CN112511717B - Mapping method and device of RGB image data, storage medium and electronic equipment - Google Patents

Abstract

The embodiment of the disclosure provides a mapping method, a mapping device, a storage medium and electronic equipment of RGB image data, wherein the method comprises the steps of acquiring an image histogram based on the RGB data of a preset image; determining a CIELab data mapping relation based on the image histogram and the output parameter value; and acquiring the RGB data mapping relation of converting the preset image into an output image based on the CIELab data mapping relation. The embodiment of the disclosure can promote the working process of printing digitization, automation and intellectualization based on an automatic mapping framework to realize the conversion of RGB image data into standard data of a printing image, wherein the intellectualization is embodied as follows: 1) One-click is finished, the system automatically provides recommended results, and the user selects a favorite result; 2) The guide type operation is carried out, the system prompts and guides an operator to gradually finish the effect preview of the expected printing, and the personalized adjustment is embodied; 3) The process integration level is high, the RGB image is input, and a result graph meeting the requirements of the pre-printing condition is output.

Description

A mapping method, device, storage medium and electronic equipment for RGB image data

technical field

Embodiments of the present disclosure relate to the technical field of image conversion, and in particular to a mapping method, device, storage medium and electronic equipment for RGB image data.

Background technique

In computer interface equipment, there are many products at the image input and output terminals, such as scanners, digital cameras, monitors, inkjet printers, color laser printers, digital printing machines, etc., and different output and input devices involve different colors. The principle of operation, for example, some are the display methods of RGB data, and some are the display methods of CMYK data.

Taking the scanner as the input and the monitor as the output as an example, because there are many manufacturers of lamps for monitors and scanners with different quality and capabilities, the displayed colors cannot be unified, and after the user's self-adjustment , there is no consistent color standard; in addition, printing ink, color toner, and ink are also produced by many brands, so that the same file can produce different works with different consumables. Printing on different devices will also be different. In order to reduce the difference in color caused by different devices and systems, perfect color management is necessary. Even for the same type of electronic equipment such as color inkjet printers, the description and definition of color will be different due to different design manufacturers. Therefore, when users use different devices for image input or output, distortion or errors will appear.

Printed image duplication works that use digital photos captured by electronic devices such as digital cameras as originals abound. Compared with the printed image duplication work that uses film or film as originals, digital photos only represent data, unlike photos or other physical originals. , customers and printing technicians can communicate according to the real object, so as to adjust the image reproduction process, and can predict the printing effect and complete the image reproduction. Digital photos can only be displayed through display devices. Since it is impossible or difficult to reproduce the scene when shooting, there is no basis for photographers and printing technicians to judge the reproduction effect of images, and it is difficult to reach a consensus on the printing and reproduction process. Existing for the duplication work of images such as digital photos, often use professional equipment, software by professional technical personnel, just can reach consensus with the client through repeatedly adjusting process.

The existing printing image reproduction process based on RGB color mode is based on ICC color management technology. By displaying RGB images on a color-corrected monitor, in a standard observation environment, experienced printing technicians, Adjust the image in professional image processing software with reference to relevant printing conditions. In the prior art, the main tasks for image duplication in RGB mode include: 1) setting the working environment to meet the requirements of ISO 3664; Characterized to meet the requirements of soft proofing specified in ISO 1486; 3) Use image adjustment software that supports ICC color management technology to display images; 4) Adjust the highlights and dark points of the image, gray balance by skilled prepress technicians , tone and color, etc., to achieve the expected printing and reproduction effect; 5) Conduct soft proofing or digital proofing, and invite customers to sign samples according to the requirements of ISO 12647-8 or ISO12647-7; 6) If an agreement is reached with the customer, delivery for printing ; 7) If no agreement is reached with the customer, repeat the process from 4) to 6). Currently using printing technology based on RGB digital image data, skilled printing technicians can achieve customer-satisfied copying effects according to corresponding standards or technical specifications. This process technology is a classic printing reproduction technology based on ICC color management. In recent years, major industry associations have developed corresponding process control specifications, such as PSO, G7, C9, etc., which are suitable for lithographic printing to meet the requirements of ISO 12747-2. Standards, process correction and control, etc. require a high degree of professionalism of operators, which are often completed by industry experts. The daily work of prepress and printing operators is to complete the image reproduction work according to the established conditions.

Artificial intelligence technology is promoting changes in all walks of life. The automated workflow from image output to printing or printing has been promoting the transformation of printing technology workflow, especially short-run printing, which requires fast and personalized design to meet mass consumption. According to the needs of consumers, the existing technology does not exist to meet the requirements of mass consumers. Through the "fool" platform or system, users can upload images to the system or platform, and complete the adjustment of the image effect by themselves according to the automatic prompt of the platform. , to achieve a preview of the printing effect, without the participation of professional and technical personnel, sign the sample immediately, and deliver the working mode of printing.

Contents of the invention

In order to improve the above problems, the purpose of the embodiments of the present disclosure is to provide a RGB image data mapping method, device, storage medium and electronic equipment, so as to solve the above problems in the prior art.

In order to solve the above technical problems, embodiments of the present disclosure adopt the following technical solutions: a mapping method of RGB image data, which includes the following steps: based on the RGB data of a predetermined image, obtaining an image histogram; based on the image histogram and Output parameter values to determine the CIELab data mapping relationship; based on the CIELab data mapping relationship, obtain the RGB data mapping relationship from the predetermined image to the output image.

In some embodiments, the acquisition of an image histogram based on the RGB data of the predetermined image includes the following steps: obtaining a predetermined image; obtaining CIELab data based on the RGB data of the predetermined image; drawing an image histogram based on the CIELab data picture.

