CN105516713A - Image quality evaluation method of road traffic closed-circuit television based on machine vision - Google Patents
Image quality evaluation method of road traffic closed-circuit television based on machine vision Download PDFInfo
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Abstract
The invention discloses an image quality evaluation method of a road traffic closed-circuit television (Closed-Circuit Television, CCTV) based on machine vision, specifically comprising: displaying preset standard images through a portable imaging device; after carrying out a complete set of steps of the CCTV system, outputting real images at a display terminal, wherein the complete set of steps comprises collection, coding, transmission, decoding and so on; photographing over against the display terminal of the CCTV system by a high definition industrial camera to obtain test images; respectively extracting vision features from the test images and the standard images by an information processing unit, comparing and analyzing the vision features, calculating the values of a series of image quality evaluation indexes; resolving the overall object evaluation results of the image quality through an image quality multi-index fusing model by the information processing unit. According to the invention, comprehensive evaluation is carried out to the complete set of CCTV system at the same time without influencing the existing device operation; the overall object evaluation results of the image quality in accord with subject evaluation can be obtained; the method and the system are simple in principle, low in cost and convenient in operation.
Description
Technical field
The present invention relates to the field of video monitoring in traffic safety, particularly a kind of road traffic closed-circuit television image quality evaluating method based on machine vision.
Background technology
In road traffic, closed-circuit television (Closed-CircuitTelevision, CCTV) as the useful supplement of other information collecting devices such as wagon detector, video monitoring means are used to monitor the magnitude of traffic flow in primary location or region, vehicle density and road occupation situation, get the live view of traffic jam intuitively and be uploaded to Control Room in real time, make operation management personnel recognize accident pattern and the order of severity rapidly and accurately, thus provide reliable basis for the formulation of emergency traffic and control strategy.
Video image quality is the key technical index weighing road traffic close-circuit television,closed-circuft televishon quality.In practical application; the factor of influential system picture quality is a lot; resolution of video camera is low, lens focus is improper or low environment illumination bad adaptability etc. will cause the original image quality of front-end collection to decline; apart from long, joint quantity, too large or ground protection is insecure etc. will the picture signal attenuation of transmission channel be caused to increase for cable, the picture quality that the low or visible angle of display device hardware aging, refreshing frequency is little etc. also will affect monitor staff and finally observe.Therefore, determination and evaluation is carried out to road traffic closed-circuit television picture quality, the quality of whole surveillance to be checked on and image enhancement has important practical significance.
The image quality evaluation of existing road traffic closed-circuit television has subjective assessment and objective evaluation two kinds of modes.Subjective evaluation method wherein, seems the most reliable because people is the ultimate consumer of image, but also exists significantly not enough: be subject to the background knowledge of evaluation personnel, observe the impact of motivation and emotional state, cannot realize repeatably accurately measuring; In order to make subjective assessment as far as possible accurately objective, need a large amount of people to be used as observer, human cost is very high and operability is poor.On the other hand, existing method for objectively evaluating, or abnormality detection can only be carried out for the damage of certain single link such as the video signal collective in CCTV system, transmission, storage, encoding and decoding, or certain single evaluation index can only be adopted to carry out analysis and inspection to image lesion, namely, in existing correlation technique, all do not relate to and comprehensive analysis is carried out to a whole set of CCTV system and considers the method for objectively evaluating image quality of multiple Damage Evaluation index simultaneously.Therefore, succinctly as subjective evaluation method can not represent evaluation result intuitively, also easily cause the subjective feeling of evaluation result and image viewing personnel inconsistent.
Summary of the invention
Given this, the object of this invention is to provide a kind of road traffic closed-circuit television image quality evaluating method based on machine vision, the method mainly utilizes the obvious standard picture of a set of visual effect and the test pattern to CCTV system display terminal shooting gained, extract visual signature respectively to compare, calculate the value of a series of images quality evaluation index, then by picture quality multi-index amalgamation model, the overall objective evaluation result of CCTV system image quality is obtained.
