CN117201768A - Image noise detection method and system for high-definition video file - Google Patents
Image noise detection method and system for high-definition video file Download PDFInfo
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Abstract
The application discloses an image noise detection method and system for a high-definition video file, relates to the field of image processing, and solves the problems that the video file is not subjected to safety detection when subjected to image noise detection, and an adaptive image noise detection method is not set, and a safety monitoring module is used for carrying out safety monitoring on an uploaded video file to be detected, generating a file safety signal or a file abnormal signal and feeding the file safety signal back to a temporary storage space; the video frame extraction module converts the video file to be detected into a plurality of frames of video images according to a time axis and sends the frames of video images to the intelligent dividing module, and the intelligent dividing module divides the video images of the video file to be detected; the detection and measurement module is used for setting the detection force of the image set corresponding to the video file to be detected; the image noise detection module detects the image noise of the video file to be detected according to the detection level.
Description
Technical Field
The application belongs to the field of image processing, and particularly relates to an image noise detection method and system for a high-definition video file.
Background
Image processing refers to a technique of analyzing an image with a computer to achieve a desired result. Also known as image processing. Image processing generally refers to digital image processing. The digital image is a large two-dimensional array obtained by photographing with equipment such as an industrial camera, a video camera, a scanner and the like, wherein the elements of the array are called pixels, and the values of the pixels are called gray values. Image processing techniques generally include image compression, enhancement and restoration, matching, description and recognition of 3 parts.
When noise detection is performed on an image of a video file, security detection is not performed on the video file, and an image noise detection method adaptive to video file setting is not provided, so that the image noise detection method and system for the high-definition video file are provided.
Disclosure of Invention
Aiming at the defects existing in the prior art, the application aims to provide an image noise detection method and an image noise detection system for a high-definition video file.
The technical problems to be solved by the application are as follows:
(1) How to detect the security of the high definition video file for detecting the image noise;
(2) How to set an adaptive image noise detection approach for high definition video files.
In order to achieve the above purpose, the present application adopts the following technical scheme:
the image noise detection method of the high-definition video file comprises the following steps of:
step S100, a file importing module uploads a video file to be detected and sends the video file to a temporary storage space, a security monitoring module carries out security monitoring on the uploaded video file to be detected, a file security signal or a file abnormality signal is generated and fed back to the temporary storage space, if the file security signal is received, the video file to be detected is sent to a video frame extracting module, if the file abnormality signal is received, the video file to be detected is sent to an isolated space, and the isolated space isolates, checks and kills the video file to be detected;
step S200, a video frame extraction module converts a video file to be detected into a plurality of frames of video images according to a time axis and sends the frames of video images to an intelligent dividing module, the intelligent dividing module divides the video images of the video file to be detected, and an image set of the video file to be detected is obtained and sent to a detection and measurement module and an image noise detection module;
step S300, a detection and measurement module sets the detection intensity of the image set corresponding to the video file to be detected, and the detection grade of the image set corresponding to the video file to be detected is obtained and sent to an image noise detection module;
step S400, the image noise detection module detects the image noise of the video file to be detected according to the detection level, a normal video picture or an abnormal video picture is obtained and sent to the display module, and the display module displays the abnormal video picture of the video file to be detected.
Further, in step S100, the safety monitoring process of the safety monitoring module is specifically as follows:
step S101, a virtual machine is built in the safety monitoring module, and a formatted test file is stored in the virtual machine;
step S102, moving a copy of a video file to be tested into a virtual machine, and playing the copy in the virtual machine;
step S103, acquiring an initial sector of a test file before playing the video file to be tested, and then acquiring a real-time sector after playing the video file to be tested;
step S104, comparing the initial sector with the real-time sector, if the real-time sector is consistent with the initial sector, generating a file security signal, and if the real-time sector is inconsistent with the initial sector, generating a file abnormality signal.
