CN106993185A - A kind of video quality diagnosing method and system - Google Patents
A kind of video quality diagnosing method and system Download PDFInfo
- Publication number
- CN106993185A CN106993185A CN201710228978.7A CN201710228978A CN106993185A CN 106993185 A CN106993185 A CN 106993185A CN 201710228978 A CN201710228978 A CN 201710228978A CN 106993185 A CN106993185 A CN 106993185A
- Authority
- CN
- China
- Prior art keywords
- average
- image
- video
- passages
- video quality
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N17/00—Diagnosis, testing or measuring for television systems or their details
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N17/00—Diagnosis, testing or measuring for television systems or their details
- H04N17/02—Diagnosis, testing or measuring for television systems or their details for colour television signals
Abstract
The invention discloses a kind of video quality diagnosing method and system, method is：Obtain video data；The video data of acquisition is carried out taking out frame, multiple image is extracted, the multiple image of extraction is converted into yuv format；The indices of the image of several yuv formats are detected respectively, according to image detection result judgement current video quality, and video quality diagnostic result are exported；Wherein, indices include that picture signal is lost, image blurring, gradation of image is abnormal, the excessively bright excessively dark, image of image is blocked, image color cast, gain disorder, the one or more in picture freeze.Analysis to video is changed into the Comprehensive Assessment to finite graph picture by video quality diagnosing method of the present invention, and the video flowing of one section of long period can be analyzed in a short time, and it is abnormal that diagnosis whether there is video quality.Anomalous video detection accuracy of the present invention is not less than 85%, and rate of failing to report is less than 10%；Under the V2 of Intel E3 1230 CPU, the road video of poll 800 is time-consuming 40 minutes, and detection efficiency improves 20%.
Description
The application is to be directed to Application No.：2015109102768, the applying date：2015-12-10, a kind of entitled " video
The divisional application of the patent of invention of quality diagnosis method and system ".
Technical field
The present invention relates to a kind of Video Analysis Technology field, specifically, be related to a kind of video quality diagnosing method and
System.
Background technology
With carrying forward vigorously for safe city, video monitoring system is increasingly popularized.But, video monitoring system may go out
Existing various failures, mainly include in terms of video quality：Vision signal loss, video blur, gradation of image are abnormal, image mistake
Bright, image is excessively dark, image is blocked, image color cast, gain disorder, picture freeze etc..Depending merely on manual inspection and maintenance needs to expend
Substantial amounts of manpower and materials, video quality diagnosis system just should be needed and given birth to, and by analyzing access video intelligent, obtain diagnosis knot
Really, and to abnormal conditions alarm.
Patent CN102395043A discloses a kind of video quality diagnosing method, comprises the following steps：S101, pass through forwarding
Server obtains video data from from long-range, to be diagnosed camera；S102, in the range of default certain time to institute
The indices for stating video data are detected, and provide diagnostic result；Wherein, step S101 and step S102 asynchronous executions,
And after the video data of buffer some amount, then detected, and diagnosis knot is provided to video quality according to testing result
Really.Above-mentioned patent passes through the image definition to video data, video-losing rate, picture colour cast degree, picture gain imbalance degree, right
Than degree, picture drastic change degree, picture freeze degree, float degree, stable striped degree of disturbance, band overlapping degree and noise size these
The detection mode of index, realizes a kind of video quality diagnosing method.
After the video data of above-mentioned patent requirements buffer some amount, then detected, if using the mode of poll,
Need to expend certain memory space.And the image analysis method being related to is more complicated.For example, it is desirable to after step slol
The step of being pre-processed before step S102 to the video data：The video data is carried out first to eliminate OSD processing；
Then the brightness of progress image is covered covers processing with complexity.Definition requires rim detection when detecting, vision signal is lost
Cluster etc. is required during detection, complexity increase causes consumption during detection to increase.
The content of the invention
The invention provides a kind of video quality diagnosing method, solve existing video quality diagnosing method complexity and consume
When more technical problem.
In order to solve the above-mentioned technical problem, the present invention is achieved using following technical scheme：
A kind of video quality diagnosing method, methods described is as follows：
Step 1：Obtain video data；
Step 2：The video data of acquisition is carried out taking out frame, multiple image is extracted, the multiple image of extraction is converted into YUV lattice
Formula；
Step 3：The indices of the image of several yuv formats are detected respectively, video is judged according to image detection result
Quality, and export video quality diagnostic result；
The indices are lost including picture signal, image blurring, gradation of image is abnormal, the excessively bright excessively dark, image of image is blocked,
One or more in image color cast, gain disorder, picture freeze.
