Digital printing image color detection method

Abstract

The invention discloses a digital printing image color detection method, and belongs to the field of image data processing and the technical field of image detection. The invention provides a printed image color detection method of an improved superpixel segmentation algorithm based on saliency and color information entropy, and the method comprises the steps: delimiting an important region of a printed image through employing an image saliency algorithm, and adaptively determining the size of a superpixel of the region according to the color information entropy of the printed image in a saliency region. Performing different superpixel division on the salient region and the non-salient region of the printed image; in the printed image color detection, the color mean values of the printed image under the same superpixel label are compared, and the label with chromatic aberration is output, so that a chromatic aberration area is marked in the image, and the color offset of the chromatic aberration area is displayed. The method is used for printing image color detection of superpixel color mean value comparison, and a more accurate color detection result conforming to human vision can be obtained; the method has a fast detection speed, and can output a color difference area position and a color offset.

Description

Technical Field

[0001] The invention belongs to the field of image data processing and image detection technology, and in particular relates to a method for detecting color of a digital printing image. Background Art

[0002] Printing image color detection methods are used to detect the color performance and color consistency of printed products, and are an important criterion for measuring the quality of printed products. Printing image color detection can provide timely feedback on color defects in the printing process, making it easier for output devices to make adjustments, thereby improving production efficiency. With the improvement of modern printing technology, the color richness of printed products is unprecedented. Traditional printing image color detection methods rely on manual and colorimeter detection equipment, which has many problems such as subjectivity, operational complexity, and low efficiency. An autonomous printing image color detection method that combines machine vision and image processing technology is crucial.

[0003] Research on color detection methods for printed images has made a lot of progress, which can be mainly divided into the following categories: 1. Extract the color histogram of the printed image in different color spaces, convert it into a feature vector and then compare it with the feature vector of qualified products; 2. Use the color histogram of the printed image as the feature input of the neural network, train the neural network to achieve automatic color detection; 3. Use the superpixel segmentation algorithm for the printed image, obtain the color mean of the superpixel and convert it into a one-dimensional vector and then compare the color difference. The first type of method can only qualitatively describe the possible color difference of the printed image. The color information of the printed image cannot reflect the spatial position relationship of the pixels, and the printed image with the wrong printing position cannot be detected; the neural network training cost proposed by the second type of method is high, which is suitable for images with small size, but not suitable for printed images; the third type of method only uses the superpixel segmentation algorithm to detect whether there is color difference in the printed image, and does not consider that the superpixel cannot well reflect the color information of the printed image in the complex color area. The above methods have not achieved good results for color detection of colorful printed images.

[0004] The SLIC superpixel segmentation algorithm is a simple linear iterative clustering algorithm that clusters pixels with high similarity in a region into the same superpixel to achieve image segmentation. Superpixels reflect local color information and better present the results of human eye observation of the region. The printed image is obtained by the SLIC algorithm with uniform and regular superpixel blocks, but in the color detection of printed images, different regions cannot be generalized. Smaller superpixel blocks are needed to divide the area with high regional contrast, so as to present the color information of the area in a detailed and reasonable manner.

[0005] Saliency is an indicator for evaluating the importance and stability of image regions. Using local contrast to detect image saliency is helpful in determining the area of ​​human visual attention. The results obtained by the image saliency detection algorithm are consistent with the observation of the human eye on the area of ​​image visual attention. Color information entropy is an indicator used to measure the uniformity of image color distribution and the amount of information. It is based on the concept of entropy in information theory and describes the color richness of an image by calculating the uncertainty or confusion of color distribution in the image. Summary of the invention

[0006] In view of the above-mentioned problems existing in the prior art, the purpose of the present invention is to provide a printed image color detection method based on an improved superpixel segmentation algorithm based on saliency and color information entropy; it is more consistent with the observation results of the human eye, takes into account the relationship between color and spatial position, has a faster detection speed, and simultaneously outputs the area where the printed image has color difference and the color offset.

