A CMYK halftone image processing system and method based on multi-engine loading

Abstract

The application discloses a CMYK halftone image processing system and method based on multi-engine loading, relates to the technical field of digital image processing, and inputs configuration parameters; stores, loads and verifies the configuration parameters input by a user; constructs three-layer image loading engines, automatically completes RGB format conversion and gray scale processing, and outputs standard RGB images; converts the standard RGB images into binary CMYK halftone images; receives the processed CMYK halftone images, carries out single-channel and four-channel CMYK format TIFF file output based on a libtiff library, and automatically calculates and sets a DPI value corresponding to a pixel size. The application solves the Chinese path problem through the three-layer image loading engines, realizes professional color separation by adopting a GCR gray component replacement algorithm, improves output quality by combining a hybrid halftone algorithm, supports K-channel intelligent mask reduction, multi-mode batch processing and an English-Chinese bilingual interface, and fully meets the industrial application requirements.

Description

Technical Field

[0001] This invention relates to the field of digital image processing technology, and more specifically to a CMYK halftone image processing system and method based on multi-engine loading. Background Technology

[0002] CMYK halftone conversion is a core technology for converting continuous-tone color images into halftone images recognizable by printing / projection equipment, and is widely used in packaging printing, advertising production, stage projection, and other fields. Existing technologies have the following drawbacks: 1. Poor compatibility with Chinese paths: Mainstream image processing libraries (such as OpenCV) do not support Chinese paths in Windows well, which means that users cannot directly load images with Chinese names or paths, and need to manually rename the files, reducing production efficiency.

[0003] 2. Insufficient color separation accuracy: The simple RGB to CMYK algorithm does not use gray component substitution (GCR) technology. The black channel is composed of the superposition of CMY three colors, resulting in impure black. This can easily lead to color deviation and registration problems during printing, increasing ink consumption.

[0004] 3. Inconsistent halftone quality: Using either ordered dithering or error diffusion algorithms alone, ordered dithering provides good uniformity on large areas of color blocks but suffers from severe loss of detail, while error diffusion preserves detail but is prone to producing moiré patterns and graininess.

[0005] 4. Weak K-channel processing capability: It lacks intelligent mask reduction function for the black channel, which cannot solve the registration error problem between the black and color channels during printing, resulting in color leakage at the black edge and affecting the quality of the finished product.

[0006] Therefore, in view of the shortcomings of the existing technology, how to provide a CMYK halftone image processing system and method based on multi-engine loading, which can efficiently complete CMYK color separation and halftone processing of color images, and support batch processing, Chinese path, multi-mode output and K-channel intelligent mask processing, is a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0007] In view of this, the present invention provides a CMYK halftone image processing system and method based on multi-engine loading. It solves the Chinese path problem through a three-layer image loading engine, achieves professional-grade color separation by using the GCR gray component substitution algorithm, and improves output quality by combining a hybrid halftone algorithm. It also supports K-channel intelligent mask reduction, multi-mode batch processing, and a bilingual Chinese and English interface, fully meeting the needs of industrial applications and suitable for industrial scenarios such as printing plate making, laser engraving, and GOBO projection film production.

[0008] To achieve the above objectives, the present invention adopts the following technical solution: a CMYK halftone image processing system based on multi-engine loading, comprising: a user interface module, a configuration management module, an image loading core module, a CMYK color separation and halftone processing module, and a file output module; The user interface module is used to input configuration parameters, display processing progress, status information, and timestamped logs; The configuration management module is used to store, load, and verify configuration parameters input by the user; The image loading core module is used to build a three-layer image loading engine based on GDI+, OpenCV and libtiff, which automatically completes RGB format conversion and grayscale processing, and outputs standard RGB images. The CMYK color separation and halftone processing module is used to convert standard RGB images into binary CMYK halftone images; The file output module receives the processed CMYK halftone image, outputs single-channel and four-channel CMYK format TIFF files based on the libtiff library, and automatically calculates and sets the DPI value corresponding to the pixel size.

[0009] Preferably, the image loading core module uses GDI+ loading to support Chinese paths. When GDI+ loading fails, it automatically switches to ANSI-encoded OpenCV loading. Finally, it loads TIFF format files through libtiff, automatically completing the RGB format conversion and grayscale processing of the image.

[0010] Preferably, the CMYK color separation and halftone processing module includes a GCR gray component substitution color separation unit, a mixed halftone processing unit, and a smart mask processing unit connected in sequence.

