CN110662024A - Video quality diagnosis method and device based on multiple frames and electronic equipment - Google Patents
Video quality diagnosis method and device based on multiple frames and electronic equipment Download PDFInfo
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- H04N17/00—Diagnosis, testing or measuring for television systems or their details
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
- H04N21/40—Client devices specifically adapted for the reception of or interaction with content, e.g. set-top-box [STB]; Operations thereof
- H04N21/43—Processing of content or additional data, e.g. demultiplexing additional data from a digital video stream; Elementary client operations, e.g. monitoring of home network or synchronising decoder's clock; Client middleware
- H04N21/44—Processing of video elementary streams, e.g. splicing a video clip retrieved from local storage with an incoming video stream or rendering scenes according to encoded video stream scene graphs
- H04N21/44008—Processing of video elementary streams, e.g. splicing a video clip retrieved from local storage with an incoming video stream or rendering scenes according to encoded video stream scene graphs involving operations for analysing video streams, e.g. detecting features or characteristics in the video stream
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- H04N7/00—Television systems
- H04N7/18—Closed-circuit television [CCTV] systems, i.e. systems in which the video signal is not broadcast
- H04N7/181—Closed-circuit television [CCTV] systems, i.e. systems in which the video signal is not broadcast for receiving images from a plurality of remote sources
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Abstract
The invention provides a video quality diagnosis method and device based on multiple frames and electronic equipment, wherein the video quality diagnosis method based on the multiple frames comprises the following steps: acquiring a video stream comprising a plurality of consecutive video frames; respectively detecting whether the diagnostic items of each video frame are qualified; acquiring the number of video frames with unqualified diagnostic items in the video stream; and judging whether the number of unqualified video frames is greater than a preset diagnosis threshold value, if so, determining that the video equipment corresponding to the video stream is abnormal, and if not, determining that the video equipment corresponding to the video stream is normal. The invention effectively solves the problem that the video quality diagnosis in the prior art is easy to have false alarm and missing report, can avoid random faults to the greatest extent so as to ensure detailed monitoring abnormal report, and has sufficient video stream and analysis data when being checked afterwards.
Description
Technical Field
The invention belongs to the technical field of image processing, particularly relates to the technical field of video image detection processing, and particularly relates to a multi-frame-based video quality diagnosis method and device and electronic equipment.
Background
At present, video monitoring systems are widely applied to various fields of railway transportation production. The monitoring system provides powerful technical support for creating safe, efficient and orderly high-speed rail operation.
Most video signals are mainly subjected to background video recording for investigation or evidence collection after the incident, and due to the fact that the number of cameras is large and the geographic positions are dispersed, the cameras cannot be manually checked regularly, and the videos of the corresponding cameras cannot be found out fuzziness or even effective videos cannot be obtained when the videos are inquired and recorded when the emergency occurs.
The figure is a flow chart of a conventional video quality diagnosis method. The traditional video quality diagnosis method is to randomly extract a frame for data analysis to obtain the quality condition of the monitoring equipment. The biggest defect is that the diagnosis result is false or missed.
The fault of the whole equipment is deduced based on the single-frame diagnosis data abnormity, the fault is the individual derivation commonality, the general theorem is formed, and the unreasonable logic exists. The environment such as temperature, humidity, static electricity, grounding system, power supply system have a great influence on the normal operation of the machine. Because the equipment is judged to be bad due to the instant bad contact of a random leaf or circuit, the equipment maintenance worker who is burdened with heavy work can cause great waste of human resources.
Disclosure of Invention
In view of the above drawbacks of the prior art, an object of the present invention is to provide a method, an apparatus and an electronic device for video quality diagnosis based on multiple frames, which are used to solve the problem that the video quality diagnosis in the prior art is prone to false alarm and false negative.
