CN103200403B - Large-scale database image storage optimization method - Google Patents
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Abstract
The invention provides a kind of large-scale database image storage optimization method, described storage optimization method comprises: eliminate time-domain redundancy and spatial domain redundancy to the frame of video stored in a database, the decision function of one judgement intraframe coding or interframe encode is set, judge that present frame is reference frame or coded frame according to described decision function, if reference frame, then compression stroke territory redundancy; If coded frame, then on the basis of compression time territory redundancy, recompress spatial domain redundancy, finally the user images record after compression is stored in corresponding database; For database increases by one for storing the reference frame table of reference frame in user images recording compressed, then add, read, delete and revise user images record.Storage optimization method of the present invention improves effectively, the memory property of optimization data storehouse image, reduces database size, improves the access efficiency of database.
Description
Technical field
The invention belongs to database field, relate to a kind of storage optimization method, particularly relate to a kind of large-scale database image storage optimization method.
Background technology
Along with the development of computer technology, people not only use single word as the carrier of information, can also be transmitted, storage information by various media.Media refer to the form of expression of information, as word, sound, image, animation etc.To " process " of various information medium, refer to that computer can obtain them, edit, store, retrieve, show, the various operation such as transmission.Multimedia technology is not the simple composite of various information medium, it is that a kind of information forms such as text, figure, image, animation and sound combines, and by computer generalization process and control, the information technology of a series of interactive operation can have been supported.The development of multimedia technology changes the use field of computer, make computer become the general tool of information-intensive society by the special product in office, laboratory, be widely used in the fields such as industrial production management, school eduaction, public information consulting, commercial advertisement, military commanding and training.And realize these function most criticals exactly effective organization and management is carried out to multimedia, this sets up the database that can process multi-medium data with regard to needing.
Such as, in enterprise's visual production management process, in order to ensure product quality and record production procedure, need in management database, store a large amount of monitoring images.Direct storage monitoring image causes database to take, and memory space is large, access speed is slow.For this reason, a lot of scheme is had at present to avoid this problem, such as database external schema and database internal schema.In the detection and filing application of monitoring or production line, need to retrieve the record meeting certain condition and consult, and no matter take which kind of image storage mode above-mentioned, its database size is all very large, to such an extent as to impact is normal uses.Although the inquiry of data and the retrieval of image can be realized by database with associating of video server, add the complexity of cost and application.
Summary of the invention
The shortcoming of prior art in view of the above, the object of the present invention is to provide a kind of large-scale database image storage optimization method, for solving the problem that database size in prior art is excessive and database operation workload is overweight.
For achieving the above object and other relevant objects, the invention provides a kind of large-scale database image storage optimization method.Described storage optimization method comprises:
S1, time-domain redundancy and spatial domain redundancy are eliminated to the frame of video stored in a database, the decision function of one judgement intraframe coding or interframe encode is set, judge that present frame is reference frame or coded frame according to described decision function, if reference frame, then intraframe coding is carried out to reference frame, compression stroke territory redundancy; If coded frame, then interframe encode is carried out to coded frame, the basis of compression time territory redundancy recompresses spatial domain redundancy, finally the user images record after compression is stored in corresponding database;
S2, for database increases by one for storing the reference frame table of reference frame in user images recording compressed, then adds, reads, deletes and revises user images record.
Preferably, described step S1 comprises:
S11, is divided into the identical current block of several sizes by the present frame of described current input, searches for immediate prediction block with it respectively to each current block in reference frame according to block matching criterion, the absolute movement vector between record current block and prediction block; Wherein, the block that the difference between each pixel in described current block and described prediction block forms, is called residual block;
S12, arranges the decision function judging intraframe coding or interframe encode, and the judgment threshold of described decision function is set to N, predicts the pixel value difference binaryzation of each pixel in block and current block, obtains binaryzation result D
i,j, wherein i, j are image pixel point coordinates; When
when being more than or equal to judgment threshold N, then judging that present frame is reference frame, then intraframe coding is carried out to described reference frame, i.e. compression stroke territory redundancy; When
when being less than judgment threshold N, then judging that present frame is coded frame, then interframe encode is carried out to described coded frame, namely on the basis of compression time territory redundancy, recompress spatial domain redundancy.