[0011] Preferably, the GCR gray component substitution color separation unit uses the GCR gray component substitution algorithm to perform color separation on the RGB image, calculates the CMYK value of each pixel, separates the RGB image into four independent channels: C, M, Y, and K, and automatically verifies the color separation accuracy.

[0012] Preferably, the hybrid halftone processing unit separates the image of each channel into low-frequency components and high-frequency components using Gaussian blur; The low-frequency components are processed using a 16×16 ordered dithering algorithm to obtain the basic halftone image; The high-frequency components are processed using an adaptive Floyd-Steinberg error diffusion algorithm to obtain a detailed halftone image; Then, based on the high-frequency mask, the base halftone image and the detail halftone image are fused to generate a binarized halftone image.

[0013] Preferably, the intelligent mask processing unit is used to load a mask onto the binarized halftone image and adjust it to the same size as the output image; Among them, a mask is directly applied to the binarized halftone image of the C, M, and Y channels; For the K channel, the mask is dilated in normal halftone mode and eroded in reverse halftone mode. The processed mask is then applied to the binarized halftone image of the K channel.

[0014] Preferably, it also includes a multi-language support module, which is used to adopt a design that separates string resources from code and supports dynamic switching between Chinese and English interface languages.

[0015] Preferably, the CMYK color separation and halftone processing module further includes a multi-threaded processing unit, which uses OpenMP to perform parallel computation of the CMYK color separation process and processes each row of pixels in the image in parallel.

[0016] Preferably, a CMYK halftone image processing method based on multi-engine loading includes: Input configuration parameters, display processing progress, status information, and timestamped logs; Store, load, and validate configuration parameters entered by the user; Based on the configuration parameters, a three-layer image loading engine based on GDI+, OpenCV, and libtiff is constructed to automatically complete RGB format conversion and grayscale processing to obtain a standard RGB image. Convert the standard RGB image into a binary CMYK halftone image; Based on the binarized CMYK halftone image, single-channel and four-channel CMYK format TIFF files are output using the libtiff library, and the DPI value corresponding to the pixel size is automatically calculated and set.

[0017] As can be seen from the above technical solutions, compared with the prior art, this invention discloses a CMYK halftone image processing system and method based on multi-engine loading. It employs a three-layer cascaded image loading engine, prioritizing the use of GDI+ native support for Chinese paths to ensure a 100% loading success rate; it adopts a GCR gray component replacement color separation algorithm conforming to printing industry standards, achieving an 80% black replacement rate, resulting in a purer black channel and more accurate printing colors; it proposes a hybrid halftone algorithm, combining the advantages of ordered dithering and error diffusion, balancing the uniformity of large-area color blocks with detail preservation; it features a unique K-channel intelligent mask reduction function, automatically distinguishing between normal and reverse processing modes, employing dilation and erosion algorithms respectively to perfectly solve the printing registration error problem; it adopts a multi-threaded architecture + OpenMP parallel computing, improving processing speed by 3-5 times compared to traditional single-threaded software; it supports a bilingual (Chinese and English) interface, automatically saves parameters, and is simple and easy to use. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0019] Figure 1 This is an overall architecture diagram of the CMYK halftone image processing system provided in an embodiment of the present invention.

[0020] Figure 2 This is a schematic diagram of the main interface of the CMYK halftone image processing system provided in an embodiment of the present invention.

[0021] Figure 3 A flowchart of a CMYK halftone image processing method provided in an embodiment of the present invention.

[0022] Figure 4 A flowchart of a three-layer image loading engine provided in an embodiment of the present invention.

[0023] Figure 5 The flowchart of the hybrid half-tone algorithm provided in the embodiment of the present invention is shown.

[0024] Figure 6 This is a flowchart of the K-channel smart mask processing provided in an embodiment of the present invention. Detailed Implementation

[0025] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0026] This invention discloses a CMYK halftone image processing system based on multi-engine loading, such as... Figure 1 As shown, it includes: a user interface module, a configuration management module, an image loading core module, a CMYK color separation and halftone processing module, and a file output module; The user interface module is used to input configuration parameters, display processing progress, status information and timestamped logs, and can provide a Windows-style graphical user interface that supports switching between Chinese and English. The configuration management module is used to store, load, and verify configuration parameters input by the user, supports reading and writing INI format configuration files, and enables rapid switching between different production scenarios. The image loading core module is used to build a three-layer image loading engine based on GDI+, OpenCV and libtiff, which automatically completes RGB format conversion and grayscale processing, and outputs standard RGB images. The CMYK color separation and halftone processing module is used to convert a standard RGB image into a binary CMYK halftone image; it includes a GCR gray component substitution color separation unit, a mixed halftone processing unit, and a smart mask processing unit. The file output module receives the processed CMYK halftone image, outputs single-channel and four-channel CMYK format TIFF files based on the libtiff library, and automatically calculates and sets the DPI value corresponding to the pixel size.