In some embodiments, the acquisition of CIELab data based on the RGB data of the predetermined image is realized through a color characteristic file embedded in the predetermined image.

In some embodiments, the determining the CIELab data mapping relationship based on the image histogram and the output parameter value includes the following steps: obtaining the first tone range of the predetermined image based on the image histogram; based on the output parameter obtain the second tone range of the output image; determine the CIELab data mapping relationship based on the first tone range and the second tone range.

In some embodiments, the acquisition of the first tone range of the predetermined image based on the image histogram is achieved by extracting the CIELab values of the brightest and darkest pixels from the image histogram; Traversing the data of the predetermined image, marking the areas where the brightest and darkest pixels are located in the image histogram; based on the areas where the brightest and darkest pixels are located, respectively determine the image white point or image black Field, so as to calculate the average brightness value of each area; based on the average brightness value, determine the first tone range of the predetermined image.

In some embodiments, the method further includes: adjusting the RGB data mapping relationship based on the color difference between the output image and the predetermined image.

In some embodiments, the adjusting the RGB data mapping relationship based on the color difference between the output image and the predetermined image includes: obtaining an output image based on the predetermined image and the RGB data mapping relationship; based on the output parameters Determine the sample CIELab value of the tone sample point in the predetermined color block; determine the comparison sample point in the output image based on the sample CIELab value; obtain based on the sample CIELab value and the comparison CIELab value of the comparison sample point Color difference: adjusting the RGB data mapping relationship based on the color difference.

The present disclosure also provides a mapping device for RGB image data, which includes the following parts:

The first acquisition module is used to obtain the image histogram based on the RGB data of the predetermined image; the determination module is used to determine the CIELab data mapping relationship based on the image histogram and output parameter values; the second acquisition module is used to Based on the CIELab data mapping relationship, the RGB data mapping relationship converted from the predetermined image to the output image is obtained.

The present disclosure also provides a storage medium storing a computer program, wherein when the computer program is executed by a processor, the steps of any one of the methods described above are implemented.

The present disclosure also provides an electronic device, including at least a memory and a processor, the memory stores a computer program, and it is characterized in that, when the processor executes the computer program on the memory, any one of the above-mentioned method steps.

The embodiment of the present disclosure focuses on the conversion stage of "conversion of RGB digital image data mapping into printing standard reference data". What is different from the prior art is that this technical solution provides an intelligent mapping system, which simplifies the prior art There are three difficulties in the implementation: first, the adjustment of RGB digital image data in the prior art needs to be completed by professional technicians using highly specialized tools; second, the cost of reaching an agreement with the customer during the adjustment process is high; Three, the modules of existing technical work are fragmented, requiring the cooperation of multiple people to complete the work.

The beneficial effect of the embodiment of the present disclosure is that the embodiment of the present disclosure provides a mapping method for converting RGB image data into RGB image data of printing image standard data using an automatic mapping framework. According to the mapping framework, the printing digital workflow can be promoted , automation and intelligence. The embodiments of the present disclosure have low requirements for user operations, and the operation process is simple. Among them, intelligence is reflected in: 1) One-click completion, the system automatically provides recommended results, and the user selects the desired result; 2) Guided operation, The system prompts and guides the operator to complete the expected printing effect preview step by step, reflecting personalized adjustments; 3) The process is highly integrated, input RGB images, and output results that meet the requirements of pre-printing conditions.

Description of drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure or the prior art, the following will briefly introduce the drawings that need to be used in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only These are some embodiments described in the present disclosure. Those skilled in the art can also obtain other drawings based on these drawings without any creative effort.

FIG. 1 is a schematic flow diagram of a mapping method of RGB image data in an embodiment of the present disclosure;

2 is a schematic structural diagram of a mapping device for RGB image data according to an embodiment of the present disclosure;

FIG. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present disclosure.

Detailed ways

Various aspects and features of the present disclosure are described herein with reference to the accompanying drawings.

It should be understood that various modifications may be made to the embodiments applied for herein. Accordingly, the above description should not be viewed as limiting, but only as exemplifications of embodiments. Those skilled in the art will envision other modifications within the scope and spirit of the disclosure.

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the disclosure and, together with the general description of the disclosure given above and the detailed description of the embodiments given below, serve to explain the principles of the disclosure. principle.

These and other characteristics of the present disclosure will become apparent from the following description of preferred forms of embodiment given as non-limiting examples with reference to the accompanying drawings.

It should also be understood that, while the disclosure has been described with reference to a few specific examples, those skilled in the art will surely be able to implement many other equivalents of the disclosure which have the features of the claims and which are therefore situated within the scope of the claims. within the limited scope of protection.

The above and other aspects, features and advantages of the present disclosure will become more apparent in view of the following detailed description when taken in conjunction with the accompanying drawings.

Specific embodiments of the present disclosure are hereinafter described with reference to the accompanying drawings; however, it is to be understood that the applied embodiments are merely examples of the disclosure, which may be embodied in various ways. Well-known and/or repetitive functions and constructions are not described in detail to avoid obscuring the disclosure with unnecessary or redundant detail. Therefore, specific structural and functional details disclosed herein are not intended to be limiting, but merely as a basis for the claims and as a representative basis for teaching one skilled in the art to variously employ the present invention in virtually any suitable detailed structure. public.

This specification may use the phrases "in one embodiment," "in another embodiment," "in yet another embodiment," or "in other embodiments," which may refer to the same or one or more of the different embodiments.