Further, the dividing process of the intelligent dividing module in the step S200 is specifically as follows;
step S201, arranging video images of a video file to be tested in sequence according to a time axis;
step S202, obtaining a transition image in a video image, and obtaining two adjacent video images of the transition image;
step S203, taking a first frame of video image in the video file to be measured as a division start image and a last frame of video image in the video file to be measured as a division end image;
step S204, simultaneously, marking the transition image as a division ending image and marking the video image of the next frame of the transition image as a division starting image;
step S205, the video images of the video file to be tested are summarized into different image sets according to the division start image and the division end image.
Further, when the transition image is played by the video file to be tested, the first video scene is switched to the second video scene, and the last frame of video image in the first video scene is recorded as the transition image.
Further, the setting process of the detection and measurement module in the step S300 specifically includes the following steps:
step S301, obtaining an image set corresponding to the obtained video file to be tested;
step S302, counting the number of frames of video images in an image set, and recording the number of frames of the video images in each image set as an image number TSi, wherein i is the number of the image set, and the images can be actually arranged in sequence by adopting Arabic numerals or English letters;
step S303, simultaneously, acquiring the starting time of dividing the starting image in the image set and the ending time of dividing the ending image in the image set, and subtracting the starting time from the ending time to obtain the duration CTi of the image set;
step S304, calculating to obtain a measurement value CDi of the image set corresponding to the video file to be measured according to the formula cdi=tsi×a1+cti×a2; wherein a1 and a2 are weight coefficients of fixed values, and a1+a2=1;
in step S305, if the measured value is smaller than the first measurement threshold, the detection level of the image set corresponding to the video file to be measured is the third detection level, if the measured value is greater than or equal to the first measurement threshold and smaller than the second measurement threshold, the detection level of the image set corresponding to the video file to be measured is the second detection level, and if the measured value is greater than or equal to the second measurement threshold, the detection level of the image set corresponding to the video file to be measured is the first detection level.
Further, the detection force of the first detection level is greater than the detection force of the second detection level, and the detection force of the second detection level is greater than the detection force of the third detection level.
Further, in step S400, the detection process of the image noise detection module is specifically as follows:
obtaining the detection times of the corresponding image set of the video file to be detected according to the detection grade;
carrying out image noise detection on video images in the image set corresponding to the video file to be detected according to the detection times;
if the video image has image noise, the video image is marked as an abnormal video image;
and if the video image does not have image noise, recording the video image as a normal video image.
Further, the first detection level corresponds to the first detection times, the second detection level corresponds to the second detection times, and the third detection level corresponds to the third detection times;
the first detection times are larger than the second detection times, and the second detection times are larger than the third detection times.
An image noise detection system of a high-definition video file comprises a file importing module, a temporary storage space, an isolated space, a data acquisition module, a safety monitoring module, a video frame extraction module, an intelligent dividing module, a detection and measurement module, an image noise monitoring module and a display module;
the file importing module is used for uploading the video file to be tested and sending the video file to be tested to the temporary storage space;
the safety monitoring module is used for carrying out safety monitoring on the uploaded video file to be tested, generating a file safety signal or a file abnormal signal, feeding the file safety signal or the file abnormal signal back to the temporary storage space, sending the video file to be tested to the video frame extraction module if the temporary storage space receives the file safety signal, and sending the video file to be tested to the isolated space if the temporary storage space receives the file abnormal signal;
the isolation space is used for isolating, searching and killing the video file to be tested;
the video frame extraction module is used for converting the video file to be detected into a multi-frame video image according to a time axis and sending the multi-frame video image to the intelligent dividing module;
the intelligent dividing module is used for dividing video images of the video files to be detected, obtaining an image set of the video files to be detected and sending the image set to the detection and measurement module and the image noise detection module;
the detection and measurement module is used for setting the detection strength of the image set corresponding to the video file to be detected, obtaining the detection grade of the image set corresponding to the video file to be detected and sending the detection grade to the image noise detection module;
the image noise detection module is used for detecting the image noise of the video file to be detected according to the detection level, obtaining a normal video picture or an abnormal video picture and sending the normal video picture or the abnormal video picture to the display module;
the display module is used for displaying the abnormal video pictures of the video file to be detected.
