Image Defect Traceability Detection Method and System Applicable to Mobile Phone Touch Screens

Abstract

The present invention relates to the field of mobile phone image analysis, and discloses an image defect traceability detection method and system applicable to a mobile phone touch screen, including the following steps: locating the abnormal position of sample image pixel points in the mobile phone, classifying the defect types of the abnormal positions of sample image pixel points by combining with a support vector machine model, and finally performing defect traceability detection and defect solution design on different classification results. The present invention can perform pixel analysis on the sample image on the mobile phone, and based on the analysis results, realize defect traceability analysis of the mobile phone hardware and software as well as the shooting process, and realize solution design, so as to achieve the purpose of preventing and solving image display problems.

Description

Technical Field

[0001] The present invention relates to the field of mobile phone image analysis, and particularly to an image defect traceability detection method and system applicable to a mobile phone touch screen. Background Art

[0002] Image blurring in a mobile phone may be related to problems of the mobile phone itself or the mobile phone touch screen (i.e., the display screen). For example, tiny stains such as dust, fingerprints, and grease may adhere to the mobile phone lens, affecting the clarity of taking pictures or videos. At the same time, damage to the camera, instability of the lens, or other hardware problems may also cause image blurring. And there are problems with the image. If the mobile phone touch screen itself is damaged or aged, such as defects inside the LCD, failures of internal components (such as the graphics card), etc., it will cause image blurring. Displaying a static image for a long time will cause a permanent image trace on the screen, which is called "image burn-in". The image burn-in phenomenon will also cause the displayed image to be blurred. To sum up, the reasons for image blurring in a mobile phone may involve multiple aspects, including the mobile phone camera, the mobile phone touch screen, as well as shooting skills and environmental conditions. Therefore, an image defect traceability detection method and system applicable to a mobile phone touch screen are proposed to determine the source of defects in mobile phone images and achieve the purpose of repairing defects by prescribing the right medicine. Summary of the Invention

[0003] The present invention overcomes the deficiencies of the prior art and provides an image defect traceability detection method and system applicable to a mobile phone touch screen.

[0004] To achieve the above object, the technical solution adopted by the present invention is as follows:

[0005] In a first aspect of the present invention, there is provided an image defect traceability detection method applicable to a mobile phone touch screen, including the following steps:

[0006] Install an image defect analysis module in the mobile phone, and mark the pixel points of the sample image with anomalies by means of image analysis of the sample image;

[0007] For the abnormal pixel points, introduce a support vector machine model in the image defect analysis module for feature analysis, and classify the defect types of the sample image based on the feature analysis results;

[0008] In the image defect analysis module, perform defect traceability detection based on the defect type of the sample image, determine the source of the defect type of the sample image, and design a defect solution in combination with the source of the defect type of the sample image.

[0009] Further, in a preferred embodiment of the present invention, the step of installing an image defect analysis module in the mobile phone and marking the pixel points of the sample image with anomalies by means of image analysis of the sample image is specifically as follows:

[0010] Determine the target mobile phone and calibrate the touch screen of the target mobile phone as the target mobile phone touch screen;

[0011] Install an image defect analysis module in the target mobile phone. The image defect analysis module is a module that can determine the type of image defects and perform traceability detection on image defects. At the same time, determine a sample image in the target mobile phone;

[0012] The sample image is an image used to test whether there are defects in the target mobile phone and the target mobile phone touch screen. Perform defect traceability detection on the target mobile phone and the target mobile phone touch screen according to the type of defects on the sample image;

[0013] Import and map the sample image into the image defect analysis module. Based on the image defect analysis module, record the pixel value of each pixel point on the sample image, calibrate it as the actual pixel value, and determine the standard pixel value threshold of each pixel point on the sample image, calibrate it as the standard pixel value threshold;

[0014] Perform pixel value analysis on all pixel points on the sample image, and calibrate the pixel points whose actual pixel values are not within the standard pixel value threshold as abnormal pixel points. At the same time, mark and highlight the abnormal pixel points on the sample image.