The first embodiment of the present disclosure relates to a mapping method of RGB image data. Through the mapping method of the embodiment of the present disclosure, it can be realized that in the case of inputting a predetermined image with RGB data, the output conforms to predetermined conditions or standards for Printed or printed images, to ensure that the color difference of the output image is within a predetermined range, as shown in Figure 1, the mapping method specifically includes the following steps:

S101. Acquire an image histogram based on RGB data of a predetermined image.

In this step, the RGB data of the predetermined image is obtained based on the input predetermined image, and an image histogram is obtained based on the RGB data of the predetermined image.

The RGB data of the predetermined image here refers to the color information of each element point in the predetermined image, and the color information is expressed in the form of RGB data. The image histogram (Image Histogram) here is widely used in the visual processing of computers, and generally obtains color change information by marking significant edges and statistical changes of colors. In simple terms, the image histogram is used to represent numbers The statistical diagram of the data distribution of the brightness distribution in the image counts the number of pixels of each brightness value in the image. Therefore, the image histogram here is the way to count the color data, and the statistical value is assigned to a series of implementation-defined Among the bins, the bins here are considered as "bars" or "group distances" in the image histogram, and their values are characteristic statistics calculated from the data. These data can be such as gradient, direction, color or any other feature. Further, said RGB data based on predetermined image, obtains image histogram, comprises the following steps:

S201. Acquire a predetermined image.

In this step, it is first necessary to obtain the input predetermined image, where the predetermined image is a predetermined image that needs to be printed, printed and other subsequent processing through user input, and the predetermined image here can be obtained through a mobile terminal, a digital camera, An image acquired by electronic devices such as a scanner and stored and displayed in the form of RGB data.

S202. Acquire CIELab data based on the RGB data of the predetermined image;

Through the predetermined image obtained in the above step S201, the RGB data in the predetermined image can be analyzed and obtained based on the predetermined image. In this step, the CIELab data can be obtained based on the RGB data of the predetermined image. It should be noted that CIELab is a color system of the International Commission on Illumination (CIE), which belongs to the color expression system. The Lab mode or Lab color space is a color model announced by the International Commission on Illumination in 1976, which is determined by the International Commission on Illumination. Theoretically, the color mode includes all colors visible to the human eye, and the Lab mode makes up for the shortcomings of the RGB and CMYK color modes. The Lab mode is composed of three channels, the first channel is the lightness, which is "L", in addition, the color of the a channel is from red to dark green, and the b channel is from blue to yellow. Among the existing color modes, in terms of expressing the color range, the Lab mode is the most complete, followed by the RGB mode, and the CMYK mode is the narrowest. That is to say, the Lab mode defines the most colors, and the Lab mode is used as a transfer medium to keep the appearance of all colors consistent, and when transferring colors between different devices, the colors will not change and have nothing to do with light and devices, and the processing speed As fast as RGB mode, several times faster than CMYK mode.

In this step, because different manufacturers of pre-printing electronic equipment have different definitions of RGB or CMYK colors, the same RGB or CMYK image data will appear in different colors on different systems and electronic equipment. Therefore, use ICC files, as the basis and standard for color conversion between different devices, enable colors to be displayed consistently on different electronic devices. In one embodiment, in the process of converting the predetermined image from RGB data to CIELab data, for example, it can be realized through the color characteristic file ICC, and the color characteristic ICC file combines all the color data related to the device (such as RGB and CMYK data ) is mapped to the device-independent Lab color model.

Specifically, considering that general electronic devices will pre-install the color characteristic ICC file, by reading the input data of the predetermined image, it is possible to determine whether the predetermined image is embedded with the color characteristic ICC by obtaining the header file information of the predetermined image. file; if the color characteristic ICC file is embedded in the header file information of the predetermined image, the conversion of "RGB-CIELAB" in the color characteristic ICC file can be performed to obtain the data of the RGB data of the predetermined image in Lab mode That is, CIELab data; if there is no ICC file embedded in the header file information of the predetermined image, the conversion can be realized in the form of installing a color characteristic ICC file.

S203, draw an image histogram based on the CIELAB data

After obtaining the CIELab data of the predetermined image by the above step S202, in this step, draw an image histogram by the RGB data of the predetermined image and the CIELab data obtained by the above step S202. Here, the method of drawing the image histogram based on the CIELab data in the Lab mode can adopt any existing method, and the present disclosure will not repeat them here.

S102. Determine a CIELab data mapping relationship based on the image histogram and output parameter values.

After obtaining the image histogram based on the RGB data of the predetermined image through the above step S101, in this step, based on the image histogram and the output parameter value, determine the mapping relationship of the CIELab value. In this step, the image histogram represents the CIELab data of the input image, that is, the predetermined image, and the CIELab value of the output image is represented by the output parameter value, and the CIELab value between the input image and the output image is determined based on these two CIELab values mapping relationship. Described based on described image histogram and output parameter value, determine CIELAB mapping relation, specifically comprise the following steps:

S301. Acquire a first tone range of the predetermined image based on the image histogram.

After obtaining the image histogram based on the above step S101, in this step, extract the CIELab values of the brightest and darkest pixels in the image histogram from the image histogram, and store the brightest pixel at the same time The RGB value and the corresponding CIELAB value.

Further, according to the RGB data and CIELab values of the brightest and darkest pixels, traverse the data of the predetermined image, mark the areas where the brightest and darkest pixels are located in the image histogram, and use intelligent The way to identify the black and white field of the image, for example, determine the continuous area of 3×3 or 5×5 as the image white field or the image black field respectively, so as to calculate the average brightness value of each area, where the brightness value is recorded in the format of CIELab value , so as to determine the first tone range of the predetermined image, the tone in the tone range is the optical performance of an area with uniform brightness in image information restoration, and the tone value is the measure of tone, in printing technology It is usually expressed by the degree of transmission or reflection of light.