An object of the present invention is achieved through the following technical solutions, and a kind of road traffic closed-circuit television image quality evaluation system based on machine vision, comprises portable type image device, high definition industrial camera and information process unit.Described portable type image device is for showing preset standard picture; Described high definition industrial camera is used for carrying out taking the test pattern after obtaining damage to CCTV system display terminal; Described information process unit is used for from test pattern, standard picture, extract visual signature respectively and compares, calculate the value of a series of images quality evaluation index, and by picture quality multi-index amalgamation model, solve the overall objective evaluation result of CCTV system image quality.
Two of object of the present invention is achieved through the following technical solutions, and a kind of road traffic closed-circuit television image quality evaluating method based on machine vision, specifically comprises the following steps:
Portable type image device is utilized to show preset standard picture;
The CCTV camera shooting standard picture of CCTV system, after a whole set of link such as coding, transmission, decoding of CCTV system, CCTV system display terminal exports can for the real image of eye-observation;
Just to CCTV system display terminal erection high definition industrial camera, real image is being taken, is obtaining test pattern;
Information process unit extracts visual signature respectively and compares from test pattern, standard picture, calculates the value of a series of images quality evaluation index;
Information process unit, by picture quality multi-index amalgamation model, considers each evaluation index value of picture quality, solves the overall objective evaluation result of CCTV system image quality.
Preferably, described portable type image device can longer-term storage and display standard picture, and display brightness is adjustable.
Preferably, described standard picture is a set of corresponding to the obvious manual construction image of different evaluation index visual effect, solid color structure standard picture is adopted for snow noise, color authenticity, contrast, moire, black and white raceway index, mosaic, edge ambiguity, geometric distortion, flicker are beated the multiple color structure standard picture that index adopts edge effect abundant, the multiple image sequence structure standard picture of fast-moving target under simple background is adopted for motion smear index.
Preferably, described image quality evaluation index both comprised simulated television image snow noise, flicker beat, moire, black and white raceway index, also comprise the mosaic of digital TV image, edge ambiguity, color authenticity, contrast, geometric distortion, motion smear index.
Preferably, described information process unit at least possesses data memory module, image quality assessment module and human-computer interaction module, described data memory module is used for storage standards image, test pattern, multi-index amalgamation model etc., described image quality assessment module is used for carrying out image procossing to standard picture and test pattern, extract visual signature, calculate each evaluation index value and obtain the overall objective evaluation result of picture quality by multi-index amalgamation model, described human-computer interaction module is for choosing the overall objective evaluation result of various types of evaluation index and output image quality.
Preferably, described picture quality multi-index amalgamation model adopts normalization Weighted Fusion model, and computing formula is as follows:
Wherein
q∈ [0,1] is picture quality multi-index amalgamation result of calculation, and value larger expression test pattern is more serious relative to the overall impairment phenomenon of standard picture; For normalization coefficient;
kfor the index number selected by this evaluation;
λ i be
ithe weight coefficient of individual index, adopts the method off-line of neural network learning to obtain, in learning sample
qvalue produced by human eye subjective assessment;
f i ∈ [0,1] is
ithe calculated value of individual index, the larger expression of value test pattern with regard to this index is more serious relative to the damage phenomenon of standard picture.
Preferably, the overall objective evaluation result of described picture quality, adopt the five grade impairment scale consistent with human eye subjective assessment to mark, computing formula is as follows:
R=5×(1－
Q)
Wherein
rthe overall objective evaluation result that ∈ [0,5] is picture quality, value larger expression test pattern is slighter relative to the overall impairment phenomenon of standard picture, and namely the picture quality of road traffic close-circuit television,closed-circuft televishon is better.
The present invention by road traffic close-circuit television,closed-circuft televishon integrally, adopts the method based on machine vision to carry out comprehensive Damage Evaluation to a whole set of CCTV system, realizes the party's ratio juris simple, easy to operate; Meanwhile, have employed the five grade impairment scale consistent with human eye subjective assessment and mark, by the Weighted Fusion to multiple Damage Evaluation index, the picture quality overall objective evaluation result consistent with subjective assessment can be obtained.In evaluation procedure, do not need to auxiliary equipment such as the special high-accuracy signals collecting of CCTV system access and signal analysis instruments, the equipment not affecting existing CCTV system completely runs, both avoided and torn open or injury that reconfiguration hardware circuit causes existing CCTV system, the cost price of evaluation system self can be made again significantly to be reduced.