[0015] Furthermore, in a preferred embodiment of the present invention, for the abnormal pixel points, introduce a support vector machine model in the image defect analysis module for feature analysis, and classify the type of defects of the sample image based on the feature analysis results. Specifically:

[0016] Introduce a support vector machine model in the image defect analysis module, and through the threshold extraction method, perform threshold extraction processing on the abnormal pixel points in the sample image to obtain an abnormal pixel point image;

[0017] In the abnormal pixel point image, construct a gray level co-occurrence matrix of pixel points, and based on the gray level co-occurrence matrix of abnormal pixel points, extract the texture features of the abnormal pixel point image. At the same time, perform Fourier transform on the abnormal pixel point image to obtain the frequency domain features of the abnormal pixel point image. Finally, extract the color features of the pixel points on the abnormal pixel point image to obtain the color features of the abnormal pixel point image;

[0018] Combine the texture features, frequency domain features, and color features of the abnormal pixel point image into a feature vector, construct a training data set based on the feature vector, and import the training data set into the support vector machine model;

[0019] Introduce a big data network, retrieve all possible defect types of the sample image based on the big data network, and retrieve all corresponding support vector machine model kernel functions for all possible defect types of the sample image, calibrate them as the target kernel functions;

[0020] Based on the objective sum function within the support vector machine model, perform cross - validation on the training data set to achieve data training of the support vector machine model, and output the defect types existing in the abnormal pixel point image after cross - validation, which are marked as the actual defect types of the image;

[0021] Among them, the actual defect types of the image include color defects, blur defects, and image quality defects of the sample image.

[0022] Furthermore, in a preferred embodiment of the present invention, in the image defect analysis module, based on the defect types of the sample image, perform defect traceability detection, determine the source of the defect types of the sample image, and design defect solutions in combination with the source of the defect types of the sample image. Specifically:

[0023] In the image defect analysis module, if the actual defect type of the sample image is a color defect, determine the standard color temperature parameter and standard color parameter of the abnormal pixel point image in the target mobile phone, combine them to form the standard color parameter, and at the same time determine the actual color parameter of the abnormal pixel point image;

[0024] Calculate the parameter difference between the standard color parameter and the actual color parameter. At the same time, preset a qualified parameter difference, detect the mobile phone system version of the target mobile phone, determine whether there is an available updated version for the target mobile phone. If so, install the available updated version for the target mobile phone, and after installation, determine whether the parameter difference between the standard color parameter and the actual color parameter is not greater than the qualified parameter difference;

[0025] If so, label the source of the color defect of the sample image as the reason of the target mobile phone system. If not, determine whether adjusting the color parameters in the target mobile phone can make the parameter difference between the standard color parameter and the actual color parameter not greater than the qualified parameter difference;

[0026] If still not, label the source of the color defect of the sample image as the reason of the target mobile phone touch screen hardware, determine the position of the abnormal pixel point image on the target mobile phone touch screen, label it as the color abnormal position of the target mobile phone touch screen, and determine the corresponding hardware in the target mobile phone touch screen for the color abnormal position of the target mobile phone touch screen, label it as a type of abnormal hardware, and based on the big data network, retrieve all solutions for hardware maintenance of the type of abnormal hardware;

[0027] In the image defect analysis module, perform defect traceability detection on the sample image with the actual defect type of blur defect in the image, and design a defect solution.

[0028] Furthermore, in a preferred embodiment of the present invention, in the image defect analysis module, perform defect traceability detection on the sample image with the actual defect type of blur defect in the image, and design a defect solution. Specifically:

[0029] In the image defect analysis module, if the actual image defect type of the sample image is a blur defect, obtain the gyroscope module of the target mobile phone and connect the gyroscope module of the target mobile phone to the image analysis module;

[0030] After the gyroscope module of the target mobile phone is connected to the image analysis module, determine the recording timestamp of the sample image, and based on the recording timestamp of the sample image, determine the jitter amplitude and jitter frequency of the gyroscope at the time of recording the sample image, and calibrate them as the actual jitter amplitude of the gyroscope and the actual jitter frequency of the gyroscope;

[0031] Based on the big data network, determine the jitter amplitude and jitter frequency corresponding to the gyroscope when the sample image does not have a blur defect, and calibrate them as the standard jitter amplitude of the gyroscope and the standard jitter frequency of the gyroscope;

[0032] If the actual jitter amplitude of the gyroscope and the actual jitter frequency of the gyroscope are greater than the corresponding standard jitter amplitude of the gyroscope and the standard jitter frequency of the gyroscope, calibrate the source of the blur defect of the sample image as the reason for shooting jitter;

[0033] Introduce the blind deconvolution algorithm in the image defect analysis module, construct an initial blur kernel based on the blind deconvolution algorithm, define the blur estimation value of the initial blur kernel, and perform a convolution operation on the sample image through the blur estimation value of the initial blur kernel to obtain an estimated value of the clear sample image;

[0034] Based on the blind deconvolution algorithm, iteratively convolve the sample image, and preset the maximum number of iterations. If the number of iterations is equal to the maximum number of iterations, stop iteratively convolving the sample image and output the iteratively convolved sample image, which is calibrated as the blind deconvolution sample image;

[0035] Among them, the blind deconvolution sample image has no blur defect;

[0036] If both the actual jitter amplitude of the gyroscope and the actual jitter frequency of the gyroscope are not greater than the corresponding standard jitter amplitude of the gyroscope and the standard jitter frequency of the gyroscope, perform a secondary traceability of the blur defect on the target mobile phone and the touch screen of the target mobile phone, and design a defect solution based on the secondary traceability result.