Preferably, the compression process of spatial domain redundancy is: by being judged as that the image of reference frame carries out JPEG compression, be stored in corresponding database after compression; When decoding to reference frame, only need the packed data of described reference frame according to jpeg decompression and restructural complete image; The compression process that the compression basis of time-domain redundancy is carried out spatial domain redundancy is again: will be judged as that the image of coded frame carries out estimation, calculate the residual error between coded frame and predictive frame described in the absolute movement vector sum between described coded frame and reference frame, Huffman encoding is adopted to the absolute movement vector between described coded frame and reference frame, after adopting discrete cosine transform to convert to the residual error between described coded frame and predictive frame and quantizing, carry out entropy code again, be finally stored in corresponding database; During to the decoding of described coded frame, need the predicted value obtaining each current block according to absolute movement vector from reference frame, then sue for peace with residual error and reconstruct complete image.
Preferably, described step S2 comprises:
S21, for database increases by one for storing the reference frame table of reference frame in user images recording compressed; Described reference frame table is to user transparent; Database comprises the tables of data for storing user images record, and the tables of data namely containing BLOB field is called subscriber's meter; Described reference frame table is by ImageID field, and KeyBLOB field and Reference field are formed; ImageID field is used for stored record sequence number, as the unique identification with reference to a line every in frame table; KeyBLOB field is used for storage of reference frames image information complete after frame data compression; Reference field is for recording by the number of times of subscriber's meter reference record;
S22, adds user images record;
S23, reads user images record;
S24, deletes user images record;
S25, amendment user images record.
Preferably, described step S22 comprises:
S221, waits request to be added, input new images, selects first image in described user images record as the Article 1 record with reference to frame, is recorded in reference frame table; When preserving the new images inputted, the last item record read in described reference frame table and the new images inputted are compared, judge that the new images inputted is reference frame or coded frame according to described decision function, the judgment threshold of described decision function is set to N, when inputted new images is with when being obtained residual error coefficient that predictive frame compares and be less than threshold value N by reference frame and absolute movement vector, the new images of input is judged as coded frame; When residual error coefficient with when being more than or equal to threshold value N, the new images of input is judged as reference frame;
S222, if the new images of input is judged as coded frame, so in subscriber's meter, add a new record, the ImageID information of reference frame associated with it and different information are combined, be recorded in the BLOB field of described subscriber's meter, upgrade the Reference field of the last item record in reference frame table simultaneously, make it add 1;
S223, if the new images of input is judged as reference frame, so adds a new record in reference frame table; Wherein, the ImageID of new images described in ImageID field record, its value is ImageID+1; KeyBLOB field store is through the binary image information of the complete described new images of JPEG compression process, and juxtaposition Reference field is 1; In subscriber's meter, increase a new record simultaneously, the ImageID of new images is kept in BLOB field.
Preferably, described step S23 comprises:
S231, waits for read requests, according to the BLOB field recorded in the subscriber's meter of database, reads the value of ImageID and searches in reference frame table;
S232, according to KeyBLOB field and the different information reconstructed image of reference frame table; The different information later stored by the BLOB field in subscriber's meter 64 and the reference frame of KeyBLOB field store by coding/decoding method also original image, and export the image of reduction.
Preferably, described step S24 comprises:
S241, waits for removal request, looks for ImageID, judge whether the Reference field of ImageID corresponding record in reference frame table is 1 in the BLOB field in subscriber's meter;
S242, judges whether the Reference field of ImageID corresponding record in reference frame table is 1, if so, from subscriber's meter and reference frame table, directly deletes this record; If not, then only from subscriber's meter, delete this record, and subtract 1 with reference to the Reference field of this record in frame table.
