CN106657817A - Processing method applied to mobile phone platform for automatically making album MV - Google Patents

Processing method applied to mobile phone platform for automatically making album MV Download PDF

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Publication number
CN106657817A
CN106657817A CN201611237751.0A CN201611237751A CN106657817A CN 106657817 A CN106657817 A CN 106657817A CN 201611237751 A CN201611237751 A CN 201611237751A CN 106657817 A CN106657817 A CN 106657817A
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photo
data
sift
processing method
ssim
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熊永春
韩晟
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HANGZHOU QUWEI SCIENCE & TECHNOLOGY Co Ltd
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HANGZHOU QUWEI SCIENCE & TECHNOLOGY Co Ltd
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Priority to CN201611237751.0A priority Critical patent/CN106657817A/en
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N5/00—Details of television systems
    • H04N5/222—Studio circuitry; Studio devices; Studio equipment
    • H04N5/262—Studio circuits, e.g. for mixing, switching-over, change of character of image, other special effects ; Cameras specially adapted for the electronic generation of special effects
    • H04N5/265—Mixing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/40—Information retrieval; Database structures therefor; File system structures therefor of multimedia data, e.g. slideshows comprising image and additional audio data
    • G06F16/43—Querying
    • G06F16/438—Presentation of query results
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/40—Information retrieval; Database structures therefor; File system structures therefor of multimedia data, e.g. slideshows comprising image and additional audio data
    • G06F16/43—Querying
    • G06F16/438—Presentation of query results
    • G06F16/4387—Presentation of query results by the use of playlists
    • G06F16/4393—Multimedia presentations, e.g. slide shows, multimedia albums
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N5/00—Details of television systems
    • H04N5/222—Studio circuitry; Studio devices; Studio equipment

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  • Engineering & Computer Science (AREA)
  • Multimedia (AREA)
  • Theoretical Computer Science (AREA)
  • Signal Processing (AREA)
  • Data Mining & Analysis (AREA)
  • Databases & Information Systems (AREA)
  • Physics & Mathematics (AREA)
  • General Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Image Analysis (AREA)
  • Image Processing (AREA)
  • Processing Or Creating Images (AREA)

Abstract

The invention discloses a processing method applied to a mobile phone platform for automatically making album MV. The method comprises an intelligent picture selection module, an intelligent beautifying effect selection module, a data management module, a rendering module and a video module. by adoption of the method disclosed by the invention, the whole process is automatically processed, a user does not to perform any operation, so that the time cost of the user for making the album MV is zero.