Accompanying drawing explanation
In order to make the object, technical solutions and advantages of the present invention clearly, below in conjunction with accompanying drawing, the present invention is described in further detail, wherein:
Fig. 1 is system construction drawing of the present invention.
Embodiment
Below with reference to accompanying drawing, the preferred embodiments of the present invention are described in detail; Should be appreciated that preferred embodiment only in order to the present invention is described, instead of in order to limit the scope of the invention.
Fig. 1 is system construction drawing of the present invention.
As shown in Figure 1, a kind of road traffic closed-circuit television image quality evaluation system based on machine vision, comprises portable type image device, high definition industrial camera and information process unit.Wherein, described portable type image device adopts panel computer, screen size 10 inches, and resolution 1920 × 1200, for showing preset standard picture; Described high definition industrial camera employing resolution is the colored CCD industrial camera of more than five mega pixels, institute joins optical lens and has zoom and aperture regulatory function, just to the erection of CCTV system display terminal, for carrying out taking the test pattern after obtaining damage to CCTV system display terminal; Described information process unit adopts industrial computer, possesses the data-interface and LCDs that are connected with high definition industrial camera, and the configuration of the aspects such as processor, hard disk, internal memory, video card meets image procossing and evaluation result calculates the demand with display.
Based on said system, the present invention also provides a kind of road traffic closed-circuit television image quality evaluating method based on machine vision, specifically comprises the following steps:
Portable type image device is utilized to show preset standard picture;
The CCTV camera shooting standard picture of CCTV system, after a whole set of link such as coding, transmission, decoding of CCTV system, CCTV system display terminal exports can for the real image of eye-observation;
Just to CCTV system display terminal erection high definition industrial camera, real image is being taken, is obtaining test pattern;
Information process unit extracts visual signature respectively and compares from test pattern, standard picture, calculates the value of a series of images quality evaluation index;
Information process unit, by picture quality multi-index amalgamation model, considers each evaluation index value of picture quality, solves the overall objective evaluation result of CCTV system image quality.
Preferably, described portable type image device can longer-term storage and display standard picture, and display brightness is adjustable.
Preferably, described standard picture is a set of corresponding to the obvious manual construction image of different evaluation index visual effect, solid color structure standard picture is adopted for snow noise, color authenticity, contrast, moire, black and white raceway index, mosaic, edge ambiguity, geometric distortion, flicker are beated the multiple color structure standard picture that index adopts edge effect abundant, the multiple image sequence structure standard picture of fast-moving target under simple background is adopted for motion smear index.
Preferably, described image quality evaluation index both comprised simulated television image snow noise, flicker beat, moire, black and white raceway index, also comprise the mosaic of digital TV image, edge ambiguity, color authenticity, contrast, geometric distortion, motion smear index.
Preferably, described information process unit at least possesses data memory module, image quality assessment module and human-computer interaction module, described data memory module is used for storage standards image, test pattern, multi-index amalgamation model etc., described image quality assessment module is used for carrying out image procossing to standard picture and test pattern, extract visual signature, calculate each evaluation index value and obtain the overall objective evaluation result of picture quality by multi-index amalgamation model, described human-computer interaction module is for choosing the overall objective evaluation result of various types of evaluation index and output image quality.
Preferably, described picture quality multi-index amalgamation model adopts normalization Weighted Fusion model, and computing formula is as follows:
Wherein
q∈ [0,1] is picture quality multi-index amalgamation result of calculation, and value larger expression test pattern is more serious relative to the overall impairment phenomenon of standard picture; For normalization coefficient;
kfor the index number selected by this evaluation;
λ i be
ithe weight coefficient of individual index, adopts the method off-line of neural network learning to obtain, in learning sample
qvalue produced by human eye subjective assessment;
f i ∈ [0,1] is
ithe calculated value of individual index, the larger expression of value test pattern with regard to this index is more serious relative to the damage phenomenon of standard picture.
Preferably, the overall objective evaluation result of described picture quality, adopt the five grade impairment scale consistent with human eye subjective assessment to mark, computing formula is as follows:
R=5×(1－
Q)
Wherein
rthe overall objective evaluation result that ∈ [0,5] is picture quality, value larger expression test pattern is slighter relative to the overall impairment phenomenon of standard picture, and namely the picture quality of road traffic close-circuit television,closed-circuft televishon is better.