To achieve the above and other related objects, an embodiment of the present invention provides a multi-frame based video quality diagnosis method, including: acquiring a video stream comprising a plurality of consecutive video frames; respectively detecting whether the diagnostic items of each video frame are qualified; acquiring the number of video frames with unqualified diagnostic items in the video stream; and judging whether the number of unqualified video frames is greater than a preset diagnosis threshold value, if so, determining that the video equipment corresponding to the video stream is abnormal, and if not, determining that the video equipment corresponding to the video stream is normal.
In an embodiment of the present application, the detecting whether the diagnostic item of each video frame is qualified includes: acquiring a video frame; selecting a diagnosis item and detecting whether the video frame is qualified under the selected diagnosis item, if so, adding one to the number of unqualified video frames of the diagnosis item, and if not, adding one to the number of qualified video frames of the diagnosis item; judging whether all the diagnosis items are finished, if so, detecting the next video frame; and if not, continuing to detect the next diagnostic item of the video frame.
In an embodiment of the present application, the acquiring the number of video frames with unqualified diagnostic items in the video stream includes: and respectively acquiring the number of unqualified video frames corresponding to each diagnosis item.
In an embodiment of the present application, the determining whether the number of the unqualified video frames is greater than a preset diagnostic threshold includes: respectively judging whether the number of unqualified video frames corresponding to each diagnostic item is greater than a preset diagnostic threshold, if the number of unqualified video frames corresponding to any diagnostic item is greater than the preset diagnostic threshold, determining that the video equipment corresponding to the video stream is abnormal, and if the number of unqualified video frames corresponding to all diagnostic items is less than the preset diagnostic threshold, determining that the video equipment corresponding to the video stream is normal.
In an embodiment of the present application, the diagnosis items include various combinations of occlusion detection, contrast anomaly detection, color cast detection, brightness anomaly detection, picture freeze detection, snow detection, jitter anomaly detection, streak detection, picture blur detection, and no-signal detection.
In an embodiment of the present application, each of the diagnosis items corresponds to a preset diagnosis threshold.
In an embodiment of the present application, the method for diagnosing video quality based on multiple frames further includes: generating a video quality diagnosis report according to the detection result; the video quality diagnosis comprises diagnosis item names, preset diagnosis threshold values corresponding to all diagnosis items, total video frame numbers, unqualified video frame numbers, abnormal rates and video equipment states.
An embodiment of the present invention also provides a multi-frame based video quality diagnosis apparatus, including: the video stream acquisition module is used for acquiring a video stream containing a plurality of continuous video frames; the diagnostic item detection module is used for respectively detecting whether the diagnostic item of each video frame is qualified; the quantity obtaining module is used for obtaining the quantity of the video frames with unqualified diagnostic items in the video stream; and the judging module is used for judging whether the number of the unqualified video frames is greater than a preset diagnosis threshold value, if so, determining that the video equipment corresponding to the video stream is abnormal, and if not, determining that the video equipment corresponding to the video stream is normal.
Embodiments of the present invention also provide an electronic device including a processor and a memory, the memory storing program instructions, the processor executing the program instructions to implement the multi-frame based video quality diagnosis method as described above.
In an embodiment of the present application, the electronic device is connected to a video output device that outputs a video stream, and acquires and detects the video stream in a polling manner.
As described above, the multi-frame-based video quality diagnosis method, apparatus and electronic device according to the present invention have the following advantages:
the invention extracts a certain number of continuous video frames, carries out quality diagnosis on each frame in sequence to obtain the numerical value of each detection item, obtains whether the diagnosis item is abnormal or not by comparing with the preset threshold value, finally carries out statistics on the data of all the diagnosis frames to obtain the number of the abnormal frames of each item, and then compares with the preset threshold value to judge whether the diagnosis item is abnormal or not.
Drawings
Fig. 1 is a schematic overall flow chart of the multi-frame-based video quality diagnosis method according to the present invention.
Fig. 2 is a schematic diagram illustrating a pathological image being cut into a plurality of sub-images according to the multi-frame-based video quality diagnosis method of the present invention.
Fig. 3 is a flowchart illustrating a method for obtaining a benign/malignant prediction model according to the multi-frame-based video quality diagnosis method of the present invention.