[0037] Furthermore, in a preferred embodiment of the present invention, the secondary traceability of the blur defect on the target mobile phone and the touch screen of the target mobile phone, and the design of a defect solution based on the secondary traceability result are specifically as follows:

[0038] If both the actual jitter amplitude of the gyroscope and the actual jitter frequency of the gyroscope are not greater than the corresponding standard jitter amplitude of the gyroscope and the standard jitter frequency of the gyroscope, then perform a hardware damage analysis on the camera of the target mobile phone to determine whether there is a hardware damage problem with the camera of the target mobile phone;

[0039] If so, label the source of the blur defect in the sample image as the hardware reason of the target mobile phone camera, retrieve the repair plan for the damaged hardware of the target mobile phone camera in the big data network and output it;

[0040] If not, update the camera application cache and the camera application program in the target mobile phone. If there is no blur defect in the sample image after the camera application cache update and the camera application program update, label the source of the blur defect in the sample image as the software reason of the target mobile phone camera;

[0041] If there is still a blur defect in the sample image after the camera application cache update and the camera application program update, label the source of the blur defect in the sample image as the hardware reason of the target mobile phone touch screen, and replace the target mobile phone touch screen to ensure that there is no blur defect in the sample image.

[0042] The second aspect of the present invention also provides an image defect traceability detection system applicable to a mobile phone touch screen. The image defect traceability detection system includes a memory and a processor. The memory stores an image defect traceability detection method. When the image defect traceability detection method is executed by the processor, the following steps are implemented:

[0043] Install an image defect analysis module in the mobile phone, and mark the pixel points of the sample image with abnormalities by analyzing the sample image;

[0044] For abnormal pixel points, introduce a support vector machine model in the image defect analysis module for feature analysis, and classify the defect types of the sample image based on the feature analysis results;

[0045] In the image defect analysis module, perform defect traceability detection based on the defect type of the sample image, determine the source of the defect type of the sample image, and design a defect solution in combination with the source of the defect type of the sample image.

[0046] The present invention solves the technical defects existing in the background technology. The present invention has the following beneficial effects: Locate the abnormal position of the pixel points of the sample image in the mobile phone, classify the defect types of the abnormal positions of the pixel points of the sample image in combination with the support vector machine model, and finally perform defect traceability detection and defect solution design on different classification results. The present invention can perform pixel analysis on the sample image on the mobile phone, and based on the analysis results, realize defect traceability analysis of the mobile phone hardware and software and the shooting process, and realize solution design, so as to achieve the purpose of preventing and solving image display problems. Description of the Drawings

[0047] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the accompanying drawings required for the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings of embodiments can also be obtained based on these drawings.

[0048] Figure 1 The flowchart of the image defect traceability detection method applicable to the mobile phone touch screen is shown;

[0049] Figure 2 The flowchart of the method for defect traceability detection based on the defect type of the sample image is shown;

[0050] Figure 3 The program view of the image defect traceability detection system applicable to the mobile phone touch screen is shown. Detailed implementation manners

[0051] In order to more clearly understand the above objects, features and advantages of the present invention, the present invention will be further described in detail below in conjunction with the accompanying drawings and specific implementation manners. It should be noted that, without conflict, the embodiments of the present application and the features in the embodiments can be combined with each other.

[0052] Many specific details are set forth in the following description in order to fully understand the present invention. However, the present invention can also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited by the specific embodiments disclosed below.

[0053] Figure 1 The flowchart of the image defect traceability detection method applicable to the mobile phone touch screen is shown, including the following steps:

[0054] S102: Install an image defect analysis module in the mobile phone, and mark the pixel points of the sample image with anomalies by analyzing the sample image;

[0055] S104: For the abnormal pixel points, introduce a support vector machine model in the image defect analysis module for feature analysis, and classify the defect types of the sample image based on the feature analysis results;

[0056] S106: In the image defect analysis module, perform defect traceability detection based on the defect type of the sample image, determine the source of the defect type of the sample image, and design a defect solution in combination with the source of the defect type of the sample image.

