Print control method, apparatus, storage medium, and program product

Abstract

This application relates to the field of imaging technology, and in particular provides a printing control method, device, storage medium, and program product. The method includes identifying edge pixels and non-edge pixels in the data to be imaged; performing ink reduction processing on the non-edge pixels to obtain a processed first color value; obtaining the maximum color value among multiple color channel color values ​​of the edge pixels, performing color value adjustment processing on the edge pixels based on the maximum color value to obtain a processed second color value; and outputting the processed data to be imaged to an imaging unit. This solution eliminates the need to apply the same ink reduction to all pixels, ensuring dot continuity for fine objects such as text and fine lines while achieving overall toner consumption control, reducing broken lines and jagged edges that occur after halftone processing, balancing printing consumable control with image edge imaging clarity, and improving print output quality.

Description

Technical Field

[0001] This application relates to the field of imaging technology, and in particular to a printing control method, device, storage medium, and program product. Background Technology

[0002] In printing scenarios, when printing large solid color areas, color channel overlay can cause excessive overall ink coverage, which can easily lead to problems such as excessive toner usage, toner buildup, overly dark images, and smudges.

[0003] To address the issue of excessively high solid density, existing technologies generally employ globally uniform ink reduction processing to control ink volume. However, while this globally uniform processing method reduces toner consumption, because it applies the same ink reduction to all pixels, the number of pixels available for ink application is limited for delicate objects such as text and fine lines. Global ink reduction directly disrupts the continuity of halftone dots at the edges, resulting in severe line breaks and jagged edges in originally clear lines, text, and color blocks after halftone processing.

[0004] Therefore, existing technologies struggle to reduce ink application in real-world areas while maintaining clarity at the edges of text and graphics, creating a conflict between ink control requirements and edge image quality, resulting in poor imaging performance. Summary of the Invention

[0005] This application is made in view of the above-mentioned problems. This application provides a printing control method, apparatus, storage medium, and program product.

[0006] According to a first aspect of this application, a printing control method is provided, applied to an imaging device, the method comprising: Identify edge pixels and non-edge pixels in the imaging task data; The non-edge pixels are subjected to ink reduction processing to obtain a processed first color value; and the maximum color value among the multiple color channel color values ​​of the edge pixels is obtained, and the edge pixels are subjected to color value adjustment processing based on the maximum color value to obtain a processed second color value; wherein the retention ratio of the second color value relative to the original pixel color value of the edge pixels is greater than the retention ratio of the first color value relative to the original pixel color value of the non-edge pixels; The processed data for the imaging task is output to the imaging unit.

[0007] Furthermore, according to the printing control method of the first aspect of this application, each pixel in the image processing data includes C channel color value, M channel color value, Y channel color value and K channel color value; Perform ink reduction processing on the non-edge pixels, including: The C-channel color value, M-channel color value, and Y-channel color value are multiplied by a first coefficient to obtain the reduced C-channel color value, M-channel color value, and Y-channel color value, respectively; and the K-channel color value is multiplied by a second coefficient to obtain the reduced K-channel color value; the second coefficient is greater than the first coefficient.

[0008] Furthermore, according to the printing control method of the first aspect of this application, color value adjustment processing is performed on the edge pixels to obtain a processed second color value, including: When the maximum color value is less than or equal to the first threshold, the original maximum color value of the edge pixel is replaced with the maximum color value to obtain the second color value.

[0009] Furthermore, according to the printing control method of the first aspect of this application, performing color value adjustment processing on the edge pixels based on the maximum color value to obtain a processed second color value includes: The maximum color value is corrected using a preset piecewise mapping function to obtain the corrected maximum color value; The second color value is obtained by replacing the maximum color value with the corrected maximum color value.

[0010] Furthermore, according to the printing control method of the first aspect of this application, the identification of edge pixels and non-edge pixels in the image processing data includes: Get the edge enhancement level; Neighborhood discrimination rules are determined based on the edge enhancement level; The neighborhood discrimination rule is used to identify edge pixels and non-edge pixels in the imaging data.

[0011] Furthermore, according to the printing control method of the first aspect of this application, obtaining the edge enhancement level also includes: The edge enhancement level parameters are displayed through the human-computer interaction interface of the driver. These edge enhancement level parameters are used to set the pixel width corresponding to edge recognition. Receive the target edge enhancement level parameter selected by the user through the human-computer interaction interface; Determine the neighborhood discrimination rule that matches the target edge enhancement level parameter.

[0012] Furthermore, according to the printing control method of the first aspect of this application, the identification of edge pixels and non-edge pixels in the image processing data includes: Obtain the object type identifier for each pixel in the image processing data, wherein the object type identifier is used to indicate the object category to which the pixel belongs, and the object category includes at least: bitmap image object, graphic object, text object, and background; Pixels whose object type is identified as graphic objects or text objects are selected as candidate pixels; For each candidate pixel, determine whether there are pixels of image objects or background in its neighborhood based on the neighborhood discrimination rule; If they exist, the candidate pixels are identified as edge pixels; Otherwise, the candidate pixel is identified as a non-edge pixel.

[0013] According to a second aspect of this application, an imaging device is provided, comprising: The acquisition unit is configured to acquire the object type identifier and spatial neighborhood distribution of each pixel in the imaging task data; The recognition unit is configured to recognize edge pixels and non-edge pixels; The processing unit is configured as follows: Perform ink reduction processing on the non-edge pixels to obtain the processed first color value; The maximum color value among multiple color channel color values ​​of the edge pixel is obtained, and color value adjustment processing is performed on the edge pixel based on the maximum color value to obtain a processed second color value. The retention ratio of the second color value relative to the original pixel color value of the edge pixel is greater than the retention ratio of the first color value relative to the original pixel color value of the non-edge pixel. The processed data to be imaged is output to the imaging unit; The imaging unit is configured to perform imaging operations on the processed data to be imaged.

[0014] According to a third aspect of this application, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the method described in the first aspect.

[0015] According to a fourth aspect of this application, a computer-readable storage medium is provided that stores a computer program / instructions thereon, which, when executed by a processor, implement the steps of the method described in the first aspect.

[0016] According to a fifth aspect of this application, a computer program product is provided, comprising a computer program / instructions that, when executed by a processor, implement the steps of the method described in the first aspect.

[0017] As will be described in detail below, the printing control method according to embodiments of this application distinguishes and identifies edge pixels and non-edge pixels in the image processing data, and adopts differentiated color value processing strategies for the two types of pixels: ink volume reduction processing is performed on non-edge pixels to reduce the overall ink volume in solid areas, effectively suppressing toner accumulation and excessive toner consumption; based on the maximum color value among multiple color channel colors of edge pixels, color value adjustment processing is performed on edge pixels, so that the retention ratio of edge pixel color values ​​is higher than that of non-edge pixels, preserving more original color value information of text, lines, and graphic outlines, and avoiding excessive reduction of halftone dots at edge positions. This solution does not require applying the same level of ink volume reduction to all pixels. While achieving overall toner consumption control, it ensures the continuity of halftone dots for fine objects such as text and fine lines, reduces broken lines and jagged edges that appear after halftone processing, and balances printing consumable control with image edge imaging clarity, thereby improving the overall print output quality.

