CN113822860A - Video quality judgment method, system, storage medium and electronic device - Google Patents
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Abstract
The application discloses a video quality judgment method, a system, a storage medium and an electronic device, wherein the video quality judgment method comprises the following steps: the collection step comprises: collecting a video through a video collecting unit; a first recognition result obtaining step: performing quality identification on the video through a CNN video quality judging unit to obtain a first identification result; video frame extraction: performing frame extraction on the video to obtain a plurality of video frames, and performing anomaly detection on the plurality of video frames to obtain a second identification result; judging the video quality: and judging the quality of the video according to the first identification result and the second identification result by the CNN video quality judging unit. The invention can accurately judge the quality of the video, pre-judge the quality of the video, assist the video monitoring personnel to know the running condition of each video device, save a large amount of labor cost and improve the detection efficiency.
Description
Technical Field
The invention belongs to the field of video quality judgment, and particularly relates to a video quality judgment method, a video quality judgment system, a storage medium and electronic equipment.
Background
With the development of artificial intelligence, the construction of smart cities and the increasing number of monitoring cameras in the cities, large-scale monitoring equipment brings new challenges to the maintenance work of monitoring systems. How to know the running condition of the front-end video equipment in time, find the fault and detect the malicious shielding and destructive illegal actions becomes the first urgent problem of the running of the video monitoring system. For thousands of monitoring cameras, the workload of detecting whether the monitoring images have faults or not is large and the efficiency is low by manpower.
The image recognition technology is rapidly developed in recent years, the convolutional neural network is applied to enable the image and video recognition direction to be developed vigorously, the Convolutional Neural Network (CNN) can be used for extracting features of the images and the videos, and after the features are extracted, a plurality of downstream tasks including classification, recognition and the like can be carried out on the images and the videos. The traditional visual method can also obtain some quality judgment of the picture by detecting the picture.
Disclosure of Invention
The embodiment of the application provides a video quality judgment method, a video quality judgment system, a storage medium and electronic equipment, and aims to at least solve the problem that the existing video quality judgment method is low in efficiency in large-scale video, manual monitoring and the like.
The invention provides a video quality judgment method, which comprises the following steps:
the collection step comprises: collecting a video through a video collecting unit;
a first recognition result obtaining step: performing quality identification on the video through a CNN video quality judging unit to obtain a first identification result;
video frame extraction: performing frame extraction on the video to obtain a plurality of video frames, and performing anomaly detection on the plurality of video frames to obtain a second identification result;
judging the video quality: and judging the quality of the video according to the first identification result and the second identification result by the CNN video quality judging unit.
In the above video quality determination method, the second identification result is an abnormal frame number, and the determining video quality includes:
if the first identification result is that the video quality is poor and the number of the abnormal frames is greater than one half of the total number of the video frames, judging that the video quality is abnormal; and if the first identification result is that the video quality is normal and the number of the abnormal frames is less than one half of the total number of the video frames, judging that the video quality is normal.
The video quality determining method described above, wherein the step of determining the video quality further includes:
and if the first identification result is that the video quality is poor and the number of the abnormal frames is less than one half of the total number of the video frames or the first identification result is that the video quality is normal and the number of the abnormal frames is greater than one half of the total number of the video frames, sending information to a video supervisor, and entering manual review.
The video quality determination method may further include performing at least one of noise detection, snow detection, streak detection, brightness detection, blur detection, color cast detection, shake detection, and picture freeze detection on the video frame.
The invention also provides a video quality judgment system, which comprises:
the acquisition module acquires a video through the video acquisition unit;
the first identification result obtaining module is used for identifying the quality of the video through a CNN video quality judging unit to obtain a first identification result;
the video frame extracting module is used for carrying out frame extracting operation on the video to obtain a plurality of video frames, carrying out anomaly detection on the plurality of video frames and obtaining a second identification result;
and the video quality judging module judges the quality of the video according to the first identification result and the second identification result through the CNN video quality judging unit.