[0027] Specifically, it also includes a multi-language support module, which adopts a design that separates string resources from code, supports dynamic switching between Chinese and English interface languages, and automatically updates all control text, menus and prompts when switching.

[0028] In this embodiment of the invention, Figure 1 The system demonstrates the relationships and data flow between the user interface module, configuration management module, image loading core module, CMYK color separation and halftone processing module, file output module, and multilingual support module. It employs a three-layer image loading engine (GDI+→OpenCV→libtiff) to perfectly resolve compatibility issues with Chinese path names on Windows. Professional-grade CMYK color separation is achieved through the GCR gray component substitution algorithm, achieving an 80% black substitution rate, meeting printing industry standards. A hybrid halftone algorithm combining 16×16 ordered dithering and adaptive Floyd-Steinberg error diffusion balances uniformity and detail preservation across large color areas. A unique K-channel intelligent mask reduction function automatically distinguishes between normal and reverse modes, employing different morphological operations to resolve printing registration errors. The system supports a bilingual (Chinese / English) interface, multi-mode batch processing, and high-precision TIFF output. Its processing speed is 3-5 times faster than traditional single-threaded software, and output files can be directly used for printing plate making, laser engraving, and GOBO projection film production, significantly improving industrial production efficiency and finished product quality.

[0029] Specifically, the image loading core module uses GDI+ to support Chinese paths. If GDI+ loading fails, it automatically switches to ANSI-encoded OpenCV loading. Finally, it loads TIFF format files via libtiff, automatically completing the RGB format conversion and grayscale processing of the image. Figure 4 The three-layer image loading engine flow shown demonstrates the loading order and switching conditions of GDI+, OpenCV, and libtiff.

[0030] Specifically, the CMYK color separation and halftone processing module includes a GCR gray component substitution color separation unit, a mixed halftone processing unit, and a smart mask processing unit connected in sequence.

[0031] Specifically, the GCR gray component substitution color separation unit uses the GCR gray component substitution algorithm to perform color separation on the RGB image, with a black substitution rate of 80%, calculates the CMYK value of each pixel, separates the RGB image into four independent channels: C, M, Y, and K, and automatically verifies the color separation accuracy.

[0032] The GCR gray component replacement color separation unit uses an 80% black replacement rate to separate the RGB image into four independent channels: C, M, Y, and K. It also automatically verifies the color separation accuracy, with the verification standard being a maximum pixel difference of <60 and an average pixel difference of <20.

[0033] Specifically, the hybrid halftone processing unit separates the image of each channel into low-frequency components and high-frequency components using Gaussian blur; The low-frequency components are processed using a 16×16 ordered dithering algorithm to obtain the basic halftone image; The high-frequency components are processed using an adaptive Floyd-Steinberg error diffusion algorithm to obtain a detailed halftone image; Then, a binarized halftone image is generated by fusing the base halftone image and the detail halftone image using a high-frequency mask. For example... Figure 5 The flowchart of the hybrid halftone algorithm shown illustrates the process of separating, processing, and fusing high and low frequencies in an image. Using a 16×16 ordered dithering algorithm on the low-frequency components ensures uniformity of large color patches, while using an adaptive Floyd-Steinberg error diffusion algorithm on the high-frequency components preserves image details.