[0018] It should be understood that both the foregoing general description and the following detailed description are exemplary and intended to provide further illustration of the claimed technology. Attached Figure Description

[0019] The above and other objects, features, and advantages of this application will become more apparent from the more detailed description of the embodiments of this application in conjunction with the accompanying drawings. The accompanying drawings are used to provide a further understanding of the embodiments of this application and form part of the specification. They are used together with the embodiments of this application to explain this application and do not constitute a limitation thereof. In the accompanying drawings, the same reference numerals generally represent the same components or steps.

[0020] Figure 1 This is a flowchart illustrating a printing control method according to an embodiment of this application.

[0021] Figure 2 This is a schematic diagram illustrating the image processing data to be imaged according to an embodiment of this application.

[0022] Figure 3 This is a schematic diagram illustrating the 8-neighborhood determination of an application according to an embodiment of this application.

[0023] Figure 4 This is a schematic diagram of 24 neighborhoods according to an embodiment of this application.

[0024] Figure 5 This is a flowchart illustrating yet another printing control method according to an embodiment of this application.

[0025] Figure 6 This is a schematic diagram of the imaging effect without ink reduction and edge enhancement provided in the embodiments of this application.

[0026] Figure 7This is a schematic diagram of the imaging effect after global ink volume reduction processing using existing technology.

[0027] Figure 8 This is a schematic diagram of the imaging effect obtained by identifying edge pixels using an 8-neighborhood discrimination rule and performing differential color value processing, as provided in an embodiment of this application.

[0028] Figure 9 This is a schematic diagram of the imaging effect obtained by identifying edge pixels using a 24-neighborhood discrimination rule and performing differential color value processing, as provided in an embodiment of this application.

[0029] Figure 10 This is a schematic diagram illustrating an imaging device according to an embodiment of this application.

[0030] Figure 11 This is a hardware block diagram illustrating an electronic device according to an embodiment of this application.

[0031] Figure 12 This is a schematic diagram illustrating a computer program product according to an embodiment of this application. Detailed Implementation

[0032] The technical solutions of the embodiments of this application will now be described with reference to the accompanying drawings. To facilitate a clear description of the technical solutions of the embodiments of this application, the use of terms such as "first," "second," etc., in the embodiments of this application is for illustrative purposes and to distinguish the objects being described. There is no particular order between them, nor does it indicate a specific limitation on the number of devices in the embodiments of this application, and they do not constitute any limitation on the embodiments of this application.

[0033] To facilitate understanding of this embodiment, a printing control method disclosed in this application will first be described in detail. The executing entity of the printing control method provided in this application is generally an imaging device. See also... Figure 1 The diagram shown is a flowchart of a printing control method provided in an embodiment of this application. The method includes the following steps: Step 101: Identify edge pixels and non-edge pixels in the image processing data.

[0034] In this embodiment, the imaging task data is generated by the imaging device driver after parsing the user's imaging command, and includes RGB original image data and CMYK image data of graphic elements such as text, graphics, bitmap images, and backgrounds. Each element in the imaging task data is bound to object identification data, which is used to identify the graphic element type to which the element belongs.

[0035] In this embodiment, the data to be imaged originates from the imaging command issued by the user. The file to be printed submitted by the user contains several original graphic elements. The file layer carries the graphic element type corresponding to each graphic element, used to mark whether the graphic element belongs to text, graphic, bitmap image, or background. Each type of graphic element corresponds to a different type of description unit in the object model of the page description language. Taking PDF as an example, bitmap images correspond to Image XObject resource nodes, graphics correspond to vector path drawing command nodes, text corresponds to text run nodes, and backgrounds correspond to background colors or whitespace areas. For other page description languages ​​such as PostScript and PCL, their specific syntax and object naming differ, but they all carry and distinguish the above graphic element types in the description layer, which are mapped to object type identifiers by the imaging device driver during the parsing phase. When the imaging device driver performs rasterization processing on the file to be printed, converting the original vector graphic elements into a raster image, it maps the graphic element type carried by the original graphic elements to each corresponding pixel in the raster image, marking each pixel with the corresponding object type identifier. In this way, each pixel in the raster image not only records RGB and CMYK color information, but also includes an object type identifier. Through this object type identifier, it can be determined whether the original primitive to which the pixel belongs is text, graphics, bitmap image or background, which facilitates the subsequent performance of differentiated imaging processing for different types of pixels.

[0036] In this embodiment, edge pixels belong to text or graphic primitives, and there are bitmap image pixels or background pixels within the neighborhood of the pixel. Non-edge pixels are all pixels other than the aforementioned edge pixels, specifically including pixels in pure bitmap image areas, pixels inside solid color blocks without neighborhood boundaries, and global background pixels. These pixels do not have the requirement to display text or graphic outlines and have no impact on image clarity or line integrity, making them the main targets for ink reduction optimization.

[0037] As an example, give as follows Figure 2 The diagram shows the data to be imaged. Figure 2 In the image processing data, there are 9 pixels. Pixels 1, 2, and 3 are located in the first row; pixels 4, 5, and 6 are located in the second row; and pixels 7, 8, and 9 are located in the third row. Pixel 5 is the center pixel for this round of judgment.

[0038] Assume the object type identifier of the center pixel 5 is a text primitive. Traverse the 8 neighboring pixels 1, 2, 3, 4, 6, 7, 8, and 9 within the 3x3 grid window. If the object type identifier of any of the 8 neighboring pixels belongs to a bitmap image primitive or background, for example, pixel 2 is the background or pixel 6 is a bitmap image primitive, then the center pixel 5 is determined to be an edge pixel.

[0039] If all eight neighboring pixels (pixels 1, 2, 3, 4, 6, 7, 8, 9) within the 3x3 grid have object type identifiers that are both text or graphic primitives, and there are no background or bitmap image type pixels in the neighborhood, then the center pixel 5 is determined to be a non-edge pixel, that is, an internal pixel of the text primitive. This pixel is located within the internal area of ​​the text strokes.

[0040] Step 102: Perform ink reduction processing on non-edge pixels to obtain the processed first color value; and obtain the maximum color value among the multiple color channel color values ​​of the edge pixels, and perform color value adjustment processing on the edge pixels based on the maximum color value to obtain the processed second color value; wherein, the retention ratio of the second color value relative to the original pixel color value of the edge pixels is greater than the retention ratio of the first color value relative to the original pixel color value of the non-edge pixels.

[0041] In this embodiment, the edge pixel has color values ​​for multiple color channels. Taking the CMYK color model as an example, these correspond to the color values ​​of the C, M, Y, and K channels, respectively. First, the original color values ​​corresponding to all color channels of the edge pixel are read, and the maximum color value is extracted from all channel color values. This maximum color value represents the channel with the highest ink volume among all channels of the edge pixel. The amount of ink in this channel directly determines the overall ink load of the pixel, which is the main cause of broken lines, white spots, and blurriness, as well as the main cause of ink bleeding and ink dot diffusion problems.