The present invention can carry out overall merit to a whole set of CCTV system while not affecting existing equipment operation completely, and the picture quality overall objective evaluation result consistent with subjective assessment can be obtained, the method and system principle realized is simple, with low cost, easy to operate.
The foregoing is only the preferred embodiments of the present invention, be not limited to the present invention, obviously, those skilled in the art can carry out various change and modification to the present invention and not depart from the spirit and scope of the present invention.Like this, if these amendments of the present invention and modification belong within the scope of the claims in the present invention and equivalent technologies thereof, then the present invention is also intended to comprise these change and modification.
Claims (7)
1., based on the road traffic closed-circuit television image quality evaluating method of machine vision, it is characterized in that, comprise the following steps:
1) portable type image device is utilized to show preset standard picture;
2) the CCTV camera shooting standard picture of CCTV system, after a whole set of link such as coding, transmission, decoding of CCTV system, CCTV system display terminal exports can for the real image of eye-observation;
3) just to CCTV system display terminal erection high definition industrial camera, real image is taken, obtains test pattern;
4) information process unit extracts visual signature respectively and compares from test pattern, standard picture, calculates the value of a series of images quality evaluation index;
5) information process unit is by picture quality multi-index amalgamation model, considers each evaluation index value of picture quality, solves the overall objective evaluation result of CCTV system image quality.
2. according to claim 1 based on the road traffic closed-circuit television image quality evaluating method of machine vision, it is characterized in that: described portable type image device can longer-term storage and display standard picture, and display brightness is adjustable.
3. according to claim 1 based on the road traffic closed-circuit television image quality evaluating method of machine vision, it is characterized in that: described standard picture is a set of corresponding to the obvious manual construction image of different evaluation index visual effect, for snow noise, color authenticity, contrast, moire, black and white raceway index adopts solid color structure standard picture, for mosaic, edge ambiguity, geometric distortion, flicker is beated the multiple color structure standard picture that index adopts edge effect abundant, the multiple image sequence structure standard picture of fast-moving target under simple background is adopted for motion smear index.
4. according to claim 1 based on the road traffic closed-circuit television image quality evaluating method of machine vision, it is characterized in that: described image quality evaluation index both comprised simulated television image snow noise, flicker beat, moire, black and white raceway index, also comprise the mosaic of digital TV image, edge ambiguity, color authenticity, contrast, geometric distortion, motion smear index.
5. according to claim 1 based on the road traffic closed-circuit television image quality evaluating method of machine vision, it is characterized in that: described information process unit at least possesses data memory module, image quality assessment module and human-computer interaction module, described data memory module is used for storage standards image, test pattern, multi-index amalgamation model etc., described image quality assessment module is used for carrying out image procossing to standard picture and test pattern, extract visual signature, calculate each evaluation index value and obtain the overall objective evaluation result of picture quality by multi-index amalgamation model, described human-computer interaction module is for choosing the overall objective evaluation result of various types of evaluation index and output image quality.
6. according to claim 1 based on the road traffic closed-circuit television image quality evaluating method of machine vision, it is characterized in that: described picture quality multi-index amalgamation model adopts normalization Weighted Fusion model, and computing formula is as follows:
Wherein
q∈ [0,1] is picture quality multi-index amalgamation result of calculation, and value larger expression test pattern is more serious relative to the overall impairment phenomenon of standard picture; For normalization coefficient;
kfor the index number selected by this evaluation;
λ i be
ithe weight coefficient of individual index, adopts the method off-line of neural network learning to obtain, in learning sample
qvalue produced by human eye subjective assessment;
f i ∈ [0,1] is
ithe calculated value of individual index, the larger expression of value test pattern with regard to this index is more serious relative to the damage phenomenon of standard picture.
7. according to claim 1 based on the road traffic closed-circuit television image quality evaluating method of machine vision, it is characterized in that: the overall objective evaluation result of described picture quality, adopt the five grade impairment scale consistent with human eye subjective assessment to mark, computing formula is as follows:
R=5×(1－
Q)
Wherein
rthe overall objective evaluation result that ∈ [0,5] is picture quality, value larger expression test pattern is slighter relative to the overall impairment phenomenon of standard picture, and namely the picture quality of road traffic close-circuit television,closed-circuft televishon is better.