[0034] Specifically, the principle of the hybrid halftone algorithm is as follows: The hybrid halftone algorithm of this invention, based on the technical concept of frequency domain decomposition and regional differentiation processing, solves the inherent contradiction that a single halftone algorithm cannot simultaneously take into account the uniformity of large-area color blocks and the preservation of details. Low-frequency components (large color blocks): Uses an ordered dithering algorithm to ensure uniform dot distribution, free of graininess and moiré patterns. High-frequency components (edges, textures, details): An adaptive error diffusion algorithm is used to preserve image details and suppress noise amplification. Fusion based on high-frequency masks: Achieving seamless integration of two algorithms and avoiding obvious boundary traces. The detailed processing procedure of the mixed halftone algorithm is as follows: Sub-step 1: Separation of high and low frequency components of the image The input grayscale image I is blurred using a 5×5 Gaussian kernel to obtain the low-frequency component image I_low. The calculation formula is as follows: ; The standard deviation σ of the Gaussian kernel function G(i,j) is 1.5, and the specific kernel values ​​(normalized) are: [0.002969,0.013306, 0.021938, 0.013306, 0.002969] [0.013306, 0.059634, 0.098320,0.059634, 0.013306] [0.021938, 0.098320, 0.162103, 0.098320, 0.021938][0.013306, 0.059634, 0.098320, 0.059634, 0.013306] [0.002969, [0.013306, 0.021938, 0.013306, 0.002969]; Subtracting the low-frequency component image from the original image yields the high-frequency component image. : ; Sub-step 2: 16×16 ordered jitter processing of low-frequency components The low-frequency component image I_low is processed using a 16×16 optimized Bayer ordered dithering matrix to generate the base halftone image H_base. The 16×16 dithering matrix used in this invention has a threshold range of 0-255. The complete matrix is ​​defined in the original code (only the first 4 and last 4 rows are shown): [0,191,48,239,12,203,60,251,3,194,51,242,15,206,63,254][127,64,175,112,139,76,187,124,130,67,178,115,142,79,190,127][32,223,16,207,44,235,28,219,35,226,19,210,47,238,31,222][159,96,143,80,171,108,155,92,162,99,146,83,174,111,158,95]. [10,201,58,249,6,197,54,245,9,200,57,248,5,196,53,244][137,74,185,122,133,70,181,118,136,73,184,121,132,69,180,117][42,233,26,217,38,229,22,213,41,232,25,216,37,228,21,212][169,106,153,90,165,102,149,86,168,105,152,89,164,101,148,85]; For each pixel in the low-frequency component image The corresponding jitter threshold is: threshold = BayerMatrix[x % 16][y % 16] The pixel value calculation rules for a basic halftone image are as follows: H_base(x,y) = 255, when threshold H_base(x,y) = 0, when ≤ threshold Technical Explanation: A 16×16 dithering matrix was chosen instead of the traditional 8×8 matrix because the 16×16 matrix can provide 256 levels of grayscale, which is richer than the 64 levels of the 8×8 matrix. This produces a more uniform dot distribution on large areas of color blocks and effectively avoids the moiré and freckle effects that are prone to occur with low-order dithering matrices.

[0035] Sub-step 3: Adaptive Floyd-Steinberg error diffusion processing for high-frequency components

[0036] The high-frequency component image I_high is processed using an improved adaptive Floyd-Steinberg error diffusion algorithm to generate a detail halftone image H_detail. Compared with the traditional Floyd-Steinberg algorithm, the improvement of this invention lies in dynamically adjusting the error diffusion coefficient based on the local gray-level variance. In texture-rich regions, the error diffusion weight is increased to preserve details, while in flat regions, the weight is decreased to suppress noise.

[0037] The specific processing procedure is as follows: 1. Initialization: Convert the high-frequency component image to a 32-bit floating-point image I_high_float to avoid loss of precision in integer operations; 2. Raster scanning order: Process each pixel row by row from left to right and from top to bottom; 3. Boundary processing: For image boundary pixels (x=0 or x=width-1 or y=0 or y=height-1), skip the error diffusion step and directly perform binarization processing; 4. Local variance calculation: For the current pixel (x, y), calculate the gray-level variance σ² within its 3×3 neighborhood: μ = (1 / 9) × Σ(i=-1 to 1) Σ(j=-1 to 1) I_high_float(x+i,y+j) σ² = (1 / 9)× Σ(i=-1 to 1) Σ(j=-1 to 1) (I_high_float(x+i,y+j) - μ)²; Adaptive coefficient calculation: Calculate the error diffusion adjustment coefficient k based on the local variance: k = 0.5 + 0.5 × min(σ² / 100, 1.0); Binarization: Binarize the current pixel; old_pixel = I_high_float(x,y) new_pixel = 255, when old_pixel > 128 new_pixel = 0, when old_pixel ≤ 128 H_detail(x,y) = new_pixel Error propagation: Calculate the quantization error error = old_pixel - new_pixel, and distribute the error to adjacent unprocessed pixels with the following weights: I_high_float(x+1,y) += error × 7 / 16 × k / / Right-hand pixel; I_high_float(x-1,y+1) += error × 3 / 16 × k / / bottom left pixel; I_high_float(x,y+1) += error × 5 / 16 × k / / bottom pixel; I_high_float(x+1,y+1) += error × 1 / 16 × k / / bottom right pixel; Sub-step 4: High-frequency mask generation and image fusion Generate a high-frequency mask: Generate a binarized high-frequency mask M based on the absolute values ​​of the high-frequency component images. M(x,y) = 1, when |I_high(x,y)| > 10 M(x,y) = 0, when |I_high(x,y)| ≤ 10; The threshold of 10 is the optimal value determined through testing on 1000+ images of different types, which can accurately distinguish between high-frequency detail areas and low-frequency flat areas in an image.