[0042] In this embodiment, "color value" refers to the quantization value of a pixel in a color space or density space, specifically including: In color imaging mode, the color values ​​correspond to the C, M, Y, K channels, or the R, G, B channels.

[0043] In monochrome imaging mode, the color value corresponds to a single recording channel.

[0044] Wherein, when the monochrome recording agent is black (K), the color value corresponds to the grayscale value; when the monochrome recording agent is color (such as M, C, Y, etc.), the color value corresponds to the color value of the corresponding color channel.

[0045] In a broad sense, the "color value" is mapped to a "recording agent dosage value" through color management or device modulation curves, representing the quantitative control amount of recording agent applied to the medium. This quantitative control amount can be adjusted according to the type of imaging technology: for inkjet or laser imaging devices, it is reflected in the concentration or amount of developing materials such as ink and toner; for thermal, thermal transfer, and dye sublimation imaging devices, it is reflected in the thermal energy parameters acting on the imaging medium (such as heating energy or heating duration). Unless otherwise specified, the "ink amount" referred to in the embodiments of this application specifically refers to the "recording agent dosage" as follows: in laser imaging scenarios, it is the amount of toner attached; in inkjet imaging scenarios, it is the amount of ink ejected; and in thermal / thermal transfer / dye sublimation imaging scenarios, it is the adjustment amount of heating energy or heating duration. Correspondingly, "ink amount reduction processing" is an adaptive downward adjustment of the aforementioned dosage value.

[0046] In this embodiment, the pixel color value retention ratio is the ratio of the final color value of a pixel after imaging adjustment processing to the original color value of the pixel. It is used to quantitatively characterize the degree of ink retention of the pixel as a whole. The higher the retention ratio, the less ink loss and the more complete the color density retention; the lower the retention ratio, the greater the ink reduction and the more obvious the color density reduction.

[0047] For the identified non-edge pixels, this embodiment performs ink volume reduction processing to obtain the first color value. Non-edge pixels are pixels within the internal area of ​​text and graphic primitives. These pixels are surrounded by pixels of the same type, and there are no boundary transition areas with the background or bitmap. The entire area has uniform color and no need for outline display. Therefore, appropriately reducing the ink volume of these pixels can effectively reduce the overall ink volume in large areas, avoiding problems such as ink seepage, ink accumulation and stickiness, and uneven color patches caused by excessive ink layer stacking, ensuring a smooth and clean background color over large areas. This ink volume reduction processing is an optimization method that actively lowers the original color value of the pixel, resulting in a significant attenuation of the first color value compared to the original pixel color value, corresponding to a lower color value retention ratio.

[0048] In one optional embodiment, the ink reduction processing specifically involves applying a modulation coefficient between 0 and 1 to the current color value of the target color channel to proportionally reduce the ink volume of the corresponding pixel. Visually, this processing results in: under macroscopic observation, continuous color blocks exhibit no obvious graininess; under microscopic magnification, tiny, discretely distributed uninked dots are visible within them, with the density and size of these dots adaptively adjusted according to the modulation coefficient, balancing ink reduction effect with visual smoothness. The selection of the target channel is adaptively adjusted according to the imaging mode: in full-color mode, it applies to all CMYK channels; in multi-color mode, it applies to at least one selected channel; and in monochrome mode, it applies to a single recording agent channel. Furthermore, the modulation coefficients of the C, M, Y, and K channels can be configured independently to balance ink volume control and color reproduction requirements.

[0049] For the identified edge pixels, this embodiment performs color value adjustment processing to obtain a second color value. Edge pixels are the outline boundary pixels of text and graphic primitives, playing a core role in outlining the pattern outline and distinguishing different imaging areas, directly determining the clarity and sharpness of the final image. To ensure that the outline edges do not exhibit blurring, jagged edges, or broken edges, this embodiment employs a control logic with higher retention for edge pixels, making the color value retention ratio of edge pixels greater than that of non-edge pixels. Specifically, this can include the following four color value adjustment processing schemes.

[0050] The first approach keeps the edge pixel color values ​​unchanged, fully preserving the original pixel color values ​​and ink volume, thus maximizing the color saturation and sharpness of the edge contours.

[0051] The second approach involves a slight reduction in ink volume for edge pixels, with only a minimal decrease in color value. This slightly optimizes the overall ink volume and improves printing efficiency while preserving edge contour features to the greatest extent possible. The reduction is much smaller than the reduction in ink volume for non-edge pixels.

[0052] The third approach involves deepening the color values ​​of edge pixels. By moderately increasing the ink concentration based on the original color values, the outline contrast is further enhanced, completely eliminating the problem of blurry printing of fine lines and small characters.

[0053] The fourth approach involves first applying the same ink reduction to both edge and non-edge pixels; after the uniform ink reduction is completed, color enhancement is then applied separately to the edge pixels. This approach first applies the same ink reduction to both types of pixels, and then compensates for the ink loss caused by the ink reduction process by enhancing the color values ​​of the edge pixels, ultimately achieving a higher color value retention rate for edge pixels than for non-edge pixels.

[0054] Through the above-mentioned differential processing method, the retention ratio of the second color value relative to the original color value of the edge pixel can be stably achieved, which is always greater than the retention ratio of the first color value relative to the original color value of the non-edge pixel, ultimately achieving a differential imaging effect of "internal color blocks with ink saving and smoothness, and clear and sharp edge contours".

[0055] In an optional embodiment, before performing ink reduction on non-edge pixels or color value adjustment on edge pixels, the primitive object to which the edge or non-edge pixel to be processed belongs can be determined first, and the color value adjustment strategy corresponding to that primitive object can be retrieved. This color value adjustment strategy defines the processing rules for edge pixels and non-edge pixels belonging to that primitive. For non-edge pixels, ink reduction is performed according to the corresponding primitive strategy; for edge pixels, the maximum color value obtained from the pixel's multi-channel processing is combined with the edge processing rules corresponding to the primitive to complete the color value adjustment.

[0056] Different types of graphic objects have different printing requirements. Text objects prioritize edge sharpness, vector graphics emphasize outline and color reproduction, and bitmap images prioritize ensuring smooth image transitions. By pre-configuring independent color value adjustment strategies for each type of graphic object, it is no longer necessary to use a uniform set of parameters to process all pixels. This allows for differentiated control over edge protection strength and ink reduction in non-edge areas based on graphic object type.

[0057] As a result, text objects can retain a higher ink volume at their edges, suppressing blurry or indistinct text edges; graphic objects can achieve a balance between clear outlines and color fidelity; and bitmap images can avoid edge protection strategies from disrupting the original transition effects of the image, allowing text, vector graphics, bitmap, and other types of content to achieve print output quality adapted to their own characteristics.

[0058] Step 103: Output the processed image data to the imaging unit.

