Platemaking exposure parameter setting method and equipment for preprinting paperboard color sequence and medium
By combining the Neugebauer chromatic overprinting model and edge detection algorithm with a local exposure adjustment strategy, the problems of insufficient global color consistency and microstructure control in complex color sequence overprinting are solved, achieving high-precision printing results.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- XIAN XIONGFENG PRINTING PACKING CO LTD
- Filing Date
- 2025-12-30
- Publication Date
- 2026-05-12
Smart Images
Figure CN122018240A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of printing plate-making data processing technology, and in particular to a method, equipment and medium for adjusting the exposure parameters of pre-printed paperboard color sequence. Background Technology
[0002] Color sequence plate making for pre-printed paperboard is a crucial step in the printing plate-making process. It typically relies on the optical properties of the inks, the color sequence arrangement, and the image structure information of the printing plate to set exposure conditions in order to obtain a printing plate that meets the requirements of overprinting colors. Traditional exposure parameter settings are mostly based on the correspondence between the ink reflectance spectrum and color standards. Overall exposure settings are completed through model prediction or empirical adjustment, combined with image analysis methods to identify the structural features of the printing plate to ensure the imaging quality of image details. With the increasing complexity of color sequences in pre-printed paperboard, the coupling relationship between ink overprinting behavior and the microstructure of the printing plate has gradually become an important research direction for exposure control in plate making.
[0003] Existing methods for predicting the spectral distribution of ink overprinting often rely on the overall color shift as the basis for parameter adjustment, which is insufficient for responding to local differences in different color sequence combinations and makes it difficult to maintain global color consistency under complex color sequence conditions. For fine lines, tiny dots, and high-density structural areas in the image, traditional methods cannot perform more targeted exposure control on microstructure areas, which can easily lead to insufficient recovery of local imaging details. Summary of the Invention
[0004] In view of the aforementioned existing problems, the present invention is proposed.
[0005] Therefore, this invention provides a method for setting the plate-making exposure parameters for the color sequence of pre-printed paperboard, which solves the problems of difficulty in ensuring global color consistency and insufficient precision in exposure control of microstructure areas under complex color sequence overprinting conditions.
[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: In a first aspect, the present invention provides a method for adjusting the exposure parameters of a pre-printed paperboard color sequence, comprising: acquiring ink spectral data, target color sequence data, and plate image data and preprocessing them; using the Neugebauer color overprinting model to perform spectral overprinting prediction on the preprocessed ink spectral data and target color sequence data, generating a predicted spectral curve, and calculating exposure parameters to generate a global exposure parameter set; using an edge detection algorithm to perform image analysis on the preprocessed plate image data, generating a plate edge feature map, and performing regional connectivity judgment and structure classification on the plate edge feature map to identify microstructure regions in the plate image and generate a microstructure partition mapping map; fusing the global exposure parameter set with the microstructure partition mapping map, and correcting the exposure of the microstructure regions according to a preset local exposure adjustment strategy library to generate an exposure parameter mapping map; performing CTP partition exposure plate making and printing verification based on the exposure parameter mapping map, detecting the reproduction quality of the microstructure regions, and obtaining qualified paperboard samples.
[0007] As a preferred embodiment of the plate-making exposure parameter tuning method for the color sequence of pre-printed paperboard according to the present invention, the ink spectral data includes the reflectance curves of each color ink and the ink optical parameters; The target color sequence data includes the printing color order, standard color values, and standard spectral curves. The page layout image data refers to the page layout dot matrix information; The preprocessing includes denoising, normalization, and image grayscale conversion.
[0008] As a preferred embodiment of the plate-making exposure parameter tuning method for the color sequence of pre-printed paperboard according to the present invention, the steps for generating the predicted spectral curve are as follows: The preprocessed ink spectral data and target color sequence data are input into the Neugebauer color overprinting model for data fusion calculation to obtain the spectral input matrix. The Neugebauer chromaticity overprinting simulation is performed based on the spectral input matrix to generate a predicted spectral curve.
[0009] As a preferred embodiment of the plate-making exposure parameter tuning method for the color sequence of pre-printed paperboard according to the present invention, the steps for generating the global exposure parameter set are as follows: The standard spectral curve is extracted from the preprocessed target color sequence data and combined with the predicted spectral curve to calculate the wavelength-by-wavelength difference, generating a spectral deviation vector. The exposure response relationship of each color ink is calculated in reverse based on the spectral deviation vector to obtain the exposure adjustment parameter set; The exposure adjustment parameter set is fused with the preset basic exposure parameters to generate a comprehensive exposure parameter matrix; Exposure time and exposure energy of each color plate are extracted from the exposure comprehensive parameter matrix to generate a global exposure parameter set.
[0010] As a preferred embodiment of the plate-making exposure parameter tuning method for the color sequence of pre-printed paperboard according to the present invention, the steps for generating the plate edge feature map are as follows: The edge detection algorithm is used to perform convolution calculation on the preprocessed layout image data to generate pixel gradient magnitude data; Threshold segmentation is performed on the pixel gradient magnitude data to extract edge pixel features and generate a layout edge feature map.
[0011] As a preferred embodiment of the plate-making exposure parameter tuning method for the color sequence of pre-printed paperboard according to the present invention, the steps for generating the microstructure partition mapping map are as follows: Perform region connectivity determination on the page edge feature map and output a connected region label matrix; Geometric features of each connected region are identified based on the connected region labeling matrix to generate a region feature dataset. Based on the regional feature dataset, regions with fine lines, tiny dots, and extreme dot density are selected to generate a set of candidate regions for microstructures. The set of candidate microstructure regions is mapped to a preset layout image coordinate system to generate a microstructure partition mapping map.
