r) associated with the ratio of the number of pixels in a dark area when the coefficient a(r) according to the second embodiment of the present invention is a multidimensional function. [Figure 14] FIG. 13 is a diagram showing an example of a landscape image for explaining a dark area, which is binarized in a brightness hierarchy having a brightness level that extracts only a window, which is a low brightness area. [Figure 15] FIG. 13 is a diagram showing an example of a landscape image for explaining a dark area, in which a brightness that extracts windows and trees, which is a low-to-middle brightness hierarchy, is set as the brightness hierarchy, and the image is binarized in the brightness hierarchy. [Figure 16] FIG. 11 is a block diagram showing an example of a functional configuration of an image processing device according to a third embodiment of the present invention. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0025] BACKGROUND OF THEINVENTION Nowadays, the widespread use of ICT (Information and Communication Technology) devices such as smartphones and tablets allows anyone to take pictures "easily" and "anywhere." While convenience has improved in this way, the diversification of usage environments has made it important to improve the visibility of images affected by lighting such as backlight and shadows.

[0026] In the field of image processing, corrections and enhancements relating to luminance and color, such as gamma correction, saturation enhancement, band enhancement, contrast enhancement, and dynamic range correction, are performed for the purpose of achieving desirable reproduction and improving visibility.

[0027] Generally, when performing the above-mentioned correction and enhancement processes, some allow you to specify the mode and strength on the software's UI (User Interface), some automatically determine the optimal reproduction parameters within the software, and some combine both. In order to automatically determine the optimal reproduction parameters, an evaluation index that shows the quality of the reproduction is required, and in many cases, image features calculated from the image histogram or frequency are used as an index, and correction is made according to the feature values.

[0028] In contrast to this, the present embodiment considers the color gamut of the output device, determines optimal reproduction parameters according to feature amounts that take into account visual characteristics, and improves visibility of dark areas.

[0029] (First embodiment) Hereinafter, an example of an embodiment of the present disclosure will be described with reference to the drawings. In each drawing, the same or equivalent components and parts are given the same reference numerals. In addition, the dimensional ratios of the drawings are exaggerated for the convenience of explanation and may differ from the actual ratios.

[0030] FIG. 1 is a diagram showing a schematic configuration of an image processing system 10 according to this embodiment.

[0031] As shown in FIG. 1, the image processing system 10 is configured by connecting an image processing device 100 and an image forming device 200 to a communication means such as a network. As described later, various networks such as the Internet, an intranet, and Ethernet (registered trademark) can be applied as the communication means. Although FIG. 1 shows an example of two image processing devices 100 and one image forming device 200, the number is not limited to this. Here, the image processing device 100 is a personal computer (PC) in this embodiment. Furthermore, the image forming device 200 has various functions such as a print function. Furthermore, the image forming device 200 may have a copy function, a facsimile function, a scanner function, etc. in addition to the print function.

[0032] FIG. 2 is a block diagram showing the hardware configuration of the image processing device 100 according to this embodiment.

[0033] 2, the image processing device 100 includes a CPU (Central Processing Unit) 101, which is an example of a processor, a ROM (Read Only Memory) 102, a RAM (Random Access Memory) 103, a storage 104, an input unit 105, a display unit 106, and a communication unit 107. Each component is connected to each other via a bus 108 so as to be able to communicate with each other.

[0034] The CPU 101 is a central processing unit, and executes various programs and controls each part. That is, the CPU 101 reads a program from the ROM 102 or the storage 104, and executes the program using the RAM 103 as a working area. The CPU 101 controls each of the above components and performs various arithmetic processing according to the program recorded in the ROM 102 or the storage 104. In this embodiment, the program is stored in the ROM 102 or the storage 104.

[0035] The ROM 102 stores various programs and various data. The RAM 103 temporarily stores programs or data as a working area. The storage 104 is configured with an HDD (Hard Disk Drive) or an SSD (Solid State Drive) and stores various programs including an operating system and various data.