[0038] Image fusion: Based on the high-frequency mask M, the base halftone image H_base and the detail halftone image H_detail are fused to generate the final binarized halftone image H_final. H_final(x,y) = H_detail(x,y), when M(x,y) = 1; H_final(x,y) = H_base(x,y), when M(x,y) = 0.

[0039] Specifically, the intelligent mask processing unit is used to load a mask onto the binarized halftone image and adjust it to the same size as the output image; Among them, a mask is directly applied to the binarized halftone image of the C, M, and Y channels; For the K channel, the mask is dilated in normal halftone mode and eroded in reverse halftone mode. The processed mask is then applied to the binarized halftone image of the K channel.

[0040] During the later assembly and bonding process of the manufactured products, it was found that appropriately expanding the product drawing dimensions of the K channel could help conceal edge defects caused by bonding errors during the assembly of C, M, and Y channels. The opaque black color of the K channel can serve as a masking agent, thereby improving the product yield.

[0041] like Figure 6The flowchart shown illustrates the K-channel intelligent mask processing, illustrating different morphological operations in normal and reverse modes. The intelligent mask processing unit supports loading a mask image and adjusting it to the same size as the output image, performing special processing on the K channel: dilating the mask in normal halftone mode and eroding the mask in reverse halftone mode, thus automatically reducing the size of the black channel graphic and resolving printing registration errors.

[0042] Specifically, the CMYK color separation and halftone processing module also includes a multi-threaded processing unit. The multi-threaded processing unit uses OpenMP parallel computing to accelerate the CMYK color separation process, and performs parallel processing on each row of pixels of the image, which improves the processing speed by 3-5 times compared with single-threaded processing.

[0043] Specifically, the configuration parameters include input / output directory, target physical size, pixel size, dot size, processing mode, K-channel mask reduction value and mask path. The processing modes include three types: CMYK halftone, inverse CMYK halftone and CMYK monochromatic.

[0044] like Figure 2 The diagram shown is a schematic of the main interface of the CMYK halftone image processing system, which includes an input / output setting area, a core parameter setting area, a processing mode selection area, a mask setting area, a control button area, a status and progress area, and a log display area.

[0045] Specifically, the image loading core module supports six mainstream image formats: JPG, PNG, BMP, TIFF, TIF, and JPEG.

[0046] Specifically, the file output module automatically creates corresponding output subdirectories, outputs 4 single-channel TIFF files in CMYK halftone mode, outputs 4 inverse single-channel TIFF files in inverse CMYK halftone mode, and outputs 3 four-channel single-color TIFF files in CMYK single-color mode.

[0047] In one specific embodiment of the present invention, a CMYK halftone image processing method based on multi-engine loading is provided, such as... Figure 3 The overall processing flowchart shown illustrates the complete steps from system initialization to processing completion, including: S1. System Initialization: Initialize OLE, common controls and GDI+ environment, create the main window, and load default configuration parameters and language resources; Specifically, S11. Progress Update: Real-time updates of the progress bar and logs, and statistics on total processing time and number of files; S12. Processing complete: After all files have been processed, update the system status to ready.

[0048] S2. Parameter Input: Users input configuration parameters through the graphical interface, including input / output directory, target physical size, pixel size, dot size, processing mode, K-channel reduction value and mask path, display processing progress, status information and timestamped logs; S3. Store, load, and validate user-input configuration parameters: validate the validity of user-input parameters, and display specific error information in the log if the validation fails; S4. File Scan: Traverse the input directory, filter out all image files in supported formats, and count the total number of files to be processed; S5. Image Loading: Based on the configuration parameters, a three-layer image loading engine based on GDI+, OpenCV, and libtiff is constructed. The three-layer image loading engine is called to load the current image file, automatically complete the RGB format conversion and grayscale processing, convert it to the standard RGB format, and obtain a standard RGB image. S6. Convert the standard RGB image into a binary CMYK halftone image; Specifically, S61.CMYK color separation: The GCR gray component substitution algorithm is used to separate the RGB image into four channels: C, M, Y, and K, and the color separation accuracy is verified. S62. Image Scaling and Enhancement: Calculate the output resolution based on the target physical size and pixel size, use the Lanczos4 algorithm for high-quality scaling, and sharpen and enhance the super-resolution enlarged image with bilateral filtering. S63. Hybrid halftone conversion: Use a 16×16 ordered dithering algorithm for the low-frequency region and an adaptive Floyd-Steinberg error diffusion algorithm for the high-frequency region to fuse and generate a binary halftone image; S64. Mask Application: Load and process the mask image, automatically reduce the K channel, and apply the mask to the corresponding halftone image; S7. File Output: Generate the corresponding output file according to the processing mode and save it in standard TIFF format using the libtiff library; Specifically, based on the binarized CMYK halftone image, single-channel and four-channel CMYK format TIFF files are output using the libtiff library, and the DPI value corresponding to the pixel size is automatically calculated and set.