[0012] In a preferred embodiment of the plate-making exposure parameter tuning method for the color sequence of pre-printed paperboard described in this invention, the steps for generating the exposure parameter mapping diagram are as follows: The global exposure parameter set and the microstructure partition mapping map are fused together by coordinate alignment to generate an exposure partition fusion dataset. Based on the exposure zone fusion dataset, the exposure correction strategy for the corresponding region is matched from the preset local exposure adjustment strategy library to generate an exposure correction strategy set; Based on the exposure correction strategy set, the exposure parameters of the microstructure region in the exposure partition fusion dataset are dynamically corrected and calculated to generate a microstructure correction exposure parameter set. The exposure parameter set for microstructure correction is integrated with the exposure parameters for the normal region to generate an exposure parameter mapping map.
[0013] In a preferred embodiment of the plate-making exposure parameter tuning method for the color sequence of pre-printed paperboard according to the present invention, the steps for obtaining a qualified paperboard sample are as follows: Based on the exposure parameter mapping, the CTP equipment is controlled to perform zoned exposure plate making operation to generate exposed printing plates, and the exposed printing plates are used to perform trial printing operations to obtain pre-printed paperboard samples. Image processing algorithms are used to acquire and extract features from the microstructure regions in the preprinted paperboard sample to obtain microstructure reproduction image data. Based on the microstructure reconstruction image data, quality analysis of the microstructure region is performed to generate microstructure reconstruction quality assessment results; The quality of the pre-printed paperboard samples is determined based on the microstructure reproduction quality assessment results. When the preset paperboard quality standards are met, qualified paperboard samples are obtained.
[0014] In a second aspect, the present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, it implements any step of the plate-making exposure parameter tuning method for the color sequence of pre-printed paperboard as described in the first aspect of the present invention.
[0015] Thirdly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of the plate-making exposure parameter tuning method for the color sequence of pre-printed paperboard as described in the first aspect of the present invention.
[0016] The beneficial effects of this invention are as follows: By using the Neugebauer color overprinting model for spectral overprinting prediction, quantitative setting of exposure based on spectral response characteristics is achieved, making exposure control calculable and consistent for different color sequence combinations, thus improving the accuracy of overall color matching; by correcting the exposure of microstructure areas according to a preset local exposure adjustment strategy library, local exposure compensation for fine lines, tiny dots, and high-density structures is achieved, improving the reproduction quality of local details on the printing plate and ensuring the imaging stability of the final paperboard sample. Attached Figure Description
[0017] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0018] Figure 1 A flowchart of the plate-making exposure parameter adjustment method for the color sequence of pre-printed paperboard.
[0019] Figure 2 A flowchart for Neugebauer chromatic overprint prediction and exposure parameter calculation.
[0020] Figure 3 This is a flowchart for extracting edge features and identifying microstructure regions on the page.
[0021] Figure 4A flowchart for CTP zone exposure plate making, printing verification, and quality judgment. Detailed Implementation
[0022] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.
[0023] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.
[0024] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.
[0025] Reference Figures 1-4 This is one embodiment of the present invention, which provides a method for adjusting the plate-making exposure parameters of the color sequence of pre-printed paperboard, including the following steps: S1. Collect ink spectral data, target color sequence data, and plate image data, and perform preprocessing.
[0026] S1.1 Ink spectral data includes reflectance curves and optical parameters of each ink color.
[0027] It should be noted that the reflectance curve is a continuous spectral data obtained by measuring the reflectance of each color ink in the visible light band (e.g., 400-700nm) using a spectrophotometer; the ink optical parameters are quantitative indicators obtained by analyzing the reflectance curve and combining it with laboratory tests, including the ink's optical density, color intensity, and transparency.
[0028] S1.2 Target color sequence data includes the order of printing colors, standard color values, and standard spectral curves.
[0029] It should be noted that the order of printing colors includes the sequence in which each ink is applied to the printing plate during the printing process, obtained from the plate design documents and process specifications; the standard color values are quantitative values in the CIE Lab color space obtained by measuring the standard color card with a spectrophotometer or according to the color standard provided by the customer; the standard spectral curve is spectral reflectance data obtained by scanning the standard color card or the color standard provided by the customer with a spectrophotometer wavelength by wavelength, describing the reflectance characteristics of the color at different wavelengths in the visible light band (such as 400–700nm), reflecting the color's ability to reflect light at each wavelength.
[0030] S1.3 Page layout image data refers to the page layout dot matrix information.
[0031] It should be noted that the layout bitmap information is binary bitmap data generated by interpreting the original layout file through a raster image processor, containing the position and size information of each dot.
[0032] S1.4 preprocessing includes denoising, normalization, and image grayscale conversion.
[0033] It should be noted that denoising processing refers to filtering random interference signals in the collected ink spectral data and page image data, eliminating high-frequency noise introduced by the measurement environment through digital filtering algorithms, and improving the signal-to-noise ratio of the data; normalization processing refers to scaling the numerical range of the denoised ink spectral data and page image data, converting data of different dimensions to a standard numerical range through linear scaling, and eliminating the influence of dimensions; image grayscale processing refers to color space conversion processing of the collected page image data, converting RGB color information into single-channel grayscale values through a weighted average method, reducing the dimensionality of the data and highlighting contour features.
[0034] S2. Use the Neugebauer color overprinting model to perform spectral overprinting prediction on the preprocessed ink spectral data and target color sequence data, generate predicted spectral curves, calculate exposure parameters, and generate a global exposure parameter set.
