High-resolution video image preprocessing method for image processing chip

Abstract

The invention belongs to the technical field of chip image processing, and particularly relates to a high-resolution video image preprocessing method for an image processing chip. According to the method, after an image is preprocessed, a regional image preprocessing image quality adjusting model is built, different image quality adjusting methods are automatically adopted through the trained model according to features of a preprocessed and divided high-resolution video image, and non-key regions and key regions of the video image are divided; respectively carrying out personalized processing on the video images of the non-key area and the key area; according to the invention, through the built image preprocessing image quality adjustment model, the calculation amount of image quality adjustment is reduced, the error of manual adjustment is reduced, and the efficiency and accuracy of image quality adjustment are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of chip image processing, and in particular relates to a high-resolution video image preprocessing method for an image processing chip. Background Art

[0002] At present, the image quality can be optimized by adjusting the picture quality (PQ) adjustment parameters of the local or overall image. Specifically, the image quality can be optimized by artificial means, solutions based on deep models, and solutions combining artificial means with deep models. In the artificial means, the picture quality adjustment parameters are manually set, and the picture quality is optimized according to the manually set picture quality adjustment parameters. In the solution based on the deep model, the video stream can be segmented by the deep model, and the scene recognition can be performed on each video segment after the scene segmentation. The picture quality adjustment parameters of the image in the video segment are determined according to the scene recognition results of each segmented video segment, and then the picture quality is optimized. In the solution combining artificial means with deep models, scene recognition is performed on the images in the video stream, and then the picture quality adjustment parameters are matched from the correspondence between the preset scene and the picture quality parameters according to the identified scene, and then the picture quality is optimized.

[0003] However, image quality optimization by manual means requires human intervention and has poor optimization effects. Image quality optimization based on deep models requires large amounts of computation and is not suitable for scenarios with limited computing power, such as terminal devices. In addition, the processing speed is slow, making it difficult to achieve real-time optimization. The solution that combines traditional solutions with deep models also has the problem of large amounts of computation and is not suitable for scenarios with limited computing power. In addition, this solution has poor optimization effects on images outside of preset scenarios. In summary, the above three existing solutions have the problem of not being able to take into account both the effect and efficiency of image quality optimization, as well as poor adaptability. Through the above analysis, the existing technologies have the following problems that need to be solved: How to automatically adjust the image quality, reduce the manual adjustment rate, improve the quality of high-resolution images to the best, use learning models other than deep learning models to optimize image quality, reduce the amount of calculation, so as to be suitable for scenarios with limited computing power such as terminal devices, improve processing speed, and achieve real-time optimization.

[0004] Therefore, it is necessary to redesign a high-resolution video image preprocessing method for image processing chips to address existing problems. Summary of the invention

[0005] In order to solve the above technical problems, the present invention proposes a high-resolution video image preprocessing method for an image processing chip.

[0006] In a first aspect of the present invention, a high-resolution video image preprocessing method for an image processing chip is provided, comprising the following steps: S1, the image processing chip acquires a first high-resolution video image; S2, performing preprocessing division of the image non-key area and key area according to the first high-resolution video image to obtain a first non-key area image and a first key area image, and collecting a first non-key area image quality adjustment method and a first key area image quality adjustment method of the first non-key area and the first key area respectively; S3, constructing a sub-region image preprocessing image quality adjustment model according to a first two-dimensional input vector composed of the first non-key region image and the first key region image and a first two-dimensional output vector composed of a corresponding first non-key region image quality adjustment method and a corresponding first key region image quality adjustment method; S4, receiving a second high-resolution video image, and the image processing chip generates a second non-key area image quality adjustment method and a second key area image quality adjustment method according to the sub-region image preprocessing image quality adjustment model; S5. Adjust the image quality of the second high-resolution video image according to the second non-key area image quality adjustment method and the second key area image quality adjustment method.

[0007] Furthermore, the regional image preprocessing image quality adjustment model is constructed using machine learning or deep learning methods.