[0057] Further, in a preferred embodiment of the present invention, an image defect analysis module is installed in the mobile phone, and by analyzing the sample image, the pixel points of the sample image with anomalies are marked. Specifically:

[0058] Determine the target mobile phone and calibrate the mobile phone touch screen of the target mobile phone as the target mobile phone touch screen;

[0059] Install an image defect analysis module in the target mobile phone. The image defect analysis module is a module that can determine the type of image defect and perform traceability detection on the image defect. At the same time, determine the sample image in the target mobile phone;

[0060] The sample image is an image used to test whether the target mobile phone and the target mobile phone touch screen have defects. Defect traceability detection of the target mobile phone and the target mobile phone touch screen is performed according to the type of defect on the sample image;

[0061] Import and map the sample image into the image defect analysis module. Based on the image defect analysis module, record the pixel value of each pixel point on the sample image, mark it as the actual pixel value, and determine the standard pixel value threshold of each pixel point on the sample image, mark it as the standard pixel value threshold;

[0062] Perform pixel value analysis on all pixel points on the sample image, and mark the pixel points whose actual pixel values are not within the standard pixel value threshold as abnormal pixel points, and at the same time mark and highlight the abnormal pixel points on the sample image.

[0063] It should be noted that the purpose of installing the image defect analysis module is to directly trace the defect source of the problematic image in the mobile phone and perform defect correction, saving time. The sample image is an image retained in the mobile phone through various methods such as shooting, uploading, and storing by the target mobile phone. This image may have defects, such as blurring defects, clarity defects, etc. In the image, not all positions have anomalies. It may only be some positions that have defects. Therefore, pixel point and pixel value analysis is performed on the image, and the position corresponding to the pixel point with abnormal pixel value is the defect position. The purpose of highlighting the abnormal pixel points is to more clearly know the positioning coordinates of the abnormal positions.

[0064] Further, in a preferred embodiment of the present invention, for the abnormal pixel points, a support vector machine model is introduced in the image defect analysis module for feature analysis, and the sample image is classified according to the feature analysis results. Specifically:

[0065] Introduce a support vector machine model in the image defect analysis module, and through the threshold extraction method, perform threshold extraction processing on the abnormal pixel points in the sample image to obtain the abnormal pixel point image;

[0066] In the abnormal pixel point image, construct the gray-level co-occurrence matrix of pixel points, and based on the gray-level co-occurrence matrix of abnormal pixel points, extract the texture features of the abnormal pixel point image. At the same time, perform Fourier transform on the abnormal pixel point image to obtain the frequency domain features of the abnormal pixel point image. Finally, extract the color features of the pixel points on the abnormal pixel point image to obtain the color features of the abnormal pixel point image;

[0067] Combine the texture features, frequency domain features, and color features of the abnormal pixel point image into a feature vector, construct a training data set based on the feature vector, and import the training data set into the support vector machine model;

[0068] Introduce a big data network, retrieve all possible defect types of the sample image based on the big data network, and retrieve all corresponding support vector machine model kernel functions based on all possible defect types of the sample image, and label them as target kernel functions;

[0069] In the support vector machine model, based on the target sum function, perform cross-validation on the training data set to realize the data training of the support vector machine model, and output the defect type existing in the abnormal pixel point image after cross-validation, which is marked as the actual defect type of the image;

[0070] Among them, the actual defect type of the image includes color defects, blur defects, and picture quality defects of the sample image.

[0071] It should be noted that the defect types include image color defects, blur defects, and picture quality defects. However, direct visual observation of the image cannot accurately judge the defect type of the image, and it needs to be carefully divided in combination with an algorithm. The algorithm needs to use a support vector machine model. Among them, the support vector machine model is a multi-step algorithm for feature analysis and defect type classification of abnormal pixel points in an image, also known as the SVM algorithm. First, perform threshold segmentation of the image to remove the image positions without abnormalities, simplify the calculation steps, and improve the defect traceability efficiency. Secondly, extract texture features and frequency domain features. The texture features further describe the abnormal positions in the image, while the frequency domain features are the necessary feature data for training the SVM model and can be obtained through Fourier transform. Construct a training data set, and train the support vector machine model based on the training data set. When training the support vector machine model, an appropriate kernel function should be selected, including linear kernel functions, polynomial kernel functions, etc. The cross-validation method includes the grid search method, etc., which is used to optimize the parameters of the SVM model to achieve the purpose of image defect type classification, and finally obtain the actual defect type of the image.