[0035] It should be noted that the Neugebauer chromatic overprinting model undergoes preprocessing. Specifically, multiple sets of ink spectral samples for training are collected, including test color patches prepared on standard printing materials using single-color, two-color, three-color, and four-color overprinting methods. The corresponding measured spectral reflectance curves are obtained by scanning each wavelength using a spectrophotometer, while simultaneously recording the corresponding area coverage combinations for each overprinted color patch. The measured spectral reflectance curves and area coverage combinations are proportionally divided into training and validation sets. In the training set, each measured spectral reflectance curve is normalized according to wavelength order, scaling the spectral reflectance values to the [0,1] interval. The area coverage combinations are then encoded according to color order and used as input to the Neugebauer chromatic overprinting model. The normalized measured spectral reflectance curves and area coverage combinations are then used as input to the model. The coverage combination encoding is input into the Neugebauer chromaticity overprinting model. By calculating the error between the predicted spectral curve output by the Neugebauer chromaticity overprinting model and the measured spectral reflectance curve, the AdamW optimizer and smoothed L1 loss function are used to perform gradient updates on the parameters of the Neugebauer chromaticity overprinting model (primary color reflectance coefficient, secondary overprinting coefficient, and area coverage estimation factor), gradually optimizing each parameter to make the predicted spectral curve closer to the measured spectral reflectance curve. Forward inference is periodically performed on the validation set, and the validation error of the Neugebauer chromaticity overprinting model on different overprinting combinations is recorded. When the validation error decreases slowly in several consecutive iterations and the validation set error does not decrease significantly further, the pre-training process is terminated, and the trained Neugebauer chromaticity overprinting model is obtained.
[0036] S2.1 Input the preprocessed ink spectral data and target color sequence data into the Neugebauer color overprinting model for data fusion calculation to obtain the spectral input matrix.
[0037] Furthermore, in the preprocessed ink spectral data, a spectral reflectance array is established for each ink according to color order. In the preprocessed target color order data, a color order index table is established according to the order of printing colors. Each color order combination in the color order index table is matched one-to-one with the spectral reflectance array of each ink. The spectral reflectance values of each ink at discrete wavelength positions under the corresponding combination are sequentially filled into the data row. All data rows are stacked according to color order combination and wavelength order to form a spectral input matrix in which the row direction represents the color order combination and the column direction represents the wavelength sampling point.
[0038] S2.2 performs Neugebauer chromaticity overprinting simulation based on the spectral input matrix to generate a predicted spectral curve.
[0039] Furthermore, using the spectral input matrix as input, the spectral reflectance values of each ink in each color sequence combination are weighted and summed according to the preset area coverage weight in the Neugebauer color overprinting model. The weighted summation operation is repeated at all wavelength sampling points to obtain the overprinted spectral reflectance values of the corresponding color sequence combination at each wavelength position. All overprinted spectral reflectance values are arranged in wavelength order to generate a predicted spectral curve.
[0040] The expression for Neugebauer's chromatic overprinting simulation is: ; in, It is a color sequence combination at wavelength The overprinted spectral reflectance value; It is the first ink at wavelength Spectral reflectance at that location; It is the first The area coverage weight of each type of ink; It is an ink label index; These are discrete wavelength sampling points; This indicates the amount of ink used in the calculation for the overprinting combination; It should be noted that the area coverage weight refers to the weight used in the Neugebauer color overprinting model to represent the proportion of halftone area occupied by each color ink in the overprinting combination. During the setting process, pixel statistics are performed on the RIP-processed image data to calculate the halftone area ratio of each ink at the same position. The halftone area ratio of all inks is then normalized according to the overprinting rules so that the area coverage weight is distributed between 0 and 1, and the sum of the area coverage weights of all inks in the same overprinting combination is fixed at 1.
[0041] S2.3 Extract the standard spectral curve from the preprocessed target color sequence data, and combine it with the predicted spectral curve to calculate the wavelength-by-wavelength difference, generating a spectral deviation vector.
[0042] Furthermore, standard spectral curves are read from the preprocessed target color sequence data. Standard spectral reflectance values are extracted at each wavelength sampling point and arranged into a standard spectral array in wavelength order. The standard spectral array and the predicted spectral curve are matched one-to-one at the same wavelength position. The difference between the spectral reflectance value of the predicted spectral curve at each wavelength position and the spectral reflectance value of the standard spectral array at the corresponding position is calculated and arranged in wavelength order to generate a spectral deviation vector.
[0043] It should be noted that a standard spectral curve is a discrete spectral data curve used to characterize the spectral reflectance distribution of a target color at different wavelengths of visible light. It is generated by scanning and measuring standard color samples or target color samples provided by customers in the visible light band wavelength by wavelength using a spectrophotometer, recording the spectral reflectance value at each wavelength position, and arranging them in wavelength order.
[0044] S2.4 Based on the spectral deviation vector, the exposure response relationship of each color ink is calculated in reverse to obtain the exposure adjustment parameter set.
[0045] Furthermore, the spectral deviation vector is assigned to each ink used in the corresponding color sequence combination according to the color sequence position, and the spectral reflectance change data of each ink under different exposure energies recorded in the preset exposure response relationship is read. For each ink, a record with a spectral deviation value close to the value is found at each wavelength position, and the exposure energy adjustment value at the wavelength position is obtained by linear calculation through the exposure energy values of adjacent records. The exposure energy adjustment value is calculated repeatedly for all wavelength positions in the spectral deviation sequence, and the weighted average method is used to synthesize all exposure energy adjustment values of each ink under the corresponding color sequence combination, and the values are arranged in order of color sequence position to generate an exposure adjustment parameter set.
[0046] The expression for the linear interpolation calculation of the exposure energy adjustment is: ; in, It is the first ink at wavelength The corresponding exposure energy adjustment amount; At wavelength Spectral deviation at that location; and This indicates that it is the first in the exposure response relationship. Record of the pre-calibrated spectral deviation between two adjacent inks and The corresponding lower and higher spectral deviation values, respectively; and Indicates and and The corresponding exposure energy record value; It should be noted that the exposure response relationship is a quantitative correspondence between exposure energy and spectral deviation established for each type of ink. The setting process is as follows: select several representative ink color blocks on a predetermined printing substrate, and perform plate making and test printing under different exposure energy conditions in sequence, recording the corresponding ink printing samples under each exposure energy; use a spectrophotometer to measure the spectrum of the ink printing samples under each exposure energy condition, and calculate the spectral deviation value of each exposure energy relative to the standard spectral curve; pair the exposure energy value with the spectral deviation value one by one to form multiple data points corresponding to the exposure energy and spectral deviation; perform interpolation processing on all data points according to ink type to generate a mapping data table between the exposure energy and spectral deviation of each ink, which constitutes the overall exposure response relationship.