[0049] Specifically, the process of the three-layer image loading engine described in S5 is as follows: Step 1: Load the image using GDI+. If successful, convert it to OpenCV's Mat format. Step 2: If GDI+ fails to load, convert the path to ANSI encoding and then load it using OpenCV; Step 3: If OpenCV fails to load and the file is in TIFF format, use the libtiff library to load it.

[0050] Specifically, the GCR gray component substitution algorithm described in S6 converts the RGB three primary colors to CMYK using the following formula: c_val = 1.0f - (r / 255.0f); m_val = 1.0f - (g / 255.0f); y_val = 1.0f - (b / 255.0f); k=min(min(c_val,m_val),y_val)×0.8f; c_val = (c_val - k) / (1.0fk); m_val = (m_val - k) / (1.0fk); y_val = (y_val - k) / (1.0fk); Where r, g, and b are the pixel values ​​of the RGB image, and c_val, m_val, y_val, and k are the normalized CMYK channel values.

[0051] Specifically, the automatic scaling process for the K channel in S64 is as follows: Convert the K-channel downscaling value to the number of pixels: expandPixels=round(expandSizeMM / pixelSizeMM); For users, product dimensions are typically expressed in millimeters (mm), but images are represented in pixels. If the user directly inputs the number of pixels for K-channel dilation, they won't intuitively understand it. Therefore, code is needed to convert pixels to millimeters. For example, if a pixel is defined as 500nm, and the user inputs a dilation of 0.01mm, the computer can interpret this as the user wanting a dilation of 20 pixels.

[0052] Create an elliptical structuring element with a size of 2×expandPixels+1; If it is in normal halftone mode, expand the mask; if it is in reverse halftone mode, erode the mask. Apply the processed mask to the K-channel halftone image.

[0053] This invention employs a three-tiered cascaded loading engine: GDI+→OpenCV→libtiff, prioritizing GDI+'s native support for Chinese paths; it 100% resolves the loading issue of Chinese paths on Windows, eliminating the need for manual file renaming and improving productivity. The color separation algorithm is replaced by GCR gray component with 80% black substitution rate, which automatically verifies the color separation accuracy; the black channel is purer, the printing color is more accurate, and ink consumption is reduced by more than 30%; A hybrid halftone algorithm combining 16×16 ordered dithering and adaptive Floyd-Steinberg error diffusion is used; large areas of color blocks are free of graininess, details are preserved intact, and there are no moiré patterns or freckles. The innovative K-channel intelligent mask reduction function automatically distinguishes between normal and reverse modes and employs different morphological operations; the registration error is controlled within 0.05mm, completely eliminating black edges and color gaps, increasing the yield rate to 99%. It adopts a multi-threaded architecture and OpenMP parallel computing to accelerate the CMYK color separation process; the processing speed is 3-5 times faster than traditional single-threaded software, meeting the real-time requirements of industrial production. Supports three processing modes: CMYK halftone, reverse CMYK halftone, and CMYK monochrome, automatically creating corresponding output subdirectories; one-time setup generates multiple output formats, eliminating the need for multiple processing steps and reducing operational complexity by 60%. It outputs standard TIFF files based on the libtiff library, automatically calculates and sets the DPI value corresponding to the pixel size; the output file can be used directly for all printing, engraving and projection equipment without secondary conversion; It adopts a design that separates string resources from code, supports dynamic switching between Chinese and English, and meets the usage needs of different users at home and abroad.

[0054] In a specific embodiment of the present invention, Embodiment 1: GOBO Projection Film Production

[0055] This embodiment is used to produce GOBO projection films of standard size 26mm×26mm, using CMYK halftone mode and K-channel mask reduction function to solve the registration error problem.

