CN102542282A - Mosaic detection method and mosaic detection device for passive images - Google Patents
Mosaic detection method and mosaic detection device for passive images Download PDFInfo
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Abstract
The invention discloses a mosaic detection method and a mosaic detection device for passive images, which are applied to the technical field of image detection. The method comprises the following steps of: carrying out edge detection on images to be detected to obtain two-value edge images; carrying out expansion on the two-value edge images by adopting rectangular structure elements; carrying out template matching on the two-value edge images after expansion processing by adopting a plurality of square templates, and overlapping matching points obtained by matching the plurality of square templates and the two-value edge images on one two-value edge image; the two-value edge image formed by overlapping is divided into a plurality of preselection blocks by adopting set thresholds, if the preslection blocks contain a plurality of matching points, one matching point is maintained for forming a template matching point map; carrying out angular-point detection on the images to be detected, wherein if angular points exist in the area coverage of one preselection block, the preselection block is set as the mosaic. For the mosaic detection method and the mosaic detection device provided by the utility model, angular-point detection and template matching are carried out simultaneously, so that the accuracy degree in detecting the mosaic distortion area is improved.
Description
Technical field
The present invention relates to technical field of image detection, relate in particular to a kind of no source images mosaic detection method and device.
Background technology
The development of Along with computer technology has also been used image recognition technology in the monitoring automatically widely at DTV fault graph picture.Hei Chang, quiet frame, mosaic are the faults that DTV occurs through regular meeting.Block-based method for video coding; Because the mistake in decoding or the transmission course; Apparent in view blocking effect may appear, and just so-called " mosaic ", the appearance of mosaic has reduced visual quality for images; How need try one's best detects position that whether every two field picture in the video have mosaic, a mosaic for this reason exactly, with the exact evaluation of auxiliary video quality.Automatic detection based on the DTV Hei Chang of image recognition technology, quiet frame has obtained using widely, and in reality, has obtained good effect.Wherein, the detection of mosaic is a difficult point, at present, and the domestic digital television monitoring system that does not also have mosaic fault image detection.
Meanwhile, scholar both domestic and external and researchist have also proposed some algorithms that detect to mosaic.Great majority still detect to the mosaic in the video, and the front and back frame that can utilize present frame like this is with reference to detecting whether contain mosaic.Concrete method comprises:
A, Storey propose the image block of directly thinking huge with the pixel value difference of front and back frame as far back as nineteen eighty-three, promptly are labeled as the mosaic distortion, and Kokaram has improved this method subsequently about 1993, added motion compensation.
B, domestic scholars have proposed a kind of algorithm based on pattern match and SVMs (SVM) in recent years and have come to detect automatically the frame that contains mosaic in the video sequence.But this method is calculated more complicated, is not easy to the application of extensive video quality evaluation.
C, combine the activity of block boundary luminance difference, local background, use three restrictive conditions to distinguish the true edge and the texture of blocking effect and image, with the position of detecting block boundary more accurately and the degree of blocking effect.This method can be used for reference the accuracy that detects with reinforcement mosaic edges of regions in the mosaic detection to a certain extent, but blocking effect and mosaic still are distinguishing, and blocking effect detects with ratio of compression has much relations, and finally the effect of enforcement also can't be estimated.
To sum up analyze, the calculation of complex that in the prior art there is the method for image detection, the reference information that needs is too much, is not easy to large-scale promotion, and there is defective in the basis that has on accuracy of detection.
Summary of the invention
The present invention provides a kind of no source images mosaic detection method and device, is used for solving the method calculation of complex of prior art image detection, and the reference information that needs is too much, is not easy to the problem of large-scale promotion.
The embodiment of the invention provides a kind of no source images mosaic detection method, comprising:
Treat detected image and carry out rim detection, obtain binary edge map about said image border to be detected;
Adopt the rectangle structure element that binary edge map is expanded, make the regular edgesization of said binary edge map;
Binary edge map after using a plurality of square templates to expansion process carries out template matches, and the match point that a plurality of square templates and binary edge map coupling is obtained is superimposed upon in the width of cloth binary edge map;
Be divided into a plurality of preliminary election pieces with the preset threshold binary edge map that forms that will superpose,, then keep one and form the template matches point diagram if comprise a plurality of match points in the said preliminary election piece;
Treat detected image and carry out Corner Detection generation Corner Detection figure;
With the template matches point diagram that obtains and Corner Detection figure comparison, if in the regional extent of said arbitrary preliminary election piece, angle point is arranged then confirm the mosaic that is of this preliminary election piece.