[0008] Furthermore, the first high-resolution video image or the second high-resolution video image is preprocessed in the non-key area and the key area of ​​the image according to the color gamut, and the inter-domain variance T between the non-key area and the key area of ​​the image is defined as: ; In the formula, is the ratio of the mean value of the color gamut R belonging to the non-key area of ​​the image to the mean value of the color gamut R of the first high-resolution video image or the second high-resolution video image, g is the mean value of the three RGB color gamuts of the first high-resolution video image or the second high-resolution video image, is the ratio of the average value of the color gamut G belonging to the key area of ​​the image to the average value of the color gamut G of the first high-resolution video image or the second high-resolution video image, is the ratio of the color gamut B mean value belonging to the key area of ​​the image to the color gamut B mean value of the first high-resolution video image or the second high-resolution video image, is the mean value of the color gamut R in the non-key area of ​​the image, is the mean value of the color gamut G in the non-key area of ​​the image, is the color gamut B mean of the non-key area of ​​the image.

[0009] Furthermore, the inter-domain variance T is used to distinguish between key areas and non-key areas, and the area where the regional mean of the RGB three color domains of the high-resolution video image is greater than the inter-domain variance T is taken as the non-key area feature vector N(T), and the area where the regional mean of the RGB three color domains of the high-resolution video image is less than the inter-domain variance T is taken as the key area feature vector M(T), thereby obtaining a two-dimensional input vector .

[0010] Furthermore, the calculation formula of the classifier based on the Fisher criterion used in the machine learning method is as follows: ; A is a two-dimensional input vector, is the output vector value composed of the corresponding image quality adjustment method, W T is the normal vector perpendicular to the hyperplane, K is the adjustment coefficient, N(T) is the feature vector of the non-key area, is the feature vector of the key area, Determinant The value of .

[0011] A high-resolution video image preprocessing system for an image processing chip is also provided, the system comprising: High-resolution image acquisition module: acquires a first high-resolution video image and also acquires a second high-resolution video image; A key area preprocessing module: connected to the high-resolution image acquisition module, obtains the first high-resolution video image, performs preprocessing division of the image non-key area and key area to obtain a first non-key area image and a first key area image, and respectively collects the first non-key area image quality adjustment method corresponding to the first non-key area and the first key area image quality adjustment method corresponding to the first key area; and also obtains the second high-resolution video image, performs preprocessing division of the image non-key area and key area to obtain a second non-key area image and a second key area image; A sub-regional image preprocessing quality adjustment module: used for receiving the first non-key region image and the first key region image obtained by preprocessing and dividing by the key region preprocessing module, the first non-key region image quality adjustment method and the first key region image quality adjustment method, constructing a sub-regional image preprocessing quality adjustment model according to a first two-dimensional input vector composed of the first non-key region image and the first key region image and a first two-dimensional output vector composed of the corresponding first non-key region image quality adjustment method and the corresponding first key region image quality adjustment method, receiving a second two-dimensional input vector composed of the second non-key region image and the second key region image of the key region preprocessing module, and the sub-regional image preprocessing quality adjustment model processes the second two-dimensional input vector to obtain a second output vector; The chip image processing module receives the second output vector of the sub-region image preprocessing image quality adjustment module, obtains the second non-key region image quality adjustment method and the second key region image quality adjustment method according to the second output vector, and performs sub-region image quality adjustment on the second high-resolution video image.

[0012] Furthermore, the regional image preprocessing image quality adjustment model is constructed using machine learning or deep learning methods.

[0013] Furthermore, the first high-resolution video image or the second high-resolution video image is preprocessed in the non-key area and the key area of ​​the image according to the color gamut, and the inter-domain variance T between the non-key area and the key area of ​​the image is defined as: ; In the formula, is the ratio of the mean value of the color gamut R belonging to the non-key area of ​​the image to the mean value of the color gamut R of the first high-resolution video image or the second high-resolution video image, g is the mean value of the three RGB color gamuts of the first high-resolution video image or the second high-resolution video image, is the ratio of the average value of the color gamut G belonging to the key area of ​​the image to the average value of the color gamut G of the first high-resolution video image or the second high-resolution video image, is the ratio of the color gamut B mean value belonging to the key area of ​​the image to the color gamut B mean value of the first high-resolution video image or the second high-resolution video image, is the mean value of the color gamut R in the non-key area of ​​the image, is the mean value of the color gamut G in the non-key area of ​​the image, is the color gamut B mean of the non-key area of ​​the image.