In the above video quality determination system, the second identification result is an abnormal frame number, and the video quality determination module includes:
if the first identification result is that the video quality is poor and the number of the abnormal frames is greater than one half of the total number of the video frames, judging that the video quality is abnormal; and if the first identification result is that the video quality is normal and the number of the abnormal frames is less than one half of the total number of the video frames, judging that the video quality is normal.
The above video quality determination system, wherein the video quality determination module further includes:
and if the first identification result is that the video quality is poor and the number of the abnormal frames is less than one half of the total number of the video frames or the first identification result is that the video quality is normal and the number of the abnormal frames is greater than one half of the total number of the video frames, sending information to a video supervisor, and entering manual review.
The video quality determination system may further include a video frame detection unit configured to perform at least one of noise detection, snow detection, streak detection, luminance detection, blur detection, color cast detection, shake detection, and picture freeze detection on the video frame.
An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the video quality determination method as described in any of the above when executing the computer program.
A storage medium having stored thereon a computer program, wherein the program when executed by a processor implements a video quality determination method as claimed in any one of the preceding claims.
The invention has the beneficial effects that:
the invention belongs to the field of computer vision in the deep learning technology. The invention can accurately judge the quality of the video, pre-judge the quality of the video, assist the video monitoring personnel to know the running condition of each video device, save a large amount of labor cost and improve the detection efficiency.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application.
In the drawings:
FIG. 1 is a flow chart of a video quality determination method of the present invention;
FIG. 2 is a general flow chart of a video quality determination method of the present invention;
FIG. 3 is a schematic diagram of a video quality determination system according to the present invention;
fig. 4 is a frame diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be described and illustrated below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments provided in the present application without any inventive step are within the scope of protection of the present application.
It is obvious that the drawings in the following description are only examples or embodiments of the present application, and that it is also possible for a person skilled in the art to apply the present application to other similar contexts on the basis of these drawings without inventive effort. Moreover, it should be appreciated that in the development of any such actual implementation, as in any engineering or design project, numerous implementation-specific decisions must be made to achieve the developers' specific goals, such as compliance with system-related and business-related constraints, which may vary from one implementation to another.
Reference in the specification to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the specification. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of ordinary skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments without conflict.
Unless defined otherwise, technical or scientific terms referred to herein shall have the ordinary meaning as understood by those of ordinary skill in the art to which this application belongs. Reference to "a," "an," "the," and similar words throughout this application are not to be construed as limiting in number, and may refer to the singular or the plural. The present application is directed to the use of the terms "including," "comprising," "having," and any variations thereof, which are intended to cover non-exclusive inclusions; for example, a process, method, system, article, or apparatus that comprises a list of steps or modules (elements) is not limited to the listed steps or elements, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus. Reference to "connected," "coupled," and the like in this application is not intended to be limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. The term "plurality" as referred to herein means two or more. "and/or" describes an association relationship of associated objects, meaning that three relationships may exist, for example, "A and/or B" may mean: a exists alone, A and B exist simultaneously, and B exists alone. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship. Reference herein to the terms "first," "second," "third," and the like, are merely to distinguish similar objects and do not denote a particular ordering for the objects.
The present invention is described in detail with reference to the embodiments shown in the drawings, but it should be understood that these embodiments are not intended to limit the present invention, and those skilled in the art should understand that functional, methodological, or structural equivalents or substitutions made by these embodiments are within the scope of the present invention.
Before describing in detail the various embodiments of the present invention, the core inventive concepts of the present invention are summarized and described in detail by the following several embodiments.