In summary, due to the adoption of the technical scheme, the beneficial effects of the application are as follows:
1. the method comprises the steps that an uploaded video file to be detected is subjected to safety monitoring through a safety monitoring module, a file safety signal or a file abnormality signal is generated, the video file to be detected is sent to a video frame extraction module if the file safety signal is generated, the video file to be detected is sent to an isolated space if the file abnormality signal is generated, the isolated space is used for isolating and checking the video file to be detected, and the method realizes safety detection on a high-definition video file needing image noise detection;
2. when the video file to be detected is safe, the video frame extraction module is utilized to convert the video file to be detected into a plurality of frames of video images according to a time axis and send the frames of video images to the intelligent dividing module, the intelligent dividing module divides the video images of the video file to be detected, an image set of the video file to be detected is obtained and sent to the detection measuring module and the image noise detecting module, the detection measuring module sets the detection intensity of the image set corresponding to the video file to be detected, the detection grade of the image set corresponding to the video file to be detected is obtained and sent to the image noise detecting module, the image noise detecting module detects the image noise of the video file to be detected according to the detection grade to obtain a normal video image or an abnormal video image, and the application sets an adaptive image noise detecting method for the high-definition video file.
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The present application is further described below with reference to the accompanying drawings for the convenience of understanding by those skilled in the art.
FIG. 1 is a flow chart of the method of the present application;
fig. 2 is an overall system block diagram of the present application.
Detailed Description
The technical solutions of the present application will be clearly and completely described in connection with the embodiments, and it is obvious that the described embodiments are only some embodiments of the present application, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
Example 1: referring to fig. 1, the present application provides an image noise detection method for a high-definition video file, which specifically includes:
step S100, a file importing module uploads a video file to be detected and sends the video file to a temporary storage space, a safety monitoring module monitors the uploaded video file to be detected safely, a file safety signal or a file abnormality signal is generated and fed back to the temporary storage space, if the temporary storage space receives the file safety signal, the video file to be detected is sent to a video frame extracting module, if the temporary storage space receives the file abnormality signal, the video file to be detected is sent to an isolated space, and the isolated space isolates, checks and kills the video file to be detected;
in step S100, the safety monitoring process of the safety monitoring module specifically includes the following steps:
step S101, a virtual machine is built in the safety monitoring module, and a formatted test file is stored in the virtual machine;
step S102, moving a copy of a video file to be tested into a virtual machine, and playing the copy in the virtual machine;
step S103, acquiring an initial sector of a test file before playing the video file to be tested, and then acquiring a real-time sector after playing the video file to be tested;
step S104, comparing the initial sector with the real-time sector, if the real-time sector is consistent with the initial sector, generating a file security signal, and if the real-time sector is inconsistent with the initial sector, generating a file abnormality signal;
step S200, a video frame extraction module converts a video file to be detected into a plurality of frames of video images according to a time axis and sends the frames of video images to an intelligent dividing module, the intelligent dividing module divides the video images of the video file to be detected, and an image set of the video file to be detected is obtained and sent to a detection and measurement module and an image noise detection module;
in this embodiment, the dividing process of the intelligent dividing module in the step S200 is specifically as follows;
step S201, arranging video images of a video file to be tested in sequence according to a time axis;
step S202, obtaining a transition image in a video image, and obtaining two adjacent video images of the transition image;