[0014] Furthermore, the inter-domain variance T is used to distinguish between key areas and non-key areas, and the area where the regional mean of the RGB three color domains of the high-resolution video image is greater than the inter-domain variance T is taken as the non-key area feature vector N(T), and the area where the regional mean of the RGB three color domains of the high-resolution video image is less than the inter-domain variance T is taken as the key area feature vector M(T), thereby obtaining the two-dimensional input vector .

[0015] Furthermore, the calculation formula of the classifier based on the Fisher criterion used in the machine learning method is as follows: ; A is a two-dimensional input vector, is the output vector value composed of the corresponding image quality adjustment method, W T is the normal vector perpendicular to the hyperplane, K is the adjustment coefficient, N(T) is the feature vector of the non-key area, is the feature vector of the key area, Determinant The value of .

[0016] The present invention discloses a method of building a regional image preprocessing image quality adjustment model after image preprocessing, and automatically adopting different image quality adjustment methods according to the characteristics of the preprocessed high-resolution video image through the trained model, and dividing the video image into non-key areas and key areas, and performing personalized processing on the video images of non-key areas and key areas respectively. The present invention reduces the amount of calculation of image quality adjustment, reduces the error of manual adjustment, and improves the efficiency and accuracy of image quality conditions by building an image preprocessing image quality adjustment model.

[0017] More embodiments and improved effects of the present invention will be further introduced in conjunction with the drawings and specific embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a flow chart of a high-resolution video image preprocessing method for an image processing chip of the present invention; Figure 2 It is a schematic diagram of a high-resolution video image preprocessing system for an image processing chip of the present invention; Figure 3 Schematic diagram of the division of non-key areas and key areas in the embodiment of the present invention; Figure 4 is a schematic diagram of an online image quality adjustment mode in an embodiment of the present invention; Figure 5 It is a schematic diagram of the structure of an electronic device for implementing the method of the present invention according to an embodiment of the present invention. DETAILED DESCRIPTION

[0019] The invention is further described below in conjunction with the accompanying drawings and specific implementation methods.

[0020] In order to solve the above technical problems, the present invention proposes a high-resolution video image preprocessing method for an image processing chip.

[0021] In a first aspect of the present invention, a high-resolution video image preprocessing method for an image processing chip is provided, comprising the following steps: S1, the image processing chip acquires a first high-resolution video image; S2, performing preprocessing division of the image non-key area and key area according to the first high-resolution video image to obtain a first non-key area image and a first key area image, and collecting a first non-key area image quality adjustment method and a first key area image quality adjustment method of the first non-key area and the first key area respectively; S3, constructing a sub-region image preprocessing image quality adjustment model according to a first two-dimensional input vector composed of the first non-key region image and the first key region image and a first two-dimensional output vector composed of a corresponding first non-key region image quality adjustment method and a corresponding first key region image quality adjustment method; S4, receiving a second high-resolution video image, and the image processing chip generates a second non-key area image quality adjustment method and a second key area image quality adjustment method according to the sub-region image preprocessing image quality adjustment model; S5. Adjust the image quality of the second high-resolution video image according to the second non-key area image quality adjustment method and the second key area image quality adjustment method.

[0022] Furthermore, the regional image preprocessing image quality adjustment model is constructed using machine learning or deep learning methods.

[0023] Furthermore, the first high-resolution video image or the second high-resolution video image is preprocessed in the non-key area and the key area of ​​the image according to the color gamut, and the inter-domain variance T between the non-key area and the key area of ​​the image is defined as: ; In the formula, is the ratio of the mean value of the color gamut R belonging to the non-key area of ​​the image to the mean value of the color gamut R of the first high-resolution video image or the second high-resolution video image, g is the mean value of the three RGB color gamuts of the first high-resolution video image or the second high-resolution video image, is the ratio of the average value of the color gamut G belonging to the key area of ​​the image to the average value of the color gamut G of the first high-resolution video image or the second high-resolution video image, is the ratio of the color gamut B mean value belonging to the key area of ​​the image to the color gamut B mean value of the first high-resolution video image or the second high-resolution video image, is the mean value of the color gamut R in the non-key area of ​​the image, is the mean value of the color gamut G in the non-key area of ​​the image, is the mean value of the color gamut B in the non-key area of ​​the image. In this example, the values ​​of the RGB three color gamuts are 0-255.