It is to use Multi-path synchronous poll that video data is obtained in video quality diagnosing method as described above, the step 1
Mode obtain video data.
Video quality diagnosing method as described above, the abnormal detection method of described image gray scale is：To multiple image point
Not Ji Suan U, V histogram, if probability of U, V value of multiple image between [128-dis, 128+dis] is all higher than given threshold,
Then think that video gradation is abnormal, wherein, dis=10.
Video quality diagnosing method as described above, the detection method that described image is blocked is：Each image is divided into n blocks
Image, judges every block of image with the presence or absence of blocking respectively, calculate each image and there is the block number blocked, if each image is blocked
Block number is more than setting value, it is believed that described image, which exists, blocks；Blocked if multiple image is present, then it is assumed that video image is blocked.
Video quality diagnosing method as described above, it is to calculate respectively to judge that every block of image whether there is the method blocked
The grey level histogram of every block of image, calculates maximum probability value Mg（i）With mean square deviation sigma（i）, i=1,2 ..., n；If P（[Mg
（i）- 10, Mg（i）+10]）＞ given thresholds thr1 and sigma（i）＜ given thresholds thr2, then it is assumed that the block image, which exists, to be hidden
Gear.
Video quality diagnosing method as described above, the detection method of described image colour cast is：Convert the image into RGB lattice
Formula；R, G, B and intensity profile histogram are calculated respectively, calculate average, the average of G passages, the average of channel B and the ash of R passages
Average is spent, the poor absolute value of average, the average of G passages, the average of channel B and the gray average of R passages is calculated and selects difference
Absolute value maximum, if the average of R passages, the average of G passages, the poor absolute value of the average of channel B and gray average
And select the maximum ＞ given thresholds thr1 of the absolute value of difference, then it is assumed that there is colour cast in described image；If multiple image is deposited
In colour cast, then it is assumed that video image colour cast.
Video quality diagnosing method as described above, if the average of R passages, the average of G passages, the average of channel B and ash
Spend the poor absolute value of average and select maximum≤given threshold thr1 of the absolute value of difference, then calculating ash is spent bright place R and led to
The average in road, the average of G passages, the poor absolute value of the average of channel B and gray average, if ash spends the equal of bright place R passages
The maximum ＞ given thresholds thr2 of the poor absolute value of value, the average of G passages, the average of channel B and gray average, then it is assumed that
There is colour cast in described image.
Video quality diagnosing method as described above, the ash spends the position that bright place is gray average ＞ given thresholds thr1
Put.
Based on the design of above-mentioned video quality diagnosing method, the invention also provides a kind of video quality diagnosis system, institute
Stating video diagnostic system includes：
Video acquiring module, for obtaining video data；
Video processing module, carries out taking out frame for the video to acquisition, extracts multiple image, the multiple image of extraction is converted into
Yuv format；
Analysis module, for being detected respectively to the indices of the image of several yuv formats, according to image detection knot
Fruit judges video quality, and exports video quality diagnostic result；
The indices are lost including picture signal, image blurring, gradation of image is abnormal, the excessively bright excessively dark, image of image is blocked,
One or more in image color cast, gain disorder, picture freeze.
Video quality diagnosis system as described above, the video acquiring module is obtained by the way of Multi-path synchronous poll
Video data.
Compared with prior art, advantages and positive effects of the present invention are：Video quality diagnosing method of the present invention will to regarding
The analysis of frequency changes into the Comprehensive Assessment to finite graph picture, and the video flowing of one section of long period can be divided in a short time
Analysis, it is abnormal that diagnosis whether there is video quality.Anomalous video detection accuracy of the present invention is not less than 85%, and rate of failing to report is less than 10%；
Under Intel E3-1230 V2 CPU, the road video of poll 800 is time-consuming 40 minutes, and detection efficiency improves 20%.
It is read in conjunction with the figure after the detailed description of embodiment of the present invention, the other features and advantages of the invention will become more
Plus it is clear.
Brief description of the drawings
Fig. 1 is the flow chart of specific embodiment of the invention video quality diagnosing method.