[0047] S2.5 fuses the exposure adjustment parameter set with the preset basic exposure parameters to generate a comprehensive exposure parameter matrix.
[0048] Furthermore, the basic exposure time and basic exposure energy of each ink under standard process conditions are read from the preset basic exposure parameters. The exposure adjustment parameters are set to the corresponding ink's exposure energy increment or decrement, and numerical calculations are performed with the ink's basic exposure time and basic exposure energy according to a predetermined proportional correction rule to obtain the corrected exposure time parameters and corrected exposure energy parameters of the ink under the color sequence combination. The corrected exposure time parameters and corrected exposure energy parameters of each ink under each color sequence combination are integrated according to the color plate identification and color sequence arrangement order to form an exposure comprehensive parameter matrix that distinguishes the color plate and color sequence position by rows and columns.
[0049] It should be noted that the basic exposure parameters are the reference exposure time and exposure energy used for each color plate. They are obtained by printing test plates made under different exposure conditions and performing optical measurements. The differences between each exposure condition and the target density and target color difference are compared, and the exposure time and exposure energy are gradually adjusted to select the exposure conditions that can stably meet the target color requirements as the basic exposure parameters. The proportional correction rule refers to the calculation rule that numerically superimposes the exposure time and exposure energy according to the fixed proportional relationship between the exposure adjustment amount and the basic exposure parameters in the exposure adjustment. It is obtained by comparing the influence of the exposure adjustment amount on the color difference change under multiple sets of test exposure conditions and selecting the proportional coefficient that is closest to the linear correspondence with the color difference change.
[0050] S2.6 extracts the exposure time and exposure energy of each color plate from the exposure comprehensive parameter matrix to generate a global exposure parameter set.
[0051] Furthermore, in the exposure comprehensive parameter matrix, the corrected exposure time parameter and corrected exposure energy parameter corresponding to each color plate are retrieved one by one according to the color plate identifier and formed into parameter pairs. At the same time, the parameters are arranged and combined in sequence according to the color sequence of the pre-printed paperboard to generate a global exposure parameter set.
[0052] S3. Use edge detection algorithms to perform image analysis on the preprocessed page image data, generate page edge feature maps, and perform regional connectivity judgment and structure classification on the page edge feature maps to identify microstructure regions in the page image and generate microstructure partition mapping maps.
[0053] S3.1 uses an edge detection algorithm to perform convolution calculation on the preprocessed layout image data to generate pixel gradient magnitude data.
[0054] Furthermore, the preprocessed layout image data is used as input, and convolution operations are performed pixel by pixel using the convolution kernel of the edge detection algorithm. During the convolution operation, the neighboring pixel values are extracted for each pixel position, and the weighted summation of the convolution kernel is performed. The convolution results in the horizontal and vertical directions are recorded as horizontal gradient values and vertical gradient values, respectively. The sum of the squares of the horizontal and vertical gradient values is calculated for each pixel position, and the square root is taken at the same pixel position to obtain the gradient magnitude of each pixel position. The gradient magnitudes of all pixel positions are arranged in the order of the pixel row and column coordinates of the preprocessed layout image data to generate pixel gradient magnitude data.
[0055] S3.2 performs threshold segmentation on the pixel gradient magnitude data, extracts edge pixel features, and generates a layout edge feature map.
[0056] Furthermore, the gradient magnitude of each pixel in the pixel gradient magnitude data is compared with a preset gradient magnitude threshold. When the gradient magnitude is greater than the gradient magnitude threshold, the pixel is marked as an edge pixel, and when the gradient magnitude is less than or equal to the gradient magnitude threshold, the pixel is marked as a non-edge pixel. The edge marking results of all pixel positions are combined according to the pixel row and column coordinate order of the preprocessed layout image data to generate a layout edge feature map.
[0057] It should be noted that the gradient magnitude threshold is a fixed value used to distinguish edge pixels from non-edge pixels. It is set based on the pixel gradient magnitude distribution obtained after convolution calculation of the preprocessed layout image data. The threshold is selected by statistically analyzing the gradient magnitude histogram of the entire image to determine the boundary between the concentrated distribution area and the high gradient area. An exemplary value range is 5%–20% of the full gradient magnitude range. If it is higher than 20%, a large number of real edge pixels will be judged as having insufficient gradient magnitude, causing edge pixels to be missed. If it is lower than 5%, a large number of non-edge pixels will also be judged as edges, causing excessive expansion of the edge area.
[0058] S3.3 performs a region connectivity determination on the edge feature map of the page and outputs a connected region label matrix.
[0059] Furthermore, the edge feature map of the page is read pixel by pixel in a pixel-by-pixel scanning order from top to bottom and from left to right. For the positions marked as edge pixels, the row and column coordinate values of the pixels are read. The edge marking status of the pixels above, to the left, to the upper left, and to the upper right of the adjacent pixels is checked respectively. When there are edge markings in adjacent pixels, the connected region numbers of the pixel and the adjacent pixels are merged and recorded as the same connected region number. When there are no edge markings in all adjacent pixels, a new connected region number is assigned to the pixel. At the same time, the connected region numbers of all pixel positions are recorded and arranged in the order of pixel row and column coordinates of the page image data to generate a connected region marking matrix.