The first embodiment is as follows:
referring to fig. 1, fig. 1 is a flowchart of a video quality determination method. As shown in fig. 1, the video quality determination method of the present invention includes:
a collection step S1: collecting a video through a video collecting unit;
first recognition result obtaining step S2: performing quality identification on the video through a CNN video quality judging unit to obtain a first identification result;
video framing step S3: performing frame extraction on the video to obtain a plurality of video frames, and performing anomaly detection on the plurality of video frames to obtain a second identification result;
video quality judgment step S4: and judging the quality of the video according to the first identification result and the second identification result by the CNN video quality judging unit.
Wherein, the second identification result is an abnormal frame number, and the step of judging the video quality comprises:
if the first identification result is that the video quality is poor and the number of the abnormal frames is greater than one half of the total number of the video frames, judging that the video quality is abnormal; and if the first identification result is that the video quality is normal and the number of the abnormal frames is less than one half of the total number of the video frames, judging that the video quality is normal.
Wherein, the step of judging the video quality further comprises:
and if the first identification result is that the video quality is poor and the number of the abnormal frames is less than one half of the total number of the video frames or the first identification result is that the video quality is normal and the number of the abnormal frames is greater than one half of the total number of the video frames, sending information to a video supervisor, and entering manual review.
The anomaly detection includes at least one of noise detection, snow detection, streak detection, brightness detection, blur detection, color cast detection, shake detection, and picture freeze detection of the video frame.
Specifically, the invention can accurately detect the quality of the video; the invention can remind the video monitoring personnel of the condition of the equipment in real time and provide the video quality report of each video equipment regularly, thereby saving a large amount of labor cost and improving the detection efficiency.
Further, the specific steps of the present invention shown in fig. 2 are as follows:
step 1, installing a video acquisition system.
And 2, identifying the video quality through a CNN video quality judging system, and entering the step 4.
And 3, performing video frame extraction while performing the step 2, performing abnormal detection on the video frame such as noise, snow, stripes, brightness, blurring, color cast, shaking, picture freezing and the like after the frame extraction is completed, and entering the step 4.
And 4, if the CNN video quality judging system judges the abnormal video and the number of the abnormal frames is more than one half of the total number of the video frames, judging that the video is abnormal. If the CNN video quality judging system judges that the video is normal and the number of the abnormal frames is less than one half of the total video frame number, the video is normal. Otherwise, step 5 is entered.
And 5, if the CNN video quality judging system judges that the abnormal frame number is less than one half of the total frame number of the video, or the CNN video quality judging system judges that the abnormal frame number is normal and the abnormal frame number is greater than one half of the total frame number of the video, entering the step 6.
And 6, sending a mobile phone reminding signal to a video supervisor to remind the video supervisor that the video quality is possibly abnormal, and suggesting manual review.
And 7, counting the failure times of each device, storing the failure times into a database, and preparing for subsequent data statistical analysis.
And 8, timing a video quality detection report according to the data in the step 7, and helping a video supervision person in charge to know the condition of each device more clearly.
Example two:
referring to fig. 3, fig. 3 is a schematic structural diagram of a video quality determination system according to the present invention. As shown in fig. 3, a video quality determination system of the present invention includes:
the acquisition module 11 acquires a video through a video acquisition unit;
a first identification result obtaining module 12, where the first identification result obtaining module 12 obtains a first identification result by performing quality identification on the video through a CNN video quality discrimination unit;
the video frame extracting module 13, wherein the video frame extracting module 13 performs frame extracting operation on the video to obtain a plurality of video frames, and performs anomaly detection on the plurality of video frames to obtain a second identification result;
a video quality judgment module 14, wherein the video quality judgment module 14 judges the quality of the video according to the first identification result and the second identification result by the CNN video quality judgment unit.
Wherein, the second recognition result is an abnormal frame number, and the module for judging video quality includes:
if the first identification result is that the video quality is poor and the number of the abnormal frames is greater than one half of the total number of the video frames, judging that the video quality is abnormal; and if the first identification result is that the video quality is normal and the number of the abnormal frames is less than one half of the total number of the video frames, judging that the video quality is normal.