Description

A kind of processing method of the automatic making photograph album MV for being applied to cell phone platform
Technical field
The present invention relates to mobile video makes field, in particular, it is related to for a kind of cell phone platform of being applied to Automatically the processing method of photograph album MV is made.
Background technology
In recent years smart mobile phone becomes increasingly popular, and its function is also stronger and stronger, and in daily life it instead of substantially Camera function.Meanwhile, people are fabricated to photograph album MV and are shared with that relatives and friends are also little by little popular to rise the photo that mobile phone shoots Come.The making of traditional photograph album MV, needs user that the picture for making photograph album MV is selected from the photograph album of mobile phone, then from very Certain landscaping effect of matching photo scene is selected in many landscaping effects by repeatedly trial, subsequently from many music In pick out matching photo scene music as MV background sound.
When making photograph album MV automatically in actual life, user has clapped a photo and can first have a look effect, if effect It is bad to attempt clapping one again, until photographing oneself satisfaction till, this also can cause to exist in mobile phone photo album a lot Similar photo.Therefore the shortcoming of this scheme is that user needs longer time to make MV into original, and secondly user selects manually The landscaping effect selected and music not necessarily match the scene of photo.So two problems in the urgent need to address：How first point be Similar photo is removed, how second point selects the landscaping effect and background sound for matching photo scene.
The content of the invention
It is an object of the invention to provide a kind of processing method of the automatic making photograph album MV for being applied to cell phone platform so that The fully automated process of whole process, it is not necessary to any operation of user, makes the time cost that user makes photograph album MV to be 0.
In order to solve above-mentioned technical problem, technical scheme is as follows：
A kind of processing method of the automatic making photograph album MV for being applied to cell phone platform, specifically includes following steps：
101) Intelligent Selection figure：The Intelligent Selection figure, by the photo in a period of time unduplicated photo is selected, and automatically The photo list that generation is chosen, and photo table data is passed to data management module；
102) intelligence beautification：By step 101) select photo by artificial intelligence deep learning method process, then oneself It is dynamic to select landscaping effect and background music, and corresponding data are transferred to into data management module；
103) data management：By step 101) and 102) data that generate carry out storage and call management；
104) render：By step 103) data message of needs is obtained in data, and they are rendered to one by one Vedio data；
105) video being generated, by step 104) data compression that generates, into corresponding video data encoder, and writes video File, generates MV.
Further, the step 101) in Intelligent Selection figure the step of it is as follows：
201) access time section：The all photos shot in a period of time are selected from user mobile phone photograph album；
202) photo sequence：Step 201) generate photo be temporally from morning to night ranked up；
203) photo is screened：Step 202) the photo application photo similarity processing method that sorted shines to judge to choose Piece, the similarity processing method includes SSIM and SIFT, and comprehensive both are screened, and thus obtain removing after similar photo Target picture；The SSIM full name are structural similarity index structural similarities, are a kind of two width figures of measurement As the index of similarity；It is Scale-invariant feature that the SIFT is Scale invariant features transform full name Transform, for one kind description of image processing field.
Further, the method that both described synthesis are screened is as follows：
The numerical value that SSIM (x, y) and SIFT (x, y) are obtained is transformed into into 0 to 100 number；
If SIFT (x, y)>50, return SIFT (x, y)；
If SIFT (x, y)>40, return SIFT (x, y) * 0.5+SSIM (x, y) * 0.5；
If SIFT (x, y)>30, return SIFT (x, y) * 0.25+SSIM (x, y) * 0.75；
If SIFT (x, y)>20, return SIFT (x, y) * 0.125+SSIM (x, y) * 0.875；
Otherwise, SSIM (x, y) is returned；
The data value for finally returning that is not less than 50, represent two figures be it is similar, it is otherwise, dissimilar.
Further, the SSIM is a kind of index of two width image similarities of measurement, and the concrete grammar that SSIM compares is Equation below (1)：