step S203, taking a first frame of video image in the video file to be measured as a division start image and a last frame of video image in the video file to be measured as a division end image;
step S204, simultaneously, marking the transition image as a division ending image and marking the video image of the next frame of the transition image as a division starting image;
step S205, according to the division start image and the division end image, inducing the video images of the video file to be tested into different image sets;
when the transition image is played by the video file to be tested, the first video scene is switched to the second video scene, the last frame of video image in the first video scene is recorded as the transition image, the judgment method of the transition image can be used for comparing the colors of pixel points, normally, the transition image is switched from one scene to the other, and the color matching of the scenes can be changed greatly;
step S300, a detection and measurement module sets the detection intensity of the image set corresponding to the video file to be detected, and the detection grade of the image set corresponding to the video file to be detected is obtained and sent to an image noise detection module;
in this embodiment, the setting process of the detection and measurement module in step S300 is specifically as follows:
step S301, obtaining an image set corresponding to the obtained video file to be tested;
step S302, counting the number of frames of video images in an image set, and recording the number of frames of the video images in each image set as an image number TSi, wherein i is the number of the image set, and the images can be actually arranged in sequence by adopting Arabic numerals or English letters;
step S303, simultaneously, acquiring the starting time of dividing the starting image in the image set and the ending time of dividing the ending image in the image set, and subtracting the starting time from the ending time to obtain the duration CTi of the image set;
step S304, calculating to obtain a measurement value CDi of the image set corresponding to the video file to be measured according to the formula cdi=tsi×a1+cti×a2; wherein a1 and a2 are weight coefficients of fixed values, and a1+a2=1;
step S305, if the measured value is smaller than the first measurement threshold, the detection level of the image set corresponding to the video file to be measured is a third detection level, if the measured value is greater than or equal to the first measurement threshold and smaller than the second measurement threshold, the detection level of the image set corresponding to the video file to be measured is a second detection level, and if the measured value is greater than or equal to the second measurement threshold, the detection level of the image set corresponding to the video file to be measured is a first detection level; wherein the detection force of the first detection level is greater than the detection force of the second detection level, and the detection force of the second detection level is greater than the detection force of the third detection level;
step S400, an image noise detection module detects image noise of a video file to be detected according to the detection level, a normal video picture or an abnormal video picture is obtained and sent to a display module, and the display module displays the abnormal video picture of the video file to be detected;
in step S400, the detection process of the image noise detection module specifically includes the following steps:
obtaining the detection times of the image set corresponding to the video file to be detected according to the detection levels, wherein the first detection level corresponds to the first detection times, the second detection level corresponds to the second detection times, the third detection level corresponds to the third detection times, the first detection times are larger than the second detection times, and the second detection times are larger than the third detection times;
carrying out image noise detection on the video images in the image set corresponding to the video file to be detected according to the detection times, if the video images have image noise, marking the video images as abnormal video images, and if the video images do not have image noise, marking the video images as normal video images; the image noise detection comprises a switch threshold method, an extremum method, a two-stage threshold method and the like, wherein the two-stage threshold method has the following ideas: the gray values of the salt and pepper noise points are usually concentrated around 255 or 0, have a rough range, and do not necessarily appear in the form of maximum or minimum values; therefore, whether the pixel value is a noise point is judged by giving a range, if the pixel gray value falls in the range, the pixel value is judged to be the noise point, otherwise, the pixel value is judged to be the signal point.