[0024] Further, the inter-domain variance T is used to distinguish between key areas and non-key areas. The area where the regional mean of the three RGB color domains of the high-resolution video image is greater than the inter-domain variance T is the non-key area feature vector N(T). If the value of the inter-domain variance T is 99, then the area where the three RGB color domain values ​​are greater than 99 is the non-key area. In this embodiment, a non-key area feature vector N(T)=|(155,215,87)| / 3=152 is taken as the key area feature vector M(T). The area where the regional mean of the three RGB color domains of the high-resolution video image is less than the inter-domain variance T is the key area feature vector M(T). Then the area where the three RGB color domain values ​​are less than 99 is the key area. In this embodiment, a key area feature vector M(T)=|(56,32,90)| / 3=59 is present. In this embodiment, |X| is represented by X rounded down, thereby obtaining a two-dimensional input vector , then the two-dimensional input vector in this embodiment is .

[0025] Furthermore, the calculation formula of the classifier based on the Fisher criterion used in the machine learning method is as follows: ; A is a two-dimensional input vector, is the output vector value composed of the corresponding image quality adjustment method, W T is the normal vector perpendicular to the hyperplane, K is the adjustment coefficient, N(T) is the feature vector of the non-key area, is the feature vector of the key area, Determinant The value of .

[0026] The two-dimensional output vector in this embodiment is the image quality adjustment method of the non-key area and the key area. The adjustment method of the non-key area and the key area includes the values ​​of color temperature C, vividness V, sharpness value B, blurriness Q and blue light E, etc., which are not limited to the five features, namely When the output vector value is greater than or equal to 0, the corresponding two-dimensional output vector is F(N,M)=( ), When the output vector value is less than 0, the corresponding two-dimensional output vector is F'(N,M)=( ). The adjustment coefficient in this embodiment has a value range of -0.01-0.015, and this coefficient only makes appropriate corrections to the output vector value obtained by classification.

[0027] A high-resolution video image preprocessing system for an image processing chip is also provided, the system comprising: High-resolution image acquisition module: acquires a first high-resolution video image and also acquires a second high-resolution video image; A key area preprocessing module: connected to the high-resolution image acquisition module, obtains the first high-resolution video image, performs preprocessing division of the image non-key area and key area to obtain a first non-key area image and a first key area image, and respectively collects the first non-key area image quality adjustment method corresponding to the first non-key area and the first key area image quality adjustment method corresponding to the first key area; and also obtains the second high-resolution video image, performs preprocessing division of the image non-key area and key area to obtain a second non-key area image and a second key area image; A sub-regional image preprocessing quality adjustment module: used for receiving the first non-key region image and the first key region image obtained by preprocessing and dividing by the key region preprocessing module, the first non-key region image quality adjustment method and the first key region image quality adjustment method, constructing a sub-regional image preprocessing quality adjustment model according to a first two-dimensional input vector composed of the first non-key region image and the first key region image and a first two-dimensional output vector composed of the corresponding first non-key region image quality adjustment method and the corresponding first key region image quality adjustment method, receiving a second two-dimensional input vector composed of the second non-key region image and the second key region image of the key region preprocessing module, and the sub-regional image preprocessing quality adjustment model processes the second two-dimensional input vector to obtain a second output vector; The chip image processing module receives the second output vector of the sub-region image preprocessing image quality adjustment module, obtains the second non-key region image quality adjustment method and the second key region image quality adjustment method according to the second output vector, and performs sub-region image quality adjustment on the second high-resolution video image.

[0028] Furthermore, the regional image preprocessing image quality adjustment model is constructed using machine learning or deep learning methods.

[0029] Furthermore, the first high-resolution video image or the second high-resolution video image is preprocessed in the non-key area and the key area of ​​the image according to the color gamut, and the inter-domain variance T between the non-key area and the key area of ​​the image is defined as: ; In the formula, is the ratio of the mean value of the color gamut R belonging to the non-key area of ​​the image to the mean value of the color gamut R of the first high-resolution video image or the second high-resolution video image, g is the mean value of the three RGB color gamuts of the first high-resolution video image or the second high-resolution video image, is the ratio of the average value of the color gamut G belonging to the key area of ​​the image to the average value of the color gamut G of the first high-resolution video image or the second high-resolution video image, is the ratio of the color gamut B mean value belonging to the key area of ​​the image to the color gamut B mean value of the first high-resolution video image or the second high-resolution video image, is the mean value of the color gamut R in the non-key area of ​​the image, is the mean value of the color gamut G in the non-key area of ​​the image, is the color gamut B mean of the non-key area of ​​the image.