S302. Acquire the second tone range of the output image based on the output parameter value.

After obtaining the first tone range of the predetermined image through the above step S301, in this step, it is also necessary to obtain the second tone range of the output image based on determining and setting the output parameter value of the image data used for printing or printing , the output parameter value here can be determined based on the purpose of the user for outputting the image, for example: for office printing/for printing/for network transmission, etc., the output parameter value is determined and set by the output purpose, the output parameter value here is for example record In the color profile ICC file. Based on, for example, the output parameter value of the color characteristic ICC file, the second tone range of the output image is extracted and obtained, and recorded in the format of CIELab value.

S303. Based on the first tone range and the second tone range, determine a CIELab data mapping relationship.

After obtaining the first tone range of the predetermined image through the above step S301 and the second tone range of the output image through step S302, in this step, based on the first tone range and the second tone range The tone range determines the tone compression ratio from the predetermined image to the output image, and determines the CIELab data mapping relationship based on the tone compression ratio.

S103. Based on the CIELAB data mapping relationship, obtain the RGB data mapping relationship for converting the predetermined image to an output image.

After the CIELab data mapping relationship is determined through the above step S102, in this step, based on the CIELAB data mapping relationship, the RGB data mapping relationship from the predetermined image to the output image is obtained, for example, it can be realized through the color characteristic file ICC, calling the embedded The image data from CIELab to RGB in the color characteristic ICC file in the predetermined image is inversely calculated to complete the mapping from the CIELab data mapping relationship to the RGB data mapping relationship.

Embodiments of the present disclosure can promote printing digital workflow, automation and intelligence. The embodiment of the present disclosure has a low threshold for user operation, simple operation process, and intelligence is reflected in: 1) One-click completion, the system automatically provides recommended results, and the user selects the desired result; 2) Guided operation, system prompts Guide the operator to complete the expected printing effect preview step by step, reflecting personalized adjustments; 3) The process is highly integrated, input RGB images, and output results that meet the requirements of pre-printing conditions.

The second embodiment of the present disclosure provides a method for mapping RGB image data, which includes the above-mentioned steps S101-S103 in the above-mentioned first embodiment, and specifically, further includes the following steps:

S104. Adjust the RGB data mapping relationship based on the color difference between the output image and the predetermined image.

On the basis of the predetermined image, the RGB data mapping relationship determined by the above step S103 can obtain an output image, and in this step, further compare between the predetermined image and the output image to correspondingly The RGB data mapping relationship is adjusted so that the output image meets the requirements of predetermined output parameter values and conditions such as color difference. Specifically, the adjustment of the RGB data mapping relationship based on the color difference between the output image and the predetermined image includes the following steps:

S401. Acquire an output image based on the predetermined image and the RGB mapping relationship.

In this step, first, based on the predetermined image and the RGB data mapping relationship, the output image is obtained, and the output image is an image conforming to the output parameter value; preferably, after the output image is obtained, the system user can The input image and the mapping result of the corresponding input image, that is, the output image, are displayed side by side in the interface, so as to facilitate user comparison, and the tone compression ratio after image conversion is output at the same time.

S402. Determine a sample CIELAB value of a tone sample point in a predetermined color block based on the output parameter value.

After obtaining the output image through the above step S301, in this step, determine the sample CIELAB value of the tone sample point in the predetermined color block based on the output parameter value, specifically,

First of all, it is necessary to determine the predetermined color block. For example, according to ISO 16760, it can be extracted from the color characteristic file ICC embedded in the predetermined image. 255 all combined RGB color blocks are used as predetermined color blocks. Further, for example, the CIELab values of highlight sample points, midtone sample points, and bright and dark sample points can be extracted from the output image according to the color characteristic file ICC, where the highlight sample points can have a CMYK value of (5%, 3 %, 3%, 0), the middle tone sample point can be a sample point with a CMYK value of (50%, 41%, 39%, 0), and the light and dark tone sample point can be a CMYK value of (65%, 53%, 51%, 95%) sample points.

S403. Determine a comparison sample point in the output image based on the sample CIELab value.

After the sample CIELab value of the tone sample point is determined through the above steps, in this step, the neutral gray curve of the output image is extracted with the target value of the sample CIELab value of the tone sample point, and the center is marked The point on the gray curve that is closest to the above three target values is used as a comparison sample point.

S404. Acquire color difference based on the sample CIELab value and the comparison CIELab value of the comparison sample point.

In this step, after obtaining the sample CIELab value and the comparison sample point, the color difference is calculated based on the sample CIELab value and the comparison CIELab value of the comparison sample point to determine the adjustment of the gray balance of the output image Effect. Here, the so-called color difference takes the maximum color difference or average color difference as the evaluation index to evaluate the effect before and after image conversion.

S405. Adjust the RGB data mapping relationship based on the color difference.

After obtaining the color difference in step S304, judge and evaluate the color difference in this step to judge whether the RGB data mapping relationship is appropriate, for example, when the average color difference satisfies ΔE00≤4 or the maximum color difference satisfies ΔEab≤6, it can be considered satisfactory effect, it is determined that the RGB data mapping relationship does not need to be adjusted. Of course, when the average color difference or the maximum color difference does not meet the above conditions, it is determined that the RGB data mapping relationship needs to be adjusted.

Embodiments of the present disclosure can promote printing digital workflow, automation and intelligence. The embodiment of the present disclosure has a low threshold for user operation, simple operation process, and intelligence is reflected in: 1) One-click completion, the system automatically provides recommended results, and the user selects the desired result; 2) Guided operation, system prompts Guide the operator to complete the expected printing effect preview step by step, reflecting personalized adjustments; 3) The process is highly integrated, input RGB images, and output results that meet the requirements of pre-printing conditions.