Preferably, described step S25 comprises:
S251, waits request to be modified;
S252, judges that the user images be modified is recorded as reference frame or coded frame; If coded frame, then give tacit consent to reference frame associated with it constant, recalculate different information, and upgrade the different information in BLOB field; If reference frame, then in reference frame table, search respective record according to ImageID, judge whether the user images record that need revise is independent reference frames further, namely judge whether the value of the Reference of respective record in reference frame table is 1;
S253, judges whether the value of the Reference of respective record in reference frame table is 1, if Reference is 1, namely judges that the user images that need revise is recorded as independent reference frames, then directly revise the KeyBLOB field contents that in reference frame table, ImageID is corresponding with it; If Reference is not 1, then need increase new record, make the value of its ImageID be the ImageID+1 of the last item record in reference frame table, KeyBLOB field store is through the image information of JPEG compression process, and juxtaposition Reference field is 1; Upgrade the BLOB field of the user images record be modified in subscriber's meter simultaneously, be updated to the ImageID of new record.
As mentioned above, large-scale database image storage optimization method of the present invention, has following beneficial effect:
1, the present invention improves, optimizes the memory property of database images effectively, decreases database size;
2, invention increases the access efficiency of database;
3, user operation of the present invention is transparent, and the middleware that can be used as in database images independently exists.
Accompanying drawing explanation
Fig. 1 is shown as the method flow diagram of large-scale database image storage optimization method of the present invention.
Fig. 2 is shown as the flow chart of large-scale database image storage optimization method step S1 of the present invention.
Fig. 3 is shown as reference frame compression process schematic diagram in large-scale database image storage optimization method step S1 of the present invention.
Fig. 4 is shown as coded frame compression process schematic diagram in large-scale database image storage optimization method step S1 of the present invention.
Fig. 5 is shown as in large-scale database image storage optimization method step S2 of the present invention and adds operational flowchart.
Fig. 6 is shown as read operation flow chart in large-scale database image storage optimization method step S2 of the present invention.
Fig. 7 is shown as deletion action flow chart in large-scale database image storage optimization method step S2 of the present invention.
Fig. 8 is shown as retouching operation flow chart in large-scale database image storage optimization method step S2 of the present invention.
Embodiment
Below by way of specific instantiation, embodiments of the present invention are described, those skilled in the art the content disclosed by this specification can understand other advantages of the present invention and effect easily.The present invention can also be implemented or be applied by embodiments different in addition, and the every details in this specification also can based on different viewpoints and application, carries out various modification or change not deviating under spirit of the present invention.
Refer to accompanying drawing.It should be noted that, the diagram provided in the present embodiment only illustrates basic conception of the present invention in a schematic way, then only the assembly relevant with the present invention is shown in graphic but not component count, shape and size when implementing according to reality is drawn, it is actual when implementing, and the kenel of each assembly, quantity and ratio can be a kind of change arbitrarily, and its assembly layout kenel also may be more complicated.
Below in conjunction with embodiment and accompanying drawing, the present invention is described in detail.
The present invention uses for reference the compression method of vision signal, in the optimizing process of database stores user image record, first introduce the concept removing Time and place redundancy compress user images record, and continue to add user images record, delete, revise, to reach reduction database size, the object that database transportation load is overweight.