Fig. 2 is the distribution histogram of specific embodiment of the invention normal picture and dropout image.
Fig. 3 is the flow chart of the picture signal loss detection of the specific embodiment of the invention.
Fig. 4 is specific embodiment of the invention gray level image gradient probability distribution histogram.
Fig. 5 is the flow chart of the image blurring detection of the specific embodiment of the invention.
Fig. 6 is the excessively bright flow chart secretly detected excessively of image of the specific embodiment of the invention.
Fig. 7 is the flow chart of the gradation of image abnormality detection of the specific embodiment of the invention.
Fig. 8 is the flow chart of the image occlusion detection of the specific embodiment of the invention.
Fig. 9 is the flow chart of the image color cast detection of the specific embodiment of the invention.
Figure 10 is the flow chart of the picture freeze detection of the specific embodiment of the invention.
Figure 11 is the system block diagram of the specific embodiment of the invention.
Embodiment
The embodiment to the present invention is described in more detail below in conjunction with the accompanying drawings：
As shown in figure 1, the present embodiment proposes a kind of video quality diagnosing method, comprise the following steps：
Step 1：Video data is obtained, the video data of a period of time is generally obtained, for example, a length of during the video data of acquisition
5s。
Step 2：The video data of acquisition is carried out taking out frame, multiple image is extracted.For example, a frame or several can be extracted with one second
Frame is not extracted, when a length of 5s video data can extract 5 width images.Common video such as H264 videos are 25 frames/second, i.e., every
Second 25 frames, then can extract the 15th, 20,28,30,60 frames.
The multiple image of extraction is converted into yuv format.
Step 3：The indices of the image of several yuv formats are detected respectively, judged according to image detection result
Video quality, and export video quality diagnostic result.
Wherein, indices include picture signal loss, image blurring, gradation of image exception, the excessively bright excessively dark, image of image
Block, image color cast, gain are disorderly, the one or more in picture freeze.
It is preferred that, video data is obtained in Video processing efficiency, step 1 for using the side of Multi-path synchronous poll in order to improve
Formula obtains video data.Specifically, the present embodiment obtains video data simultaneously to multi-channel video, when having obtained wherein all the way, i.e.,
The road video data of acquisition is carried out into step 2 to carry out taking out frame processing, while video carries out obtaining video data all the way under
Operation.For example, it is desired to which 10 road videos are carried out with quality diagnosis, the present embodiment can carry out acquisition video simultaneously to 4 road videos simultaneously
The operation of data, when having obtained wherein all the way, the present embodiment to the 5th road video obtain the operation of video data immediately；
When having obtained all the way again, the present embodiment to the 6th road video obtain the operation of video data immediately, circulates successively.Appoint
Anticipate the moment, the present embodiment carries out obtaining the operation of video data simultaneously to 4 road videos simultaneously.
The specific detection method detected below to the indices of the image of yuv format is illustrated：
1）Picture signal loss detection：
As shown in Fig. 2 the gray value of dropout image major part pixel concentrates on Mg（Gradation of image distribution probability maximum
Corresponding gray value）Near, even gradation of image is largely focused in interval [Mg-10, Mg+10], then is dropout.
Specifically, as shown in figure 3, calculate the grey level histogram of image first, the corresponding gray value Mg of maximum probability is calculated,
If P（[Mg-10, Mg+10]）＞ 0.7, then it is assumed that the dropout of the image.Multiple image is handled successively, if multiple image is equal
Dropout, then it is assumed that the road vision signal is lost, and exports the road vision signal loss alarm information.
2）Image blurring detection：
As shown in figure 4, the gray value of blurred picture consecutive points be more or less the same-gradient is more or less the same, and in gradient distribution histogram
In, 90% Grad is concentrated in [0,10] interval.
Specifically, as shown in figure 5, first calculate gray level image Grad, formed gradient probability distribution histogram, if P
（[0,10]）＞ 0.9, then it is assumed that this is image blurring.Multiple image is handled successively, if multiple image is obscured, then it is assumed that the road is regarded
Frequently it is image blurring, export the road video image fuzzy alarm information.
3）The excessively bright excessively dark detection of image：
By contrasting the average gray of brightness abnormal image and normal picture under unified scene, bright hourly value was found>Setting
Threshold value Thr1, excessively dark hourly value<After given threshold Thr2, and this characteristic presentation full figure, i.e. piecemeal processing, each block is also in
Existing this feature.