The third embodiment of the present disclosure provides a mapping device for RGB image data. Through the mapping device of the embodiment of the present disclosure, when a predetermined image with RGB data is input, the output image for Printed or printed images, to ensure that the color difference of the output image is within a predetermined range, as shown in Figure 2, the mapping device includes a first acquisition module 10, a determination module 20 and a second acquisition module 30, which are coupled to each other ,specifically:

The first acquiring module 10 is configured to acquire an image histogram based on RGB data of a predetermined image.

Through the first obtaining module 10, the RGB data of the predetermined image is obtained based on the input predetermined image, and the image histogram is obtained based on the RGB data of the predetermined image.

The RGB data of the predetermined image here refers to the color information of each element point in the predetermined image, and the color information is expressed in the form of RGB data. The image histogram (Image Histogram) here is widely used in the visual processing of computers, and generally obtains color change information by marking significant edges and statistical changes of colors. In simple terms, the image histogram is used to represent numbers The statistical diagram of the data distribution of the brightness distribution in the image counts the number of pixels of each brightness value in the image. Therefore, the image histogram here is the way to count the color data, and the statistical value is assigned to a series of implementation-defined Among the bins, the bins here are considered as "bars" or "group distances" in the image histogram, and their values are characteristic statistics calculated from the data. These data can be such as gradient, direction, color or any other feature. Further, the RGB data based on the predetermined image obtains an image histogram, and the first acquisition module 10 includes the following parts:

The first acquisition unit is used to acquire a predetermined image.

Through the first acquisition unit, it is first necessary to acquire the input predetermined image, where the predetermined image is a predetermined image that needs to be printed, printed and other subsequent processing through user input, and the predetermined image here can be obtained through a mobile terminal, Digital cameras, scanners and other electronic devices acquire and store and display images in the form of RGB data.

A second acquisition unit, which is used to acquire CIELab data based on the RGB data of the predetermined image;

Through the predetermined image acquired by the first acquisition unit, the RGB data in the predetermined image can be analyzed and obtained based on the predetermined image, and the CIELab data can be obtained based on the RGB data of the predetermined image by the second acquisition unit. It should be noted that CIELab is a color system of the International Commission on Illumination (CIE), which belongs to the color expression system. The Lab mode or Lab color space is a color model announced by the International Commission on Illumination in 1976 and is determined by the International Commission on Illumination. Theoretically, the color mode includes all colors visible to the human eye, and the Lab mode makes up for the shortcomings of the RGB and CMYK color modes. Lab mode is composed of three channels, the first channel is lightness, which is "L", in addition, the color of channel a is from red to dark green, and channel b is from blue to yellow. Among the existing color modes, the Lab mode is the most complete in expressing the color range, followed by the RGB mode, and the narrowest is the CMYK mode. That is to say, the Lab mode defines the most colors, and the Lab mode is used as a transfer medium to keep the appearance of all colors consistent, and when transferring colors between different devices, the colors will not change and have nothing to do with light and devices, and the processing speed As fast as RGB mode, several times faster than CMYK mode.

Through the second acquisition unit, because different manufacturers of pre-printing electronic equipment have different definitions of RGB or CMYK colors, the same RGB or CMYK image data will present different colors on different systems and electronic equipment, Therefore, ICC files are used as the basis and standard for color conversion between different devices, so that colors can be consistently displayed on different electronic devices. In one embodiment, in the process of converting the predetermined image from RGB data to CIELab data, for example, it can be realized through the color characteristic file ICC, and the color characteristic ICC file combines all the color data related to the device (such as RGB and CMYK data ) is mapped to the device-independent Lab color model.

Specifically, considering that general electronic devices will pre-install the color characteristic ICC file, by reading the input data of the predetermined image, it is possible to determine whether the predetermined image is embedded with the color characteristic ICC by obtaining the header file information of the predetermined image. file; if the color characteristic ICC file is embedded in the header file information of the predetermined image, the conversion of "RGB-CIELAB" in the color characteristic ICC file can be performed to obtain the data of the RGB data of the predetermined image in Lab mode That is, CIELab data; if there is no ICC file embedded in the header file information of the predetermined image, the conversion can be realized in the form of installing a color characteristic ICC file.

A drawing unit for drawing an image histogram based on the CIELAB data

After obtaining the CIELab data of the predetermined image by the second obtaining unit, draw an image histogram by the RGB data of the predetermined image and the CIELab data obtained by the second obtaining unit by the drawing unit. Here, the method of drawing the image histogram based on the CIELab data in the Lab mode can adopt any existing method, and the present disclosure will not repeat them here.

A determining module 20, configured to determine the CIELab data mapping relationship based on the image histogram and output parameter values.

After the image histogram is obtained based on the RGB data of the predetermined image by the first acquisition module 10, the mapping relationship of the CIELab value is determined by the determination module 20 based on the image histogram and the output parameter value. Through the determination module 20, the image histogram represents the situation of the CIELab data of the input image, that is, the predetermined image, and the CIELab value of the output image is represented by the output parameter value, and the CIELab value between the input image and the output image is determined based on these two CIELab values mapping relationship. Described based on described image histogram and output parameter value, determine CIELAB mapping relationship, described determination module 20 specifically comprises the following parts:

A third acquiring unit, configured to acquire the first tone range of the predetermined image based on the image histogram.