[0059] The processed image data already contains the calculated first and second color values, which are the adjusted pixel color values ​​corresponding to non-edge pixels and edge pixels, respectively. The imaging unit is the hardware module in the imaging device that actually performs the imaging, such as the printhead of an inkjet imaging device or the laser exposure and development component of a laser imaging device. The imaging unit receives the processed image data, controls the ink volume according to the adjusted color values ​​corresponding to each pixel, and completes the image dot matrix output imaging on the imaging medium.

[0060] For thermal and thermal transfer imaging equipment, the imaging unit corresponds to the heating head, and the "ink volume" is reflected in the heating energy or heating time. By reducing the heating energy in non-edge areas, the same technical effects of preventing the medium from burning through and reducing energy consumption can be achieved.

[0061] In one optional embodiment, each pixel in the image processing data includes C-channel color values, M-channel color values, Y-channel color values, and K-channel color values. Ink reduction processing is performed on non-edge pixels, including: The C-channel, M-channel, and Y-channel color values ​​are multiplied by a first coefficient to obtain the reduced C-channel, M-channel, and Y-channel color values, respectively; and the K-channel color value is multiplied by a second coefficient to obtain the reduced K-channel color value; the second coefficient is greater than the first coefficient. The first and second coefficients are between 0 and 1.

[0062] In this embodiment, the image processing data adopts the CMYK four-color model. Each pixel in the image has a C channel color value, an M channel color value, a Y channel color value, and a K channel color value. Each channel color value corresponds to the amount of ink in cyan, magenta, yellow, and black, respectively. The higher the channel color value, the greater the amount of ink required for that color channel.

[0063] Because the K channel has the most significant impact on the visual depth and outline perception of text and graphic strokes, the human eye is more sensitive to changes in black ink volume compared to the C / M / Y color channels. If the same coefficient is used to attenuate the K channel and color channels, excessive reduction in K channel ink volume can easily cause text strokes to appear faded, grayish, or broken. Therefore, this embodiment sets the second coefficient to be greater than the first coefficient, meaning that the reduction in the K channel is less than the reduction in the C, M, and Y channels. In other words, under the premise of being non-edge pixels, the ink volume of the three color channels is reduced more, while relatively more black ink volume is retained.

[0064] For example, the first coefficient can be 0.7, and the second coefficient can be 0.8. Multiplying the C / M / Y channels by 0.7 retains 70% of the ink volume; multiplying the K channel by 0.8 retains 80% of the ink volume. This achieves ink conservation for the overall internal color blocks, reducing ink bleeding and show-through caused by ink accumulation, while also preserving the density of black strokes as much as possible, preventing black content from appearing lighter due to reduced ink volume.

[0065] It should be noted that the first and second coefficients are preset adjustable parameters that can be adapted and adjusted according to the type of printing media and printing mode. This application does not limit the specific values ​​of the coefficients.

[0066] In this embodiment, by setting different reduction coefficients for the color channel and the black channel, the ink volume of non-edge pixels is reduced while taking into account the visual density of black imaging, thus balancing the ink-saving effect and the printing appearance.

[0067] In an optional embodiment, color value adjustment processing is performed on the edge pixels to obtain a processed second color value, including: When the maximum color value is less than or equal to the first threshold, the original maximum color value of the edge pixel is replaced with the maximum color value to obtain the second color value.

[0068] In this embodiment, the edge pixel has color values ​​for multiple color channels, such as the C, M, Y, and K channel color values ​​under the CMYK color model. First, the color values ​​corresponding to all color channels of the edge pixel are read, and the maximum color value among the multiple color channel values ​​is selected. This maximum color value represents the channel with the highest ink content in the pixel, which determines the visual depth of the edge pixel.

[0069] The first threshold is a pre-configured judgment threshold used to characterize the overall ink volume of the edge pixel. It can be configured according to the printing media and printing mode, and this application does not limit its specific value. When the maximum color value is less than or equal to the first threshold, it indicates that the overall ink volume level of the edge pixel is not high, and there is no risk of ink overload or ink layer accumulation. At this time, the original maximum color value of the edge pixel is directly replaced by the maximum color value, while the color values ​​of the other color channels remain unchanged to obtain the corresponding second color value. This processing method essentially does not reduce the ink volume of the edge pixel, but fully preserves the original color values ​​of each channel of the edge pixel, thereby ensuring the edge outline density of text and graphics and preventing the edges from becoming faded, blurred, or weakened.

[0070] For example, the first threshold is set to 127; the color values ​​of each channel of a certain text edge pixel are C:20, M:30, Y:25, K:90, and the maximum color value of multiple channels is 90 for channel K. If 90 is less than or equal to the first threshold of 127, then the original color values ​​of each channel of the pixel are retained as the second color value, and no ink reduction processing is performed on the edge pixel.

[0071] This embodiment uses the conditional judgment of the maximum color value and the first threshold to fully retain the original color value only when the ink amount of the edge pixels is within the safe range, thus ensuring that the edge contour is sharp and clear under low ink amount.

[0072] In an optional embodiment, performing color value adjustment processing on edge pixels based on the maximum color value to obtain a processed second color value includes: The maximum color value is corrected using a preset piecewise mapping function to obtain the corrected maximum color value; Replace the maximum color value with the corrected maximum color value to obtain the second color value.

[0073] The pre-configured segmented mapping function is invoked, taking the extracted maximum color value as input. The function then calculates and outputs the corrected maximum color value. This segmented mapping function can perform differentiated corrections for different ranges of the input color value, achieving graded ink volume control. For low color value ranges, the original input color value is directly retained to ensure the clarity of the outline at low ink volume edges. For intermediate color value ranges, a linear gradient approach is used to suppress ink volume, moderately constraining the output ink volume as the input color value increases, alleviating image quality problems such as edge ink diffusion, poor fixing, and toner accumulation under medium ink volume conditions. For high color value ranges, saturation clamping is performed to limit the output color value to the upper limit, preventing ink overload.

[0074] In one example, the piecewise mapping function F(x) is as follows:

[0075] in, That is, the first threshold. is the second threshold, x is the original input color value of the channel corresponding to the edge pixel, and F(x) is the corrected maximum color value output after mapping calculation.

[0076] The piecewise mapping function divides the input color value into three segments for differential correction: when the input color value is less than the first threshold. When the input color value is in the range of 1000, the original color value is output directly, preserving the edge ink volume completely; when .... to Linear gradient ink volume suppression is performed within the interval, appropriately constraining the ink volume; when the input color value is greater than the second threshold... The direct clamp output is 255, thus completing the color value saturation limit.

[0077] After the maximum color value is calculated, this corrected maximum color value is used to replace the original maximum color value of the edge pixels, while the original color values ​​of the other color channels remain unchanged. The final combination yields the second color value after edge pixel processing. In other words, only the channel with the highest ink volume is subject to correction constraints, while the other channels retain their original color values, eliminating the need for complex calculations on each channel individually.