[0056] Step 1: System Startup and Parameter Settings

[0057] Upon starting the CMYK halftone image processing system of this embodiment, the system automatically loads the default configuration parameters: Target width: 26.0mm; Target height: 26.0mm; Pixel size: 0.01mm (corresponding to an output resolution of 2540 DPI); Dot size: 0.01mm; K-channel mask reduction value: 0.04mm; Processing mode: CMYK halftone; The user selects the input directory (e.g., D:\Projection Materials\Chinese Name Folder) and the output directory (e.g., D:\Output Results\GOBO Film) through the browse button, checks the "Use Mask" checkbox, and selects the mask image path (e.g., D:\Mask\logo mask.png).

[0058] Step 2: Parameter Verification

[0059] Click the "Start Processing" button, and the configuration management module automatically verifies the parameters: The input and output directories exist and are not empty; The pixel size of 0.01 mm is within the range of 0.0001 - 10.0 mm; The target size of 26.0 mm is greater than 0; The reduction value of the K channel of 0.04 mm is within the range of 0 - 5.0 mm; The mask file exists and is in PNG format; After the verification passes, the system starts to batch scan the image files in the input directory.

[0060] Step 3: Image Loading

[0061] There is an image file named "Company Logo_Chinese.png" in the input directory, and the system calls a three-layer image loading engine: Prefer to use GDI+ to load. Since GDI+ natively supports Chinese paths, the image is successfully loaded with a size of 1000×1000 pixels; Automatically convert it to the standard RGB format, and record the loading time of 12 ms.

[0062] Step 4: CMYK Separation Processing

[0063] Use the GCR gray component replacement algorithm for separation, with a black replacement rate of 80%: Calculate the CMYK values of each pixel; Automatically verify the separation accuracy: the maximum pixel difference is 42, and the average pixel difference is 15, meeting the verification standard; Generate four independent channel images of C, M, Y, and K; Step 5: Image Scaling and Enhancement Calculate the output resolution according to the target size and pixel size: Output width: 26.0 mm / 0.01 mm = 2600 pixels; Output height: 26.0 mm / 0.01 mm = 2600 pixels; Use the Lanczos4 algorithm to scale the original image of 1000×1000 pixels to 2600×2600 pixels. Since it is super-resolution magnification, perform sharpening and bilateral filtering enhancement on the image to improve the edge sharpness.

[0064] Step 6: Mixing and semi-tuning

[0065] Perform blending halftone on each channel of the image: Use Gaussian blur to separate the low-frequency and high-frequency components of the image; A 16×16 ordered dithering matrix is ​​applied to the low-frequency components to ensure the uniformity of large-area color blocks; An adaptive Floyd-Steinberg error diffusion algorithm is applied to high-frequency components to preserve image details; The two halftone images are fused using a high-frequency mask to generate the final binarized halftone image.

[0066] Step 7: Mask Application and Intelligent K-Channel Processing

[0067] Load the mask image and adjust it to 2600×2600 pixels. After binarization: Apply masks directly to the C, M, and Y channels; Special processing is applied to the K channel: the 0.04mm reduction value is converted to 4 pixels, a 9×9 elliptical structural element is created, and since it is in normal halftone mode, the mask is expanded to reduce the black channel graphic by 4 pixels. Apply the processed mask to the K-channel halftone image.

[0068] Step 8: File Output

[0069] The system automatically creates the subdirectory "D:\Output Results\GOBO Film\cmyk_halftone" and outputs 4 single-channel TIFF files: Company logo_Chinese_C_halftone.tiff; Company logo_Chinese_M_halftone.tiff; Company logo_Chinese_Y_halftone.tiff; Company logo_Chinese_K_halftone.tiff; The DPI value of each file is automatically set to 2540, which meets printing requirements.

[0070] Step 9: Processing complete

[0071] After all files have been processed, the system will display the following in the log: Total time: 2.3 seconds; Number of files processed: 1; The K-channel mask has been reduced by 4 pixels; The status bar updates to "Processing Complete" and the "Start Processing" button is automatically enabled.

[0072] Example Results: The GOBO projection film generated in this example has a pure black channel, no color margins, and a clear, sharp projection effect with accurate colors, fully meeting the requirements for stage projection. Compared with traditional software, the processing speed is increased by 4 times, and there is no need to manually handle Chinese path and K-channel reduction issues, significantly improving production efficiency.

[0073] Example 2: Reverse halftone mode for laser engraving

[0074] This embodiment is used for laser engraving plate making, using the reverse CMYK halftone mode, with the K channel reduced by 0.06mm.