Specifically, as shown in fig. 6, to fragmental image processing, being divided into n block images, the gray average of every block of image of calculating
Avrg（i）, i=1,2 ..., n；Global gray average Average=(∑ Avrg (i))/n is calculated, if Average ＞ Thr1, and
Avrg（i）＞ Thr1, i=1,2 ..., n, then it is assumed that image is excessively bright.If Average ＜ Thr2, and Avrg（i）＜ Thr2, i=1,
2nd ..., n, then it is assumed that image is excessively dark.Multiple image is handled successively, if multiple image is excessively bright, then it is assumed that the road video image mistake
It is bright, export the excessively bright warning message of the road video image；If multiple image is excessively dark, then it is assumed that the road video image is excessively dark, output
The excessively dark warning message of the road video image.
4）Gradation of image abnormality detection：
U, V value of gray scale abnormal image are concentrated near 128（U, V span [0,255]）, even image U, V values are most of
Concentrate in interval [128-dis, 128+dis], then it is abnormal for gray scale（Typically take dis=10）.
Specifically, as shown in fig. 7, U, the V histogram of image are calculated, if P（[128-dis, 128+dis]）＞ 0.9, then recognize
It is abnormal for gradation of image, wherein, dis=10.Multiple image is handled respectively, and the road is thought if multiple image is gray scale exception
Video gradation is abnormal, exports the road video gradation abnormal alarm information.
5）Image occlusion detection：
Shielded image shield portions gray scale is presented gray value and compares concentration and the less feature of mean square deviation, by image block, difference
Grey level histogram and mean square deviation are calculated, gray value concentration and mean square deviation exist less than the block of certain threshold value blocks.
Specifically, as shown in figure 8, image is divided into n block images, every block of image is judged respectively with the presence or absence of blocking, respectively
The grey level histogram of every block of image is calculated, maximum probability value Mg is calculated（i）With mean square deviation sigma（i）, i=1,2 ..., n；If P
（[Mg（i）- 10, Mg（i）+10]）＞ given thresholds thr1 and sigma（i）＜ given thresholds thr2, then it is assumed that the block image is deposited
Blocking.Calculate image and there is the block number blocked, if blocking block number more than setting value, it is believed that image, which exists, to be blocked.If several figures
Blocked as existing, then it is assumed that the road video has image and blocked, and exports the road video pictures and blocks warning message.
6）Image color cast is detected：
Colour cast image RGB triple channels average and gray average difference are larger, and local colour cast image usually has colour cast at brighter place,
Cross bright place's triple channel deviation average it is larger when to there is colour cast abnormal.
As shown in figure 9, converting the image into rgb format；R, G, B and intensity profile histogram are calculated respectively；Calculate R passages
Average, the average of G passages, the average of channel B and gray average, calculate the averages of R passages, the average of G passages, channel B
The poor absolute value of average and gray average and the maximum MAX_distance for selecting poor absolute value, if the average of R passages,
The average of G passages, the poor absolute value of the average of channel B and gray average and the maximum MAX_ for selecting poor absolute value
Distance ＞ given thresholds thr1, then it is assumed that described image has colour cast.If the average of R passages, the average of G passages, channel B
Average and gray average poor absolute value and select difference absolute value maximum MAX_distance≤given threshold
Thr1, then calculate ash spend bright place R passages average, the average of G passages, the average of channel B and gray average it is poor absolute
Value, if ash spends the poor absolute value of average, the average of G passages, the average of channel B and the gray average of bright place R passages most
Big value ＞ given thresholds thr2, then it is assumed that described image has colour cast.Wherein, ash spends bright place for gray average ＞ given thresholds
Thr1 position.
Multiple image is handled successively, and multiple image has colour cast Ze Weigai roads video and there is colour cast, exports the road video
Colour cast warning message.
7）Gain disorder detection：
The gray average of multiple image is calculated, if the difference of the gray average of any two images is more than given threshold, it is believed that should
Road video gain is disorderly, exports the disorderly warning message of the road video gain.
8）Picture freeze is detected：
As shown in Figure 10, freeze that video freeze is motionless in a picture, then the figure difference of front and rear two frame is 0, will front and rear two frames ash
Image subtraction is spent, the number NUM that statistics gray level is 0, if NUM ＞ given thresholds thr, then it is assumed that video pictures freeze, is exported
The road video pictures freeze warning message.