[0060] S3.4 Based on the connected region labeling matrix, geometric features of each connected region are identified to generate a region feature dataset.
[0061] Furthermore, the connected region label matrix is grouped according to the connected region number. All pixel coordinates corresponding to each connected region number are collected into a pixel coordinate set. Within each pixel coordinate set, the minimum row coordinate, maximum row coordinate, minimum column coordinate, and maximum column coordinate of the boundary pixels are calculated in row and column coordinate order. At the same time, the number of pixels belonging to each pixel coordinate set is counted as the region area value. The pixel coordinates of the boundary positions are checked row by row and column by column to form the contour point sequence of the connected region. The horizontal span value and vertical span value of the connected region are calculated according to the row and column span in the pixel coordinate set. At the same time, the directional change between points in the contour point sequence is calculated to extract the shape and direction attributes of the connected region. The region area value, horizontal span value, vertical span value, contour point sequence, and shape and direction attributes are combined in the order of connected region number to generate a region feature dataset.
[0062] S3.5 filters regions with fine lines, tiny dots, and extreme dot density based on the regional feature dataset, generating a set of candidate regions for microstructures.
[0063] Furthermore, in the regional feature dataset, the area value, horizontal span value, vertical span value, contour point sequence, and shape direction attribute of each connected region are read one by one according to the connected region number. The smaller value of the horizontal span value and the vertical span value is compared with a preset thin line width threshold. If the smaller value is lower than the thin line width threshold, the connected region number is registered as a thin line region number. The area value of the region is compared with a preset micro-dot area threshold. If the area value of the region is lower than the micro-dot area threshold, the region number is registered as a micro-dot region number. Pixel density statistics are performed on the contour point sequence according to a fixed grid division method. If the number of edge pixels in a certain grid region is higher than a preset extreme dot density threshold, the region number is registered as an extreme dot density region number. After all region numbers have been determined, all connected region numbers registered as thin line region numbers, micro-dot region numbers, and extreme dot density region numbers are combined to generate a set of microstructure candidate regions.
[0064] It should be noted that the fine line width threshold is a numerical parameter used to distinguish between normal lines and fine line areas. It is set based on the minimum line width range that can be stably reproduced during common pre-printed paperboard plate-making processes. Continuous line width data is obtained by microscopic measurement of line elements in representative sample prints. After recording the imaging integrity corresponding to different line widths in multiple exposure tests, the maximum line width that can be stably and accurately reproduced is selected as the upper limit threshold. An exemplary value range is 20~80 micrometers. Values above 80 micrometers will misjudge some normal lines as fine line areas, while values below 20 micrometers will cause some truly fine lines to be unrecognizable. The micro dot area threshold is an area parameter used to distinguish between micro dots and normal dot areas. It is set based on the minimum formable area of dots under CTP plate-making conditions. This is determined by statistically analyzing the dot matrix data generated by RIP. The pixel distribution of a single dot is set based on microscopic observation of the dot formation in the test print samples. An exemplary value range is 3 to 20 pixels. When the value is higher than 20 pixels, larger dots will be incorrectly classified as small dot areas. When the value is lower than 3 pixels, real small dot areas will be missed. The extreme dot density threshold is used to identify areas with significantly high local dot distribution density. The setting is based on dividing the edge feature map of the page into a fixed grid (e.g., a 5×5 pixel grid) and counting the number of edge pixels in each grid area. At the same time, the density range that best reflects abnormal aggregation is selected based on the dot aggregation curves of a large number of page samples. An exemplary value range is 15 to 40 pixels. When the value is higher than 40 pixels, the dot aggregation area will not be identified. When the value is lower than 15 pixels, the normal dot density area will be misjudged as an extreme aggregation area.
[0065] S3.6 maps the set of candidate microstructure regions to a preset layout image coordinate system to generate a microstructure partition mapping map.
[0066] Furthermore, all pixel coordinates in each pixel coordinate set are extracted from the microstructure candidate region set, and repositioned according to the row and column coordinates of the preset layout image coordinate system. At the same time, microstructure marker values are written at the corresponding row and column coordinate positions (e.g., using the value 1 to represent a pixel in a microstructure region and using the value 0 to represent a pixel in a non-microstructure region). The pixel positions in the layout image coordinate system that have been marked with microstructure marker values are uniformly regarded as microstructure region positions, and the pixel positions that have not been marked with microstructure marker values are regarded as non-microstructure region positions. The marking results of all pixel positions are combined into a matrix structure according to the row and column order of the layout image coordinate system, and the microstructure partition mapping map is output.
[0067] It should be noted that the page layout image coordinate system refers to a two-dimensional pixel coordinate framework established according to the row and column order of the page layout image data. It is a coordinate system directly constructed based on the resolution of the generated bitmap and the pixel arrangement after the RIP performs bitmap processing on the page layout content.
[0068] S4. The global exposure parameter set is fused with the microstructure partition mapping map, and the exposure of the microstructure region is corrected according to the preset local exposure adjustment strategy library to generate an exposure parameter mapping map.
[0069] S4.1 performs coordinate alignment and fusion of the global exposure parameter set and the microstructure partition mapping map to generate an exposure partition fusion dataset.
[0070] Furthermore, for each pixel, the corresponding exposure time parameter and exposure energy parameter are extracted from the global exposure parameter set according to the color plate relationship, and the microstructure marker value in the microstructure partition mapping map is read pixel by pixel; the exposure time parameter, exposure energy parameter and microstructure marker value are recorded at each pixel position, and combined into a fusion data record according to the pixel row and column coordinate order. All fusion data records are combined according to the row and column order of the page image coordinate system to generate an exposure partition fusion dataset.
[0071] S4.2 Based on the exposure partition fusion dataset, match the exposure correction strategy for the corresponding region from the preset local exposure adjustment strategy library to generate an exposure correction strategy set.