Fig. 4 is a block diagram illustrating an overall schematic structure of the multi-frame based video quality diagnosis apparatus according to the present invention.
Fig. 5 is a block diagram illustrating the schematic structure of a benign/malignant prediction model training generation module in the multi-frame-based video quality diagnosis apparatus according to the present invention.
Fig. 6 is a schematic structural diagram of an electronic terminal according to an embodiment of the present application.
Fig. 7 and 8 are diagrams showing effects of an application example of the multi-frame based video quality diagnosis method of the present invention.
Description of the element reference numerals
100 multi-frame based video quality diagnosis apparatus
110 video stream acquisition module
120 diagnostic item detection module
130 quantity acquisition module
140 judging module
1101 processor
1102 memory
S110 to S160
S121 to S127
Detailed Description
The embodiments of the present invention are described below with reference to specific embodiments, and other advantages and effects of the present invention will be easily understood by those skilled in the art from the disclosure of the present specification. The invention is capable of other and different embodiments and of being practiced or of being carried out in various ways, and its several details are capable of modification in various respects, all without departing from the spirit and scope of the present invention. It is to be noted that the features in the following embodiments and examples may be combined with each other without conflict.
It should be noted that the drawings provided in the following embodiments are only for illustrating the basic idea of the present invention, and the components related to the present invention are only shown in the drawings rather than drawn according to the number, shape and size of the components in actual implementation, and the type, quantity and proportion of the components in actual implementation may be changed freely, and the layout of the components may be more complicated.
The embodiment aims to provide a video quality diagnosis method and device based on multiple frames and electronic equipment, and is used for solving the problems that false alarm and false alarm are easy to occur in video quality diagnosis in the prior art.
The principle and implementation of the multi-frame based video quality diagnosis method, apparatus and electronic device of the present invention will be described in detail below, so that those skilled in the art can understand the multi-frame based video quality diagnosis method, apparatus and electronic device without creative efforts.
As shown in fig. 2, the present embodiment provides a multi-frame based video quality diagnosis method, which includes the following steps:
step S110, acquiring a video stream containing a plurality of continuous video frames;
step S120, whether the diagnosis items of each video frame are qualified is respectively detected;
step S130, acquiring the number of video frames with unqualified diagnostic items in the video stream;
step S140, determining whether the number of unqualified video frames is greater than a preset diagnosis threshold, if yes, continuing to execute step S150: determining that the video device corresponding to the video stream is abnormal, if not, continuing to execute step S160: and determining that the video equipment corresponding to the video stream is normal.
The following describes steps S110 to S160 of the multi-frame based video quality diagnosis method according to the present embodiment in detail.
In step S110, a video stream including a plurality of consecutive video frames is acquired.
For example, a video stream containing a plurality of video frames in succession is acquired from a video output device such as a video server (DVS), a web camera (IPC), or the like.
Step S120, respectively detecting whether the diagnostic item of each video frame is qualified.
Specifically, as shown in fig. 3, in the present embodiment, the detecting whether the diagnosis item of each video frame is qualified includes:
step S121, acquiring a video frame;
step S122, selecting a diagnosis item;
step S123, detecting whether the video frame is qualified under the selected diagnosis item, if yes, continuing to execute step S124: adding one to the number of unqualified video frames of the diagnostic item, if not, continuing to execute step S125: adding one to the number of qualified video frames of the diagnostic item;
step S126, determining whether all the diagnostic items have been performed, if yes, continuing to execute step S127: detecting the next video frame; if not, the process returns to step S122: and continuing to detect the next diagnostic item of the video frame.
Step S130, acquiring the number of video frames with unqualified diagnostic items in the video stream.
In this embodiment, the acquiring the number of video frames with unqualified diagnostic items in the video stream includes: and respectively acquiring the number of unqualified video frames corresponding to each diagnosis item.
Step S140, determining whether the number of unqualified video frames is greater than a preset diagnosis threshold, if yes, continuing to execute step S150: determining that the video device corresponding to the video stream is abnormal, if not, continuing to execute step S160: and determining that the video equipment corresponding to the video stream is normal.