[0007] The present invention provides the following technical solution: a method for detecting color of a digital printed image, comprising the following steps:

[0008] S1: Obtain a salient region map of the prepress image by using an image saliency detection algorithm based on convex hull prior, segment the foreground and background of the prepress image, and obtain a saliency mask map; image saliency detection related algorithms can be used in this application;

[0009] S2: Adaptively control the superpixel size based on the color information entropy of the salient area to optimize the original superpixel segmentation algorithm;

[0010] S3: Use the optimized superpixel algorithm to complete superpixel segmentation of the prepress image, and save the image pixel position and the corresponding superpixel label information;

[0011] S4: Use the sample image of the qualified printed product as the post-printing standard pattern, perform color correction on the post-printing standard pattern, and perform color correction and image registration on the post-printing test pattern;

[0012] S5: Obtain the pixel at the corresponding position of the printed image according to the superpixel label, calculate the color mean of each superpixel area in the Lab color space, calculate the color difference of the same superpixel block by the color difference formula, and output the superpixel label greater than the threshold;

[0013] S6: Draw the superpixel boundary of the corresponding position on the post-printing standard image according to the output superpixel label, and save the deviation between the superpixel label value and the corresponding area in the three components of L, a, and b as a file.

[0014] Furthermore, the specific process of S1 is as follows:

[0015] S1.1: Obtain a salient region map of the prepress image using an image saliency detection algorithm;

[0016] S1.2: Using the Otsu method, an image processing technique for automatically determining the image binarization threshold, the threshold is automatically calculated by minimizing the intra-class variance or maximizing the inter-class variance to distinguish the foreground and background of the prepress image, and a binary image of the salient area is obtained; the salient area of ​​the image is set to white, and the non-salient area of ​​the image is set to black.

[0017] Furthermore, the specific process of S2 is as follows:

[0018] S2.1: Extract the salient region M from the salient binary image obtained in step S1.2; use the grayscale information of the prepress image to replace the number of colors in the color space dimension for measurement, and obtain the color information entropy E in the salient region:

[0019]

[0020] Where, L h represents the number of gray levels contained in the image under the salient area, p i Represents the probability histogram at the i-th gray level;

[0021] S2.2: Set the superpixel area A of the salient region, and the expression for A is as follows:

[0022]

[0023] Where n is the number of pixels in the salient area, r is the number of horizontal pixels in the image, c is the number of vertical pixels in the image, and ω2 is the control weight of the superpixel size, which is used to adjust the influence of color information entropy. In superpixel segmentation, the superpixel size l in the non-salient area can change, and the salient area also changes accordingly. The following table is used to solve ω2:

[0024] ω2=5·l 2 .

[0025] Furthermore, the specific process of S3 is as follows:

[0026] S3.1: Use the improved superpixel segmentation algorithm to perform superpixel segmentation on the prepress image and construct the improved superpixel segmentation map G a (x,y);

[0027] G a (x,y)=(1-M(x,y))S na (x,y)+M(x,y)S ya (x,y)

[0028] Where S nais the superpixel segmentation map of the non-salient area, S ya is the superpixel segmentation map of the salient area; S(x,y) is the label of the superpixel area to which the pixel (x,y) belongs, S na (x,y), S ya (x, y) represent the pixel label values ​​in their respective regions; M(x, y) represents pixels in the salient region, and 1-M(x, y) represents pixels in the non-salient region;

[0029] S3.2: Use the simple linear clustering SLIC superpixel algorithm to obtain the pixel label S in the salient area ya (x,y); Set the superpixel size in the salient area to The cluster centers under the salient areas are Search for similar pixels in the range and compare the similarity between the pixels and the cluster center in the Lab color space;

[0030] S3.3: Use the method in step S3.2 to perform superpixel segmentation on the non-salient area and obtain the pixel label S under the salient area na (x, y); the superpixel size in the non-salient area is l, and the cluster center searches for similar pixels in the range of 2l×2l; the superpixel label value of the non-salient area is connected after the label value of the salient area.

[0031] Furthermore, in S3, when the size of the pre-press image is inconsistent with the size of the post-press image, the super-pixel label of the pre-press image is interpolated according to the pixel size of the post-press image, so that the super-pixel label can reflect each pixel of the post-press image; each pixel in the post-press image is assigned a corresponding label value; and the super-pixel color reflection effect of the post-press image is the same as the super-pixel color of the pre-press image.