[0072] Furthermore, based on the microstructure marker values in the exposure partition fusion dataset, it is determined whether the pixel location belongs to a microstructure region (e.g., a microstructure marker value of 1 indicates a microstructure region, and a microstructure marker value of 0 indicates a non-microstructure region). Combining the microstructure type markers (e.g., fine line structure, micro dot structure, and extreme dot density structure) in the microstructure candidate region set, when the pixel belongs to a microstructure region, the corresponding exposure correction strategy is matched from the preset local exposure adjustment strategy library according to the microstructure type (e.g., fine line structure, micro dot structure, and extreme dot density structure). When the pixel belongs to a non-microstructure region, the default exposure strategy is recorded, and the exposure correction strategy is combined with the row and column coordinates of the pixel location to form a strategy record. All strategy records are integrated to generate an exposure correction strategy set.
[0073] It should be noted that the local exposure adjustment strategy library is a collection of strategies for correcting the exposure of microstructure regions. It is a parameter rule library established based on multiple exposure experiments on fine line structures, micro dot structures and extreme dot density structures and recording the imaging performance under different exposure conditions. It includes fine line exposure compensation strategies, micro dot energy enhancement strategies and extreme dot density energy suppression strategies. For example, the fine line exposure compensation strategy is implemented using the following parameterization: ; in, It is the exposure time correction amount; It is the compensation coefficient for fine lines; It is the threshold for the width of the fine lines; It is a pixel The actual measured line width at the location; It should be noted that the fine line compensation coefficient is determined by preparing test patterns containing different line widths, setting a fixed exposure time gradient (e.g., ±30%) based on the baseline exposure time, conducting plate-making experiments, measuring the edge sharpness index under each line width condition, and determining the optimal compensation coefficient through linear regression analysis. The fine line width threshold is a numerical parameter used to distinguish between ordinary lines and fine line areas. Based on the minimum line width range that can be stably reproduced during the pre-printed paperboard plate-making process, the maximum line width that can be stably and accurately reproduced is selected as the upper limit threshold after microscopic measurement of the line elements of representative plate samples and recording the imaging integrity through multiple exposure experiments. An exemplary value range is 20-80 micrometers to avoid misjudging normal lines when the value is higher than 80 micrometers and missing true fine lines when the value is lower than 20 micrometers.
[0074] S4.3 Based on the exposure correction strategy set, dynamically correct the exposure parameters of the microstructure region in the exposure partition fusion dataset, and generate a microstructure correction exposure parameter set.
[0075] Furthermore, based on the exposure correction strategy set, the exposure time correction and exposure energy correction for each pixel position are calculated using a linear correction method. The exposure time parameters and exposure energy parameters for the corresponding pixel positions in the exposure partition fusion dataset are numerically adjusted. The adjusted exposure time parameters and exposure energy parameters are combined with the row and column coordinates of the pixel position to generate a microstructure correction exposure parameter set.
[0076] The expressions for calculating the exposure time and exposure energy parameters at each pixel location are as follows: ; ; in, pixels in the layout image coordinate system The exposure time parameters after position correction; pixels in the layout image coordinate system The base exposure time parameter for the location; The pixels are calculated based on the exposure correction strategy. Position exposure time correction; It is the exposure time correction factor; pixels in the layout image coordinate system Exposure energy parameters after position correction; Pixels in the layout image coordinate system The base exposure energy parameters for the location; The pixels are calculated based on the exposure correction strategy. Position exposure energy correction; It is the exposure energy correction factor; It should be noted that, This involves conducting multiple exposure time perturbation experiments on representative fine line areas under standard plate-making conditions, recording the line width offset under different exposure times, and selecting the most stable adjustment coefficient that allows the line width offset to change within a controllable range. An example value is 10. This involves conducting graded energy perturbation experiments on areas with small and high-density dots to analyze the impact of exposure energy changes on dot area shift and local density shift, and selecting the optimal adjustment coefficient that maintains a linear response relationship between the two shifts. An example value of 12 is used.
[0077] S4.4 integrates the microstructure correction exposure parameter set with the exposure parameters of the normal region to generate an exposure parameter mapping map.
[0078] Furthermore, based on the microstructure-corrected exposure parameter set, the corrected parameter records are read according to the pixel row and column coordinates and the corrected exposure time parameters and exposure energy parameters are written into the corresponding positions in the page image coordinate system. The microstructure marker values in the exposure partition fusion dataset represent the positions of non-microstructure regions. The original exposure time parameters and exposure energy parameters are read and written into the page image coordinate system to generate an exposure parameter mapping map.
[0079] S5. Based on the exposure parameter mapping diagram, perform CTP zone exposure plate making and printing verification, detect the reproduction quality of microstructure areas, and obtain qualified paperboard samples.
[0080] S5.1 Controls the CTP equipment to perform zoned exposure plate-making operation according to the exposure parameter mapping diagram, generates exposed printing plates, and uses the exposed printing plates to perform trial printing operations to obtain pre-printed paperboard samples.
[0081] Furthermore, based on the exposure parameter mapping map, the exposure time and exposure energy parameters are obtained pixel by pixel in the row and column coordinate order of the image coordinate system and loaded into the CTP device. This allows the CTP device to perform zoned exposure operations according to pixel position during the plate-making process to control the exposure amount of different areas. After exposure is completed, the exposed printing plate is removed and loaded into the printing equipment. In the printing equipment, the inking, imprinting, and paper feeding steps are completed sequentially according to the standard printing process to obtain a pre-printed paperboard sample.
[0082] S5.2 uses image processing algorithms to acquire images and extract features from the microstructure regions in the pre-printed paperboard sample, obtaining microstructure reproduction image data.