In this embodiment, each of the diagnosis items corresponds to a preset diagnosis threshold.
Specifically, in this embodiment, the determining whether the number of the unqualified video frames is greater than the preset diagnostic threshold includes:
respectively judging whether the number of unqualified video frames corresponding to each diagnostic item is greater than a preset diagnostic threshold, if the number of unqualified video frames corresponding to any diagnostic item is greater than the preset diagnostic threshold, determining that the video equipment corresponding to the video stream is abnormal, and if the number of unqualified video frames corresponding to all diagnostic items is less than the preset diagnostic threshold, determining that the video equipment corresponding to the video stream is normal.
In this embodiment, the diagnostic items include various combinations of occlusion detection, contrast anomaly detection, color cast detection, brightness anomaly detection, picture freeze detection, snow detection, jitter anomaly detection, streak detection, picture blur detection, and no-signal detection.
The diagnostic items and the respective corresponding diagnostic thresholds can be referred to table 1.
TABLE 1 diagnostic items and diagnostic threshold configuration Table
| Numbering | Diagnostic items | Diagnostic threshold | Percentage of |
| 1 | Without |
5 | 20％ |
| 2 | Picture freezing | 5 | 20％ |
| 3 | Color cast | 50 | 20％ |
| 4 | |
85 | 20％ |
| 5 | |
40 | 20％ |
| 6 | |
80 | 20％ |
| 7 | |
30 | 20％ |
| 8 | |
40 | 20％ |
| 9 | |
40 | 20％ |
| 10 | |
40 | 20％ |
As shown in fig. 4, the overall method principle of the multi-frame-based video quality diagnosis method of the present embodiment is as follows:
extracting a certain number of continuous video frames (for example, 500 frames), sequentially performing quality diagnosis on each frame to obtain a numerical value of each detection item, and comparing the numerical value with a preset threshold value to obtain whether the diagnosis item is abnormal or not. And finally, counting the data of all the diagnosis frames to obtain the number of abnormal frames of each item, and comparing the number with a preset threshold value to judge whether the diagnosis item of the equipment is abnormal.
The specific process is as follows:
1) taking a piece of video stream (streaming media);
2) taking a frame from the stream media, calculating whether the required diagnosis item data is within the specified threshold, if so, the diagnosis item in the frame is qualified, and the count of the number of the qualified diagnosis items is increased by 1. If not, the frame is diagnosed as abnormal. Adding 1 to the number of abnormal frames in the diagnosis item;
3) and continuously and repeatedly taking the next frame, and diagnosing each diagnosis item in sequence to obtain the qualified frame number and the abnormal frame number of each diagnosis item.
4) And after the specified frame number is processed, counting the abnormal frame number of each diagnosis item.
5) And alarming the equipment of which the abnormal frame number exceeds the threshold value.
In this embodiment, the method for diagnosing video quality based on multiple frames further includes: generating a video quality diagnosis report according to the detection result; the video quality diagnosis comprises diagnosis item names, preset diagnosis threshold values corresponding to all diagnosis items, total video frame numbers, unqualified video frame numbers, abnormal rates and video equipment states.
As can be seen from the above, the multi-frame-based video quality diagnosis method of this embodiment sequentially obtains real-time code streams (video streams) from video output devices for a certain duration in a round-robin manner according to user requirements, performs decoding analysis, and further determines whether a quality failure problem exists in the video images, performs intelligent analysis, determination, and early warning on the quality problem existing in the video images, detects a large number of video output devices in a short time, and stores the diagnosis result in a designated database. A user can check all diagnosis results through an intelligent mobile terminal, such as a mobile phone, generate a required report and provide the report to an operation and maintenance unit in time so as to overhaul faulty equipment as soon as possible.
The multi-frame-based video quality diagnosis method of the embodiment carries out operation maintenance on the existing security system and timely grasps the working condition of each camera; and timely reminding the damaged camera in the key monitoring area to be repaired, and killing the risk in the germination stage.