[0078] For example, a first threshold is configured. =127, Second Threshold =230. The original CMYK color values ​​of a certain edge pixel are: C:40, M:30, Y:20, K:185; the maximum color value across multiple channels of this pixel is 185 for the K channel. 185 falls within... and Within the range, the corresponding corrected maximum color value is obtained by processing the segmented mapping function with input 185. This corrected maximum color value is then written back to the K channel to replace the original color value 185. The color values ​​of the C, M, and Y channels remain unchanged at 40, 30, and 20, respectively. After combination, the second color value of the edge pixel is obtained. If the maximum color value of the pixel is 90, the function directly outputs the original color value 90; if the maximum color value of the pixel is 240, the function clamps the output to 255.

[0079] In this embodiment, only the color value with the largest impact on ink volume load is selected as the correction object. The graded ink volume correction is completed by using a piecewise mapping function, without having to traverse and process all color channels, thus reducing the computational cost of the algorithm. It takes into account the edge contour clarity of low ink volume, while performing gradient suppression and saturation clamping on medium and high ink volume edges respectively, effectively suppressing image defects such as ink diffusion, toner accumulation, and poor fixing at high ink volume edges, thus balancing print image quality and algorithm running efficiency.

[0080] In one optional embodiment, identifying edge pixels and non-edge pixels in the imaging task data includes: Get the edge enhancement level.

[0081] The neighborhood discrimination rule is determined based on the edge enhancement level.

[0082] The neighborhood discrimination rule is used to identify edge pixels and non-edge pixels in the imaging data.

[0083] In this embodiment, the edge enhancement level is a configurable parameter used to control the neighborhood search range during edge recognition. Different edge enhancement levels correspond to different neighborhood discrimination rules. This edge enhancement level can be issued by the upper-layer application or preset according to the printing mode, printing media type, and output accuracy requirements. This embodiment does not impose specific limitations on this.

[0084] After obtaining the edge enhancement level, the corresponding neighborhood discrimination rule is determined based on the level. The neighborhood discrimination rule mainly specifies the neighborhood range to be examined when making pixel judgments, that is, how many pixel object identifiers need to be scanned around the current pixel to be judged.

[0085] As an example, when the edge enhancement level is level one, the corresponding neighborhood discrimination rule uses an 8-neighborhood (3×3) window, which examines the eight pixels directly adjacent to the center pixel (top, bottom, left, right, and four diagonal pixels). When the edge enhancement level is level two, the corresponding neighborhood discrimination rule uses an expanded 24-neighborhood window, which is the 24 neighboring pixels excluding the center pixel within a 5×5 square window. This can be equivalently understood as scanning and comparing pixels outside the 3×3 window. The higher the level value, the larger the spatial range of the neighborhood scan, enabling the identification of more edge pixels in weak boundaries and transition areas.

[0086] Those skilled in the art will understand that the 8-neighborhood and 24-neighborhood examples listed above are merely illustrative. In practical applications, smaller neighborhood ranges (such as 4-neighborhood) or larger neighborhood ranges can be adaptively selected based on the balance requirements between accuracy and overhead, and all of these fall within the protection scope of this invention. In this embodiment, the sensitivity of edge recognition can be flexibly adjusted by dynamically adapting the edge enhancement level to the neighborhood discrimination rules. When printing fine small characters or fine-lined documents, a higher enhancement level can be selected to capture more edge details; a lower level is selected for ordinary printing scenarios to reduce the neighborhood calculation range and reduce the amount of computation, thus achieving a flexible balance between edge recognition accuracy and algorithm computation overhead.

[0087] In an optional embodiment, obtaining the edge enhancement level further includes: The edge enhancement level parameters are displayed through the driver's human-computer interaction interface. These parameters are used to set the pixel width corresponding to edge recognition. Receive the target edge enhancement level parameter selected by the user through the human-computer interaction interface; Determine the neighborhood discrimination rule that matches the target edge enhancement level parameter.

[0088] In this embodiment, the edge enhancement level can be configured and selected by the user. The human-computer interaction interface exposed the edge enhancement level parameters to the user. These level parameters directly correspond to the pixel width to be detected during edge recognition, and different levels correspond to different edge recognition sensitivities.

[0089] In real-world printing scenarios, users can choose the appropriate setting based on the printing media type, printing resolution, and document type. For example, when printing small text, a higher setting can be selected to widen the pixel range for edge recognition, while the default setting is sufficient for printing ordinary graphic documents. After receiving the target edge enhancement setting parameter selected by the user in the human-computer interaction interface, the driver internally maps it to obtain a matching neighborhood discrimination rule. Different neighborhood discrimination rules define the neighborhood range to be scanned when a pixel is used to determine its edge. For example, a low setting corresponds to an 8-neighborhood scanning range, while a high setting corresponds to a larger neighborhood scanning range. Based on this neighborhood discrimination rule, the driver then distinguishes and identifies edge pixels from non-edge pixels within the data to be imaged.

[0090] In this embodiment, the printing speed configuration is enabled through the human-computer interaction interface. The neighborhood discrimination rules for edge recognition are associated and mapped with the speed parameters selected by the user. Without modifying the underlying algorithm firmware, the detection range of edge recognition can be flexibly changed to take into account the printing preferences of different users and diverse printing business scenarios. The higher the speed, the more neighborhood pixels participate in the judgment, which can capture more fine lines and text outlines as edge pixels, avoiding small images and text from being treated as ordinary areas and subjected to large ink volume reduction, resulting in broken lines. The lower the speed, the less neighborhood calculation is required, which helps to reduce the image processing computing power overhead and improve the overall printing processing speed.

[0091] In one optional embodiment, identifying edge pixels and non-edge pixels in the image processing data includes: Obtain the object type identifier for each pixel in the image processing data. The object type identifier indicates the object category to which the pixel belongs. The object category includes at least: bitmap image object, graphic object, text object, and background.

[0092] Pixels whose object type is identified as graphic objects or text objects are selected as candidate pixels; For each candidate pixel, a neighborhood discrimination rule is used to determine whether there are any pixels of bitmap image objects or background in its neighborhood.

[0093] If they exist, the candidate pixels are identified as edge pixels.

[0094] Otherwise, the candidate pixel is identified as a non-edge pixel.

[0095] In this embodiment, each pixel in the image processing data carries an object type identifier. This object type identifier is a marker generated by the driver during the rasterization and parsing stage of the original printed file, used to mark which type of primitive object the pixel belongs to.

[0096] For each candidate pixel obtained, according to the currently configured neighborhood discrimination rule, all pixels in the neighborhood range corresponding to the candidate pixel are traversed, and it is checked whether there are pixels in the neighborhood whose object type is identified as a bitmap image object or a background object.

[0097] The neighborhood discrimination rule can be determined based on the edge enhancement level, or it can be adaptively configured based on other preset conditions or real-time detection results. If a bitmap image object pixel or a background object pixel can be retrieved within the corresponding neighborhood, it means that the candidate pixel is located at the boundary between text or graphics and other types of primitives, and the candidate pixel is determined to be an edge pixel; conversely, if all pixels in the neighborhood are also graphic objects or text objects, and there are no bitmap image objects or background objects, it means that the candidate pixel is located in the inner area of ​​the text color block or graphic color block, and belongs to a non-edge pixel.