[0036] The input unit 105 includes a pointing device such as a mouse, and a keyboard, and is used to perform various input operations.

[0037] Display unit 106 is an example of a first device, and is, for example, a liquid crystal display. Display unit 106 displays various information under the control of CPU 101. Display unit 106 may also function as input unit 105 by employing a touch panel system. In this specification, the liquid crystal display will be described as the input device.

[0038] The communication unit 107 is for communicating with the image forming device 200 and other devices such as a server device not shown, and uses standards such as public lines, the Internet, an intranet, Ethernet (registered trademark), FDDI, and Wi-Fi (registered trademark).

[0039] FIG. 3 is a block diagram showing the hardware configuration of the image forming apparatus 200 according to this embodiment.

[0040] 3, the image forming apparatus 200 includes a CPU (Central Processing Unit) 201, which is an example of a processor, a ROM (Read Only Memory) 202, a RAM (Random Access Memory) 203, a storage 204, an input unit 205, a display unit 206, a document reading unit 207, an image forming unit 208, and a communication unit 209. Each component is connected to each other via a bus 210 so as to be able to communicate with each other.

[0041] The CPU 201 is a central processing unit, and executes various programs and controls each part. That is, the CPU 201 reads a program from the ROM 202 or the storage 204, and executes the program using the RAM 203 as a working area. The CPU 201 controls each of the above components and performs various arithmetic processing according to the program recorded in the ROM 202 or the storage 204. In this embodiment, the program is stored in the ROM 202 or the storage 204.

[0042] The ROM 202 stores various programs and various data. The RAM 203 temporarily stores programs or data as a working area. The storage 204 is configured with an HDD (Hard Disk Drive) or an SSD (Solid State Drive) and stores various programs including an operating system and various data.

[0043] The input unit 205 includes a pointing device such as a mouse, and a keyboard, and is used to perform various input operations.

[0044] The display unit 206 is, for example, a liquid crystal display. The display unit 206 displays various information under the control of the CPU 201. The display unit 206 may also function as the input unit 205 by adopting a touch panel system.

[0045] The document reading unit 207 takes in documents placed on a paper feed tray of an automatic feeder (not shown) provided on the top of the image forming apparatus 200, one sheet at a time, and optically reads the documents to obtain image information. Alternatively, the document reading unit 207 optically reads a document placed on a tray such as a platen glass to obtain image information.

[0046] The image forming unit 208 is an example of a second device, and forms, i.e., prints, on a recording medium such as paper an image based on image information obtained from the image processing device 100 or image information obtained by reading by the document reading unit 207. In this specification, the image forming unit will be described as an output device.

[0047] The communication unit 209 is for communicating with the image processing device 100 and other devices such as a server device not shown, and uses standards such as public lines, the Internet, an intranet, Ethernet (registered trademark), FDDI, and Wi-Fi (registered trademark).

[0048] A communication unit 209 connects the image forming apparatus 200 to a public line, and transmits and receives image information obtained by reading with the document reading unit 207 to and from other image forming apparatuses having a FAX (Facsimile) function.

[0049] The image processing device 100 uses the above hardware resources to realize various functions. The functional configuration realized by the image processing device 100 will be described below.

[0050] FIG. 4 is a block diagram showing an example of the functional configuration of the image processing device 100.

[0051] (Color space conversion unit 11) The color space conversion unit 11 converts an input device RGB image such as sRGB displayed on a display into a lightness-chromaticity color space, for example, the CIE L*, a*, b* color space. Here, the CIE L*, a*, b* components respectively represent lightness, red-green chromaticity, and blue-yellow chromaticity. In this embodiment, a* and b* are chromaticity components, and L* is a luminance component. Any color space that can be separated into light and dark components and chromaticity components, such as Lαβ, CAM02, HSV, etc., may be used.