[0032] Furthermore, the specific process of S5 is as follows:

[0033] S5.1: Segment the image G according to the superpixel obtained in S3 a (x, y), obtain the color mean of each superpixel in the Lab color space corresponding to the post-printing standard image in the superpixel segmentation image; construct the feature vector E of the post-printing standard image, and obtain the feature vector F of the post-printing image to be tested in the same way. The vectors E and F are composed of multidimensional vectors, expressed as E = [E1, E2, ..., E i ] and F=[F1,F2,...,F i ], where each E i and F i is a three-dimensional vector representing the color mean of the corresponding superpixel;

[0034] S5.2: Calculate the difference D between two eigenvectors at the same position f, where D f The standard color difference calculation formula for ΔE in Lab space is output as D f The position of the feature vector greater than the threshold is the corresponding superpixel label:

[0035]

[0036] In the formula is the number of pixels contained in the i-th superpixel in E, and p1 is a pixel under the corresponding superpixel. is the number of pixels contained in the i-th superpixel in F, and p2 is a pixel under the corresponding superpixel. L(p1), a(p1), and b(p1) represent the values ​​of the three components of the p1 pixel in the Lab color space; L(p2), a(p2), and b(p2) represent the values ​​of the three components of the p2 pixel in the Lab color space. is the mean of the three components L, a, and b of the i-th superpixel area in E; is the mean of the three components L, a, and b of the i-th superpixel area in F.

[0037] By adopting the above technology, compared with the prior art, the beneficial effects of the present invention are as follows:

[0038] The present invention combines image saliency with color information entropy to adaptively divide the superpixel size of the salient area and the non-salient area of ​​the printed image for printed image color detection by comparing the superpixel color means; it is capable of obtaining a more accurate color detection result that conforms to human vision; the method of the present invention has a faster detection speed and can output the color difference area position and color offset at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 The figure is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0040] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments of the specification. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0041] On the contrary, the present invention covers any substitution, modification, equivalent method and scheme made on the essence and scope of the present invention as defined by the claims. Further, in order to make the public have a better understanding of the present invention, some specific details are described in detail in the detailed description of the present invention below. Those skilled in the art can fully understand the present invention without the description of these details.

[0042] by Figure 1Taking the process of the printed image color detection method in as an example, the whole process from pre-printing image processing to post-printing image color detection to the final output of color difference area position and color offset is explained in detail. The specific steps are as follows:

[0043] S1: Obtain the salient region map of the pre-press image through the image saliency detection algorithm, segment the foreground and background of the image, and obtain the saliency mask map. The specific process is as follows:

[0044] S1.1: Obtain a salient region map of the prepress image through an image saliency detection algorithm based on convex hull prior.

[0045] S1.2: Using the Otsu method, by minimizing the intra-class variance, the binarization threshold of the salient region image is automatically calculated to distinguish the foreground from the background of the image, and obtain a binary image of the salient region. The salient region of the image is white, and the non-salient region of the image is black.

[0046] S2: Obtain a salient region M of the salient image according to S1.2. Determine the richness of the region content through the color information contained in the pre-press image corresponding to the M region, and adaptively set the superpixel size of the region segmentation. The specific process is as follows:

[0047] S2.1: Extract the salient region M from the salient binary image obtained in step S1.2. Select the prepress image region corresponding to M. Use the grayscale information of the prepress image region to calculate the information entropy and obtain the color information entropy E under the salient region:

[0048]

[0049] Where L h represents the number of gray levels contained in the image under the salient area, p i Represents the probability histogram at the i-th gray level.

[0050] S2.2: Set the superpixel area A of the salient region, and the expression for A is as follows:

[0051]

[0052] Where n is the number of pixels in the salient area, r is the number of horizontal pixels in the prepress image, and c is the number of vertical pixels in the prepress image.

[0053] S3: Use the improved superpixel algorithm to complete superpixel segmentation of the prepress image, and save the image pixel position and the corresponding superpixel label information; the specific process is as follows:

[0054] S3.1: Use the improved superpixel segmentation algorithm to perform superpixel segmentation on the prepress image and construct the improved superpixel segmentation map G a (x,y):

[0055] G a (x,y)=(1-M(x,y))S na (x,y)+M(x,y)S ya (x,y)

[0056] Where S na is the superpixel segmentation map of the non-salient area, S ya is the superpixel segmentation map of the salient area. S(x,y) is the label of the superpixel area to which the pixel (x,y) belongs, so S na (x,y), S ya (x, y) represents the pixel label value in its region. M(x, y) represents the pixels in the salient region, and 1-M(x, y) represents the pixels in the non-salient region.

[0057] S3.2: Use the simple linear clustering SLIC superpixel algorithm to obtain the pixel label S in the salient area ya (x,y). Set the superpixel size in the salient area to The cluster centers under the salient areas of the image are The similarity between the pixel and the cluster center in Lab color space is compared. Each similar pixel is assigned the same label as the cluster center.