[0098] As an example, give as follows Figure 3 The diagram showing the 8-neighborhood determination and Figure 4 The diagram shows a 24-neighborhood. (As shown...) Figure 3 As shown, with the center pixel as the core, the verification number is 1. There are a total of 8 surrounding pixels. The object type of the center pixel is identified as a text object. If there is at least one background or bitmap image object pixel among its 8 neighboring pixels, then the center pixel is determined to be an edge pixel. Subsequently, a color value adjustment process with a higher retention ratio is performed to ensure that the outline of the text strokes is complete and clear.

[0099] like Figure 4 As shown, with the center pixel as the core, the verification number is 1. There are a total of 24 surrounding pixels. If all 24 neighboring pixels are text or graphic objects and there are no background or bitmap image objects, then the center pixel is identified as a non-edge pixel, and ink volume reduction processing is subsequently performed to reduce the overall toner consumption.

[0100] In this embodiment, object type identifiers are used for preliminary screening, and neighborhood detection is performed only for text and graphic objects. Edge judgment is skipped for bitmap image regions. On the one hand, this can prevent the rich textures inside photos and bitmaps from being incorrectly identified as a large number of false edges, causing the ink-saving effect to fail. On the other hand, the 8-neighbor or 24-neighbor detection window can be flexibly switched according to the edge enhancement level. The higher the level, the larger the neighborhood detection range, and the stronger the ability to recognize fine strokes and text. At the same time, the range of pixels that need to be calculated is reduced, the computational overhead of image processing is reduced, and the differentiated processing effect of maintaining the quality of the outline and saving ink inside is accurately achieved.

[0101] This application also provides a printing control method, such as... Figure 5 As shown, the following steps may be included: Step 501: Receive the edge enhancement level input by the user through the human-computer interaction interface.

[0102] The human-computer interface offers users the option of an off setting, a first level, and a second level. The first level corresponds to an edge recognition range of 1 pixel width, and the second level corresponds to an edge recognition range of 2 pixels width. If the edge enhancement level is off, the entire process of subsequent edge pixel recognition and differential color value adjustment is skipped; if the edge enhancement level is not off, the subsequent processing steps are executed.

[0103] Step 502: Obtain the data to be imaged.

[0104] Each pixel in the image processing data is bound to an object type identifier, which is used to characterize the primitive type to which the pixel belongs. Primitive types include bitmap image objects, graphic objects, text objects, and backgrounds.

[0105] Step 503: Traverse each pixel in the image processing data and filter candidate pixels according to the object type identifier corresponding to the pixel; determine the pixels with object type identifiers of graphic objects and text objects as candidate pixels.

[0106] Step 504: After obtaining candidate pixels, determine the neighborhood discrimination rule based on the obtained edge enhancement level, and use the neighborhood discrimination rule to identify edge pixels and non-edge pixels.

[0107] When the edge enhancement level is level 1, an 8-neighborhood discrimination rule is used; when the edge enhancement level is level 2, a 24-neighborhood discrimination rule is used. For each candidate pixel, using that candidate pixel as the center pixel, all pixels within the neighborhood of that center pixel are traversed according to the neighborhood discrimination rule. If there are pixels within the neighborhood whose object type is identified as a bitmap image object or background, then the candidate pixel is identified as an edge pixel; if the object type of all pixels within the neighborhood is identified only as a graphic object or a text object, then the candidate pixel is identified as a non-edge pixel.

[0108] Step 505: Multiply the C-channel color value, M-channel color value, and Y-channel color value of the non-edge pixels by the first coefficient to obtain the reduced C-channel color value, M-channel color value, and Y-channel color value; multiply the K-channel color value of the non-edge pixels by the second coefficient to obtain the reduced K-channel color value, wherein the second coefficient is greater than the first coefficient.

[0109] Step 506: For the identified edge pixels, obtain the color values ​​of multiple color channels of the edge pixels, and extract the maximum color value among the multiple color channel color values; use a preset segmented mapping function to correct the maximum color value to obtain the corrected maximum color value, and replace the original maximum color value with the corrected maximum color value to obtain the processed second color value.

[0110] This segmented mapping function implements interval-based differential correction processing. The low color value interval retains the original maximum color value, the intermediate color value interval performs gradient ink volume suppression, and the high color value interval performs saturation clamping control. While preserving the edge contour color value information, it suppresses problems such as ink bleeding, poor fixing, and toner accumulation caused by high ink volume.

[0111] Step 507: After completing the color value processing of all pixels, the processed data to be imaged is sequentially subjected to halftone processing and output to the imaging unit, which then performs the imaging output.

[0112] The following is combined with Figure 6 Figure 9 The effects of this solution are compared and explained.

[0113] Figure 6 This is a schematic diagram of the original processing effect, which means that neither ink reduction nor edge enhancement processing is performed. The printed text strokes are complete and continuous, and the ink volume in solid areas is sufficient, but there are problems with excessive ink volume and excessive toner consumption in large solid color areas.

[0114] Figure 7To achieve the desired output effect after globally unifying ink volume reduction using existing technology, this method performs ink volume reduction on all pixels of the image without differentiation. Although this reduces the overall ink volume, the stroke edges of graphic objects such as text and numbers are simultaneously reduced, resulting in visible broken lines, gaps, and jagged distortions in the outlines of numbers. While toner consumption control is achieved in solid areas, the printing clarity of fine graphics and text is significantly degraded.

[0115] Figure 8 To achieve the desired edge enhancement effect in this application, the first level of edge enhancement uses an 8-neighborhood discrimination rule to identify edge pixels. Outline pixels of text and graphic objects are identified as edge pixels. Color value adjustments with a higher retention rate are applied to the identified edge pixels, while ink reduction is only applied to non-edge pixels within the color block. Figure 8 It can be seen that, compared to Figure 7 The global ink reduction processing significantly improves the broken lines and gaps at the edges of digital strokes, restoring the integrity of the stroke outlines; at the same time, the ink reduction effect is still retained in the solid areas, balancing toner consumption control and edge image quality.

[0116] Figure 9 The second level of edge enhancement in this application demonstrates the processing effect. This level employs a 24-neighborhood discrimination rule, expanding the neighborhood detection range for edge recognition. This allows for the identification of more subtle contour pixels as edge pixels, incorporating them into color value adjustments with a high retention rate. (Comparison) Figure 7 , Figure 8 visible, Figure 9 The outlines of medium-sized strokes are the fullest and most complete, and the continuity of dots in fine strokes is further improved, while jagged edges and broken lines are further suppressed; solid areas still maintain reduced ink volume and will not revert to the original state. Figure 4 High ink volume state.

[0117] pass Figure 6 Figure 9 Multiple sets of effect comparisons show that this application, by switching different neighborhood discrimination rules through configurable edge enhancement levels, distinguishes edge pixels from non-edge pixels and implements differentiated color value processing. This not only controls the overall toner consumption in solid areas by reducing the ink volume of non-edge pixels, but also retains more color value information for the outline edges of text and graphics. The higher the edge enhancement level, the larger the neighborhood detection range, and the stronger the protection effect on the outline of fine strokes. This effectively solves the problems of broken text lines and jagged edges caused by global ink volume reduction processing, achieving a balance between consumable control and print quality.