Wherein x, y are two images for comparing, μxIt is the mean value of x, μyIt is the mean value of y, δx 2It is the variance of x, δy 2It is the variance of y, δxyIt is the standard deviation of x and y.c1=(k1L)2,c2=(k2L)2It is for maintaining stable constant, k1、k2It is Constant, L is the scope of pixel value, depending on the size of real image pixel.
Further, the SIFT is Scale invariant features transform, for one kind description of image processing field；SIFT is special Levy the concrete grammar equation below (2) for comparing：
Wherein x, y are two images for comparing, niIt is characterized a number, nfIt is image x two picture registration parts The number of interior all characteristic points, α, β are constant；The niAccording to RANSAC methods obtain two images transformation matrix H and Feature point number n of matchingi, the RANSAC methods are the sample data set comprising abnormal data according to one group, calculate number According to mathematical model parameter, the method for obtaining effective sample data, the nfObtained according to transformation matrix H.
Further, the step 102) intelligence beautification method it is as follows：
501) photo is read：From step 101) generate photo list in read a photo；
502) object is recognized：Step 501) photo that obtains identifies main thing by artificial intelligence deep learning method Body, and set up object list data；
503) weighting is sorted out：According to step 502) set up table data, successively according to listed object according to respective rule Scene classification is carried out, and weighting carries out statistical computation；
504) scene is selected：According to step 503) result of statistical computation is carried out into scene selection, beautification effect is chosen automatically Fruit and background sound.
Further, the step 502) in artificial intelligence deep learning method adopt Deep Learning intelligence sides Method or deep neural network.
Further, the step 504) in landscaping effect and background sound matching will be stamped to it in database Their scene tag.
Further, the step 103) in data management include photo files, background sound file, landscaping effect The storage of configuration file, and for step 104) render the data that input is provided.
Compared to existing technology advantage is the present invention：
1, the fully automated process of whole process, it is not necessary to any operation of user makes user make the time cost of photograph album MV To be 0.
2. Intelligent Selection module is applied, and it is that user is satisfied generally to select the photo for coming.
3. the intelligent landscaping effect selecting module of application, with the landscaping effect and background sound of matching photo, produces fineness Photograph album MV.
4. intelligent processing method is adopted, the photograph album MV for producing can consider more factors, making typically manual than user It is more preferable.
Description of the drawings
Fig. 1 is a kind of process chart of the processing method of the automatic making photograph album MV for being applied to cell phone platform of the present invention；
Fig. 2 is a kind of stream of the Intelligent Selection figure of the processing method of the automatic making photograph album MV for being applied to cell phone platform of the present invention Cheng Tu；
Fig. 3 is a kind of stream of the intelligence beautification of processing method of the automatic making photograph album MV for being applied to cell phone platform of the present invention Cheng Tu.
Specific embodiment
With reference to the accompanying drawings and detailed description the present invention is further described.
As shown in figure 1, a kind of processing method of the automatic making photograph album MV for being applied to cell phone platform, specifically includes following step Suddenly：
101) Intelligent Selection figure：The Intelligent Selection figure, by the photo in a period of time unduplicated photo is selected, and automatically The photo list that generation is chosen, and photo table data is passed to data management module.As shown in Fig. 2 the tool of Intelligent Selection figure Body step is as follows：
201) access time section：The all photos shot in a period of time are selected from user mobile phone photograph album.
202) photo sequence：Step 201) generate photo be temporally from morning to night ranked up, this is sequentially exactly The precedence of user's actual photographed these photos.
203) photo is screened：Step 202) the photo application photo similarity processing method that sorted shines to judge to choose Piece, the similarity processing method includes SSIM and SIFT, and comprehensive both are screened, i.e., previous similar with next Zhang Jinhang Degree judges and screens, if both are dissimilar, then previous is selected into, otherwise previous discarding, then latter and it after One carry out similarity judgement and screening again, by that analogy.Thus finally give and remove the target picture after similar photo.