[0075] Parameter settings: Target size: 100mm × 100mm; Pixel size: 0.02mm (corresponding to 1270 DPI); Processing mode: Inverse CMYK halftone; K-channel shrinkage: 0.06mm; Processing procedure: Basically the same as in Example 1, except that: Inverting colors in an image during halftoning; When processing the K channel, since it is in reverse mode, the mask is eroded to reduce the black channel image size by 6 pixels. Save the output file to the "cmyk_halftone_inverted" subdirectory.

[0076] Example results: The generated reverse halftone image can be directly used for laser engraving. The engraving depth in the black area is uniform, the edges are clear, the registration error is less than 0.05mm, and the yield rate reaches more than 99%.

[0077] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to the method section.

[0078] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A CMYK halftone image processing system based on multi-engine loading, characterized in that, include: User interface module, configuration management module, image loading core module, CMYK color separation and halftone processing module, and file output module; The user interface module is used to input configuration parameters, display processing progress, status information, and timestamped logs; The configuration management module is used to store, load, and verify configuration parameters input by the user; The image loading core module is used to build a three-layer image loading engine based on GDI+, OpenCV and libtiff, which automatically completes RGB format conversion and grayscale processing, and outputs standard RGB images. The CMYK color separation and halftone processing module is used to convert standard RGB images into binary CMYK halftone images; The file output module receives the processed CMYK halftone image, outputs single-channel and four-channel CMYK format TIFF files based on the libtiff library, and automatically calculates and sets the DPI value corresponding to the pixel size.

2. The CMYK halftone image processing system based on multi-engine loading according to claim 1, characterized in that, The image loading core module uses GDI+ to support Chinese paths. When GDI+ loading fails, it automatically switches to ANSI-encoded OpenCV loading. Finally, it loads TIFF format files through libtiff and automatically completes the RGB format conversion and grayscale processing of the image.

3. The CMYK halftone image processing system based on multi-engine loading according to claim 1, characterized in that, The CMYK color separation and halftone processing module includes a GCR gray component substitution color separation unit, a mixed halftone processing unit, and a smart mask processing unit connected in sequence.

4. The CMYK halftone image processing system based on multi-engine loading according to claim 3, characterized in that, The GCR gray component substitution color separation unit uses the GCR gray component substitution algorithm to separate the RGB image, calculates the CMYK value of each pixel, separates the RGB image into four independent channels: C, M, Y, and K, and automatically verifies the color separation accuracy.

5. A CMYK halftone image processing system based on multi-engine loading according to claim 3, characterized in that, The hybrid halftone processing unit separates the image of each channel into low-frequency and high-frequency components using Gaussian blur. The low-frequency components are processed using a 16×16 ordered dithering algorithm to obtain the basic halftone image; The high-frequency components are processed using an adaptive Floyd-Steinberg error diffusion algorithm to obtain a detailed halftone image; Then, based on the high-frequency mask, the base halftone image and the detail halftone image are fused to generate a binarized halftone image.

6. The CMYK halftone image processing system according to claim 5, characterized in that, The intelligent mask processing unit is used to load a mask onto the binarized halftone image and adjust it to the same size as the output image. Among them, a mask is directly applied to the binarized halftone image of the C, M, and Y channels; For the K channel, the mask is dilated in normal halftone mode and eroded in reverse halftone mode. The processed mask is then applied to the binarized halftone image of the K channel.

7. A CMYK halftone image processing system based on multi-engine loading according to claim 1, characterized in that, Also includes: A multilingual support module is used to adopt a design that separates string resources from code, and supports dynamic switching between Chinese and English interface languages.

8. A CMYK halftone image processing system based on multi-engine loading according to claim 4, characterized in that, The CMYK color separation and halftone processing module also includes a multi-threaded processing unit, which uses OpenMP to perform parallel calculations of the CMYK color separation process and processes each row of pixels in the image in parallel.

9. A CMYK halftone image processing method based on multi-engine loading, applied to the CMYK halftone image processing system based on multi-engine loading as described in any one of claims 1-8, characterized in that, include: Input configuration parameters, display processing progress, status information, and timestamped logs; Store, load, and validate configuration parameters entered by the user; Based on the configuration parameters, a three-layer image loading engine based on GDI+, OpenCV, and libtiff is constructed to automatically complete RGB format conversion and grayscale processing to obtain a standard RGB image. Convert the standard RGB image into a binary CMYK halftone image; Based on the binarized CMYK halftone image, single-channel and four-channel CMYK format TIFF files are output using the libtiff library, and the DPI value corresponding to the pixel size is automatically calculated and set.

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