After the image histogram is obtained based on the first acquisition module 10, the CIELab values of the brightest and darkest pixels in the image histogram are extracted from the image histogram by the third acquisition unit, and stored simultaneously The RGB value of the brightest pixel and the corresponding CIELAB value.

Further, according to the RGB data and CIELab values of the brightest and darkest pixels, traverse the data of the predetermined image, mark the areas where the brightest and darkest pixels are located in the image histogram, and use intelligent The way to identify the black and white field of the image, for example, determine the continuous area of 3×3 or 5×5 as the image white field or the image black field respectively, so as to calculate the average brightness value of each area, where the brightness value is recorded in the format of CIELab value , so as to determine the first tone range of the predetermined image, the tone in the tone range is the optical performance of an area with uniform brightness in image information restoration, and the tone value is the measure of tone, in printing technology It is usually expressed by the degree of transmission or reflection of light.

A fourth acquiring unit, configured to acquire the second tone range of the output image based on the output parameter value.

When the first tone range of the predetermined image is acquired by the third acquisition unit, the fourth acquisition unit also needs to obtain the output image based on the determination and setting of the output parameter value of the image data for printing or printing. In the second tone range, the output parameter value here can be determined based on the purpose of the user for outputting the image, for example: for office printing/for printing/for network transmission, etc., determine and set the output parameter value through the output purpose, here Output parameter values are recorded, for example, in a color characteristic ICC file. Based on, for example, the output parameter value of the color characteristic ICC file, the second tone range of the output image is extracted and obtained, and recorded in the format of CIELab value.

A first determining unit, configured to determine a CIELab data mapping relationship based on the first tone range and the second tone range.

After the first tone range of the predetermined image is acquired by the third acquisition unit and the second tone range of the output image is acquired by the fourth acquisition unit, the first determination unit based on the first tone The tone range and the second tone range determine a tone compression ratio from the predetermined image to an output image, and a CIELab data mapping relationship is determined based on the tone compression ratio.

The second acquiring module 30 is used for obtaining the RGB data mapping relationship of converting the predetermined image to an output image based on the CIELAB data mapping relationship.

After the CIELab data mapping relationship is determined by the determining module 20, the RGB data mapping relationship of converting the predetermined image to the output image is obtained by the second acquisition module 30 based on the CIELAB data mapping relationship, for example, it can be realized by a color characteristic file ICC Invoking the image data inverse operation from CIELab to RGB in the color characteristic ICC file embedded in the predetermined image to complete the mapping from the CIELab data mapping relationship to the RGB data mapping relationship.

Embodiments of the present disclosure can promote printing digital workflow, automation and intelligence. The embodiment of the present disclosure has a low threshold for user operation, simple operation process, and intelligence is reflected in: 1) One-click completion, the system automatically provides recommended results, and the user selects the desired result; 2) Guided operation, system prompts Guide the operator to complete the expected printing effect preview step by step, reflecting personalized adjustments; 3) The process is highly integrated, input RGB images, and output results that meet the requirements of pre-printing conditions.

The fourth embodiment of the present disclosure provides a mapping device for RGB image data, which includes the first acquisition module 10, the determination module 20, and the second acquisition module 30 in the above third embodiment, specifically, it also includes the following part:

An adjustment module, configured to adjust the RGB data mapping relationship based on the color difference between the output image and the predetermined image.

On the basis of the predetermined image, the RGB data mapping relationship obtained by the second obtaining module 30 can obtain an output image, and the adjustment module can further compare between the predetermined image and the output image The RGB data mapping relationship is adjusted accordingly, so that the output image meets the requirements of predetermined output parameter values, color difference and other conditions. Specifically, based on the color difference between the output image and the predetermined image, the adjustment module includes the following parts:

A fifth acquiring unit, configured to acquire an output image based on the predetermined image and the RGB mapping relationship.

The fifth acquisition unit first acquires the output image based on the predetermined image and the RGB data mapping relationship, and the output image is an image conforming to the output parameter value; preferably, after the output image is acquired, it can be In the user interface of the system, the input image and the mapping result of the corresponding input image, that is, the output image, are displayed side by side to facilitate user comparison, and the tone compression ratio after image conversion is output at the same time.

The second determination unit is used to determine the sample CIELAB value of the tone sample point in the predetermined color block based on the output parameter value.

After the output image is acquired by the fifth acquisition unit, the sample CIELAB value of the tone sample point is determined in the predetermined color block by the second determination unit based on the output parameter value, specifically,

First of all, it is necessary to determine the predetermined color block. For example, according to ISO 16760, it can be extracted from the color characteristic file ICC embedded in the predetermined image. 255 all combined RGB color blocks are used as predetermined color blocks. Further, for example, the CIELab values of highlight sample points, midtone sample points, and bright and dark sample points can be extracted from the output image according to the color characteristic file ICC, where the highlight sample points can have a CMYK value of (5%, 3 %, 3%, 0), the middle tone sample point can be a sample point with a CMYK value of (50%, 41%, 39%, 0), and the light and dark tone sample point can be a CMYK value of (65%, 53%, 51%, 95%) sample points.

A third determining unit, configured to determine a comparison sample point in the output image based on the sample CIELab value.

After the sample CIELab value of the tone sample point is determined by the second determination unit, the neutral gray curve of the output image is extracted by the third determination unit with the target value of the sample CIELab value of the tone sample point, And mark the points closest to the above three target values on the neutral gray curve as comparison sample points.

A sixth acquisition unit, configured to acquire a color difference based on the sample CIELab value and the comparison CIELab value of the comparison sample point.