The present embodiment provides a kind of large-scale database image storage optimization method, and as shown in Figure 1, described database images storage optimization method comprises:
S1, to the frame of video stored in a database, (frame is exactly a secondary static picture, continuous print frame forms frame of video) in the present frame of current input eliminate time-domain redundancy and spatial domain redundancy, the decision function of one judgement intraframe coding or interframe encode is set, judge that present frame is reference frame or coded frame according to described decision function, if reference frame, then intraframe coding is carried out to reference frame, compression stroke territory redundancy; If coded frame, then interframe encode is carried out to coded frame, the basis of compression time territory redundancy recompresses spatial domain redundancy, finally the user images record after compression is stored in corresponding database.This step, as shown in Figure 2, comprising:
S11, is divided into the identical current block of several sizes by the present frame of described current input, searches for immediate prediction block with it respectively to each current block in reference frame according to block matching criterion, the absolute movement vector between record current block and prediction block; Wherein, the block that the difference between each pixel in described current block and described prediction block forms, is called residual block, and therefore, each current block of described present frame can with a residual block and an absolute movement vector representation.
S12, arranges the decision function judging intraframe coding or interframe encode, and the judgment threshold of described decision function is set to N, predicts the pixel value difference binaryzation of each pixel in block and current block, obtains binaryzation result D
i,j, wherein i, j are image pixel point coordinates; When
when being more than or equal to judgment threshold N, then judging that present frame is reference frame, then intraframe coding is carried out to described reference frame, i.e. compression stroke territory redundancy; When
when being less than judgment threshold N, then judging that present frame is coded frame, then interframe encode is carried out to described coded frame, namely on the basis of compression time territory redundancy, recompress spatial domain redundancy.Wherein, as shown in Figure 3, the compression process of spatial domain redundancy, by being judged as that the image of reference frame carries out JPEG compression, is stored in after compression in corresponding database.When decoding to reference frame, only need the packed data of described reference frame according to jpeg decompression and restructural complete image.As shown in Figure 4, the compression basis of time-domain redundancy is carried out the compression process of spatial domain redundancy again, to be judged as that the image of coded frame carries out estimation, calculate the residual error between coded frame and predictive frame described in the absolute movement vector sum between described coded frame and reference frame, Huffman (Huffman) is adopted to encode to the absolute movement vector between described coded frame and reference frame, after adopting discrete cosine transform (DCT) to convert to the residual error between described coded frame and predictive frame and quantizing, carry out entropy code again, be finally stored in corresponding database.During to the decoding of described coded frame, need the predicted value obtaining each current block according to absolute movement vector from reference frame, then sue for peace with residual error and reconstruct complete image.Carried out the thinking compressed by estimation in step S1 reference video compression, sub-picture every in the user's picture record stored in a database is regarded as the frame in sequence image, in picture frame, compression stroke territory redundancy utilizes the information redundancy of adjacent interline image to realize time-domain compression simultaneously, thus reduces the size of database.
In order to improve access efficiency and the size of continuation minimizing database, continue to perform step S2
S2, for database increases by one for storing the reference frame table of reference frame in user images recording compressed, then adds, reads, deletes and revises user images record;
S21, for database increases by one for storing the reference frame table of reference frame in user images recording compressed; Described reference frame table is to user transparent; Database comprises the tables of data for storing user images record, is namely called subscriber's meter containing the tables of data of BLOB field, and this step is accessed to improve and reduce the size of database.Described reference frame table is by ImageID field, and KeyBLOB field and Reference field are formed.Described tables of data is as shown in table 1.Wherein, ImageID field is used for stored record sequence number, as the unique identification with reference to a line every in frame table; KeyBLOB field is used for storage of reference frames image information complete after frame data compression; Reference field is for recording by the number of times of subscriber's meter reference record.
Table 1: tables of data
| Colum | Type | Attribute |
| [ImageID] | [bigint] | PRIMARY KEY，NOT NULL, |
| [KeyBLOB] | [image] | NOT NULL, |
| [Reference] | [bigint] | NOT NULL, |
In order to keep the structure of database Central Plains subscriber's meter constant, ensure consistency.In subscriber's meter, the content of amended BLOB field is made up of the compressed information of ImageID, absolute movement vector sum residual error, the wherein LSN at the ImageID information storage of reference frames place of 64bits, the implication of the ImageID field in remaining reference frame table is identical, for associated user table and reference frame table.Convenient in order to describe, absolute movement vector sum residual error is referred to as different information.