According to said method the present invention a kind of no source images mosaic pick-up unit is provided also, comprises:
Pretreatment unit is used to treat detected image and carries out rim detection, obtains the binary edge map about said image border to be detected, and adopts the rectangle structure element that binary edge map is expanded, and makes the regular edgesization of said binary edge map;
Matching unit, the binary edge map after being used to use a plurality of square templates to expansion process carries out template matches, and the match point that a plurality of square templates and binary edge map coupling is obtained is superimposed upon in the width of cloth binary edge map; Be divided into a plurality of preliminary election pieces with the preset threshold binary edge map that forms that will superpose,, then keep one and form the template matches point diagram if comprise a plurality of match points in the said preliminary election piece;
The Corner Detection unit is used to treat detected image and carries out Corner Detection generation Corner Detection figure;
Mosaic is confirmed the unit, is used for the comparison with the template matches point diagram that obtains and Corner Detection figure, if in the regional extent of said arbitrary preliminary election piece, angle point is arranged then confirm the mosaic that is of this preliminary election piece.
Mosaic detection method and device that the embodiment of the invention provided are used the method that Corner Detection and template matches are carried out simultaneously, have both improved the levels of precision of detection mosaic distortion zone, can flexible Application on a plurality of application platforms such as image or video.
Description of drawings
Fig. 1 is a kind of process flow diagram that does not have source images mosaic detection method of the embodiment of the invention;
Fig. 2 is the synoptic diagram of image to be detected;
The edge binary images that Fig. 3 adopts the canny operator to obtain for the embodiment of the invention;
Fig. 4 is the edge binary images after morphology expands among the real template matches point diagram of the present invention embodiment;
Two class templates that use in the template matches process in Fig. 5 embodiment of the invention;
Template matches point diagram in Fig. 6 embodiment of the invention;
Corner Detection figure in Fig. 7 embodiment of the invention;
A kind of no source images mosaic pick-up unit structural drawing in Fig. 8 embodiment of the invention.
Embodiment
The embodiment of the invention provides a kind of no source images mosaic detection method, and this method comprises: treat detected image and carry out rim detection, obtain the binary edge map about said image border to be detected; Adopt the rectangle structure element that binary edge map is expanded, make the regular edgesization of said binary edge map; Binary edge map after using a plurality of square templates to expansion process carries out template matches, and the match point that a plurality of square templates and binary edge map coupling obtains is superimposed upon forms the template matches point diagram among the width of cloth figure; Said template matches point diagram is divided into a plurality of preliminary election pieces with preset threshold, if comprise a plurality of match points in the said preliminary election piece then keep one; Treat detected image and carry out Corner Detection generation Corner Detection figure; With the template matches point diagram that obtains and Corner Detection figure comparison, if in the regional extent of said arbitrary preliminary election piece, angle point is arranged then confirm the mosaic that is of this preliminary election piece.
As shown in Figure 1, the embodiment of the invention provides a kind of no source images mosaic detection method, and concrete steps comprise:
Step 101 is treated detected image (as shown in Figure 2) and is carried out rim detection, obtains the binary edge map (as shown in Figure 3) about said image border to be detected;
Treat in embodiments of the present invention when detected image is carried out rim detection and can adopt the Canny operator, obtain binary edge map edge, but be not limited to this a kind of detection mode about the edge.
Step 102 adopts the rectangle structure element that binary edge map is expanded (as shown in Figure 4), makes the regular edgesization of said binary edge map;
Can adopt two structural elements [0 00 successively in embodiments of the present invention; 111; 000] and [0 10; 010; 01 0] image is expanded.After carrying out expansion process, can obtain the relatively accentuated edges image s-edge of rule.
Step 103, the binary edge map after using a plurality of square templates to expansion process carries out template matches, and the match point that a plurality of square templates and binary edge map coupling obtain is superimposed upon in the width of cloth binary edge map;
As shown in Figure 5, a plurality of square templates of selecting in the embodiment of the invention comprise: 1 square template and 4 half square templates.