Based on the design of above-mentioned video quality diagnosing method, as shown in figure 11, the present embodiment also proposed a kind of video matter
Diagnostic system is measured, including：
Video acquiring module, for obtaining video data, video data is obtained by the way of Multi-path synchronous poll.
Video processing module, carries out taking out frame for the video to acquisition, extracts multiple image, the multiple image of extraction is turned
Change yuv format into；
Analysis module, the indices for the image to yuv format are detected, are judged to regard according to image detection result
Frequency quality, and video quality diagnostic result is exported, the specific detection process of indices is as described above, be no longer described in detail herein.
Wherein, indices include picture signal loss, image blurring, gradation of image exception, the excessively bright excessively dark, image of image
Block, image color cast, gain are disorderly, the one or more in picture freeze.
Certainly, described above is not limitation of the present invention, and the present invention is also not limited to the example above, this technology neck
The variations, modifications, additions or substitutions that the those of ordinary skill in domain is made in the essential scope of the present invention, should also belong to this hair
Bright protection domain.
Claims (7)
1. a kind of video quality diagnosing method, it is characterised in that methods described is as follows：
Step 1：Obtain video data；
Step 2：The video data of acquisition is carried out taking out frame, multiple image is extracted, the multiple image of extraction is converted into YUV lattice
Formula；
Step 3：The index to the image of several yuv formats is detected respectively, and video quality is judged according to image detection result,
And export video quality diagnostic result；
The index of described image includes image color cast, and the detection method of described image colour cast is：Convert the image into rgb format；
R, G, B and intensity profile histogram are calculated respectively, and average, the average of G passages, the average of channel B and the gray scale for calculating R passages are equal
Value, calculates the poor absolute value of average, the average of G passages, the average of channel B and the gray average of R passages and selects the exhausted of difference
To the maximum of value, if the average of R passages, the average of G passages, the average of channel B and the poor absolute value of gray average and choosing
Select the maximum ＞ given thresholds thr1 of the absolute value of difference, then it is assumed that described image has colour cast；If multiple image exists inclined
Color, then it is assumed that video image colour cast.
2. video quality diagnosing method according to claim 1, it is characterised in that obtain video data in the step 1
To obtain video data by the way of Multi-path synchronous poll.
3. video quality diagnosing method according to claim 1 or 2, it is characterised in that the index of described image includes figure
As gray scale exception, the abnormal detection method of described image gray scale is：U, V histogram are calculated multiple image respectively, if several figures
Probability of U, V value of picture between [128-dis, 128+dis] is all higher than given threshold, then it is assumed that video gradation is abnormal, wherein,
dis=10。
4. video quality diagnosing method according to claim 1, it is characterised in that if the average of R passages, G passages is equal
Value, the poor absolute value of the average of channel B and gray average and the maximum≤given threshold thr1 for selecting poor absolute value,
The poor absolute value that ash spends average, the average of G passages, the average of channel B and the gray average of bright place R passages is then calculated, if
Ash spends the maximum ＞ of the poor absolute value of average, the average of G passages, the average of channel B and the gray average of bright place R passages
Given threshold thr2, then it is assumed that described image has colour cast.
5. video quality diagnosing method according to claim 4, it is characterised in that：The ash spends bright place for gray average
＞ given thresholds thr1 position.
6. a kind of video quality diagnosis system, it is characterised in that the video diagnostic system includes：
Video acquiring module, for obtaining video data；
Video processing module, carries out taking out frame for the video to acquisition, extracts multiple image, the multiple image of extraction is converted into
Yuv format；
Analysis module, is detected for the index respectively to the image of several yuv formats, is sentenced according to image detection result
Disconnected video quality, and export video quality diagnostic result；
The index of described image includes image color cast, and the detection method of described image colour cast is：Convert the image into rgb format；
R, G, B and intensity profile histogram are calculated respectively, and average, the average of G passages, the average of channel B and the gray scale for calculating R passages are equal
Value, calculates the poor absolute value of average, the average of G passages, the average of channel B and the gray average of R passages and selects the exhausted of difference
To the maximum of value, if the average of R passages, the average of G passages, the average of channel B and the poor absolute value of gray average and choosing
Select the maximum ＞ given thresholds thr1 of the absolute value of difference, then it is assumed that described image has colour cast；If multiple image exists inclined
Color, then it is assumed that video image colour cast.