S22, adds user images record; Add operation concrete as shown in Figure 5, comprising:
S221, waits request to be added, input new images, selects first image in described user images record as the Article 1 record with reference to frame, is recorded in reference frame table; When preserving the new images inputted, the last item record read in described reference frame table and the new images inputted are compared, judge that the new images inputted is reference frame or coded frame according to described decision function, the judgment threshold of described decision function is set to N, when inputted new images is with when being obtained residual error coefficient that predictive frame compares and be less than threshold value N by reference frame and absolute movement vector, the new images of input is judged as coded frame; When residual error coefficient with when being more than or equal to threshold value N, the new images of input is judged as reference frame;
S222, if the new images of input is judged as coded frame, so in subscriber's meter, add a new record, the ImageID information of reference frame associated with it and different information are combined, be recorded in the BLOB field of described subscriber's meter, upgrade the Reference field of the last item record in reference frame table simultaneously, make it add 1;
S223, if the new images of input is judged as reference frame, so adds a new record in reference frame table; Wherein, the ImageID of new images described in ImageID field record, its value is ImageID+1; KeyBLOB field store is through the binary image information of the complete described new images of JPEG compression process, and juxtaposition Reference field is 1; In subscriber's meter, increase a new record simultaneously, the ImageID of new images is kept in BLOB field; End user image record adds process.
S23, reads user images record; Read operation is concrete as shown in Figure 6, comprising:
S231, waits for read requests, according to the BLOB field recorded in the subscriber's meter of database, reads the value of ImageID and searches in reference frame table;
S232, according to KeyBLOB field and the different information reconstructed image of reference frame table; The different information later stored by the BLOB field in subscriber's meter 64 and the reference frame of KeyBLOB field store by coding/decoding method also original image, and export the image of reduction; End user image record reads process.
S24, deletes user images record; Deletion action is concrete as shown in Figure 7, comprising:
S241, waits for removal request, looks for ImageID, judge whether the Reference field of ImageID corresponding record in reference frame table is 1 in the BLOB field in subscriber's meter;
S242, judges whether the Reference field of ImageID corresponding record in reference frame table is 1, if so, from subscriber's meter and reference frame table, directly deletes this record; If not, then only from subscriber's meter, delete this record, and subtract 1 with reference to the Reference field of this record in frame table.
S25, amendment user images record; Retouching operation is concrete as shown in Figure 8, comprising:
S251, waits request to be modified;
S252, judges that the user images be modified is recorded as reference frame or coded frame; If coded frame, then give tacit consent to reference frame associated with it constant, recalculate different information, and upgrade the different information in BLOB field; If reference frame, then in reference frame table, search respective record according to ImageID, judge whether the user images record that need revise is independent reference frames further, namely judge whether the value of the Reference of respective record in reference frame table is 1; This step is to ensure that other image can not be impaired because of the order of execution amendment user images record;
S253, judges whether the value of the Reference of respective record in reference frame table is 1, if Reference is 1, namely judges that the user images that need revise is recorded as independent reference frames, then directly revise the KeyBLOB field contents that in reference frame table, ImageID is corresponding with it; If Reference is not 1, then need increase new record, make the value of its ImageID be the ImageID+1 of the last item record in reference frame table, KeyBLOB field store is through the image information of JPEG compression process, and juxtaposition Reference field is 1; Upgrade the BLOB field of the user images record be modified in subscriber's meter simultaneously, be updated to the ImageID of new record.
Large-scale database image storage optimization method described in this patent, has following advantages:
The first, used for reference the compression method of vision signal, in the process of database purchase file and picture, introduced the concept removing Time and place redundancy, effectively improve the memory property of database images.Namely, first the frame data compression completing single image reduces spatial information redundancy, then the correlation of time between adjacent image is removed along the line direction of database, effectively can reduce the memory space of static images sequence like this, improve access speed, optimize existing database images memory property to a certain extent, especially to the situation that the content change of still image is inviolent, effect of optimization is more remarkable.