[0072] Figure 2 The method flow chart for defect traceability detection based on the defect type of the sample image is shown, including the following steps:

[0073] S202: In the image defect analysis module, perform defect traceability detection based on the defect type of the sample image, determine the source of the defect type of the sample image, and design a defect solution in combination with the source of the defect type of the sample image;

[0074] S204: In the image defect analysis module, perform defect traceability detection on the sample image whose actual image defect type is a blurred defect, and design a defect solution;

[0075] S206: Perform secondary traceability on the target mobile phone and the touch screen of the target mobile phone for blurred defects, and design a defect solution based on the results of the secondary traceability.

[0076] Further, in a preferred embodiment of the present invention, in the image defect analysis module, performing defect traceability detection based on the defect type of the sample image, determining the source of the defect type of the sample image, and designing a defect solution in combination with the source of the defect type of the sample image is specifically as follows:

[0077] In the image defect analysis module, if the actual image defect type of the sample image is a color defect, determine the standard color temperature parameter and the standard color parameter of the abnormal pixel point image in the target mobile phone, and combine to form the standard color parameter, and at the same time determine the actual color parameter of the abnormal pixel point image;

[0078] Calculate the parameter difference between the standard color parameter and the actual color parameter, and at the same time preset a qualified parameter difference, detect the mobile phone system version of the target mobile phone, determine whether there is an available updated version for the target mobile phone. If so, install the available updated version for the target mobile phone, and after installation, determine whether the parameter difference between the standard color parameter and the actual color parameter is not greater than the qualified parameter difference;

[0079] If so, label the source of the color defect of the sample image as the reason of the target mobile phone system. If not, determine whether adjusting the color parameters in the target mobile phone can make the parameter difference between the standard color parameter and the actual color parameter not greater than the qualified parameter difference;

[0080] If still not, label the source of the color defect of the sample image as the reason of the hardware of the touch screen of the target mobile phone, determine the position of the abnormal pixel point image on the touch screen of the target mobile phone, label it as the color abnormal position of the touch screen of the target mobile phone, and determine the corresponding hardware of the color abnormal position of the touch screen of the target mobile phone in the touch screen of the target mobile phone, label it as a type of abnormal hardware, and based on the big data network, retrieve all solutions for hardware maintenance of the type of abnormal hardware.

[0081] It should be noted that first, analyze whether there are color defects in the sample image. By analyzing the color parameters of the lossless version of the sample image, that is, the standard color parameters and the actual color parameters, obtain the parameter difference, and compare it with the standard parameter difference to determine whether there are color defects. Because different mobile phones, mobile phones with better performance may display colors closer to the real ones, but if the parameter difference is large, it is not a problem of mobile phone performance but other problems directly occur in the mobile phone. First, judge the mobile phone version. An old version may affect the authenticity of the mobile phone's color display. Secondly, analyze the mobile phone touch screen, judge the abnormal position of the sample image corresponding to the mobile phone touch screen, and this position is the position of the hardware abnormality. And through the big data network search, retrieve all the solutions for hardware maintenance of a type of abnormal hardware.

[0082] Furthermore, in a preferred embodiment of the present invention, in the image defect analysis module, perform defect traceability detection on the sample image whose actual image defect type is a blur defect, and design a defect solution, specifically:

[0083] In the image defect analysis module, if the actual image defect type of the sample image is a blur defect, obtain the gyroscope module of the target mobile phone, and connect the gyroscope module of the target mobile phone to the image analysis module;

[0084] After connecting the gyroscope module of the target mobile phone to the image analysis module, determine the recording timestamp of the sample image, and based on the recording timestamp of the sample image, determine the jitter amplitude and jitter frequency of the gyroscope when the sample image is recorded, and calibrate them as the actual jitter amplitude of the gyroscope and the actual jitter frequency of the gyroscope;

[0085] Based on the big data network, determine the jitter amplitude and jitter frequency corresponding to the gyroscope when the sample image does not have a blur defect, and calibrate them as the standard jitter amplitude of the gyroscope and the standard jitter frequency of the gyroscope;

[0086] If the actual jitter amplitude of the gyroscope and the actual jitter frequency of the gyroscope are greater than the corresponding standard jitter amplitude of the gyroscope and the standard jitter frequency of the gyroscope, then calibrate the source of the blur defect of the sample image as the reason of shooting jitter;

[0087] Introduce a blind deconvolution algorithm in the image defect analysis module, construct an initial blur kernel based on the blind deconvolution algorithm, and define the blur estimation value of the initial blur kernel. Perform a convolution operation on the sample image through the blur estimation value of the initial blur kernel to obtain an estimated value of the clear sample image;