[0058] S3.3: The operation is the same as S3.2. Superpixel segmentation is performed on the non-salient area of ​​the image to obtain the pixel label S in the salient area. na (x, y). The superpixel size in the non-salient area is l, and the cluster center searches for similar pixels in the range of 2l×2l. The superpixel label value of the non-salient area is connected to the label value of the salient area, and the two are combined to obtain the complete G a (x,y).

[0059] S3.4: If the size of the pre-press image is not the same as the size of the post-press image, the obtained superpixel label of the pre-press image is interpolated according to the pixel size of the post-press image so that the superpixel label can reflect every pixel of the post-press image. Each pixel in the post-press image is assigned a corresponding label value. The superpixel color reflection effect of the post-press image is the same as the superpixel color of the pre-press image.

[0060] S4: Perform color correction on the standard image after printing and color correction and registration on the image to be tested after printing, where the standard image after printing is a sample image of a qualified printed product. The specific process is as follows:

[0061] S4.1: Use Epson 12000XL scanner to collect data of post-printing standard pattern, and obtain image data of post-printing standard image through the color correction function of the system;

[0062] S4.2: Use the Epson 12000XL scanner to collect data on the post-printing test pattern, and use the system's built-in color correction function to obtain the image data of the post-printing defect image. Use the SIFT image registration algorithm to correct the post-printing defect image to achieve the corresponding position between the post-printing standard image and the post-printing defect image;

[0063] S5: Obtain the pixels at the corresponding position of the printed image according to the superpixel label, calculate the color mean of the area in the Lab color space, calculate the color difference of the same superpixel block through the color difference formula, and output the superpixel label greater than the threshold. The specific process is as follows:

[0064] S5.1: Segment the image G according to the superpixel obtained in S3 a (x, y), obtain the color mean of each superpixel of the standard image after printing in Lab color space. Construct the feature vector E of the standard image after printing. Obtain the feature vector F of the image to be tested after printing in the same way. Vectors E and F are composed of multidimensional vectors, expressed as E = [E1, E2, ..., E i ] and F=[F1,F2,...,F i ], where each E i and F i It is a three-dimensional vector representing the color mean of the corresponding superpixel.

[0065] S5.2: Calculate the difference D between two eigenvectors at the same position f , where D f The standard color difference calculation formula for ΔE in Lab space is output as D f The position of the feature vector greater than the threshold is the corresponding superpixel label i:

[0066]

[0067] In the formula is the number of pixels contained in the i-th superpixel in E, and p1 is a pixel under the corresponding superpixel. is the number of pixels contained in the i-th superpixel in F, and p2 is a pixel under the corresponding superpixel. L(p1), a(p1), and b(p1) represent the values ​​of the three components of the p1 pixel in the Lab color space; L(p2), a(p2), and b(p2) represent the values ​​of the three components of the p2 pixel in the Lab color space. is the mean of the three components L, a, and b of the i-th superpixel area in E; is the mean of the three components L, a, and b of the i-th superpixel area in F.

[0068] S6: Draw the superpixel boundary of the corresponding position on the post-printing standard image according to the output superpixel label, and save the deviation of the three components L, a, and b in the area corresponding to the label value as a file. The specific process is as follows:

[0069] S6.1: Draw the superpixel boundary of the corresponding position according to the output superpixel label, and present the drawn superpixel boundary on the standard image after printing. Finally, an image with the outline of the color difference area marked on the standard image after printing is obtained. The outline is presented in the form of the superpixel outline of the corresponding area. The outline obtained in the non-salient area is large, and the outline obtained in the salient area is small.

[0070] S6.2: Save the deviation of the color mean of the area corresponding to the output superpixel label value in the three components of L, a, and b as a file.

[0071] In this embodiment, the pre-press image and the super pixel size l are input, and the G obtained by S3.4 is a (x, y), input the post-printing standard image and the post-printing test image into the G a (x, y) determines the superpixel feature vector E and superpixel feature vector F, and compares them according to the calculation method in S5.2. The calculation process of formula S5.2 is as follows: Get the superpixel E with the same label value of 31 31 With F 31 , calculated based on their mean in three parts f . Due to D f If the value is greater than the set threshold, the label value of the corresponding superpixel is output as 31, and E 31 With F 31 The offsets of the three components L, a, and b are shown in Table 1. The relevant parameters in S5.2 are calculated during the execution of the method in this paper and are not used as output results.