Wherein, the module for judging video quality further comprises:
and if the first identification result is that the video quality is poor and the number of the abnormal frames is less than one half of the total number of the video frames or the first identification result is that the video quality is normal and the number of the abnormal frames is greater than one half of the total number of the video frames, sending information to a video supervisor, and entering manual review.
The anomaly detection includes at least one of noise detection, snow detection, streak detection, brightness detection, blur detection, color cast detection, shake detection, and picture freeze detection of the video frame.
Example three:
referring to fig. 4, this embodiment discloses a specific implementation of an electronic device. The electronic device may include a processor 81 and a memory 82 storing computer program instructions.
Specifically, the processor 81 may include a Central Processing Unit (CPU), or A Specific Integrated Circuit (ASIC), or may be configured to implement one or more Integrated circuits of the embodiments of the present Application.
The memory 82 may be used to store or cache various data files for processing and/or communication use, as well as possible computer program instructions executed by the processor 81.
The processor 81 realizes any one of the video quality determination methods in the above-described embodiments by reading and executing computer program instructions stored in the memory 82.
In some of these embodiments, the electronic device may also include a communication interface 83 and a bus 80. As shown in fig. 4, the processor 81, the memory 82, and the communication interface 83 are connected via the bus 80 to complete communication therebetween.
The communication interface 83 is used for implementing communication between modules, devices, units and/or equipment in the embodiment of the present application. The communication port 83 may also be implemented with other components such as: the data communication is carried out among external equipment, image/data acquisition equipment, a database, external storage, an image/data processing workstation and the like.
The bus 80 includes hardware, software, or both to couple the components of the electronic device to one another. Bus 80 includes, but is not limited to, at least one of the following: data Bus (Data Bus), Address Bus (Address Bus), Control Bus (Control Bus), Expansion Bus (Expansion Bus), and Local Bus (Local Bus). By way of example, and not limitation, Bus 80 may include an Accelerated Graphics Port (AGP) or other Graphics Bus, an Enhanced Industry Standard Architecture (EISA) Bus, a Front-Side Bus (FSB), a Hyper Transport (HT) Interconnect, an ISA (ISA) Bus, an InfiniBand (InfiniBand) Interconnect, a Low Pin Count (LPC) Bus, a memory Bus, a microchannel Architecture (MCA) Bus, a PCI (Peripheral Component Interconnect) Bus, a PCI-Express (PCI-X) Bus, a Serial Advanced Technology Attachment (SATA) Bus, a Video Electronics Bus (audio Electronics Association), abbreviated VLB) bus or other suitable bus or a combination of two or more of these. Bus 80 may include one or more buses, where appropriate. Although specific buses are described and shown in the embodiments of the application, any suitable buses or interconnects are contemplated by the application.
The electronic device may determine based on the video quality to implement the method described in connection with fig. 1.
In addition, in combination with the video quality determination method in the foregoing embodiments, the embodiments of the present application may provide a computer-readable storage medium to implement. The computer readable storage medium having stored thereon computer program instructions; the computer program instructions, when executed by a processor, implement any of the video quality determination methods in the above embodiments.
The technical features of the embodiments described above may be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the embodiments described above are not described, but should be considered as being within the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
In summary, the video quality judgment method has the beneficial effects that the video quality judgment method can accurately judge the quality of the video, pre-judge the quality of the video, assist video supervision personnel to know the operation condition of each video device, save a large amount of labor cost and improve the detection efficiency.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.
Claims (10)
1. A video quality judgment method is characterized by comprising the following steps:
the collection step comprises: collecting a video through a video collecting unit;
a first recognition result obtaining step: performing quality identification on the video through a CNN video quality judging unit to obtain a first identification result;
video frame extraction: performing frame extraction on the video to obtain a plurality of video frames, and performing anomaly detection on the plurality of video frames to obtain a second identification result;
judging the video quality: and judging the quality of the video according to the first identification result and the second identification result by the CNN video quality judging unit.