[0052] (Visibility Reproduction Part 14) The visibility reproducing unit 14 generates a luminance reproduced image by performing reproduction to enhance visibility on the luminance image. In this embodiment, the process of performing such reproduction is a reproduction process using the Retinex principle. The reproduction process using the Retinex principle will be explained using the reproduction formula of Equation 1. Note that in this specification, a hat symbol is placed directly above a character in a formula, but is placed after the character in a sentence.

[0053]

number

[0054] Here, taking the reproduction formula of Equation 1 as an example, I(x,y) is the pixel value of the original image, and α is the reproduction parameter for enhancement. R(x,y) is the pixel value of the estimated reflectance component, and I^(x,y) represents the pixel value of the reproduced image. Therefore, if α=0, I^(x,y) represents the pixel value of the original image, and if α=1, it represents the pixel value of the reflectance image. Note that several Retinex reproduction formulas have been proposed, but they are not limited to Formula 1 and any formula that changes the weight of the reflectance component can be used. Also, (x,y) represents the position of the image. If the pixel value for position (x, y) in the luminance image is I(x, y), the Retinex model separates I(x, y) into an illumination component L(x, y) and a reflectance component R(x, y) as follows:

[0055]

number

[0056] As can be seen from Equation 2, separating one value into two values ​​is generally called an ill-posed problem, in which one of the illumination and reflectance components cannot be found without estimating the other using some method.

[0057] Therefore, the visibility reproduction unit 14 estimates L(x,y) based on I(x,y). It is known that one characteristic of visual perception is that the perceived amount of light entering a single point (a single pixel in an image) on the retina affects the average surrounding luminance of the single point. The surrounding luminance corresponds to the estimated illumination component, and the illumination estimation model is the convolution of the following function:

[0058]

number

[0059] Here, k is a coefficient for normalization so that the integration over the number of pixels of the image processing filter size becomes 1, and σ represents the degree of smoothing (scale). The larger σ is, the more blurred the convolution image becomes. Note that the above formula 3 is just an example, and any filter that produces a similar result (smoothing) may be used. For example, a bilateral filter, known as a smoothing filter that preserves edges, is a modified form of formula 2, and this may also be used.

[0060] According to the Retinex principle, it is known that human vision estimates illumination from the periphery of the area of ​​interest. The smoothed image represents the estimated illumination, but since the appropriate scale varies depending on the scene, for example, it is desirable to estimate the illumination by performing a weighted average from scale 1 to scale N of the multi-layer image as shown in the following formula 4.

[0061]

number

[0062] where L(x,y) is the pixel of the estimated illumination image, G n (x,y) is the scale n in Equation 1, I(x,y) is the pixel of the image, and W n is the weight of scale n, and the symbol "×" surrounded by "○" represents convolution. n L(x, y) may be simply set to 1 / N, or may be variable depending on the layer. The L(x, y) calculated in this way is set as the illumination component image.

[0063] According to Equation 2, the reflectance R(x, y) can be calculated based on the estimated illumination component as shown in Equation 5 below.

[0064]

number

[0065] That is, the reflectance R(x,y) is calculated using the pixel value I(x,y) of the luminance image and the pixel value L(x,y) of the illumination component image. An image with the reflectance R(x,y) as its pixel value is the reflectance image.

[0066] Also, for example, as shown in Equation 1, a visibility reproduction luminance image is generated by the reflectance R(x, y) and luminance image I(x, y) of Equation 5, and the reproduction parameter α.

[0067] Visibility is greatly affected by this reproduction parameter α. For this reason, in this embodiment, the reproduction parameter analysis unit 12 estimates reproduction parameters in the input device space from the feature amounts of the image, and the output device optimum reproduction parameter calculation unit 13 determines optimum reproduction parameters taking into account the color gamut of the output device.