7. video quality diagnosis system according to claim 6, it is characterised in that the video acquiring module uses multichannel
The mode of synchronous polling obtains video data.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710228978.7A CN106993185A (en) | 2015-12-10 | 2015-12-10 | A kind of video quality diagnosing method and system |
Applications Claiming Priority (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710228978.7A CN106993185A (en) | 2015-12-10 | 2015-12-10 | A kind of video quality diagnosing method and system |
| CN201510910276.8A CN105430384B (en) | 2015-12-10 | 2015-12-10 | A kind of video quality diagnosing method and system |
Related Parent Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201510910276.8A Division CN105430384B (en) | 2015-12-10 | 2015-12-10 | A kind of video quality diagnosing method and system |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN106993185A true CN106993185A (en) | 2017-07-28 |
Family
ID=55508281
Family Applications (3)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201510910276.8A Active CN105430384B (en) | 2015-12-10 | 2015-12-10 | A kind of video quality diagnosing method and system |
| CN201710228737.2A Pending CN107071398A (en) | 2015-12-10 | 2015-12-10 | A kind of video quality diagnosing method and system |
| CN201710228978.7A Pending CN106993185A (en) | 2015-12-10 | 2015-12-10 | A kind of video quality diagnosing method and system |
Family Applications Before (2)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201510910276.8A Active CN105430384B (en) | 2015-12-10 | 2015-12-10 | A kind of video quality diagnosing method and system |
| CN201710228737.2A Pending CN107071398A (en) | 2015-12-10 | 2015-12-10 | A kind of video quality diagnosing method and system |
Country Status (1)
| Country | Link |
|---|---|
| CN (3) | CN105430384B (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107493509A (en) * | 2017-09-25 | 2017-12-19 | 中国联合网络通信集团有限公司 | Video quality monitoring method and device |
| CN112804520A (en) * | 2021-01-11 | 2021-05-14 | 山东高速信息工程有限公司 | High-speed monitoring video quality detection method |
Families Citing this family (20)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107610499A (en) * | 2016-07-11 | 2018-01-19 | 富士通株式会社 | Detection method, detection means and the electronic equipment of parking stall state |
| CN106898020B (en) * | 2017-02-23 | 2020-09-22 | 江苏长天智远交通科技有限公司 | Monitoring image contrast abnormity identification method and system based on video processing |
| CN109167997B (en) * | 2017-03-30 | 2020-04-28 | 安徽四创电子股份有限公司 | Video quality diagnosis system and method |
| CN106920398A (en) * | 2017-04-27 | 2017-07-04 | 深圳大图科创技术开发有限公司 | A kind of intelligent vehicle license plate recognition system |
| CN107734323B (en) * | 2017-09-13 | 2019-08-16 | 深圳市华星光电技术有限公司 | A kind of algorithm checking system and method |
| CN108337505B (en) * | 2018-01-30 | 2020-11-10 | 百度在线网络技术（北京）有限公司 | Information acquisition method and device |
| CN108377389A (en) * | 2018-04-03 | 2018-08-07 | 北京中智润邦科技有限公司 | Video quality diagnostic device and monitor supervision platform system |
| CN109413413B (en) * | 2018-09-26 | 2020-06-05 | 央视国际网络无锡有限公司 | Video quality evaluation method and device |
| CN109391844A (en) * | 2018-11-20 | 2019-02-26 | 国网安徽省电力有限公司信息通信分公司 | Video quality diagnosing method and system based on video conference scene |
| CN109587449B (en) * | 2018-12-14 | 2020-08-04 | 安徽三人信息科技有限公司 | Remote court trial method |
| CN111340749B (en) * | 2018-12-17 | 2023-08-29 | 丰翼科技(深圳)有限公司 | Image quality detection method, device, equipment and storage medium |
| CN111008596B (en) * | 2019-12-05 | 2020-12-25 | 西安科技大学 | Abnormal video cleaning method based on characteristic expected subgraph correction classification |
| CN113011226A (en) * | 2019-12-19 | 2021-06-22 | 合肥君正科技有限公司 | Method for detecting shielding of color objects in monitoring picture in vehicle |
| CN111083468B (en) * | 2019-12-23 | 2021-08-20 | 杭州小影创新科技股份有限公司 | Short video quality evaluation method and system based on image gradient |