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Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107945558A (en) * | 2017-12-21 | 2018-04-20 | 路斌 | It is a kind of that path method and system are seen based on Big Dipper location-based service |
| CN108428232A (en) * | 2018-03-20 | 2018-08-21 | 合肥工业大学 | A kind of blind appraisal procedure of cartoon image quality |
| CN109996063A (en) * | 2019-04-04 | 2019-07-09 | 广东省安心加科技有限公司 | Video image flower screen detection method, device, computer equipment and storage medium |
| CN110009590A (en) * | 2019-04-12 | 2019-07-12 | 北京理工大学 | A kind of high-quality colour image demosaicing methods based on convolutional neural networks |
| CN110942429A (en) * | 2019-01-17 | 2020-03-31 | 张红梅 | Computer quality judging method |
| CN112153371A (en) * | 2020-08-24 | 2020-12-29 | 珠海格力电器股份有限公司 | Image quality detection method, device, storage medium and product detection method |
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101378519A (en) * | 2008-09-28 | 2009-03-04 | 宁波大学 | Method for evaluating quality-lose referrence image quality base on Contourlet transformation |
| US20090096916A1 (en) * | 2007-10-15 | 2009-04-16 | Samsung Electronics Co., Ltd. | Camera and photographing method thereof |
| CN101448173A (en) * | 2008-10-24 | 2009-06-03 | 华为技术有限公司 | Method for evaluating Internet video quality, device and system thereof |
| CN102244806A (en) * | 2011-06-27 | 2011-11-16 | 广州视源电子科技有限公司 | Method and system for automatically testing reception performance parameters of digital television |
| CN105100789A (en) * | 2015-07-22 | 2015-11-25 | 天津科技大学 | Method for evaluating video quality |
-
2015
- 2015-12-24 CN CN201510988970.1A patent/CN105516713A/en not_active Withdrawn
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20090096916A1 (en) * | 2007-10-15 | 2009-04-16 | Samsung Electronics Co., Ltd. | Camera and photographing method thereof |
| CN101378519A (en) * | 2008-09-28 | 2009-03-04 | 宁波大学 | Method for evaluating quality-lose referrence image quality base on Contourlet transformation |
| CN101448173A (en) * | 2008-10-24 | 2009-06-03 | 华为技术有限公司 | Method for evaluating Internet video quality, device and system thereof |
| CN102244806A (en) * | 2011-06-27 | 2011-11-16 | 广州视源电子科技有限公司 | Method and system for automatically testing reception performance parameters of digital television |
| CN105100789A (en) * | 2015-07-22 | 2015-11-25 | 天津科技大学 | Method for evaluating video quality |
Cited By (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107945558A (en) * | 2017-12-21 | 2018-04-20 | 路斌 | It is a kind of that path method and system are seen based on Big Dipper location-based service |
| CN108428232A (en) * | 2018-03-20 | 2018-08-21 | 合肥工业大学 | A kind of blind appraisal procedure of cartoon image quality |
| CN108428232B (en) * | 2018-03-20 | 2019-07-19 | 合肥工业大学 | A kind of blind appraisal procedure of cartoon image quality |
| CN110942429A (en) * | 2019-01-17 | 2020-03-31 | 张红梅 | Computer quality judging method |
| CN110942429B (en) * | 2019-01-17 | 2020-07-24 | 刘子健 | Computer quality judging method |
| CN109996063A (en) * | 2019-04-04 | 2019-07-09 | 广东省安心加科技有限公司 | Video image flower screen detection method, device, computer equipment and storage medium |
| CN109996063B (en) * | 2019-04-04 | 2020-08-11 | 广东省安心加科技有限公司 | Video image screen splash detection method and device, computer equipment and storage medium |
| CN110009590A (en) * | 2019-04-12 | 2019-07-12 | 北京理工大学 | A kind of high-quality colour image demosaicing methods based on convolutional neural networks |
| CN112153371A (en) * | 2020-08-24 | 2020-12-29 | 珠海格力电器股份有限公司 | Image quality detection method, device, storage medium and product detection method |
| 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 |
| CN112215145A (en) * | 2020-10-12 | 2021-01-12 | 招商局重庆交通科研设计院有限公司 | Tunnel lighting lamp cleaning effect monitoring method based on image comparison |
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