[0068] (Reproduction parameter analysis part 12) The reproduction parameter analysis unit 12 estimates reproduction parameters from the feature quantities of the image in the input device space. Here, the landscape images of Figs. 5 and 6 are used as examples for explanation. In Fig. 5, the tree parts and the windows of the building are dark areas with poor visibility, and in Fig. 6, the tree parts and the building parts are dark areas with poor visibility. The brightness of the tree is the same in Fig. 5 and Fig. 6, but the brightness of the building is darker in Fig. 6, so the visibility of the entire image is worse in Fig. 6. Therefore, Fig. 6 is judged to be an image in which the strength of the reproduction parameters should be strengthened.

[0069] As in the above example, if the dark areas have the same brightness, the larger the dark area, the worse the visibility will be, so the two image feature quantities used are the ratio of the number of pixels in the dark area to the number of pixels in the entire image (the size of the dark area) and the average brightness of the dark area. Here, an explanation will be given using the brightness component. Let L*(x,y) be the brightness image converted from the input device RGB image. Then, let L*'(x,y) be the image obtained by binarizing L*(x,y) based on a certain threshold, with the maximum being 1 and the minimum being 0.

[0070] The ratio r of the number of pixels in the dark area, which is a feature amount, can be expressed as in Equation 6, where N' is the total number of pixels in the image and N is the number of pixels in the dark area.

[0071]

number

[0072] Moreover, the average luminance L*ave of the dark area can be expressed as in Equation 7 using L*(x, y) and L*'(x, y).

[0073]

number

[0074] The reproduction parameter α is calculated using the feature amounts calculated by Equations 6 and 7, but here, an example will be described in which the reproduction parameter α is calculated based on the average luminance of the dark area. First, when the reproduction parameters are estimated using only the average luminance of the dark area, they can be expressed as in Equation 8.

[0075]

number

[0076] Here, a is a coefficient determined so as to minimize the error of the reproduction parameters determined in advance by a sensory evaluation experiment or the like, using the least squares method or the like.

[0077] (Output device optimum reproduction parameter calculation unit 13) The output device optimum reproduction parameter calculation unit 13 determines the output device optimum reproduction parameters using the compression ratios of the color gamuts of the input device (liquid crystal display) and the output device (image forming unit 208), which are stored in advance as data.

[0078] In general, when the color gamuts of an input device and an output device are different, it is necessary to convert colors that cannot be reproduced by the output device into colors that can be reproduced. An example will be described with reference to Figs. 8 and 9. Fig. 8 shows the input device and output device color gamut by cutting out the a*b* plane. Fig. 9 shows the input device and output device color gamut by cutting out the L*a* plane from the L*a*b* space. Here, since the input device has a larger color gamut than the output device, it is necessary to convert the colors of the input device into colors that can be reproduced by the output device, such as point A to point A' and point B to point B'. Conversion methods include, for example, a method of converting so as to minimize the color difference using a scale called "color difference" that quantifies the color difference, and a method of minimizing the color difference while maintaining the hue.

[0079] The color gamut of these devices is generally a collection of a variety of colors. If the device is a liquid crystal display (display unit 106), the color displayed on the liquid crystal display can be directly measured, and if the device is a printer (image forming unit 208), the color gamut of the device can be defined by measuring a sample patch output onto paper or the like. Note that the colors to be measured are often data determined by international standards such as ISO.

[0080] Here, the ratio of the gamut sizes between the two devices, i.e., the volume ratio between the two devices in the L*a*b* space, is expressed as the compression ratio. For example, the volumes of the gamut (second gamut) of the output device are compared to the gamut (first gamut) of the input device in Figures 8 and 9, and when the volume ratio is 0.5, the compression ratio is set to 50%. Then, the optimal reproduction parameters are determined using this compression ratio.