| CN111541891B (en) * | 2020-04-20 | 2022-08-09 | 江苏唯侓机器人科技有限公司 | Method for determining defects of camera based on image |
| CN112153371B (en) * | 2020-08-24 | 2021-07-20 | 珠海格力电器股份有限公司 | Image quality detection method, device, storage medium and product detection method |
| CN112949390B (en) * | 2021-01-28 | 2024-03-15 | 浙江大华技术股份有限公司 | Event detection method and device based on video quality |
| CN113271457B (en) * | 2021-05-13 | 2022-03-15 | 云从科技集团股份有限公司 | Video data abnormality determination method and apparatus, storage medium, and control apparatus |
| 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 |
| CN115118934A (en) * | 2022-06-28 | 2022-09-27 | 广州阿凡提电子科技有限公司 | Live broadcast effect monitoring processing method and system |
Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101917607A (en) * | 2010-07-22 | 2010-12-15 | 浪潮电子信息产业股份有限公司 | Embedded system multi-channel video capture method |
| CN102169544A (en) * | 2011-04-18 | 2011-08-31 | 苏州市慧视通讯科技有限公司 | Face-shielding detecting method based on multi-feature fusion |
| CN102395043A (en) * | 2011-11-11 | 2012-03-28 | 北京声迅电子股份有限公司 | Video quality diagnosing method |
| CN102421008A (en) * | 2011-12-07 | 2012-04-18 | 浙江捷尚视觉科技有限公司 | Intelligent video quality detecting system |
| CN102572502A (en) * | 2010-12-17 | 2012-07-11 | 北京东方文骏软件科技有限责任公司 | Selecting method of keyframe for video quality evaluation |
| CN102724541A (en) * | 2012-05-21 | 2012-10-10 | 浙江捷尚视觉科技有限公司 | Intelligent diagnosis and recovery method for monitoring images |
| CN102857788A (en) * | 2012-08-30 | 2013-01-02 | 西可通信技术设备（河源）有限公司 | Video quality diagnostic system and video quality diagnostic method |
| CN103067734A (en) * | 2012-12-12 | 2013-04-24 | 成都三泰电子实业股份有限公司 | Video image color cast detecting method of video quality diagnostic system |
| CN103096120A (en) * | 2011-10-28 | 2013-05-08 | 浙江大华技术股份有限公司 | Video image abnormal state detecting method and device |
| 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 |
| CN104182983A (en) * | 2014-08-27 | 2014-12-03 | 重庆大学 | Highway monitoring video definition detection method based on corner features |
| CN104346817A (en) * | 2013-07-25 | 2015-02-11 | 浙江大华技术股份有限公司 | Video image color cast detection method and device |
| CN104504707A (en) * | 2014-12-26 | 2015-04-08 | 西安理工大学 | Foreign matter blocking detecting method of monitoring camera video images |
| CN104680504A (en) * | 2013-11-26 | 2015-06-03 | 杭州海康威视数字技术股份有限公司 | Scene change detection method and device thereof |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7362354B2 (en) * | 2002-02-12 | 2008-04-22 | Hewlett-Packard Development Company, L.P. | Method and system for assessing the photo quality of a captured image in a digital still camera |
| CN102006459A (en) * | 2010-11-09 | 2011-04-06 | 中国电信股份有限公司 | Intelligent video image diagnosis system and method |
| CN104539936B (en) * | 2014-11-12 | 2017-09-12 | 广州中国科学院先进技术研究所 | There are the system and method for snow noise in a kind of monitoring video |
-
2015
- 2015-12-10 CN CN201510910276.8A patent/CN105430384B/en active Active
- 2015-12-10 CN CN201710228737.2A patent/CN107071398A/en active Pending
- 2015-12-10 CN CN201710228978.7A patent/CN106993185A/en active Pending
Patent Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101917607A (en) * | 2010-07-22 | 2010-12-15 | 浪潮电子信息产业股份有限公司 | Embedded system multi-channel video capture method |
| CN102572502A (en) * | 2010-12-17 | 2012-07-11 | 北京东方文骏软件科技有限责任公司 | Selecting method of keyframe for video quality evaluation |
| CN102169544A (en) * | 2011-04-18 | 2011-08-31 | 苏州市慧视通讯科技有限公司 | Face-shielding detecting method based on multi-feature fusion |
| CN103096120A (en) * | 2011-10-28 | 2013-05-08 | 浙江大华技术股份有限公司 | Video image abnormal state detecting method and device |
| CN102395043A (en) * | 2011-11-11 | 2012-03-28 | 北京声迅电子股份有限公司 | Video quality diagnosing method |
| CN102421008A (en) * | 2011-12-07 | 2012-04-18 | 浙江捷尚视觉科技有限公司 | Intelligent video quality detecting system |