Example 2: based on another concept of the same application, referring to fig. 2, an image noise detection system of a high-definition video file is provided, which comprises a file importing module, a temporary storage space, an isolated space, a data acquisition module, a safety monitoring module, a video frame extraction module, an intelligent dividing module, a detection and measurement module, an image noise monitoring module and a display module;
in this embodiment, the file import module is configured to upload a video file to be tested, and send the video file to be tested to the temporary storage space;
the safety monitoring module is used for carrying out safety monitoring on the uploaded video file to be detected, and the safety monitoring process is specifically as follows:
the security monitoring module is internally provided with a virtual machine, and the virtual machine is internally provided with a formatted test file; moving the copy of the video file to be tested into a virtual machine, and playing in the virtual machine; acquiring an initial sector of a test file before playing a video file to be tested, and then acquiring a real-time sector after playing the video file to be tested; comparing the initial sector with the real-time sector; if the real-time sector is consistent with the initial sector, generating a file security signal; if the real-time sector is inconsistent with the initial sector, generating a file abnormality signal; the sector comparison can adopt an image-text comparison mode or a contour comparison mode, and is not particularly limited;
the security monitoring module feeds back a file security signal or a file abnormality signal to the temporary storage space, if the temporary storage space receives the file security signal, the video file to be tested is sent to the video frame extraction module, and if the temporary storage space receives the file abnormality signal, the video file to be tested is sent to the isolated space, and the isolated space is used for isolating, checking and killing the video file to be tested;
the video frame extraction module is used for converting the video file to be detected into a multi-frame video image according to a time axis and sending the multi-frame video image to the intelligent dividing module; the intelligent dividing module is used for dividing video images of the video files to be detected, and the dividing process is specifically as follows:
sequentially arranging video images of the video file to be tested according to a time axis; acquiring a transition image in a video image to obtain two adjacent video images of the transition image; taking a first frame of video image in the video file to be measured as a division start image and taking a last frame of video image in the video file to be measured as a division end image; meanwhile, the transition image is marked as a division ending image, and the video image of the next frame of the transition image is marked as a division starting image; according to the division starting image and the division ending image, inducing the video images of the video file to be detected into different image sets; in this embodiment, when the transition image is a video file to be tested and played, the first video scene is switched to the second video scene, the last frame of video image in the first video scene is recorded as the transition image, and the judgment method of the transition image can be compared by pixel point colors, and normally, the transition image is switched from one scene to another, so that the color matching of the scene can be changed greatly;
the intelligent dividing module sends an image set of the video file to be detected to the detection and measurement module and the image noise detection module; the detection and measurement module is used for setting the detection force of the image set corresponding to the video file to be detected, and the setting process is specifically as follows:
acquiring an image set corresponding to the obtained video file to be tested; counting the number of frames of video images in the image set, and recording the number of frames of the video images in each image set as an image number TSi, wherein i is the number of the image set, and the images can be actually arranged in sequence by adopting Arabic numerals or English letters; meanwhile, acquiring the starting time of the image set division starting image and the ending time of the image set division ending image, and subtracting the starting time from the ending time to obtain the duration CTi of the image set; calculating to obtain a measured value CDi of an image set corresponding to the video file to be measured through a formula CDi=TSi×a1+CTi×a2; wherein a1 and a2 are weight coefficients of fixed values, and a1+a2=1; if the measured value is smaller than the first measured threshold value, the detection level of the image set corresponding to the video file to be measured is a third detection level; if the measured value is larger than or equal to the first measured threshold value and smaller than the second measured threshold value, the detection grade of the image set corresponding to the video file to be detected is a second detection grade; if the measured value is greater than or equal to the second measured threshold value, the detection grade of the image set corresponding to the video file to be detected is a first detection grade;
wherein the detection force of the first detection level is greater than the detection force of the second detection level, and the detection force of the second detection level is greater than the detection force of the third detection level;
the detection and measurement module sends the detection grade of the image set corresponding to the video file to be detected to the image noise detection module, and the image noise detection module is used for detecting the image noise of the video file to be detected according to the detection grade, and the detection process is specifically as follows:
obtaining the detection times of the image set corresponding to the video file to be detected according to the detection levels, wherein the first detection level corresponds to the first detection times, the second detection level corresponds to the second detection times, the third detection level corresponds to the third detection times, the first detection times are larger than the second detection times, and the second detection times are larger than the third detection times; carrying out image noise detection on video images in the image set corresponding to the video file to be detected by combining the detection times, wherein the image noise detection comprises a switch threshold method, an extremum method, a two-stage threshold method and the like, and the two-stage threshold method has the following ideas: the gray values of the salt and pepper noise points are usually concentrated around 255 or 0, have a rough range, and do not necessarily appear in the form of maximum or minimum values; judging whether the pixel value is a noise point or not by giving a range, if the pixel gray value is in the range, judging the pixel value as the noise point, otherwise, judging the pixel value as a signal point; if the video image has image noise, the video image is marked as an abnormal video image; if the video image does not have image noise, the video image is marked as a normal video image;
the image noise detection module is used for sending a normal video picture or an abnormal video picture to the display module, and the display module is used for displaying the abnormal video picture of the video file to be detected;
in the application, if a corresponding calculation formula appears, the calculation formulas are all dimensionality-removed and numerical calculation, and the weight coefficient, the proportion coefficient and other coefficients in the formulas are set to be a result value obtained by quantizing each parameter, so long as the proportion relation between the parameter and the result value is not influenced.