[0081] Specifically, since the input device has a wider color gamut, colored areas are reproduced vividly and dark areas are reproduced darkly. Therefore, if the reproduction parameters are calculated in the color gamut of the input device, they will be larger than the reproduction parameters calculated in the color gamut of the output device, which has a narrower color gamut, and will be too strong in the output device space. Therefore, from the relationship shown in FIG. 10, a coefficient K is calculated, which is the degree of reduction of the reproduction parameters when the compression rate is 50%, by changing the reproduction parameters from the reference value (1.0) when the color gamuts of the input device and the output device are the same. The optimum reproduction parameter α' for the output device can be expressed as in Equation 9 using this coefficient K and the reproduction parameter α calculated by the reproduction parameter analysis unit 12.

[0082]

number

[0083] Finally, by calculating α in Equation 1 by the visibility reproduction unit 14 as α' in Equation 9, it is possible to generate a visibility reproduction luminance image that takes into account the color gamut of the output device.

[0084] Here, the relationship between the compression ratio and the degree of reduction in the reproduction parameter is shown as a linear line, but any function shape, including a nonlinear curve, may be used as long as the value of the degree of reduction in the reproduction parameter decreases as the compression ratio increases, or the value does not change. Also, although the volume is used as an example of calculating the compression ratio, for example, the volume of only the dark area where L* is 20 or less may be used, or the compression ratio may be calculated on a single axis of L*.

[0085] (Inverse color space conversion unit 15) The inverse color space converter 15 performs inverse conversion from the CIE L*, a*, b* color space to the RGB color space, thereby generating a visibility reproduction luminance image, and generating an image to be printed by the image forming unit 208, which is an example of an output device.

[0086] Next, the operation of the image processing device 100 will be described.

[0087] FIG. 11 is a flowchart showing an example of the operation of the image processing device 100 when generating a luminance reproduction image.

[0088] 11, in step S100, the average luminance of the dark area is calculated by the CPU 101 of the image processing device 100 when outputting to an output device. Then, the process proceeds to the next step S101.

[0089] In step S101, reproduction parameters are calculated based on the average luminance calculated in step S100 by the CPU 101 of the image processing device 100. Then, the process proceeds to the next step S102. In step S102, the CPU 101 of the image processing device 100 generates a luminance reproduction image using Equation 1 based on the luminance image to be output to the output device and the reproduction parameters calculated in step S101. Then, the process ends.

[0090] Second Embodiment Next, a second embodiment will be described. In the above-mentioned first embodiment, an example was described in which the reproduction parameters were estimated using only the average luminance of the dark area (see Equation 8). In the present second embodiment, the reproduction parameters are estimated taking into account the ratio of the size (number of pixels) of the dark area.

[0091] Here, the following description will focus on the differences from the first embodiment described above, and descriptions of overlapping parts will be simplified or omitted.

[0092] Here, in order to take into account the ratio of the number of pixels in the dark area, the coefficient a in Equation 8 described in the first embodiment is replaced with coefficient a(r), which is a function of the ratio of the number of pixels in the dark area, as shown in Equation 10.

[0093]

number

[0094] For example, if it is assumed that the coefficient a(r) of Equation 10 is a linear function, it can be expressed as Equation 11.

[0095]

number

[0096]

number

[0097] As shown in Equation 12, by making coefficient a in Equation 8 a function of the ratio of the number of pixels in the dark area, it becomes possible to estimate reproduction parameters that take the ratio of the number of pixels in the dark area into consideration. To explain this using a diagram, in the case of Equation 8, as shown in Figure 7, the coefficient is constant regardless of the ratio of the size (number of pixels) of the dark area. On the other hand, in the case of Equation 11, as shown in Figure 12, the coefficient changes depending on the ratio of the number of pixels in the dark area. In this way, by increasing the coefficient as the ratio of the number of pixels in the dark area increases, the reproduction parameters can be increased even if the average luminance of the dark area is the same. As before, a 0 , a 1 is a coefficient, and may be a coefficient determined by the least squares method or the like so as to minimize the error of the reproduction parameters determined in advance by a sensory evaluation experiment or the like.