Through the sixth acquisition unit, after acquiring the sample CIELab value and the comparison sample point, calculate the color difference based on the sample CIELab value and the comparison CIELab value of the comparison sample point, to determine the gray balance adjustment of the output image Effect. Here, the so-called color difference takes the maximum color difference or average color difference as the evaluation index to evaluate the effect before and after image conversion.

An adjustment unit, configured to adjust the RGB data mapping relationship based on the color difference.

After the color difference is obtained by the sixth acquisition unit, the adjustment unit judges and evaluates the color difference to determine whether the RGB data mapping relationship is appropriate, for example, when the average color difference satisfies ΔE00≤4 or the maximum color difference satisfies ΔEab≤6, It can be considered that a satisfactory effect is achieved, and it is determined that the RGB data mapping relationship does not need to be adjusted. Of course, when the average color difference or the maximum color difference does not meet the above conditions, it is determined that the RGB data mapping relationship needs to be adjusted.

Embodiments of the present disclosure can promote printing digital workflow, automation and intelligence. The embodiment of the present disclosure has a low threshold for user operation, simple operation process, and intelligence is reflected in: 1) One-click completion, the system automatically provides recommended results, and the user selects the desired result; 2) Guided operation, system prompts Guide the operator to complete the expected printing effect preview step by step, reflecting personalized adjustments; 3) The process is highly integrated, input RGB images, and output results that meet the requirements of pre-printing conditions.

The fifth embodiment of the present disclosure provides a storage medium, which is a computer-readable medium and stores a computer program, and when the computer program is executed by a processor, it realizes the functions provided by the first embodiment and the third embodiment of the present disclosure. The method comprises the following steps S11 to S13:

S11. Obtain an image histogram based on the RGB data of the predetermined image;

S12, based on the image histogram and the output parameter value, determine the CIELab data mapping relationship;

S13. Based on the CIELab data mapping relationship, obtain the RGB data mapping relationship for converting the predetermined image to an output image.

Further, when the computer program is executed by the processor, other methods provided by the first embodiment and the third embodiment of the present disclosure are realized

Embodiments of the present disclosure can promote printing digital workflow, automation and intelligence. The embodiment of the present disclosure has a low threshold for user operation, simple operation process, and intelligence is reflected in: 1) One-click completion, the system automatically provides recommended results, and the user selects the desired result; 2) Guided operation, system prompts Guide the operator to complete the expected printing effect preview step by step, reflecting personalized adjustments; 3) The process is highly integrated, input RGB images, and output results that meet the requirements of pre-printing conditions.

The sixth embodiment of the present disclosure provides an electronic device. The schematic structural diagram of the electronic device can be shown in FIG. The computer program on 901 implements the method provided by any embodiment of the present disclosure. Exemplarily, the computer program steps of the electronic device are as follows S21 to S23:

S21. Obtain an image histogram based on the RGB data of the predetermined image;

S22, based on the image histogram and the output parameter value, determine the CIELab data mapping relationship;

S23. Based on the CIELab data mapping relationship, obtain the RGB data mapping relationship for converting the predetermined image to an output image.

Further, the processor 902 also executes the computer programs in the above-mentioned first embodiment and the second embodiment

Embodiments of the present disclosure can promote printing digital workflow, automation and intelligence. The embodiment of the present disclosure has a low threshold for user operation, simple operation process, and intelligence is reflected in: 1) One-click completion, the system automatically provides recommended results, and the user selects the desired result; 2) Guided operation, system prompts Guide the operator to complete the expected printing effect preview step by step, reflecting personalized adjustments; 3) The process is highly integrated, input RGB images, and output results that meet the requirements of pre-printing conditions.

The above-mentioned storage medium may be included in the above-mentioned electronic device, or may exist independently without being assembled into the electronic device.

The above-mentioned storage medium carries one or more programs, and when the above-mentioned one or more programs are executed by the electronic device, the electronic device: acquires at least two Internet Protocol addresses; sends a message including at least two Internet Protocol addresses to the node evaluation device The node evaluation request, wherein, the node evaluation device selects an IP address from at least two IP addresses and returns it; receives the IP address returned by the node evaluation device; wherein, the obtained IP address indicates the IP address in the content distribution network edge nodes.

Alternatively, the above-mentioned storage medium carries one or more programs, and when the above-mentioned one or more programs are executed by the electronic device, the electronic device: receives a node evaluation request including at least two Internet protocol addresses; In the protocol address, select an IP address; and return the selected IP address; wherein, the received IP address indicates an edge node in the content distribution network.

Computer program code for carrying out operations of the present disclosure may be written in one or more programming languages, or combinations thereof, including but not limited to object-oriented programming languages—such as Java, Smalltalk, C++, and Includes conventional procedural programming languages - such as "C" or similar programming languages. The program code may execute entirely on the passenger computer, partly on the passenger computer, as a stand-alone software package, partly on the passenger computer and partly on a remote computer, or entirely on the remote computer or server. Where a remote computer is involved, the remote computer may be connected to the passenger computer via any kind of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via an Internet Service Provider). Internet connection).

It should be noted that the storage medium mentioned above in the present disclosure may be a computer-readable signal medium or a computer-readable storage medium, or any combination of the above two. A computer readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to, electrical connections with one or more wires, portable computer diskettes, hard disks, random access memory (RAM), read-only memory (ROM), erasable Programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination of the above. In the present disclosure, a computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In the present disclosure, however, a computer-readable signal medium may include a data signal in baseband or propagated as part of a carrier wave carrying computer-readable program code therein. Such propagated data signals may take many forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the foregoing. A computer-readable signal medium may also be any storage medium other than a computer-readable storage medium that can transmit, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code contained on a storage medium may be transmitted by any suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

The flowchart and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in a flowchart or block diagram may represent a module, program segment, or portion of code that contains one or more logical functions for implementing specified executable instructions. It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or they may sometimes be executed in the reverse order, depending upon the functionality involved. It should also be noted that each block of the block diagrams and/or flowchart illustrations, and combinations of blocks in the block diagrams and/or flowchart illustrations, can be implemented by a dedicated hardware-based system that performs the specified functions or operations , or may be implemented by a combination of dedicated hardware and computer instructions.