[0088] Based on the blind deconvolution algorithm, iteratively convolve the sample image, and preset the maximum number of iterations. If the number of iterations is equal to the maximum number of iterations, stop iteratively convolving the sample image, and output the iteratively convolved sample image, which is calibrated as the blind deconvolution sample image;

[0089] Among them, the blind deconvolution sample image has no blurring defect;

[0090] If both the actual jitter amplitude and the actual jitter frequency of the gyroscope are not greater than the corresponding gyroscope standard jitter amplitude and gyroscope standard jitter frequency, perform secondary traceability of the blurring defect on the target mobile phone and the touch screen of the target mobile phone, and design a defect solution based on the secondary traceability result.

[0091] It should be noted that without color defects, it is necessary to analyze the blurring defect of the sample image. The blurring defect of the sample image may be a problem of the mobile phone itself or a problem of jitter during shooting. First, analyze whether it is a jitter problem and how to correct the image blurring caused by jitter. Obtain the gyroscope module of the mobile phone. The gyroscope can record the jitter frequency, rate, amplitude and other internal modules of the mobile phone that affect the shooting clarity. The gyroscope records the jitter parameters when the sample image is taken, that is, the actual jitter parameters, and retrieves the standard jitter parameters for comparison. If the actual value is greater than the standard value, the source of the blurring defect of the sample image is marked as the shooting jitter reason, because the actual jitter amplitude and frequency are relatively high, resulting in image blurring. The sample image obtained under the shooting jitter reason can be deblurred by the blind deconvolution algorithm. Blind deconvolution is a method of estimating the blur kernel and the clear image through iterative optimization. It assumes that the blurred image is obtained by convolving the clear image and the blur kernel. First, initialize an estimate of the blur kernel, and then use this estimate and the blurred image for deconvolution operation to obtain an estimate of the clear image. Then, update the estimate of the blur kernel according to the difference between the estimate of the clear image and the original blurred image. Through multiple iterations, continuously optimize the estimates of the blur kernel and the clear image until the convergence condition is reached, that is, the number of iterations is equal to the maximum number of iterations, and finally obtain the blind deconvolution sample image.

[0092] Further, in a preferred embodiment of the present invention, the secondary traceability of the blurring defect on the target mobile phone and the touch screen of the target mobile phone, and the design of a defect solution based on the secondary traceability result are specifically as follows:

[0093] If both the actual jitter amplitude and the actual jitter frequency of the gyroscope are not greater than the corresponding gyroscope standard jitter amplitude and gyroscope standard jitter frequency, perform a hardware damage analysis on the camera of the target mobile phone to determine whether there is a hardware damage problem with the camera of the target mobile phone;

[0094] If so, mark the source of the blurring defect of the sample image as the hardware reason of the camera of the target mobile phone, retrieve the repair plan of the damaged hardware of the camera of the target mobile phone in the big data network and output it;

[0095] If not, update the camera application cache and the camera application in the target mobile phone. If there is no blurring defect in the sample image after the camera application cache update and the camera application update, label the source of the blurring defect of the sample image as the reason of the camera software of the target mobile phone;

[0096] If there is still a blurring defect in the sample image after the camera application cache update and the camera application update, label the source of the blurring defect of the sample image as the reason of the touch screen hardware of the target mobile phone, and replace the touch screen of the target mobile phone to ensure that there is no blurring defect in the sample image.

[0097] It should be noted that both the actual jitter amplitude and the actual jitter frequency of the gyroscope are not greater than the corresponding standard jitter amplitude and standard jitter frequency of the gyroscope, which proves that the blurring defect of the sample image has nothing to do with shooting and is related to the mobile phone hardware. It may be that there are damages such as breakage in the mobile phone camera hardware, or the internal application cache of the mobile phone is too high, resulting in insufficient memory being occupied when the image is output, thus causing the image to be blurred. It may also be the reason of the touch screen hardware, and the touch screen needs to be directly replaced.

[0098] As Figure 3 shown, the second aspect of the present invention also provides an image defect traceability detection system applicable to a mobile phone touch screen. The image defect traceability detection system includes a memory 31 and a processor 32. An image defect traceability detection method is stored in the memory 31. When the image defect traceability detection method is executed by the processor 32, the following steps are implemented:

[0099] Install an image defect analysis module in the mobile phone, and mark the pixel points of the sample image with abnormalities by analyzing the sample image;

[0100] For the abnormal pixel points, introduce a support vector machine model in the image defect analysis module for feature analysis, and classify the defect types of the sample image based on the feature analysis results;

[0101] In the image defect analysis module, perform defect traceability detection based on the defect type of the sample image, determine the source of the defect type of the sample image, and design a defect solution in combination with the source of the defect type of the sample image.