[0083] Furthermore, the pre-printed paperboard sample is placed in an image acquisition device under constant illumination. A high-resolution imaging device scans the entire surface of the pre-printed paperboard sample line by line to acquire image data covering the entire surface area. Based on the pixel row and column coordinates of the microstructure regions recorded in the microstructure partitioning map, corresponding microstructure region image blocks are extracted from the pre-printed paperboard sample image data according to their coordinate positions. For each microstructure region image block, image grayscale processing, local contrast enhancement processing, and geometric distortion correction processing are sequentially performed to generate standardized microstructure region images. Image processing algorithms are applied to the standardized microstructure region images to perform edge detection, binarization segmentation, and connected component extraction, extracting pixel sets of line contours, dot boundaries, and local dot clusters. Furthermore, line width, dot area, dot shape, and local density distribution feature parameters are calculated to generate a microstructure reproduction feature parameter set. The standardized microstructure region image corresponding to each microstructure region is combined with the microstructure reproduction feature parameter set according to the row and column coordinate order of the microstructure region to obtain microstructure reproduction image data.
[0084] It should be noted that the image processing algorithm is a set of computational methods that perform grayscale processing, edge detection processing, region segmentation processing, and connected component extraction processing on image data. It is used to identify the line contours, dot boundaries, and local aggregation patterns of microstructure regions from the image data of pre-printed paperboard samples. Specifically, it sequentially performs pixel grayscale value calculation, gradient extraction based on convolution kernel, threshold segmentation, and region connectivity analysis on the cropped microstructure region image to quantize and extract the line width, dot area, dot shape, and local density distribution features from the image.
[0085] S5.3 performs quality analysis on microstructure regions based on microstructure reconstruction image data and generates microstructure reconstruction quality assessment results.
[0086] Furthermore, in the microstructure reconstruction image data, line width offset, dot area offset, dot shape offset, and local density distribution offset are calculated according to the microstructure type. Each offset is compared with the corresponding preset line width offset threshold, dot area offset threshold, dot shape offset threshold, and local density distribution offset threshold. When the offset falls within the corresponding offset threshold range, it is recorded as a structure item that meets the quality requirements. When the offset exceeds the corresponding offset threshold range, it is recorded as a structure item that does not meet the quality requirements. The structure item results of all microstructure regions are statistically analyzed in row and column coordinate order to form a quality judgment record of microstructure reconstruction quality. The quality judgment records are then integrated to generate a microstructure reconstruction quality evaluation result.
[0087] It should be noted that the line width offset threshold is based on the microscopic width measurement data and statistical results of line edge sharpness of line test prints obtained under multiple exposure times and exposure energy conditions. The maximum acceptable offset is set by analyzing the offset distribution of line width under different exposure conditions. An exemplary value range is 2-10 micrometers. Values above 10 micrometers will cause lines that have already shown significant widening or breakage trends to still be recorded as normal, while values below 2 micrometers will cause lines within the normal texture fluctuation range to be misjudged as having quality abnormalities. The dot area offset threshold is based on the difference distribution between the measured RIP dot matrix area and the actual area of the test print dots under different exposure energy, halftone angle, and dot percentage conditions. Combined with dot gain curves and dot collapse curves, the acceptable range for area offset is determined. An exemplary value range is 1-8 pixels. Values above 8 pixels will cause dots with significant area changes to be judged as normal, while values below 1 pixel will result in normal dots being judged as abnormal. Fluctuating halftone dots are misjudged as abnormal. The halftone dot shape offset threshold is determined by statistically analyzing the aspect ratio, roundness, and edge curvature of halftone dots collected under different exposure conditions. The allowable shape fluctuation range is determined by fitting and analyzing the shape offset of multiple sets of halftone dot deformation samples. An exemplary value range is 0.02~0.10 (shape offset ratio). A value higher than 0.10 will cause severely deformed halftone dots to be judged as normal, while a value lower than 0.02 will cause halftone dots with slight shape fluctuations to be misjudged as abnormal. The local density distribution offset threshold is determined by statistically analyzing the number of pixels at the edge of halftone dots under a fixed grid division method and combining it with the local density change distribution map in the test print sample. The density offset range is set by analyzing the pixel difference distribution between high-density areas and normal density areas. An exemplary value range is 5~20 pixels. A value higher than 20 pixels will cause abnormal clustering phenomena to be judged as normal, while a value lower than 5 pixels will cause normal density fluctuation areas to be misjudged as clustering abnormalities.
[0088] S5.4 Based on the microstructure reproduction quality assessment results, the quality of the pre-printed paperboard sample is determined. When the preset paperboard quality standard is met, a qualified paperboard sample is obtained.
[0089] Furthermore, the quality judgment records corresponding to each microstructure area are checked one by one according to the microstructure reproduction quality assessment results, and all quality judgment records are compared with the preset paperboard quality standards. If the quality judgment record of any microstructure area does not meet the paperboard quality standards, the pre-printed paperboard sample is marked as a non-compliant paperboard sample. If the quality judgment records of all microstructure areas meet the paperboard quality standards, the pre-printed paperboard sample is marked as a compliant paperboard sample.
[0090] It should be noted that the paperboard quality standard is based on the statistical range of line width offset, dot area offset, dot shape offset, and local density distribution offset of a large number of pre-printed paperboard samples. The comprehensive quality judgment standard is obtained by measuring the image integrity, edge sharpness, and dot stability item by item, and selecting the offset range that can stably meet the printing color sequence requirements and has no defects that can be seen by the naked eye as the threshold combination setting.
[0091] This embodiment also provides a computer device applicable to the plate-making exposure parameter setting method for the color sequence of pre-printed paperboard, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the plate-making exposure parameter setting method for the color sequence of pre-printed paperboard as proposed in the above embodiment.
[0092] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.
[0093] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements the plate-making exposure parameter tuning method for achieving the color sequence of pre-printed paperboard as proposed in the above embodiments. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.