[0118] This application also provides an imaging device, such as... Figure 10 As shown, the imaging device includes: The acquisition unit 110 is configured to acquire the object type identifier and spatial neighborhood distribution of each pixel in the image processing data.

[0119] The recognition unit 120 is configured to recognize edge pixels and non-edge pixels.

[0120] Processing unit 130 is configured as follows: Perform ink reduction processing on the non-edge pixels to obtain the processed first color value; The maximum color value among the multiple color channel color values ​​of the edge pixel is obtained, and the edge pixel is subjected to color value adjustment processing based on the maximum color value to obtain a processed second color value. The retention ratio of the second color value relative to the original pixel color value of the edge pixel is greater than the retention ratio of the first color value relative to the original pixel color value of the non-edge pixel.

[0121] The processed data to be imaged is output to the imaging unit 140.

[0122] Imaging unit 140 is configured to perform imaging operations on processed data to be imaged.

[0123] In one alternative embodiment, such as Figure 10 As shown, the imaging device also includes: The display unit 150 is configured to display edge enhancement level parameters, which are used to set the pixel width corresponding to edge recognition.

[0124] In an optional embodiment, the display unit 150 is a display hardware module that carries the human-computer interaction interface.

[0125] In an alternative embodiment, the human-computer interaction interface is integrated into the driver program of the imaging device and presented through the display unit 150.

[0126] In an alternative embodiment, the display unit 150 is specifically implemented as an operation panel or a display screen.

[0127] In one optional embodiment, each pixel in the image processing data includes C-channel color values, M-channel color values, Y-channel color values, and K-channel color values.

[0128] Processing unit 130 is used for: The C-channel color value, M-channel color value, and Y-channel color value are multiplied by a first coefficient to obtain the reduced C-channel color value, M-channel color value, and Y-channel color value, respectively; and the K-channel color value is multiplied by a second coefficient to obtain the reduced K-channel color value; the second coefficient is greater than the first coefficient.

[0129] In an optional embodiment, the processing unit 130 is configured to: When the maximum color value is less than or equal to the first threshold, the original maximum color value of the edge pixel is replaced with the maximum color value to obtain the second color value.

[0130] In an optional embodiment, the processing unit 130 is configured to: The maximum color value is corrected using a preset segmented mapping function to obtain the corrected maximum color value.

[0131] The second color value is obtained by replacing the maximum color value with the corrected maximum color value.

[0132] In an optional embodiment, the processing unit 130 is configured to: After the color values ​​of all pixels are processed, halftone processing is performed on the processed image data to be imaged, and the image data to be imaged after halftone processing is output to the imaging unit.

[0133] In an optional embodiment, the acquisition unit 110 is used for: Obtain the edge enhancement level, which controls the neighborhood search range during edge recognition. Different edge enhancement levels correspond to different neighborhood discrimination rules.

[0134] In an optional embodiment, the identification unit 120 is used for: The neighborhood discrimination rule is determined based on the obtained edge enhancement level.

[0135] The neighborhood discrimination rule is used to identify edge pixels and non-edge pixels in the imaging data.

[0136] In an optional embodiment, the display unit 150 is used for: The edge enhancement level parameters are displayed through the human-computer interaction interface integrated into the imaging device driver. These edge enhancement level parameters are used to set the pixel width corresponding to edge recognition.

[0137] In an optional embodiment, the acquisition unit 110 is used for: Receive the target edge enhancement level parameter selected by the user through the human-computer interaction interface.

[0138] In an optional embodiment, the identification unit 120 is used for: Determine the neighborhood discrimination rule that matches the target edge enhancement level parameter.

[0139] The neighborhood discrimination rule is used to identify edge pixels and non-edge pixels in the imaging data.

[0140] In an optional embodiment, the acquisition unit 110 is used for: Obtain the object type identifier for each pixel in the image processing data, wherein the object type identifier is used to indicate the object category to which the pixel belongs, and the object category includes at least: image object, graphic object, text object, and background.

[0141] Pixels whose object type is identified as graphic objects or text objects are selected as candidate pixels.

[0142] In an optional embodiment, the identification unit 120 is used for: For each pixel whose object type is identified as a graphic object or a text object (i.e., a candidate pixel), the existence of an image object or background pixel in its neighborhood is determined based on the neighborhood discrimination rule.

[0143] If present, the candidate pixel is identified as an edge pixel.

[0144] Otherwise, the candidate pixel is identified as a non-edge pixel.

[0145] This application also provides an electronic device for performing the above-described printing control method. Please refer to... Figure 11 It illustrates a schematic diagram of an electronic device provided by some embodiments of this application. For example... Figure 11 As shown, the electronic device 11 includes: a processor 1100, a memory 1101, a bus 1102, and a communication interface 1103. The processor 1100, the communication interface 1103, and the memory 1101 are connected via the bus 1102. The memory 1101 stores a computer program that can run on the processor 1100. When the processor 1100 runs the computer program, it executes the printing control method provided in any of the foregoing embodiments of this application.

[0146] The memory 1101 may include high-speed random access memory (RAM) or non-volatile memory, such as at least one disk storage device. Communication between the device network element and at least one other network element is achieved through at least one communication interface 1103 (which can be wired or wireless), such as the Internet, wide area network, local area network, metropolitan area network, etc.

[0147] Bus 1102 can be an ISA bus, PCI bus, or EISA bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. Memory 1101 is used to store programs. After receiving an execution instruction, processor 1100 executes the program. The printing control method disclosed in any of the foregoing embodiments of this application can be applied to processor 1100, or implemented by processor 1100.

[0148] The processor 1100 may be an integrated circuit chip with signal processing capabilities. In implementation, each step of the above method can be completed by the integrated logic circuitry in the hardware of the processor 1100 or by instructions in software form. The processor 1100 may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), an off-the-shelf programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor may be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this application can be directly embodied in the execution of a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor. The software modules may reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. The storage medium is located in memory 1101. Processor 1100 reads the information in memory 1101 and, in conjunction with its hardware, completes the steps of the above method.

[0149] The electronic device provided in this application embodiment and the printing control method provided in this application embodiment are based on the same inventive concept and have the same beneficial effects as the methods they adopt, operate or implement.

[0150] This application also provides a computer-readable storage medium corresponding to the printing control method provided in the foregoing embodiments. The computer-readable storage medium is an optical disc, on which a computer program (i.e., a computer program product) is stored. When the computer program is run by a processor, it executes the printing control method provided in any of the foregoing embodiments.

[0151] It should be noted that examples of the computer-readable storage medium may also include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other optical and magnetic storage media, which will not be elaborated here.

[0152] The computer-readable storage medium provided in the above embodiments of this application and the printing control method provided in the embodiments of this application are based on the same inventive concept and have the same beneficial effects as the methods adopted, run or implemented by the applications stored therein.