As shown in fig. 5, the present embodiment also provides a multi-frame based video quality diagnosis apparatus 100, where the multi-frame based video quality diagnosis apparatus 100 includes: a video stream acquiring module 110, a diagnostic item detecting module 120, a quantity acquiring module 130 and a judging module 140.
In this embodiment, the video stream acquiring module 110 is configured to acquire a video stream including a plurality of consecutive video frames; the diagnostic item detection module 120 is configured to detect whether the diagnostic item of each video frame is qualified; the number obtaining module 130 is configured to obtain the number of video frames in the video stream, for which the diagnostic items are not qualified; the determining module 140 is configured to determine whether the number of the unqualified video frames is greater than a preset diagnosis threshold, determine that the video device corresponding to the video stream is abnormal if the number of the unqualified video frames is greater than the preset diagnosis threshold, and determine that the video device corresponding to the video stream is normal if the number of the unqualified video frames is not greater than the preset diagnosis threshold.
The multi-frame based video quality diagnosis apparatus 100 of this embodiment is a virtual apparatus corresponding to the multi-frame based video quality diagnosis method, and the principle is the same as the implementation principle of the multi-frame based video quality diagnosis method, and specific principles of the video stream obtaining module 110, the diagnosis item detecting module 120, the quantity obtaining module 130, and the determining module 140 in the multi-frame based video quality diagnosis apparatus 100 are not repeated herein.
Embodiments of the present invention also provide an electronic device, such as but not limited to an image detection device, an image processing device, etc., as shown in fig. 6, the electronic device processor 1101 and the memory 1102; the memory 1102 is connected to the processor 1101 through a system bus and performs communication with the processor 1101, the memory 1102 is used for storing computer programs, and the processor 1101 is used for running the computer programs, so that the electronic device executes the multi-frame based video quality diagnosis method. The multi-frame based video quality diagnosis method has been described in detail above, and will not be described herein again.
In this embodiment, the electronic device is connected to a video output device that outputs a video stream, and acquires and detects the video stream in a polling manner.
The electronic equipment is connected with a video server (DVS), a network camera (IPC) and the like through an IP network, performs video quality analysis in a polling mode, and gives an alarm signal to remind a worker to check and process in time when the video quality is found to be abnormal.
The electronic equipment is applied with a multi-frame-based video quality diagnosis method, is an application of a video quality diagnosis technology in monitoring system maintenance, and comprises a set of intelligent video fault analysis and early warning system, wherein a real-time code stream with a certain time length is sequentially obtained from front-end equipment in a round-robin mode according to user requirements, decoding analysis is carried out, whether a quality fault problem exists in a video image is further judged, the quality problem existing in the video image is intelligently analyzed, judged and early warned, a large number of front-end equipment are detected in a short time, and the diagnosis result is stored in a specified database. The user can check all diagnosis results through the client connected with the electronic equipment, generate a required report and provide the report to an operation and maintenance unit in time so as to overhaul the fault equipment as soon as possible.
The electronic equipment with the multi-frame-based video quality diagnosis method can operate and maintain the existing security system and timely master the working condition of each camera; and timely reminding the damaged camera in the key monitoring area to be repaired, and killing the risk in the germination stage.
Embodiments of the present invention also provide a computer-readable storage medium, such as a memory configured to store various types of data to support operations at a device. Examples of such data include instructions, messages, pictures, etc. for any application or method operating on the electronic device. The memory may be implemented by any type or combination of volatile or non-volatile memory devices, such as Static Random Access Memory (SRAM), high speed random access memory (high speed ram), Electrically Erasable Programmable Read Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Read Only Memory (PROM), Read Only Memory (ROM), magnetic memory, flash memory, magnetic or optical disks, or the like. The memory stores program instructions that, when executed, implement a multi-frame based video quality diagnostic method as described above. The method for diagnosing video quality based on multiple frames has been described in detail above, and will not be described herein again.