The preferred embodiments of the application disclosed above are intended only to assist in the explanation of the application. The preferred embodiments are not intended to be exhaustive or to limit the application to the precise form disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the application and the practical application, to thereby enable others skilled in the art to best understand and utilize the application. The application is limited only by the claims and the full scope and equivalents thereof.
Claims (9)
1. The image noise detection method for the high-definition video file is characterized by comprising the following steps of:
step S100, a file importing module uploads a video file to be detected and sends the video file to a temporary storage space, a safety monitoring module monitors the uploaded video file to be detected safely, a file safety signal or a file abnormality signal is generated and fed back to the temporary storage space, if the file safety signal is generated, the video file to be detected is sent to a video frame extracting module, if the file abnormality signal is generated, the video file to be detected is sent to an isolated space, and the isolated space isolates and kills the video file to be detected;
step S200, a video frame extraction module converts a video file to be detected into a plurality of frames of video images according to a time axis and sends the frames of video images to an intelligent dividing module, the intelligent dividing module divides the video images of the video file to be detected, and an image set of the video file to be detected is obtained and sent to a detection and measurement module and an image noise detection module;
step S300, a detection and measurement module sets the detection intensity of the image set corresponding to the video file to be detected, and the detection grade of the image set corresponding to the video file to be detected is obtained and sent to an image noise detection module;
step S400, the image noise detection module detects the image noise of the video file to be detected according to the detection level, a normal video picture or an abnormal video picture is obtained and sent to the display module, and the display module displays the abnormal video picture of the video file to be detected.
2. The method for detecting image noise of high-definition video file according to claim 1, wherein in step S100, the safety monitoring process of the safety monitoring module is specifically as follows:
step S101, a virtual machine is built in the safety monitoring module, and a formatted test file is stored in the virtual machine;
step S102, moving a copy of a video file to be tested into a virtual machine, and playing the copy in the virtual machine;
step S103, acquiring an initial sector of a test file before playing the video file to be tested, and then acquiring a real-time sector after playing the video file to be tested;
step S104, comparing the initial sector with the real-time sector, if the real-time sector is consistent with the initial sector, generating a file security signal, and if the real-time sector is inconsistent with the initial sector, generating a file abnormality signal.
3. The method for detecting image noise of high-definition video file according to claim 1, wherein the dividing process of the intelligent dividing module in step S200 is specifically as follows;
step S201, arranging video images of a video file to be tested in sequence according to a time axis;
step S202, obtaining a transition image in a video image, and obtaining two adjacent video images of the transition image;
step S203, taking a first frame of video image in the video file to be measured as a division start image and a last frame of video image in the video file to be measured as a division end image;
step S204, simultaneously, marking the transition image as a division ending image and marking the video image of the next frame of the transition image as a division starting image;
step S205, the video images of the video file to be tested are summarized into different image sets according to the division start image and the division end image.
4. A method for detecting image noise of a high definition video file according to claim 3, wherein when the transition image is a video file to be detected, the transition image is switched from a first video scene to a second video scene, and the last frame of video image in the first video scene is recorded as the transition image.