[0102] The above is only the specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of changes or substitutions, which should all be covered within the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.

Claims

1. An image defect tracing detection method applicable to a mobile phone touch screen, characterized in that: The following steps are involved: An image defect analysis module is installed in the mobile phone, and the pixel points of the sample image with abnormalities are marked by performing image analysis on the sample image; For abnormal pixels, a support vector machine model is introduced into the image defect analysis module to perform feature analysis, and the defect type of the sample image is classified based on the feature analysis results; In the image defect analysis module, defect source tracing detection is performed based on the defect type of the sample image to determine the source of the defect type of the sample image, and a defect solution is designed in combination with the source of the defect type of the sample image; Among them, in the image defect analysis module, defect tracing detection is performed based on the defect type of the sample image, the source of the defect type of the sample image is determined, and a defect solution is designed in combination with the source of the defect type of the sample image, specifically: In the image defect analysis module, if the actual image defect type of the sample image is a color defect, the standard color temperature parameters and standard color parameters of the abnormal pixel image are determined in the target mobile phone, and combined to form the standard color parameters, and the actual color parameters of the abnormal pixel image are determined at the same time; Calculate the parameter difference between the standard color parameter and the actual color parameter, preset the qualified parameter difference, perform a mobile phone system version detection on the target mobile phone, determine whether there is an available update version for the target mobile phone, and if so, install the available update version on the target mobile phone, and determine whether the parameter difference between the standard color parameter and the actual color parameter is not greater than the qualified parameter difference after installation; If yes, the source of the color defect of the sample image is calibrated as the target mobile phone system cause. If no, it is determined whether the color parameter adjustment in the target mobile phone can make the parameter difference between the standard color parameter and the actual color parameter not greater than the qualified parameter difference. If still not, then the color defect source of the sample image is calibrated as the hardware reason of the target mobile phone touch screen, the position of the abnormal pixel image on the target mobile phone touch screen is determined, and calibrated as the color abnormality position of the target mobile phone touch screen, and the hardware corresponding to the color abnormality position of the target mobile phone touch screen in the target mobile phone touch screen is determined, and calibrated as a type of abnormal hardware, and based on the big data network, all solutions for hardware maintenance of a type of abnormal hardware are retrieved; In the image defect analysis module, defect tracing detection is performed on sample images whose actual defect type is fuzzy defect, and defect solutions are designed.

2. The image defect tracing detection method for mobile phone touch screen according to claim 1, characterized in that: The image defect analysis module is installed in the mobile phone, and the pixel points of the sample image with abnormalities are marked by performing image analysis on the sample image, specifically: Determine the target mobile phone, and calibrate the touch screen of the target mobile phone as the touch screen of the target mobile phone; Installing an image defect analysis module in the target mobile phone, wherein the image defect analysis module is a module that can determine the type of image defects and perform traceability detection on image defects, and determining a sample image in the target mobile phone; The sample image is an image used to test whether the target mobile phone and the touch screen of the target mobile phone have defects, and the target mobile phone and the touch screen of the target mobile phone are defect traceable according to the defect type on the sample image; Importing and mapping the sample image into the image defect analysis module, based on the image defect analysis module, recording the pixel value of each pixel point on the sample image, calibrating it to the actual pixel value, and determining the standard pixel value threshold of each pixel point on the sample image, calibrating it to the standard pixel value threshold; Pixel value analysis is performed on all pixels on the sample image, and pixels whose actual pixel values ​​are not within the standard pixel value threshold are marked as abnormal pixels. At the same time, the abnormal pixels are marked and highlighted on the sample image.