The multi-frame based video quality diagnosis method can be applied to various types of electronic devices. The electronic device is, for example, a controller, such as an arm (advanced RISC machines) controller, an fpga (field programmable Gate array) controller, a soc (system on chip) controller, a dsp (digital signal processing) controller, or an mcu (micro controller unit) controller. The electronic device may also be, for example, a computer that includes components such as memory, a memory controller, one or more processing units (CPUs), a peripheral interface, RF circuitry, audio circuitry, speakers, a microphone, an input/output (I/O) subsystem, a display screen, other output or control devices, and external ports; the computer includes, but is not limited to, Personal computers such as desktop computers, notebook computers, tablet computers, smart phones, smart televisions, Personal Digital Assistants (PDAs), and the like. In other embodiments, the electronic device may also be a server, and the server may be arranged on one or more entity servers according to various factors such as functions, loads, and the like, or may be formed by a distributed or centralized server cluster, which is not limited in this embodiment.
In an actual implementation manner, the electronic device is, for example, an electronic device installed with an Android operating system or an iOS operating system, or an operating system such as Palm OS, Symbian, Black Berry OS, or Windows Phone.
In an exemplary embodiment, the electronic device may be implemented by one or more Application Specific Integrated Circuits (ASICs), Digital Signal Processors (DSPs), Digital Signal Processing Devices (DSPDs), Programmable Logic Devices (PLDs), Field Programmable Gate Arrays (FPGAs), controllers, micro-controllers, microprocessors, cameras, or other electronic components for performing the above-described multi-frame based video quality diagnosis method.
It should be noted that the above-mentioned system bus may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The system bus may be divided into an address bus, a data bus, a control bus, and the like. For ease of illustration, only one thick line is shown in FIG. 6, but this is not intended to represent only one bus or type of bus. The communication interface is used for realizing communication between the database access device and other equipment (such as a client, a read-write library and a read-only library). The Memory may include a Random Access Memory (RAM), and may further include a non-volatile Memory (non-volatile Memory), such as at least one disk Memory.
The Processor 1101 may be a general-purpose Processor, and includes a Central Processing Unit (CPU), a Network Processor (NP), and the like; the device can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, a discrete Gate or transistor logic device, or a discrete hardware component.
Those of ordinary skill in the art will understand that: all or part of the steps for implementing the above method embodiments may be performed by hardware associated with a computer program. The aforementioned computer program may be stored in a computer readable storage medium. When executed, the program performs steps comprising the method embodiments described above; and the aforementioned storage medium includes: various media that can store program codes, such as ROM, RAM, magnetic or optical disks.
Fig. 7 shows the diagnosis of a faulty camera, and from fig. 7, it can be seen that 400 frames are diagnosed in total, and each frame diagnoses 10 items. Contrast abnormity and brightness abnormity and shading abnormity are very obvious. The electronic equipment reminds the user to overhaul the equipment.
Fig. 8 shows the diagnosis of a normal camera. It can be seen from fig. 8 that the system has diagnosed 400 frames after taking the streaming media, and each frame diagnoses 10 items. The picture is fuzzy, the color cast is abnormal, but because the abnormal is the environmental factor problem, the abnormal existence of the partial frame can be tolerated through the threshold value. And reporting to the user that the equipment is in a normal operation state. But this is likely to be an abnormal false positive if using conventional diagnostic methods.
From the above, the report of random faults can be avoided as much as possible based on the multi-frame video quality diagnosis method, the detailed monitoring abnormal report can be ensured, sufficient video streams and analysis data are provided when the abnormal report is checked afterwards, and sufficient and reliable data comparison and evidence are provided after the equipment is repaired.
In summary, the present invention extracts a certain number of consecutive video frames, performs quality diagnosis on each frame in sequence to obtain a value of each detection item, obtains whether the diagnosis item is abnormal by comparing the value with a preset threshold, finally performs statistics on data of all diagnosis frames to obtain an abnormal frame number of each item, and compares the abnormal frame number with the preset threshold to determine whether the diagnosis item is abnormal, thereby effectively solving the problem that the video quality diagnosis in the prior art is easy to have false alarm and missing report, avoiding random faults to the greatest extent, ensuring detailed monitoring of abnormal reports, and having sufficient video stream and analysis data to check afterwards. Therefore, the invention effectively overcomes various defects in the prior art and has high industrial utilization value.