[0030] Furthermore, the inter-domain variance T is used to distinguish between key areas and non-key areas, and the area where the regional mean of the RGB three color domains of the high-resolution video image is greater than the inter-domain variance T is taken as the non-key area feature vector N(T), and the area where the regional mean of the RGB three color domains of the high-resolution video image is less than the inter-domain variance T is taken as the key area feature vector M(T), thereby obtaining the two-dimensional input vector .

[0031] Furthermore, the calculation formula of the classifier based on the Fisher criterion used in the machine learning method is as follows: ; A is a two-dimensional input vector, is the output vector value composed of the corresponding image quality adjustment method, W T is the normal vector perpendicular to the hyperplane, K is the adjustment coefficient, N(T) is the feature vector of the non-key area, is the feature vector of the key area, Determinant The value of .

[0032] The present invention discloses a method of building a regional image preprocessing image quality adjustment model after image preprocessing, and automatically adopting different image quality adjustment methods according to the characteristics of the preprocessed high-resolution video image through the trained model, and dividing the video image into non-key areas and key areas, and performing personalized processing on the video images of non-key areas and key areas respectively. The present invention reduces the amount of calculation of image quality adjustment, reduces the error of manual adjustment, and improves the efficiency and accuracy of image quality conditions by building an image preprocessing image quality adjustment model.

[0033] The prior art mentioned in the above background technology section and specific embodiment section of the present invention can be used as part of the present invention to understand the meaning of some technical features or parameters.

Claims

1. A high-resolution video image preprocessing method for an image processing chip, characterized in that: The following steps are involved: S1, the image processing chip acquires a first high-resolution video image; S2, performing preprocessing division of the image non-key area and key area according to the first high-resolution video image to obtain a first non-key area image and a first key area image, and collecting a first non-key area image quality adjustment method and a first key area image quality adjustment method of the first non-key area and the first key area respectively; S3, constructing a sub-region image preprocessing image quality adjustment model according to a first two-dimensional input vector composed of the first non-key region image and the first key region image and a first two-dimensional output vector composed of a corresponding first non-key region image quality adjustment method and a corresponding first key region image quality adjustment method; S4, receiving a second high-resolution video image, and the image processing chip generates a second non-key area image quality adjustment method and a second key area image quality adjustment method according to the sub-region image preprocessing image quality adjustment model; S5. Adjust the image quality of the second high-resolution video image according to the second non-key area image quality adjustment method and the second key area image quality adjustment method.

2. A high-resolution video image preprocessing method for an image processing chip as claimed in claim 1, characterized in that: The regional image preprocessing image quality adjustment model is constructed using a machine learning or deep learning method.

3. A high-resolution video image preprocessing method for an image processing chip as claimed in claim 2, characterized in that: The first high-resolution video image or the second high-resolution video image is preprocessed in the non-key area and the key area according to the color gamut, and the inter-domain variance T between the non-key area and the key area is defined as: ； In the formula, is the ratio of the mean value of the color gamut R belonging to the non-key area of ​​the image to the mean value of the color gamut R of the first high-resolution video image or the second high-resolution video image, g is the mean value of the three RGB color gamuts of the first high-resolution video image or the second high-resolution video image, is the ratio of the average value of the color gamut G belonging to the key area of ​​the image to the average value of the color gamut G of the first high-resolution video image or the second high-resolution video image, is the ratio of the color gamut B mean value belonging to the key area of ​​the image to the color gamut B mean value of the first high-resolution video image or the second high-resolution video image, is the mean value of the color gamut R in the non-key area of ​​the image, is the mean value of the color gamut G in the non-key area of ​​the image, is the color gamut B mean of the non-key area of ​​the image.

4. A high-resolution video image preprocessing method for an image processing chip as claimed in claim 3, characterized in that: The inter-domain variance T is used to distinguish between key areas and non-key areas, and the area where the regional mean of the RGB three color domains of the high-resolution video image is greater than the inter-domain variance T is the non-key area feature vector N(T), and the area where the regional mean of the RGB three color domains of the high-resolution video image is less than the inter-domain variance T is the key area feature vector M(T), thereby obtaining a two-dimensional input vector .