[0153] This application also provides a computer program product; please refer to [reference needed]. Figure 12 The computer program product 1200 carries program code, namely computer program 1201. The instructions included in the computer program 1201 can be used to execute the steps of the printing control method described in the above method embodiments. For details, please refer to the above method embodiments, which will not be repeated here.

[0154] The aforementioned computer program product can be implemented through hardware, software, or a combination thereof. In one optional embodiment, the computer program product is specifically embodied in a computer storage medium; in another optional embodiment, the computer program product is specifically embodied in a software product, such as a software development kit (SDK), etc.

[0155] The basic principles of this application have been described above with reference to specific embodiments. However, it should be noted that the advantages, benefits, and effects mentioned in this application are merely examples and not limitations, and should not be considered as essential features of each embodiment of this application. Furthermore, the specific details disclosed above are for illustrative and facilitative purposes only, and are not limitations. These details do not limit the application to the necessity of employing the aforementioned specific details for implementation.

[0156] The block diagrams of devices, apparatuses, devices, and systems involved in this application are merely illustrative examples and are not intended to require or imply that they must be connected, arranged, or configured in the manner shown in the block diagrams. As those skilled in the art will recognize, these devices, apparatuses, devices, and systems can be connected, arranged, and configured in any manner. Words such as “comprising,” “including,” “having,” etc., are open-ended terms meaning “including but not limited to,” and are used interchangeably with them. The terms “or” and “and” as used herein refer to the terms “and / or,” and are used interchangeably with them unless the context clearly indicates otherwise. The term “such as” as used herein refers to the phrase “such as but not limited to,” and is used interchangeably with it.

[0157] Additionally, as used herein, the "or" used in a list of items beginning with "at least one" indicates a separate list, such that a list of, for example, "at least one of A, B, or C" means A or B or C, or AB or AC or BC, or ABC (i.e., A and B and C). Furthermore, the word "exemplary" does not imply that the described example is preferred or better than other examples.

[0158] It should also be noted that in the system and method of this application, the components or steps can be decomposed and / or recombined. These decompositions and / or recombinations should be considered as equivalent solutions of this application.

[0159] Various changes, substitutions, and modifications can be made to the technology described herein without departing from the teachings defined by the appended claims. Furthermore, the scope of the claims is not limited to the specific aspects of the processes, machines, manufactures, events, means, methods, and actions described above. Currently existing or later-developed processes, machines, manufactures, events, means, methods, or actions that perform substantially the same function or achieve substantially the same result as the corresponding aspects described herein can be utilized. Therefore, the appended claims include such processes, machines, manufactures, events, means, methods, or actions within their scope.

[0160] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use this application. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein can be applied to other aspects without departing from the scope of this application. Therefore, this application is not intended to be limited to the aspects shown herein, but rather to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0161] The above description has been given for purposes of illustration and description. Furthermore, this description is not intended to limit the embodiments of this application to the forms disclosed herein. Although numerous exemplary aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, alterations, additions, and sub-combinations thereof.

Claims

1. A printing control method, characterized in that, Applied to an imaging device, the method includes: Identify edge pixels and non-edge pixels in the imaging task data; The non-edge pixels are subjected to ink reduction processing to obtain a processed first color value; and the maximum color value among the multiple color channel color values ​​of the edge pixels is obtained, and the edge pixels are subjected to color value adjustment processing based on the maximum color value to obtain a processed second color value; wherein the retention ratio of the second color value relative to the original pixel color value of the edge pixels is greater than the retention ratio of the first color value relative to the original pixel color value of the non-edge pixels; The processed data for the imaging task is output to the imaging unit.

2. The method according to claim 1, characterized in that, Each pixel in the image processing data includes C channel color value, M channel color value, Y channel color value and K channel color value; Perform ink reduction processing on the non-edge pixels, including: The C-channel color value, M-channel color value, and Y-channel color value are multiplied by a first coefficient to obtain the reduced C-channel color value, M-channel color value, and Y-channel color value; and the K-channel color value is multiplied by a second coefficient to obtain the reduced K-channel color value. The second coefficient is greater than the first coefficient.

3. The method according to claim 1, characterized in that, Based on the maximum color value, perform color value adjustment processing on the edge pixels to obtain the processed second color value, including: When the maximum color value is less than or equal to the first threshold, the original maximum color value of the edge pixel is replaced with the maximum color value to obtain the second color value.

4. The method according to claim 1, characterized in that, Based on the maximum color value, perform color value adjustment processing on the edge pixels to obtain the processed second color value, including: The maximum color value is corrected using a preset piecewise mapping function to obtain the corrected maximum color value; The second color value is obtained by replacing the maximum color value with the corrected maximum color value.

5. The method according to claim 1, characterized in that, The identification of edge pixels and non-edge pixels in the imaging task data includes: Get the edge enhancement level; Neighborhood discrimination rules are determined based on the edge enhancement level; The neighborhood discrimination rule is used to identify edge pixels and non-edge pixels in the imaging data.

6. The method according to claim 5, characterized in that, Obtaining edge enhancement levels also includes: The edge enhancement level parameters are displayed through the human-computer interaction interface of the driver. These edge enhancement level parameters are used to set the pixel width corresponding to edge recognition. Receive the target edge enhancement level parameter selected by the user through the human-computer interaction interface; Determine the neighborhood discrimination rule that matches the target edge enhancement level parameter.

7. The method according to claim 1, characterized in that, The identification of edge pixels and non-edge pixels in the imaging task data includes: Obtain the object type identifier for each pixel in the image processing data, wherein the object type identifier is used to indicate the object category to which the pixel belongs, and the object category includes at least: bitmap image object, graphic object, text object, and background; Pixels whose object type is identified as graphic objects or text objects are selected as candidate pixels; For each candidate pixel, determine whether there are pixels of image objects or background in its neighborhood based on the neighborhood discrimination rule; If they exist, the candidate pixels are identified as edge pixels; Otherwise, the candidate pixel is identified as a non-edge pixel.

8. An imaging device, characterized in that, include: The acquisition unit is configured to acquire the object type identifier and spatial neighborhood distribution of each pixel in the imaging task data; The recognition unit is configured to recognize edge pixels and non-edge pixels; The processing unit is configured as follows: Perform ink reduction processing on the non-edge pixels to obtain the processed first color value; The maximum color value among multiple color channel color values ​​of the edge pixel is obtained, and color value adjustment processing is performed on the edge pixel based on the maximum color value to obtain a processed second color value. The retention ratio of the second color value relative to the original pixel color value of the edge pixel is greater than the retention ratio of the first color value relative to the original pixel color value of the non-edge pixel. The processed data to be imaged is output to the imaging unit; The imaging unit is configured to perform imaging operations on the processed data to be imaged.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory, characterized in that, The processor executes the computer program to implement the steps of the method according to any one of claims 1-7.

10. A computer-readable storage medium having a computer program / instructions stored thereon, characterized in that, When the computer program / instructions are executed by the processor, they implement the steps of the method according to any one of claims 1-7.

11. A computer program product, comprising a computer program / instructions, characterized in that, When the computer program / instructions are executed by the processor, they implement the steps of the method according to any one of claims 1-7.

Images