The units involved in the embodiments described in the present disclosure may be implemented by software or by hardware. Among them, the name of the unit does not constitute a limitation on the unit itself under certain circumstances.

The functions described above herein may be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: Field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), System on Chips (SOCs), Complex Programmable Logical device (CPLD) and so on.

In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media would include one or more wire-based electrical connections, portable computer disks, hard disks, Random Access Memory (RAM), Read Only Memory (ROM), Erasable Programmable Read Only Memory (EPROM or flash memory), optical fiber, compact disk read only memory (CD-ROM), optical storage, magnetic storage, or any suitable combination of the foregoing.

The above description is only a preferred embodiment of the present disclosure and an illustration of the applied technical principles. Those skilled in the art should understand that the disclosure scope involved in this disclosure is not limited to the technical solution formed by the specific combination of the above-mentioned technical features, but also covers the technical solutions formed by the above-mentioned technical features or Other technical solutions formed by any combination of equivalent features. For example, a technical solution formed by replacing the above-mentioned features with (but not limited to) technical features with similar functions disclosed in this disclosure.

In addition, while operations are depicted in a particular order, this should not be understood as requiring that these operations be performed in the particular order shown or performed in sequential order. Under certain circumstances, multitasking and parallel processing may be advantageous. Likewise, while the above discussion contains several specific implementation details, these should not be construed as limitations on the scope of the present disclosure. Certain features that are described in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable subcombination.

Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are merely example forms of implementing the claims.

Multiple embodiments of the present disclosure have been described in detail above, but the present disclosure is not limited to these specific embodiments. Those skilled in the art can make various variations and modifications on the basis of the concept of the disclosure. These variations and modifications All should fall within the scope of protection claimed by the present disclosure.

Claims (10)

1. a mapping method of RGB image data, is characterized in that, comprises the following steps: Based on the RGB data of the predetermined image, an image histogram is obtained; wherein, based on the RGB data of the predetermined image, CIELab data is obtained; Based on the CIELab data, to draw an image histogram; Based on the image histogram and the output parameter value, determine the CIELab data mapping relationship; wherein, The output parameter value is specifically the CIELab value of the output image; The determination of the CIELab data mapping relationship is specifically to determine the mapping relationship of the CIELab value between the predetermined image and the output image; Based on the CIELab data mapping relationship, the RGB data mapping relationship converted from the predetermined image to the output image is obtained. 2. The mapping method according to claim 1, wherein said obtaining an image histogram based on the RGB data of the predetermined image further comprises obtaining the predetermined image. 3. The mapping method according to claim 2, wherein the acquisition of CIELab data based on the RGB data of the predetermined image is realized through a color characteristic file embedded in the predetermined image. 4. mapping method according to claim 1, is characterized in that, described based on described image histogram and output parameter value, determines CIELab data mapping relation, comprises the following steps: Acquiring the first tone range of the predetermined image based on the image histogram; Obtaining the second tone range of the output image based on the output parameter value; Based on the first tone range and the second tone range, a CIELab data mapping relationship is determined. 5. The mapping method according to claim 4, wherein the acquisition of the first tone range of the predetermined image based on the image histogram is achieved in the following manner: Extract the CIELab value of the brightest and darkest pixel points from the image histogram; Traversing the data of the predetermined image, marking the areas where the brightest and darkest pixels are located in the image histogram; Based on the areas where the brightest and darkest pixels are located, respectively determine the image white field or image black field, so as to calculate the average brightness value of each area; Based on the average brightness value, a first tone range of the predetermined image is determined. 6. The mapping method according to claim 1, further comprising: Adjusting the RGB data mapping relationship based on the color difference between the output image and the predetermined image. 7. The mapping method according to claim 6, wherein the adjustment of the RGB data mapping relationship based on the color difference between the output image and the predetermined image comprises: Acquiring an output image based on the predetermined image and the RGB data mapping relationship; determining a sample CIELab value of a tone sample point in a predetermined color block based on the output parameter value; determining a comparison sample point in the output image based on the sample CIELab value; Acquiring color difference based on the CIELab value of the sample and the comparative CIELab value of the comparison sample point; Adjusting the RGB data mapping relationship based on the color difference. 8. A mapping device for RGB image data, comprising the following parts: The first acquisition module is used to acquire an image histogram based on the RGB data of the predetermined image; wherein, based on the RGB data of the predetermined image, CIELab data is obtained; Based on the CIELab data, to draw an image histogram; Determination module, which is used to determine the CIELab data mapping relationship based on the image histogram and output parameter values; wherein, The output parameter value is specifically the CIELab value of the output image; The determination of the CIELab data mapping relationship is specifically to determine the mapping relationship of the CIELab value between the predetermined image and the output image; The second acquisition module is configured to acquire the RGB data mapping relationship from the predetermined image to the output image based on the CIELab data mapping relationship. 9. A storage medium storing a computer program, wherein the computer program implements the steps of the method according to any one of claims 1 to 7 when the computer program is executed by a processor. 10. An electronic device, comprising at least a memory and a processor, and a computer program is stored on the memory, wherein the processor implements any one of claims 1 to 7 when executing the computer program on the memory. steps of the method described in the item.

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