[0072]

[0073] Table 1 Some examples of color deviation in superpixel regions

[0074]

[0075] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for detecting color of a digital printed image, characterized in that: The following steps are involved: S1: Obtain a salient region map of the prepress image through an image saliency detection algorithm based on convex hull prior, segment the foreground and background of the prepress image, and obtain a saliency mask map; S2: Adaptively control the superpixel size based on the color information entropy of the salient area of ​​the pre-press image corresponding to the saliency mask map, and optimize the original superpixel segmentation algorithm; S3: Use the optimized superpixel algorithm to complete superpixel segmentation of the prepress image, and save the image pixel position and the corresponding superpixel label information; S4: Use the sample image of the qualified printed product as the post-printing standard pattern, perform color correction on the post-printing standard pattern, and perform color correction and image registration on the post-printing test pattern; S5: Obtain the pixel at the corresponding position of the printed image according to the superpixel label, calculate the color mean of each superpixel area in the Lab color space, calculate the color difference of the same superpixel block by the color difference formula, and output the superpixel label greater than the threshold; S6: Draw the superpixel boundary of the corresponding position on the post-printing standard image according to the output superpixel label, and save the deviation between the superpixel label value and the corresponding area in the three components of L, a, and b as a file.

2. A digital printing image color detection method according to claim 1, characterized in that: The specific process of S1 is as follows: S1.1: Obtain a salient region map of the prepress image using an image saliency detection algorithm; S1.2: Using the Otsu method, an image processing technique for automatically determining the image binarization threshold, the threshold is automatically calculated by minimizing the intra-class variance or maximizing the inter-class variance to distinguish the foreground and background of the prepress image and obtain a binary image of the salient area.

3. A digital printing image color detection method according to claim 2, characterized in that: The specific process of S2 is as follows: S2.1: Extract the salient region M from the salient binary image obtained in step S1.2; select the prepress image region corresponding to M, use the grayscale information of the prepress image to replace the number of colors in the color space dimension for measurement, and obtain the color information entropy E in the salient region: Where, L h Represents the number of gray levels contained in the image under the salient area, p i Represents the probability histogram at the i-th gray level; S2.2: Set the superpixel area A of the salient region, and the expression for A is as follows: Where n is the number of pixels in the salient area, r is the number of horizontal pixels in the image, c is the number of vertical pixels in the image, and ω2 is the control weight of the superpixel size, which is used to adjust the influence of color information entropy. The following table is used to solve ω2: ω2=5·l 2 。 4. A digital printing image color detection method according to claim 3, characterized in that: The specific process of S3 is as follows: S3.1: Use the improved superpixel segmentation algorithm to perform superpixel segmentation on the prepress image and construct an improved superpixel segmentation map; S3.2: Use the simple linear clustering SLIC superpixel algorithm to obtain the pixel label S in the salient area ya (x,y); Set the superpixel size in the salient area to The cluster centers under the salient areas are Search for similar pixels in the range and compare the similarity between the pixels and the cluster center in the Lab color space; S3.3: Use the method in step S3.2 to perform superpixel segmentation on the non-salient area and obtain the pixel label S in the salient area na (x, y); the superpixel size in the non-salient area is l, and the cluster center searches for similar pixels in the range of 2l×2l; the superpixel label value of the non-salient area is connected after the label value of the salient area.

5. A digital printing image color detection method according to claim 4, characterized in that: In S3, when the size of the pre-press image is inconsistent with the size of the post-press image, the super-pixel label of the pre-press image is interpolated according to the pixel size of the post-press image, so that the super-pixel label can reflect each pixel of the post-press image; each pixel in the post-press image is assigned a corresponding label value; the super-pixel color reflection effect of the post-press image is the same as the super-pixel color of the pre-press image.

6. A digital printing image color detection method according to claim 5, characterized in that: The specific process of S5 is as follows: S5.1: According to the superpixel segmentation image obtained in S3, the color mean of each superpixel of the post-printing standard image corresponding to the superpixel segmentation image in the Lab color space is obtained; a feature vector E of the post-printing standard image is constructed, and a feature vector F of the post-printing image to be tested is obtained in the same manner; S5.2: Calculate the difference D between two eigenvectors at the same position f , where D f The standard color difference calculation formula for ΔE in Lab space is output as D f The position of the feature vector greater than the threshold is the corresponding superpixel label.

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