| CN102724541A (en) * | 2012-05-21 | 2012-10-10 | 浙江捷尚视觉科技有限公司 | Intelligent diagnosis and recovery method for monitoring images |
| CN102857788A (en) * | 2012-08-30 | 2013-01-02 | 西可通信技术设备（河源）有限公司 | Video quality diagnostic system and video quality diagnostic method |
| CN103067734A (en) * | 2012-12-12 | 2013-04-24 | 成都三泰电子实业股份有限公司 | Video image color cast detecting method of video quality diagnostic system |
| CN104346817A (en) * | 2013-07-25 | 2015-02-11 | 浙江大华技术股份有限公司 | Video image color cast detection method and device |
| CN104680504A (en) * | 2013-11-26 | 2015-06-03 | 杭州海康威视数字技术股份有限公司 | Scene change detection method and device thereof |
| 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 |
| CN104182983A (en) * | 2014-08-27 | 2014-12-03 | 重庆大学 | Highway monitoring video definition detection method based on corner features |
| CN104504707A (en) * | 2014-12-26 | 2015-04-08 | 西安理工大学 | Foreign matter blocking detecting method of monitoring camera video images |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107493509A (en) * | 2017-09-25 | 2017-12-19 | 中国联合网络通信集团有限公司 | Video quality monitoring method and device |
| CN112804520A (en) * | 2021-01-11 | 2021-05-14 | 山东高速信息工程有限公司 | High-speed monitoring video quality detection method |
| CN112804520B (en) * | 2021-01-11 | 2023-10-24 | 山东高速信息集团有限公司 | High-speed monitoring video quality detection method |
Also Published As
| Publication number | Publication date |
|---|---|
| CN105430384B (en) | 2018-05-29 |
| CN105430384A (en) | 2016-03-23 |
| CN107071398A (en) | 2017-08-18 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN105430384B (en) | A kind of video quality diagnosing method and system | |
| ES2681294T3 (en) | Image processing system and computer readable recording medium | |
| EP2587452B1 (en) | Method for identification of dental caries in polychromatic images | |
| US9811746B2 (en) | Method and system for detecting traffic lights | |
| US9524558B2 (en) | Method, system and software module for foreground extraction | |
| US7822275B2 (en) | Method for detecting water regions in video | |
| US8189913B2 (en) | Method for detecting shadow of object | |
| CN103686148B (en) | A kind of method of the automatic detection video image clarity based on Digital Image Processing | |
| CN109803096B (en) | Display method and system based on pulse signals | |
| CN104394377B (en) | A kind of fuzzy abnormal recognition methods of monitoring image and device | |
| CN103096120A (en) | Video image abnormal state detecting method and device | |
| US9489591B2 (en) | Haze image discriminating apparatus and discriminating method | |
| CN112367520B (en) | Video quality diagnosis system based on artificial intelligence | |
| JP2010097412A (en) | Smoke detecting apparatus | |
| CN102306307A (en) | Positioning method of fixed point noise in color microscopic image sequence | |
| CN104883539B (en) | A kind of monitoring method and system anti-tamper for region-of-interest | |
| US9147257B2 (en) | Consecutive thin edge detection system and method for enhancing a color filter array image | |
| CN103034997A (en) | Foreground detection method for separation of foreground and background of surveillance video | |
| CN110688979A (en) | Illegal vehicle tracking method and device | |
| CN102625028A (en) | Method and apparatus for detecting static logo existing in video | |
| CN111179197B (en) | Contrast enhancement method and device | |
| EP2290945B1 (en) | Picture correcting device, picture correcting program and picture correcting method | |
| CN110798680B (en) | Single-frame no-reference self-adaptive video snowflake noise detection method | |
| CN111145219B (en) | Efficient video moving target detection method based on Codebook principle | |
| CN111402189B (en) | Video image color cast detection device and method |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20170728 |
|
| RJ01 | Rejection of invention patent application after publication |