[0094] In summary, this invention achieves quantitative setting of exposure based on spectral response characteristics by using the Neugebauer color overprinting model for spectral overprint prediction, making exposure control calculable and consistent for different color sequence combinations, thus improving the accuracy of overall color matching; and by correcting the exposure of microstructure areas according to a preset local exposure adjustment strategy library, it achieves local exposure compensation for fine lines, tiny dots, and high-density structures, improving the reproduction quality of local details on the printing plate and ensuring the imaging stability of the final paperboard sample.
[0095] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.
Claims
1. A method for setting plate-making exposure parameters for the color sequence of pre-printed paperboard, characterized in that: include, Collect ink spectral data, target color sequence data, and plate image data, and perform preprocessing; The Neugebauer chromaticity overprinting model is used to perform spectral overprinting prediction on the preprocessed ink spectral data and target color sequence data, generate predicted spectral curves, calculate exposure parameters, and generate a global exposure parameter set. Image analysis is performed on the preprocessed page image data using edge detection algorithms to generate page edge feature maps. The page edge feature maps are then used to determine regional connectivity and classify structures to identify microstructure regions in the page image and generate microstructure partitioning maps. The global exposure parameter set is fused with the microstructure partition mapping map, and the exposure of the microstructure region is corrected according to the preset local exposure adjustment strategy library to generate an exposure parameter mapping map. Based on the exposure parameter mapping, CTP zone exposure plate making and printing verification are performed to detect the reproduction quality of microstructure areas and obtain qualified paperboard samples.
2. The method for setting plate-making exposure parameters for the color sequence of pre-printed paperboard as described in claim 1, characterized in that: The ink spectral data includes the reflectance curves of each color ink and the ink optical parameters; The target color sequence data includes the printing color order, standard color values, and standard spectral curves. The page layout image data refers to the page layout dot matrix information; The preprocessing includes denoising, normalization, and image grayscale conversion.
3. The method for setting plate-making exposure parameters for the color sequence of pre-printed paperboard as described in claim 1, characterized in that: The steps for generating the predicted spectral curve are as follows: The preprocessed ink spectral data and target color sequence data are input into the Neugebauer color overprinting model for data fusion calculation to obtain the spectral input matrix. The Neugebauer chromaticity overprinting simulation is performed based on the spectral input matrix to generate a predicted spectral curve.
4. The method for setting plate-making exposure parameters for the color sequence of pre-printed paperboard as described in claim 1, characterized in that: The steps for generating the global exposure parameter set are as follows: The standard spectral curve is extracted from the preprocessed target color sequence data and combined with the predicted spectral curve to calculate the wavelength-by-wavelength difference, generating a spectral deviation vector. The exposure response relationship of each color ink is calculated in reverse based on the spectral deviation vector to obtain the exposure adjustment parameter set; The exposure adjustment parameter set is fused with the preset basic exposure parameters to generate a comprehensive exposure parameter matrix; Exposure time and exposure energy of each color plate are extracted from the exposure comprehensive parameter matrix to generate a global exposure parameter set.
5. The method for setting plate-making exposure parameters for the color sequence of pre-printed paperboard as described in claim 1, characterized in that: The steps for generating the layout edge feature map are as follows: The edge detection algorithm is used to perform convolution calculation on the preprocessed layout image data to generate pixel gradient magnitude data; Threshold segmentation is performed on the pixel gradient magnitude data to extract edge pixel features and generate a layout edge feature map.
6. The method for setting plate-making exposure parameters for the color sequence of pre-printed paperboard as described in claim 1, characterized in that: The steps for generating the microstructure partitioning map are as follows: Perform region connectivity determination on the page edge feature map and output the connected region label matrix; Geometric features of each connected region are identified based on the connected region labeling matrix to generate a region feature dataset. Based on the regional feature dataset, regions with fine lines, tiny dots, and extreme dot density are selected to generate a set of candidate regions for microstructures. The set of candidate microstructure regions is mapped to a preset layout image coordinate system to generate a microstructure partition mapping map.
7. The method for setting plate-making exposure parameters for the color sequence of pre-printed paperboard as described in claim 1, characterized in that: The steps for generating the exposure parameter mapping are as follows: The global exposure parameter set and the microstructure partition mapping map are fused together by coordinate alignment to generate an exposure partition fusion dataset. Based on the exposure zone fusion dataset, the exposure correction strategy for the corresponding region is matched from the preset local exposure adjustment strategy library to generate an exposure correction strategy set; Based on the exposure correction strategy set, the exposure parameters of the microstructure region in the exposure partition fusion dataset are dynamically corrected and calculated to generate a microstructure correction exposure parameter set. The exposure parameter set for microstructure correction is integrated with the exposure parameters for the normal region to generate an exposure parameter mapping map.
8. The method for setting plate-making exposure parameters for the color sequence of pre-printed paperboard as described in claim 1, characterized in that: The steps for obtaining qualified cardboard samples are as follows: Based on the exposure parameter mapping, the CTP equipment is controlled to perform zoned exposure plate making operation to generate exposed printing plates, and the exposed printing plates are used to perform trial printing operations to obtain pre-printed paperboard samples. Image processing algorithms are used to acquire and extract features from the microstructure regions in the preprinted paperboard sample to obtain microstructure reproduction image data. Based on the microstructure reconstruction image data, quality analysis of the microstructure region is performed to generate microstructure reconstruction quality assessment results; The quality of the pre-printed paperboard samples is determined based on the microstructure reproduction quality assessment results. When the preset paperboard quality standards are met, qualified paperboard samples are obtained.
9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the plate-making exposure parameter setting method for the color sequence of pre-printed paperboard as described in any one of claims 1 to 8.
10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the plate-making exposure parameter setting method for the color sequence of pre-printed paperboard as described in any one of claims 1 to 8.