SSIM (the structural similarity index) structural similarity, is a kind of two width image phases of measurement Like the index of degree, the concrete grammar that SSIM compares is equation below (1)：
Wherein x, y are two images for comparing, μxIt is the mean value of x, μyIt is the mean value of y, δx 2It is the variance of x, δy 2It is the variance of y, δxyIt is the standard deviation of x and y.c1=(k1L)2,c2=(k2L)2It is for maintaining stable constant, k1、k2It is Constant, L is the scope of pixel value, depending on the size of real image pixel.
The SIFT is Scale invariant features transform (Scale-invariant feature transform, SIFT), is used In one kind description of image processing field；The concrete grammar equation below (2) that SIFT feature compares：
Wherein x, y are two images for comparing, niIt is characterized a number, nfIt is image x two picture registration parts The number of interior all characteristic points, α, β are constant；The niAccording to RANSAC methods obtain two images transformation matrix H and Feature point number n of matchingi, the RANSAC methods are the sample data set comprising abnormal data according to one group, calculate number According to mathematical model parameter, the method for obtaining effective sample data, the nfObtained according to transformation matrix H.
Comprehensive SSIM and SIFT will the expression of SSIM and SIFT be respectively SSIM (x, y) and SIFT (x, y), and The numerical value that SSIM (x, y) and SIFT (x, y) are obtained is transformed into 0 to 100 number, i.e. SSIM (x, y) and obtains 0 to 100 number, SIFT (x, y) obtains 0 to 100 number.
If SIFT (x, y)>50, return SIFT (x, y)；
If SIFT (x, y)>40, return SIFT (x, y) * 0.5+SSIM (x, y) * 0.5；
If SIFT (x, y)>30, return SIFT (x, y) * 0.25+SSIM (x, y) * 0.75；
If SIFT (x, y)>20, return SIFT (x, y) * 0.125+SSIM (x, y) * 0.875；
Otherwise, SSIM (x, y) is returned.
As long as the data value for finally returning that is not less than 50, represent two figures be it is similar, it is otherwise, dissimilar.
102) intelligence beautification, by step 101) select photo by artificial intelligence deep learning method process, this will knowledge The primary objects list not gone out in single photo, according to the object list for identifying, identifies that the photo likelihood ratio is higher Scene.For example, if the tree etc. that has mountain and water in object list may be considered tourism scene, if there is child in object list May be considered mother and baby's scene.Then the scene for going out to all photo arrays again, by scene type weighted statistical, weights highest Scene is exactly the scene for screening photo.Then landscaping effect and background music are automatically selected, and by corresponding data transfer To data management module.As shown in figure 3, the concrete grammar step of intelligence beautification is as follows：
501) photo is read：From step 101) generate photo list in read a photo；
502) object is recognized：Step 501) photo that obtains identifies main thing by artificial intelligence deep learning method Body, and set up object list data.The artificial intelligence deep learning method is using Deep Learning intelligent methods or depth Neutral net.
503) weighting is sorted out：According to step 502) set up table data, successively according to listed object according to respective rule Scene classification is carried out, and weighting carries out statistical computation.
504) scene is selected：According to step 503) result of statistical computation is carried out into scene selection, beautification effect is chosen automatically Fruit and background sound.Landscaping effect therein and background sound will be stamped to it in database and match their scene mark Sign, when the result of statistical computation carries out obtaining consistent with the label of scene after scene selection, arbitrarily select a landscaping effect and Background sound gives photograph album MV.
103) data management：By step 101) and 102) data that generate carry out storage and call management.Data include photo File, background sound file, the storage of the configuration file of landscaping effect, and the data that input is provided are rendered for step 104.
104) render：By step 103) data message of needs is obtained in data, and they are rendered to one by one Vedio data.
105) video is generated：By step 104) generate vedio data Video coding number is compressed into by encryption algorithm According to, and these data are write in last video file, generate MV.
The above is only the preferred embodiment of the present invention, it is noted that for the ordinary skill people of the art Member, without departing from the inventive concept of the premise, can also make some improvements and modifications, and these improvements and modifications also should be regarded as In the scope of the present invention.