Step 104 is divided into a plurality of preliminary election pieces with the preset threshold binary edge map that forms that will superpose, if comprise a plurality of match points in the said preliminary election piece, then keeps one and forms template matches point diagram (as shown in Figure 6);
In concrete applied environment, because MPEG-2 is based on the compaction coding method of piece, therefore always piece or bar of impaired distortion in images zone.Mosaic fault graph picture in the digital TV video frequency just is meant this mistake based on piece or bar.So the size of mosaic is rule very, all is 16*16 generally, so the size of template is elected 16*16 as.
So said preset threshold 16*16 in embodiments of the present invention.Wherein, determine whether that a plurality of methods in same preliminary election piece are: the horizontal ordinate of employing point judges whether in same that divided by 16 method the point that keeps at last can be the place, the lower right corner at the preliminary election piece.
Step 105 is treated detected image and is carried out Corner Detection generation Corner Detection figure (as shown in Figure 7);
Adopt the Harris Corner Detection in embodiments of the present invention, wherein the threshold value of Harris angle point response can be made as 20, and this threshold value can be adjusted according to actual needs.
Step 106 is with the template matches point diagram that obtains and Corner Detection figure comparison, if in the regional extent of said arbitrary preliminary election piece, angle point is arranged then confirm the mosaic that is of this preliminary election piece.
Embodiment of the invention step 103 uses a plurality of square templates that said binary edge map is carried out template matches, and concrete implementation can be:
Change into each square template and binary edge map template bianry image of the same size;
Respectively template bianry image and binary edge map are carried out two-dimension fourier transform;
The correlativity of calculation template bianry image and binary edge map; And obtain the correlativity spectrogram; Wherein the implementation for correlation detection can be: with said binary edge map Rotate 180 degree, and the convolutional calculation that postrotational binary edge map and template bianry image are carried out Fast Fourier Transform (FFT).
According to spectrum peak in the said correlativity spectrogram is the match point that coordinate is confirmed square template and image to be detected.
As shown in Figure 8, according to the said method embodiment of the invention a kind of no source images mosaic pick-up unit is provided also, comprise that pretreatment unit 801, matching unit 802, Corner Detection unit 803 and mosaic confirm unit 804:
Matching unit 802, the binary edge map after being used to use a plurality of square templates to expansion process carries out template matches, and the match point that a plurality of square templates and binary edge map coupling is obtained is superimposed upon in the width of cloth binary edge map; Be divided into a plurality of preliminary election pieces with the preset threshold binary edge map that forms that will superpose,, then keep one and form the template matches point diagram if comprise a plurality of match points in the said preliminary election piece;
Mosaic is confirmed unit 804, is used for the comparison with the template matches point diagram that obtains and Corner Detection figure, if in the regional extent of said arbitrary preliminary election piece, angle point is arranged then confirm the mosaic that is of this preliminary election piece.
The a plurality of square templates of said matching unit 802 uses carry out template matches to said binary edge map and comprise:
Change into each square template and binary edge map template bianry image of the same size;
Respectively template bianry image and binary edge map are carried out two-dimension fourier transform;
The correlativity of calculation template bianry image and binary edge map, and obtain the correlativity spectrogram;
According to spectrum peak in the said correlativity spectrogram is the match point that coordinate is confirmed square template and image to be detected.
The correlativity of said matching unit 802 calculation template bianry images and binary edge map comprises:
With said binary edge map Rotate 180 degree, and the convolutional calculation that postrotational binary edge map and template bianry image are carried out Fast Fourier Transform (FFT).
Use mosaic detection method and device that the embodiment of the invention provided; Use the method that Corner Detection and template matches are carried out simultaneously; Both improved the levels of precision that detects the mosaic distortion zone, can flexible Application on a plurality of application platforms such as image or video.
The present invention passes through characteristics such as regular to mosaic shape size, that grey scale change is obvious; Especially use morphological method to strengthen local edge; Point after the piecemeal processing template coupling is avoided repetition, combines checking mosaic characteristic to edge and angle point, has realized the detection to mosaic effectively.
Method of the present invention is not limited to the embodiment described in the embodiment, and those skilled in the art's technical scheme according to the present invention draws other embodiment, belongs to technological innovation scope of the present invention equally.
Obviously, those skilled in the art can carry out various changes and modification to the present invention and not break away from the spirit and scope of the present invention.Like this, belong within the scope of claim of the present invention and equivalent technologies thereof if of the present invention these are revised with modification, then the present invention also is intended to comprise these changes and modification interior.