5. A high-resolution video image preprocessing method for an image processing chip as claimed in claim 4, characterized in that: The calculation formula of the classifier based on Fisher criterion used in the machine learning method is as follows: ； A is a two-dimensional input vector, is the output vector value composed of the corresponding image quality adjustment method, W T is the normal vector perpendicular to the hyperplane, K is the adjustment coefficient, N(T) is the feature vector of the non-key area, is the feature vector of the key area, Determinant The value of .

6. A high-resolution video image preprocessing system for an image processing chip, characterized in that: The system comprises: High-resolution image acquisition module: acquires a first high-resolution video image and also acquires a second high-resolution video image; A key area preprocessing module: connected to the high-resolution image acquisition module, obtains the first high-resolution video image, performs preprocessing division of the image non-key area and key area to obtain a first non-key area image and a first key area image, and respectively collects the first non-key area image quality adjustment method corresponding to the first non-key area and the first key area image quality adjustment method corresponding to the first key area; and also obtains the second high-resolution video image, performs preprocessing division of the image non-key area and key area to obtain a second non-key area image and a second key area image; A sub-regional image preprocessing quality adjustment module: used for receiving the first non-key region image and the first key region image obtained by preprocessing and dividing by the key region preprocessing module, the first non-key region image quality adjustment method and the first key region image quality adjustment method, constructing a sub-regional image preprocessing quality adjustment model according to a first two-dimensional input vector composed of the first non-key region image and the first key region image and a first two-dimensional output vector composed of the corresponding first non-key region image quality adjustment method and the corresponding first key region image quality adjustment method, receiving a second two-dimensional input vector composed of the second non-key region image and the second key region image of the key region preprocessing module, and the sub-regional image preprocessing quality adjustment model processes the second two-dimensional input vector to obtain a second output vector; The chip image processing module receives the second output vector of the sub-region image preprocessing image quality adjustment module, obtains the second non-key region image quality adjustment method and the second key region image quality adjustment method according to the second output vector, and performs sub-region image quality adjustment on the second high-resolution video image.

7. A high-resolution video image preprocessing system for an image processing chip as claimed in claim 6, characterized in that: The regional image preprocessing image quality adjustment model is constructed using a machine learning or deep learning method.

8. A high-resolution video image preprocessing system for an image processing chip as claimed in claim 7, characterized in that: The first high-resolution video image or the second high-resolution video image is preprocessed in the non-key area and the key area according to the color gamut, and the inter-domain variance T between the non-key area and the key area is defined as: ； In the formula, is the ratio of the mean value of the color gamut R belonging to the non-key area of ​​the image to the mean value of the color gamut R of the first high-resolution video image or the second high-resolution video image, g is the mean value of the three RGB color gamuts of the first high-resolution video image or the second high-resolution video image, is the ratio of the average value of the color gamut G belonging to the key area of ​​the image to the average value of the color gamut G of the first high-resolution video image or the second high-resolution video image, is the ratio of the color gamut B mean value belonging to the key area of ​​the image to the color gamut B mean value of the first high-resolution video image or the second high-resolution video image, is the mean value of the color gamut R in the non-key area of ​​the image, is the mean value of the color gamut G in the non-key area of ​​the image, is the color gamut B mean of the non-key area of ​​the image.

9. A high-resolution video image preprocessing system for an image processing chip as claimed in claim 8, characterized in that: The inter-domain variance T is used to distinguish between key areas and non-key areas, and the area where the regional mean of the RGB three color domains of the high-resolution video image is greater than the inter-domain variance T is the non-key area feature vector N(T), and the area where the regional mean of the RGB three color domains of the high-resolution video image is less than the inter-domain variance T is the key area feature vector M(T), thereby obtaining the two-dimensional input vector .

10. A high-resolution video image preprocessing system for an image processing chip as claimed in claim 9, characterized in that: The calculation formula of the classifier based on Fisher criterion used in the machine learning method is as follows: ； A is a two-dimensional input vector, is the output vector value composed of the corresponding image quality adjustment method, W T is the normal vector perpendicular to the hyperplane, K is the adjustment coefficient, N(T) is the feature vector of the non-key area, is the feature vector of the key area, Determinant The value of .

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