[0098] As an example, the coefficient a(r) is assumed to be a linear function. However, by making it a multidimensional function, the relationship of the coefficient to the ratio of the number of pixels in the dark region becomes nonlinear, as shown in Fig. 13, and the reproduction parameters can be estimated with higher accuracy. Specifically, if a(r) is assumed to be a multi-dimensional function, it can be expressed as in Equation 13.

[0099]

number

[0100] In addition, in the examples so far, feature amounts are calculated based on threshold values ​​at a certain brightness level, and reproduction parameters are calculated. However, as shown in Equation 14, feature amounts may be calculated at multiple brightness levels, and reproduction parameters may be determined.

[0101]

number

[0102] For example, when n = 1, Equation 14 can be expanded to be expressed as Equation 15.

[0103]

number

[0104] A method of setting multiple brightness hierarchies will be described with reference to FIG. 5. For example, the first brightness hierarchies are the brightness levels that extract only the windows, which are the low brightness areas in FIG. 5. An image binarized with this brightness hierarchies is shown in FIG. 14. The feature amount calculated with this brightness hierarchies is expressed as the first term on the right side of Equation 15. Next, the second brightness hierarchies are the brightness levels that extract the windows and trees, which are the low and medium brightness hierarchies in FIG. 5. An image binarized with this brightness hierarchies is shown in FIG. 15. The feature amount calculated with this brightness hierarchies is expressed as the second term on the right side of Equation 15. In other words, the reproduction parameters are determined using the average brightness for each brightness hierarchies in which the dark areas are divided into multiple brightness hierarchies. In this way, by using multiple brightness hierarchies, it is possible to take into account the feature quantities in multiple brightness hierarchies, and it is possible to change the weighting of the parameters depending on the brightness hierarchies, making it possible to make more versatile estimations. 0 , a 1 The coefficients related to may be determined so that the error is minimized by using the least squares method or the like to minimize the reproduction parameters determined in advance by a sensory evaluation experiment or the like, or may be determined so that the weight is increased for the lightness hierarchical layers of the dark areas.

[0105] (Third embodiment) Next, a third embodiment will be described.

[0106] In the first and second embodiments described above, the compression ratio of the color gamut of the output device relative to the input device is stored as information in advance, and calculations are performed uniformly using this compression ratio, so the compression ratio varies depending on the device, but does not vary depending on the image. In the third embodiment, the compression ratio is calculated based on the pixel information of the input image, so it is characterized in that the compression ratio varies depending on the image. This makes it possible to determine the optimum reproduction parameters for the output device more appropriately depending on the image.

[0107] Here, the following description will focus on the differences from the first and second embodiments described above, and descriptions of overlapping parts will be simplified or omitted.

[0108] A block diagram showing the third embodiment of the present invention is shown in Fig. 16. The color space conversion unit 11, reproduction parameter analysis unit 12, output device optimum reproduction parameter calculation unit 13, visibility reproduction unit 14, and inverse color space conversion unit 15 are the same as those in the first embodiment.

[0109] The color gamut compression color space conversion unit 21 converts the RGB image of the input device into a color space of lightness and chromaticity compressed to the color gamut of the output device, for example, the CIE L*, a*, b* color space, based on the profile of the output device stored in advance. Here, the CIE L*, a*, b* components respectively represent lightness, chromaticity in the red-green direction, and chromaticity in the blue-yellow direction. In this embodiment, a* and b* are chromaticity components, and L* is a luminance component. Any color space that can be separated into light and dark components and chromaticity components, such as Lαβ, CAM02, HSV, etc., may be used.