3. The image defect tracing detection method applicable to mobile phone touch screens according to claim 1, characterized in that: For abnormal pixels, a support vector machine model is introduced into the image defect analysis module to perform feature analysis, and the defect type of the sample image is classified based on the feature analysis results, specifically: The support vector machine model is introduced into the image defect analysis module, and the threshold extraction method is used to perform threshold extraction processing on abnormal pixels in the sample image to obtain an abnormal pixel image; In the abnormal pixel image, a pixel grayscale co-occurrence matrix is ​​constructed, and based on the grayscale co-occurrence matrix of the abnormal pixel, the texture features of the abnormal pixel image are extracted, and at the same time, the abnormal pixel image is subjected to Fourier transform to obtain the frequency domain features of the abnormal pixel image, and finally, the color features of the pixels on the abnormal pixel image are extracted to obtain the color features of the abnormal pixel image; Combining the texture features, frequency domain features, and color features of the abnormal pixel point image into a feature vector, constructing a training data set based on the feature vector, and importing the training data set into the support vector machine model; A big data network is introduced, and all possible defect types of the sample image are retrieved based on the big data network, and all corresponding support vector machine model kernel functions are retrieved based on all possible defect types of the sample image, and calibrated as target kernel functions; Based on the objective and function, the training data set is cross-validated in the support vector machine model to implement data training of the support vector machine model, and the defect type existing in the abnormal pixel image is output after cross-validation and marked as the actual defect type of the image; The actual image defect types include sample image color defects, blur defects and image quality defects.

4. The image defect tracing detection method for a mobile phone touch screen according to claim 1, characterized in that: In the image defect analysis module, defect tracing detection is performed on sample images whose actual defect type is blur defect, and defect solutions are designed, specifically: In the image defect analysis module, if the actual image defect type of the sample image is a blur defect, a gyroscope module of the target mobile phone is obtained, and the gyroscope module of the target mobile phone is connected to the image analysis module; After the gyroscope module of the target mobile phone is connected to the image analysis module, the recording timestamp of the sample image is determined, and based on the recording timestamp of the sample image, the jitter amplitude and jitter frequency of the gyroscope when the sample image is recorded are determined, and calibrated as the actual jitter amplitude and the actual jitter frequency of the gyroscope; Based on the big data network, the jitter amplitude and jitter frequency of the gyroscope corresponding to the sample image without blur defects are determined, and calibrated as the standard jitter amplitude and standard jitter frequency of the gyroscope; If the actual jitter amplitude and the actual jitter frequency of the gyroscope are greater than the corresponding standard jitter amplitude and the standard jitter frequency of the gyroscope, the blur defect source of the sample image is calibrated as the cause of shooting jitter; In the image defect analysis module, a blind deconvolution algorithm is introduced. An initial blur kernel is constructed based on the blind deconvolution algorithm, and a blur estimation value of the initial blur kernel is defined. The sample image is convolved with the blur estimation value of the initial blur kernel to obtain a clear estimate of the sample image. Based on the blind deconvolution algorithm, the sample image is iteratively convolved, and the maximum number of iterations is preset. If the number of iterations is equal to the maximum number of iterations, the iterative convolution of the sample image is stopped, and the sample image after iterative convolution is output and calibrated as the blind deconvolution sample image; Wherein, the blind deconvolution sample image does not have blur defects; If the actual jitter amplitude and actual jitter frequency of the gyroscope are not greater than the corresponding standard jitter amplitude and standard jitter frequency of the gyroscope, perform secondary fuzzy defect traceability on the target mobile phone and the target mobile phone touch screen, and design a defect solution based on the secondary traceability results.

5. The image defect tracing detection method applicable to mobile phone touch screens according to claim 1, characterized in that: The fuzzy defect secondary tracing of the target mobile phone and the target mobile phone touch screen, and the defect solution design based on the secondary tracing result, are specifically as follows: If the actual jitter amplitude and the actual jitter frequency of the gyroscope are not greater than the corresponding standard jitter amplitude and the standard jitter frequency of the gyroscope, a hardware damage analysis is performed on the camera of the target mobile phone to determine whether the camera of the target mobile phone has a hardware damage problem; If so, the blur defect source of the sample image is calibrated as the hardware cause of the target mobile phone camera, and the repair plan of the damaged hardware of the target mobile phone camera is retrieved and output in the big data network; If not, then update the camera application cache and the camera application in the target mobile phone. If the sample image does not have blur defects after the camera application cache and the camera application are updated, then calibrate the source of the blur defects of the sample image as the target mobile phone camera software cause; If the sample image still has blur defects after the camera application cache is updated and the camera application is updated, the source of the blur defect of the sample image is calibrated as a hardware problem of the target mobile phone touch screen, and the target mobile phone touch screen is replaced to ensure that the sample image does not have blur defects.

6. Image defect tracing detection system suitable for mobile phone touch screen, characterized in that: The image defect tracing detection system includes a memory and a processor, wherein the memory stores an image defect tracing detection method program. When the image defect tracing detection method program is executed by the processor, the image defect tracing detection method steps as described in any one of claims 1 to 5 are implemented.

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