5. The method for detecting image noise of high-definition video file according to claim 1, wherein the setting process of the detection and measurement module in step S300 is specifically as follows:
step S301, obtaining an image set corresponding to the obtained video file to be tested;
step S302, counting the number of frames of the video images in the image set, and recording the number of frames of the video images in each image set as the number of images;
step S303, simultaneously, acquiring the starting time of dividing the starting image in the image set and the ending time of dividing the ending image in the image set, and subtracting the starting time from the ending time to obtain the duration of the image set;
step S304, calculating a measured value of an image set corresponding to the video file to be measured;
in step S305, if the measured value is smaller than the first measurement threshold, the detection level of the image set corresponding to the video file to be measured is the third detection level, if the measured value is greater than or equal to the first measurement threshold and smaller than the second measurement threshold, the detection level of the image set corresponding to the video file to be measured is the second detection level, and if the measured value is greater than or equal to the second measurement threshold, the detection level of the image set corresponding to the video file to be measured is the first detection level.
6. The method of claim 5, wherein the first level of detection is greater than the second level of detection, and the second level of detection is greater than the third level of detection.
7. The method according to claim 1, wherein in step S400, the detection process of the image noise detection module is specifically as follows:
obtaining the detection times of the corresponding image set of the video file to be detected according to the detection grade;
carrying out image noise detection on video images in the image set corresponding to the video file to be detected according to the detection times;
if the video image has image noise, the video image is marked as an abnormal video image;
and if the video image does not have image noise, recording the video image as a normal video image.
8. The method for detecting image noise of high-definition video file according to claim 7, wherein the first detection level corresponds to a first detection number, the second detection level corresponds to a second detection number, and the third detection level corresponds to a third detection number;
the first detection times are larger than the second detection times, and the second detection times are larger than the third detection times.
9. The image noise detection system of the high-definition video file is characterized by comprising a file importing module, a temporary storage space, an isolation space, a data acquisition module, a safety monitoring module, a video frame extraction module, an intelligent dividing module, a detection and measurement module, an image noise monitoring module and a display module;
the file importing module is used for uploading the video file to be tested and sending the video file to be tested to the temporary storage space;
the safety monitoring module is used for carrying out safety monitoring on the uploaded video file to be tested, generating a file safety signal or a file abnormal signal, feeding the file safety signal or the file abnormal signal back to the temporary storage space, sending the video file to be tested to the video frame extraction module if the temporary storage space receives the file safety signal, and sending the video file to be tested to the isolated space if the temporary storage space receives the file abnormal signal;
the isolation space is used for isolating, searching and killing the video file to be tested;
the video frame extraction module is used for converting the video file to be detected into a multi-frame video image according to a time axis and sending the multi-frame video image to the intelligent dividing module;
the intelligent dividing module is used for dividing video images of the video files to be detected, obtaining an image set of the video files to be detected and sending the image set to the detection and measurement module and the image noise detection module;
the detection and measurement module is used for setting the detection strength of the image set corresponding to the video file to be detected, obtaining the detection grade of the image set corresponding to the video file to be detected and sending the detection grade to the image noise detection module;
the image noise detection module is used for detecting the image noise of the video file to be detected according to the detection level, obtaining a normal video picture or an abnormal video picture and sending the normal video picture or the abnormal video picture to the display module;
the display module is used for displaying the abnormal video pictures of the video file to be detected.
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| CN104539936A (en) * | 2014-11-12 | 2015-04-22 | 广州中国科学院先进技术研究所 | System and method for monitoring snow noise of monitor video |
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| CN115941934A (en) * | 2022-11-24 | 2023-04-07 | 中国电子产品可靠性与环境试验研究所（（工业和信息化部电子第五研究所）（中国赛宝实验室）） | Multi-frame Fusion Video Noise Evaluation Method Based on Deep Convolutional Neural Network |
| CN116051477A (en) * | 2022-12-26 | 2023-05-02 | 新奥特（北京）视频技术有限公司 | Image noise detection method and device for ultra-high definition video file |
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| CN104539936A (en) * | 2014-11-12 | 2015-04-22 | 广州中国科学院先进技术研究所 | System and method for monitoring snow noise of monitor video |
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