2. The video quality judgment method according to claim 1, wherein the second recognition result is an abnormal frame number, and the judging video quality step comprises:
if the first identification result is that the video quality is poor and the number of the abnormal frames is greater than one half of the total number of the video frames, judging that the video quality is abnormal; and if the first identification result is that the video quality is normal and the number of the abnormal frames is less than one half of the total number of the video frames, judging that the video quality is normal.
3. The video quality determination method of claim 2, wherein the determining the video quality further comprises:
and if the first identification result is that the video quality is poor and the number of the abnormal frames is less than one half of the total number of the video frames or the first identification result is that the video quality is normal and the number of the abnormal frames is greater than one half of the total number of the video frames, sending information to a video supervisor, and entering manual review.
4. The video quality determination method of claim 1, wherein the anomaly detection comprises at least one of noise detection, snow detection, streak detection, brightness detection, blur detection, color cast detection, jitter detection, and picture freeze detection of the video frame.
5. A video quality determination system, comprising:
the acquisition module acquires a video through the video acquisition unit;
the first identification result obtaining module is used for identifying the quality of the video through a CNN video quality judging unit to obtain a first identification result;
the video frame extracting module is used for carrying out frame extracting operation on the video to obtain a plurality of video frames, carrying out anomaly detection on the plurality of video frames and obtaining a second identification result;
and the video quality judging module judges the quality of the video according to the first identification result and the second identification result through the CNN video quality judging unit.
6. The video quality determination system of claim 5 wherein the second identification result is an abnormal frame number, and the means for determining video quality comprises:
if the first identification result is that the video quality is poor and the number of the abnormal frames is greater than one half of the total number of the video frames, judging that the video quality is abnormal; and if the first identification result is that the video quality is normal and the number of the abnormal frames is less than one half of the total number of the video frames, judging that the video quality is normal.
7. The video quality determination system of claim 6 wherein the determine video quality module further comprises:
and if the first identification result is that the video quality is poor and the number of the abnormal frames is less than one half of the total number of the video frames or the first identification result is that the video quality is normal and the number of the abnormal frames is greater than one half of the total number of the video frames, sending information to a video supervisor, and entering manual review.
8. The video quality determination system of claim 5, wherein the anomaly detection comprises at least one of noise detection, snow detection, streak detection, brightness detection, blur detection, color cast detection, jitter detection, and picture freeze detection of the video frame.
9. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor implements the video quality determination method according to any one of claims 1 to 4 when executing the computer program.
10. A storage medium on which a computer program is stored, which program, when being executed by a processor, is adapted to carry out the video quality determination method according to any one of claims 1 to 4.
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| CN108174198A (en) * | 2018-03-07 | 2018-06-15 | 江苏中软智能系统有限公司 | A kind of video image quality diagnostic analysis detection device and application system |
| CN109816625A (en) * | 2018-11-27 | 2019-05-28 | 广东电网有限责任公司 | A kind of video quality score implementation method |
| CN110662024A (en) * | 2019-10-31 | 2020-01-07 | 上海中铁通信信号测试有限公司 | Video quality diagnosis method and device based on multiple frames and electronic equipment |
| CN112367520A (en) * | 2020-11-11 | 2021-02-12 | 郑州师范学院 | Video quality diagnosis system based on artificial intelligence |
| CN112545525A (en) * | 2020-11-30 | 2021-03-26 | 中国科学技术大学 | Electrocardiogram data classification method, device and system |
| CN112669270A (en) * | 2020-12-21 | 2021-04-16 | 北京金山云网络技术有限公司 | Video quality prediction method and device and server |
| CN112672143A (en) * | 2020-12-21 | 2021-04-16 | 北京金山云网络技术有限公司 | Video quality evaluation method and device and server |
| CN112966596A (en) * | 2021-03-04 | 2021-06-15 | 北京秒针人工智能科技有限公司 | Video optical character recognition system method and system |
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