Claims (8)
1. a no source images mosaic detection method is characterized in that, comprising:
Treat detected image and carry out rim detection, obtain binary edge map about said image border to be detected;
Adopt the rectangle structure element that binary edge map is expanded, make the regular edgesization of said binary edge map;
Binary edge map after using a plurality of square templates to expansion process carries out template matches, and the match point that a plurality of square templates and binary edge map coupling is obtained is superimposed upon in the width of cloth binary edge map;
Be divided into a plurality of preliminary election pieces with the preset threshold binary edge map that forms that will superpose,, then keep one and form the template matches point diagram if comprise a plurality of match points in the said preliminary election piece;
Treat detected image and carry out Corner Detection generation Corner Detection figure;
With the template matches point diagram that obtains and Corner Detection figure comparison, if in the regional extent of said arbitrary preliminary election piece, angle point is arranged then confirm the mosaic that is of this preliminary election piece.
2. the method for claim 1 is characterized in that, said a plurality of square templates comprise 1 square template and 4 half square templates.
3. according to claim 1 or claim 2 method is characterized in that said rectangle structure element is [0 00; 111; 00 0] and [0 10; 010; 01 0].
4. the method for claim 1 is characterized in that, uses a plurality of square templates that said binary edge map is carried out template matches and comprises:
Change into each square template and binary edge map template bianry image of the same size;
Respectively template bianry image and binary edge map are carried out two-dimension fourier transform;
The correlativity of calculation template bianry image and binary edge map, and obtain the correlativity spectrogram;
According to spectrum peak in the said correlativity spectrogram is the match point that coordinate is confirmed square template and image to be detected.
5. method as claimed in claim 4 is characterized in that, the correlativity of said calculation template bianry image and binary edge map comprises:
With said binary edge map Rotate 180 degree, and the convolutional calculation that postrotational binary edge map and template bianry image are carried out Fast Fourier Transform (FFT).
6. a no source images mosaic pick-up unit is characterized in that, comprising:
Pretreatment unit is used to treat detected image and carries out rim detection, obtains the binary edge map about said image border to be detected, and adopts the rectangle structure element that binary edge map is expanded, and makes the regular edgesization of said binary edge map;
Matching unit, the binary edge map after being used to use a plurality of square templates to expansion process carries out template matches, and the match point that a plurality of square templates and binary edge map coupling is obtained is superimposed upon in the width of cloth binary edge map; Be divided into a plurality of preliminary election pieces with the preset threshold binary edge map that forms that will superpose,, then keep one and form the template matches point diagram if comprise a plurality of match points in the said preliminary election piece;
The Corner Detection unit is used to treat detected image and carries out Corner Detection generation Corner Detection figure;
Mosaic is confirmed the unit, is used for the comparison with the template matches point diagram that obtains and Corner Detection figure, if in the regional extent of said arbitrary preliminary election piece, angle point is arranged then confirm the mosaic that is of this preliminary election piece.
7. device as claimed in claim 6 is characterized in that, said matching unit uses a plurality of square templates that said binary edge map is carried out template matches to comprise:
Change into each square template and binary edge map template bianry image of the same size;
Respectively template bianry image and binary edge map are carried out two-dimension fourier transform;
The correlativity of calculation template bianry image and binary edge map, and obtain the correlativity spectrogram;
According to spectrum peak in the said correlativity spectrogram is the match point that coordinate is confirmed square template and image to be detected.
8. device as claimed in claim 6 is characterized in that, the correlativity of said matching unit calculation template bianry image and binary edge map comprises:
With said binary edge map Rotate 180 degree, and the convolutional calculation that postrotational binary edge map and template bianry image are carried out Fast Fourier Transform (FFT).