The foregoing embodiments are merely illustrative of the principles and utilities of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or change the above-mentioned embodiments without departing from the spirit and scope of the present invention. Accordingly, it is intended that all equivalent modifications or changes which can be made by those skilled in the art without departing from the spirit and technical spirit of the present invention be covered by the claims of the present invention.
Claims (10)
1. A multi-frame based video quality diagnosis method, comprising:
acquiring a video stream comprising a plurality of consecutive video frames;
respectively detecting whether the diagnostic items of each video frame are qualified;
acquiring the number of video frames with unqualified diagnostic items in the video stream;
and judging whether the number of unqualified video frames is greater than a preset diagnosis threshold value, if so, determining that the video equipment corresponding to the video stream is abnormal, and if not, determining that the video equipment corresponding to the video stream is normal.
2. The multi-frame based video quality diagnosis method according to claim 1, wherein said detecting whether the diagnosis item of each video frame is qualified comprises:
acquiring a video frame;
selecting a diagnosis item and detecting whether the video frame is qualified under the selected diagnosis item, if so, adding one to the number of unqualified video frames of the diagnosis item, and if not, adding one to the number of qualified video frames of the diagnosis item;
judging whether all the diagnosis items are finished, if so, detecting the next video frame; and if not, continuing to detect the next diagnostic item of the video frame.
3. The multi-frame based video quality diagnostic method according to claim 2, wherein said obtaining the number of video frames in the video stream whose diagnostic items are not qualified comprises:
and respectively acquiring the number of unqualified video frames corresponding to each diagnosis item.
4. The multi-frame based video quality diagnostic method according to claim 1, wherein said determining whether the number of failed video frames is greater than a preset diagnostic threshold comprises:
respectively judging whether the number of unqualified video frames corresponding to each diagnostic item is greater than a preset diagnostic threshold, if the number of unqualified video frames corresponding to any diagnostic item is greater than the preset diagnostic threshold, determining that the video equipment corresponding to the video stream is abnormal, and if the number of unqualified video frames corresponding to all diagnostic items is less than the preset diagnostic threshold, determining that the video equipment corresponding to the video stream is normal.
5. The multi-frame based video quality diagnostic method according to any one of claims 1 to 4, wherein the diagnostic items comprise various combinations of occlusion detection, contrast anomaly detection, color cast detection, brightness anomaly detection, picture freeze detection, snowflake detection, judder anomaly detection, streak detection, picture blur detection, no-signal detection.
6. The multi-frame based video quality diagnosis method according to any one of claims 1 to 4, wherein each of said diagnosis items corresponds to a preset diagnosis threshold.
7. The multi-frame based video quality diagnostic method according to any one of claims 1 to 4, wherein the multi-frame based video quality diagnostic method further comprises:
generating a video quality diagnosis report according to the detection result;
the video quality diagnosis comprises diagnosis item names, preset diagnosis threshold values corresponding to all diagnosis items, total video frame numbers, unqualified video frame numbers, abnormal rates and video equipment states.
8. A multi-frame based video quality diagnostic apparatus, comprising:
the video stream acquisition module is used for acquiring a video stream containing a plurality of continuous video frames;
the diagnostic item detection module is used for respectively detecting whether the diagnostic item of each video frame is qualified;
the quantity obtaining module is used for obtaining the quantity of the video frames with unqualified diagnostic items in the video stream;
and the judging module is used for judging whether the number of the unqualified video frames is greater than a preset diagnosis threshold value, if so, determining that the video equipment corresponding to the video stream is abnormal, and if not, determining that the video equipment corresponding to the video stream is normal.
9. An electronic device comprising a processor and a memory, the memory storing program instructions, the processor executing the program instructions to implement the multi-frame based video quality diagnosis method according to any one of claims 1 to 7.