[0110] The color gamut compression ratio calculation unit 22 calculates a compression ratio by comparing, for each pixel, the chromaticity and luminance components of the RGB image of the input device converted by the color space conversion unit 11 with the chromaticity and luminance components compressed to the color gamut of the output device converted by the color gamut compression color space conversion unit 21. The calculation method is basically the same as in the first and second embodiments, but the data used in the calculation is the input image information. That is, the compression ratio of each pixel of the RGB image is calculated based on the a*, b* and L* of all pixels acquired by the color gamut compression color space conversion unit 21 and the a*, b* and L* of all pixels converted by the color space conversion unit 11.

[0111] In the above embodiments, the image processing program is pre-stored (installed) in the ROM 102 or the memory unit (storage) 104, but the present invention is not limited to this. The program may be provided in a form recorded on a recording medium such as a CD-ROM (Compact Disk Read Only Memory), a DVD-ROM (Digital Versatile Disk Read Only Memory), or a USB (Universal Serial Bus) memory. The program may also be downloaded from an external device via a network.

[0112] The present invention is not limited to the first embodiment described above, and various modifications and applications are possible without departing from the gist of the present invention.

[0113] In addition, in each of the above-mentioned embodiments, the term "processor" refers to a processor in a broad sense, and includes general-purpose processors (e.g., CPU: Central Processing Unit, etc.) and dedicated processors (e.g., GPU: Graphics Processing Unit, ASIC: Application Specific Integrated Circuit, FPGA: Field Programmable Gate Array, programmable logic device, etc.).

[0114] In addition, the operations of the processor in the above-mentioned embodiments may be performed not only by one processor but also by multiple processors located at physically separate locations working together. The order of the operations of the processor is not limited to the order described in the above-mentioned embodiments, and may be changed as appropriate. [Explanation of symbols]

[0115] 100 Image processing device 101 CPU 102 ROM 103 RAM 104 Storage 105 Input section 106 Display section 107 Communications Department 108 Bus 200 Image forming device 201 CPU 202 ROM 203 RAM 204 Storage 205 Input section 206 Display section 207 Document Reading Department 208 Image forming section 209 Communications Department 210 Bus 11 Color space conversion section 12 Reproduction parameter analysis section 13 Output device optimum reproduction parameter calculation section 14 Visibility Reproduction Section 15 Inverse color space conversion unit 21 Color Gamut Compression Color Space Conversion Unit 22 Color Gamut Compression Ratio Calculation Unit

Claims

1. A processor is provided. The processor, adjusting the reproduction parameters by changing a degree of change of the reproduction parameters for correcting the visibility of the image from a reference value in accordance with a ratio of a second color gamut that represents a range in which the image can be represented by the second device to a first color gamut that represents a range in which the image can be represented by the first device; The image processing device derives the changed reproduction parameters using an average luminance of a dark area where pixel values ​​satisfy a predetermined criterion and an average luminance for each of a plurality of brightness hierarchies into which the dark area is divided.

2. A processor is provided. The processor, adjusting the reproduction parameters by changing a degree of change of the reproduction parameters for correcting the visibility of the image from a reference value in accordance with a ratio of a second color gamut that represents a range in which the image can be represented by the second device to a first color gamut that represents a range in which the image can be represented by the first device; The image processing device in which the changed reproduction parameters are derived from an average luminance of a dark area in which pixel values ​​satisfy a predetermined criterion and a size of the dark area.

3. The image processing apparatus according to claim 2 , wherein the reproduction parameter is higher as the size of the dark area increases.

4. 4. The image processing device according to claim 1, wherein the degree of change from the reference value increases as the compression ratio, which is the ratio of the second color gamut to the first color gamut, increases.

5. The processor, adjusting the reproduction parameters by changing a degree of change of the reproduction parameters from the reference values ​​in accordance with a ratio of a second color gamut representing a range in which the pixels of an image can be represented by the second device to a first color gamut representing a range in which the pixels of an image can be represented by the first device; The image processing device according to any one of claims 1 to 4.

6. An image processing program for causing a computer to function as the image processing device according to any one of claims 1 to 5.

Images