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Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103473772A (en) * | 2013-09-05 | 2013-12-25 | 北京捷成世纪科技股份有限公司 | Method and device for detecting mosaic image |
| WO2015168893A1 (en) * | 2014-05-08 | 2015-11-12 | 华为终端有限公司 | Video quality detection method and device |
| CN106372584A (en) * | 2016-08-26 | 2017-02-01 | 浙江银江研究院有限公司 | Video image mosaic detection method |
| CN106447660A (en) * | 2016-09-27 | 2017-02-22 | 百度在线网络技术（北京）有限公司 | Image detection method and device |
| CN107563970A (en) * | 2017-08-09 | 2018-01-09 | 中国科学院西安光学精密机械研究所 | A kind of detection method of video image mosaic |
| CN107818568A (en) * | 2017-09-29 | 2018-03-20 | 昆明理工大学 | A kind of video mosaic detection method |
| CN108364282A (en) * | 2018-01-15 | 2018-08-03 | 北京华兴宏视技术发展有限公司 | Image-mosaics detection method, image-mosaics detecting system |
| TWI697871B (en) * | 2019-04-01 | 2020-07-01 | 中華電信股份有限公司 | Inspection system for image containing mosaic and method thereof |
| CN113542864A (en) * | 2020-04-24 | 2021-10-22 | 腾讯科技（深圳）有限公司 | Video flower screen area detection method, device, equipment and readable storage medium |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101110122A (en) * | 2007-08-31 | 2008-01-23 | 北京工业大学 | Large cultural heritage picture pattern split-joint method based on characteristic |
| CN101236603A (en) * | 2008-02-28 | 2008-08-06 | 上海交通大学 | Vehicle intelligent back vision method |
| CN101551900A (en) * | 2008-03-31 | 2009-10-07 | 新奥特（北京）视频技术有限公司 | Detection method for video mosaic images |
-
2010
- 2010-12-16 CN CN201010608117XA patent/CN102542282B/en not_active Expired - Fee Related
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101110122A (en) * | 2007-08-31 | 2008-01-23 | 北京工业大学 | Large cultural heritage picture pattern split-joint method based on characteristic |
| CN101236603A (en) * | 2008-02-28 | 2008-08-06 | 上海交通大学 | Vehicle intelligent back vision method |
| CN101551900A (en) * | 2008-03-31 | 2009-10-07 | 新奥特（北京）视频技术有限公司 | Detection method for video mosaic images |
Non-Patent Citations (1)
| Title |
|---|
| 司文丽等: "马赛克故障图像的分析与检测", 《有线电视技术》, no. 5, 31 December 2008 (2008-12-31), pages 113 - 116 * |
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| CN103473772A (en) * | 2013-09-05 | 2013-12-25 | 北京捷成世纪科技股份有限公司 | Method and device for detecting mosaic image |
| CN103473772B (en) * | 2013-09-05 | 2017-04-19 | 北京捷成世纪科技股份有限公司 | Method and device for detecting mosaic image |
| WO2015168893A1 (en) * | 2014-05-08 | 2015-11-12 | 华为终端有限公司 | Video quality detection method and device |
| CN105264896A (en) * | 2014-05-08 | 2016-01-20 | 华为终端有限公司 | Video quality detection method and device |
| CN106372584A (en) * | 2016-08-26 | 2017-02-01 | 浙江银江研究院有限公司 | Video image mosaic detection method |
| CN106372584B (en) * | 2016-08-26 | 2019-06-11 | 浙江银江研究院有限公司 | A kind of video image mosaic detection method |
| CN106447660B (en) * | 2016-09-27 | 2019-01-25 | 百度在线网络技术（北京）有限公司 | Picture detection method and device |
| CN106447660A (en) * | 2016-09-27 | 2017-02-22 | 百度在线网络技术（北京）有限公司 | Image detection method and device |
| CN107563970A (en) * | 2017-08-09 | 2018-01-09 | 中国科学院西安光学精密机械研究所 | A kind of detection method of video image mosaic |
| CN107818568A (en) * | 2017-09-29 | 2018-03-20 | 昆明理工大学 | A kind of video mosaic detection method |
| CN108364282A (en) * | 2018-01-15 | 2018-08-03 | 北京华兴宏视技术发展有限公司 | Image-mosaics detection method, image-mosaics detecting system |
| CN108364282B (en) * | 2018-01-15 | 2022-02-11 | 北京华兴宏视技术发展有限公司 | Image mosaic detection method and image mosaic detection system |
| TWI697871B (en) * | 2019-04-01 | 2020-07-01 | 中華電信股份有限公司 | Inspection system for image containing mosaic and method thereof |
| CN113542864A (en) * | 2020-04-24 | 2021-10-22 | 腾讯科技（深圳）有限公司 | Video flower screen area detection method, device, equipment and readable storage medium |
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