10. The electronic device of claim 9, wherein the electronic device is connected to a video output device that outputs a video stream, and wherein the video stream is obtained and detected by polling.
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111225204A (en) * | 2020-01-21 | 2020-06-02 | 上海悦易网络信息技术有限公司 | Camera shake detection method and device |
| CN111639235A (en) * | 2020-06-01 | 2020-09-08 | 重庆紫光华山智安科技有限公司 | Video recording quality detection method and device, storage medium and electronic equipment |
| CN112235540A (en) * | 2020-10-09 | 2021-01-15 | 北京航空航天大学 | Intelligent video monitoring system for screen display fault recognition alarm |
| CN113191389A (en) * | 2021-03-31 | 2021-07-30 | 中国石油大学(华东) | Submarine pipeline autonomous inspection method and device based on optical vision technology |
| CN113822860A (en) * | 2021-08-30 | 2021-12-21 | 上海明略人工智能(集团)有限公司 | Video quality judgment method, system, storage medium and electronic device |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103702111A (en) * | 2013-12-24 | 2014-04-02 | 北京佳讯飞鸿电气股份有限公司 | Method for detecting camera video color cast |
| CN104301712A (en) * | 2014-08-25 | 2015-01-21 | 浙江工业大学 | Monitoring camera shaking detection method based on video analysis |
| CN106658172A (en) * | 2016-12-30 | 2017-05-10 | 广州虎牙信息科技有限公司 | Video image abnormity judgment method and device and terminal device |
| CN109151463A (en) * | 2018-11-07 | 2019-01-04 | 浩云科技股份有限公司 | A kind of video quality diagnosis system and video quality analysis method |
| CN109996062A (en) * | 2019-04-04 | 2019-07-09 | 广东省安心加科技有限公司 | Video image quality detection method, device, computer equipment and storage medium |
-
2019
- 2019-10-31 CN CN201911053811.7A patent/CN110662024A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103702111A (en) * | 2013-12-24 | 2014-04-02 | 北京佳讯飞鸿电气股份有限公司 | Method for detecting camera video color cast |
| CN104301712A (en) * | 2014-08-25 | 2015-01-21 | 浙江工业大学 | Monitoring camera shaking detection method based on video analysis |
| CN106658172A (en) * | 2016-12-30 | 2017-05-10 | 广州虎牙信息科技有限公司 | Video image abnormity judgment method and device and terminal device |
| CN109151463A (en) * | 2018-11-07 | 2019-01-04 | 浩云科技股份有限公司 | A kind of video quality diagnosis system and video quality analysis method |
| CN109996062A (en) * | 2019-04-04 | 2019-07-09 | 广东省安心加科技有限公司 | Video image quality detection method, device, computer equipment and storage medium |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111225204A (en) * | 2020-01-21 | 2020-06-02 | 上海悦易网络信息技术有限公司 | Camera shake detection method and device |
| CN111639235A (en) * | 2020-06-01 | 2020-09-08 | 重庆紫光华山智安科技有限公司 | Video recording quality detection method and device, storage medium and electronic equipment |
| CN111639235B (en) * | 2020-06-01 | 2023-08-25 | 重庆紫光华山智安科技有限公司 | Video recording quality detection method and device, storage medium and electronic equipment |
| CN112235540A (en) * | 2020-10-09 | 2021-01-15 | 北京航空航天大学 | Intelligent video monitoring system for screen display fault recognition alarm |
| CN113191389A (en) * | 2021-03-31 | 2021-07-30 | 中国石油大学(华东) | Submarine pipeline autonomous inspection method and device based on optical vision technology |
| CN113191389B (en) * | 2021-03-31 | 2022-10-11 | 中国石油大学(华东) | Submarine pipeline autonomous inspection method and device based on optical vision technology |
| CN113822860A (en) * | 2021-08-30 | 2021-12-21 | 上海明略人工智能(集团)有限公司 | Video quality judgment method, system, storage medium and electronic device |
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