Claims (9)

1. a kind of processing method of the automatic making photograph album MV for being applied to cell phone platform, it is characterised in that specifically include following step Suddenly：
101) Intelligent Selection figure：The Intelligent Selection figure, by the photo in a period of time unduplicated photo is selected, and is automatically generated The photo list chosen, and photo table data is passed to data management module；
102) intelligence beautification：By step 101) select photo by artificial intelligence deep learning method process, then select automatically Landscaping effect and background music are selected, and corresponding data are transferred to into data management module；
103) data management：By step 101) and 102) data that generate carry out storage and call management；
104) render：By step 103) obtain the data message of needs in data, and they are rendered to video one by one View data；
105) video is generated：By step 104) data compression that generates, into corresponding video data encoder, and writes video file, Generate MV.
2. the processing method of a kind of automatic making photograph album MV for being applied to cell phone platform according to claim 1, its feature Be, the step 101) in Intelligent Selection figure the step of it is as follows：
201) access time section：The all photos shot in a period of time are selected from user mobile phone photograph album；
202) photo sequence：Step 201) generate photo be temporally from morning to night ranked up；
203) photo is screened：Step 202) the photo application photo similarity processing method that sorted judging to choose photo, The similarity processing method includes SSIM and SIFT, and comprehensive both are screened, and thus obtain removing the mesh after similar photo Mark photo；The SSIM full name are structural similarity index structural similarities, are a kind of two width images of measurement The index of similarity；It is Scale-invariant feature that the SIFT is Scale invariant features transform full name Transform, for one kind description of image processing field.
3. the processing method of a kind of automatic making photograph album MV for being applied to cell phone platform according to claim 2, its feature It is that the method that both described synthesis are screened is as follows：
The numerical value that SSIM (x, y) and SIFT (x, y) are obtained is transformed into into 0 to 100 number；
If SIFT (x, y)>50, return SIFT (x, y)；
If SIFT (x, y)>40, return SIFT (x, y) * 0.5+SSIM (x, y) * 0.5；
If SIFT (x, y)>30, return SIFT (x, y) * 0.25+SSIM (x, y) * 0.75；
If SIFT (x, y)>20, return SIFT (x, y) * 0.125+SSIM (x, y) * 0.875；
Otherwise, SSIM (x, y) is returned；
The data value for finally returning that is not less than 50, represent two figures be it is similar, it is otherwise, dissimilar.
4. the processing method of a kind of automatic making photograph album MV for being applied to cell phone platform according to claim 2, its feature It is that the SSIM is a kind of index of two width image similarities of measurement, the concrete grammar that SSIM compares is equation below (1)：
Wherein x, y are two images for comparing, μxIt is the mean value of x, μyIt is the mean value of y, δx 2It is the variance of x, δy 2It is y Variance, δxyIt is the standard deviation of x and y.c1=(k1L)2,c2=(k2L)2It is for maintaining stable constant, k1、k2It is constant, L It is the scope of pixel value, depending on the size of real image pixel.
5. the processing method of a kind of automatic making photograph album MV for being applied to cell phone platform according to claim 2, its feature It is that the SIFT is Scale invariant features transform, for one kind description of image processing field；It is concrete that SIFT feature compares Method equation below (2)：
Wherein x, y are two images for comparing, niIt is characterized a number, nfIt is image x in two picture registration parts The number of all characteristic points, α, β are constant；The niTransformation matrix H and the matching of two images are obtained according to RANSAC methods Feature point number ni, the RANSAC methods are the sample data set comprising abnormal data according to one group, calculate data Mathematical model parameter, the method for obtaining effective sample data, the nfObtained according to transformation matrix H.
6. the processing method of a kind of automatic making photograph album MV for being applied to cell phone platform according to claim 1, its feature Be, the step 102) intelligence beautification method it is as follows：
501) photo is read：From step 101) generate photo list in read a photo；
502) object is recognized：Step 501) photo that obtains identifies primary objects by artificial intelligence deep learning method, and Set up object list data；
503) weighting is sorted out：According to step 502) set up table data, carried out according to respective rule according to listed object successively Scene is sorted out, and weighting carries out statistical computation；
504) scene is selected：According to step 503) result of statistical computation is carried out into scene selection, choose automatically landscaping effect and Background sound.
7. the processing method of a kind of automatic making photograph album MV for being applied to cell phone platform according to claim 6, its feature Be, the step 502) in artificial intelligence deep learning method using Deep Learning intelligent methods or depth nerve Network.
8. the processing method of a kind of automatic making photograph album MV for being applied to cell phone platform according to claim 6, its feature Be, the step 504) in landscaping effect and background sound it will be stamped in database matching their scene mark Sign.
9. the processing method of a kind of automatic making photograph album MV for being applied to cell phone platform according to claim 1, its feature Be, the step 103) in data management include that photo files, background sound file, the configuration file of landscaping effect are deposited Storage, and for step 104) render the data that input is provided.
CN201611237751.0A 2016-12-28 2016-12-28 Processing method applied to mobile phone platform for automatically making album MV Pending CN106657817A (en)

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CN109167937A (en) * 2018-11-05 2019-01-08 北京达佳互联信息技术有限公司 Video distribution method, apparatus, terminal and storage medium
WO2019062716A1 (en) * 2017-09-30 2019-04-04 腾讯科技（深圳）有限公司 Method and apparatus for generating music
CN110246523A (en) * 2019-04-26 2019-09-17 广东虎彩影像有限公司 A kind of video generating system and its method
CN111309957A (en) * 2020-03-17 2020-06-19 杭州趣维科技有限公司 Method for automatically generating travel photo album MV
CN112463998A (en) * 2020-11-25 2021-03-09 京东方科技集团股份有限公司 Album resource processing method, apparatus, electronic device and storage medium

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CN111309957A (en) * 2020-03-17 2020-06-19 杭州趣维科技有限公司 Method for automatically generating travel photo album MV
CN112463998A (en) * 2020-11-25 2021-03-09 京东方科技集团股份有限公司 Album resource processing method, apparatus, electronic device and storage medium

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