The second, can reliable memory monitoring image, improve production reliability and maintaining enterprise prestige has very great meaning.
3rd, transparent to user operation, independently can exist as the middleware of database images.
4th, frame data compression algorithm can be any one in current all image compression algorithms, has room for improvement.
In sum, the present invention effectively overcomes various shortcoming of the prior art and tool high industrial utilization.
Above-described embodiment is illustrative principle of the present invention and effect thereof only, but not for limiting the present invention.Any person skilled in the art scholar all without prejudice under spirit of the present invention and category, can modify above-described embodiment or changes.Therefore, such as have in art usually know the knowledgeable do not depart from complete under disclosed spirit and technological thought all equivalence modify or change, must be contained by claim of the present invention.
Claims (8)
1. a large-scale database image storage optimization method, is characterized in that, described storage optimization method comprises:
S1, time-domain redundancy and spatial domain redundancy are eliminated to the frame of video stored in a database, the decision function of one judgement intraframe coding or interframe encode is set, judge that present frame is reference frame or coded frame according to described decision function, if reference frame, then intraframe coding is carried out to reference frame, compression stroke territory redundancy; If coded frame, then interframe encode is carried out to coded frame, the basis of compression time territory redundancy recompresses spatial domain redundancy, finally the user images record after compression is stored in corresponding database;
S2, for database increases by one for storing the reference frame table of reference frame in user images recording compressed, then adds, reads, deletes and revises user images record.
2. large-scale database image storage optimization method according to claim 1, is characterized in that: described step S1 comprises:
S11, is divided into the identical current block of several sizes by the present frame of described current input, searches for immediate prediction block with it respectively to each current block in reference frame according to block matching criterion, the absolute movement vector between record current block and prediction block; Wherein, the block that the difference between each pixel in described current block and described prediction block forms, is called residual block;
S12, arranges the decision function judging intraframe coding or interframe encode, and the judgment threshold of described decision function is set to N, predicts the pixel value difference binaryzation of each pixel in block and current block, obtains binaryzation result D
i,j, wherein i, j are image pixel point coordinates; When
when being more than or equal to judgment threshold N, then judging that present frame is reference frame, then intraframe coding is carried out to described reference frame, i.e. compression stroke territory redundancy; When
when being less than judgment threshold N, then judging that present frame is coded frame, then interframe encode is carried out to described coded frame, namely on the basis of compression time territory redundancy, recompress spatial domain redundancy.
3. large-scale database image storage optimization method according to claim 2, is characterized in that: the compression process of spatial domain redundancy is: by being judged as that the image of reference frame carries out JPEG compression, be stored in corresponding database after compression; When decoding to reference frame, only need the packed data of described reference frame according to jpeg decompression and restructural complete image; The compression process that the compression basis of time-domain redundancy is carried out spatial domain redundancy is again: will be judged as that the image of coded frame carries out estimation, calculate the residual error between coded frame and predictive frame described in the absolute movement vector sum between described coded frame and reference frame, Huffman encoding is adopted to the absolute movement vector between described coded frame and reference frame, after adopting discrete cosine transform to convert to the residual error between described coded frame and predictive frame and quantizing, carry out entropy code again, be finally stored in corresponding database; During to the decoding of described coded frame, need the predicted value obtaining each current block according to absolute movement vector from reference frame, then sue for peace with residual error and reconstruct complete image.
4. large-scale database image storage optimization method according to claim 1, is characterized in that: described step S2 comprises:
S21, for database increases by one for storing the reference frame table of reference frame in user images recording compressed; Described reference frame table is to user transparent; Database comprises the tables of data for storing user images record, and the tables of data namely containing BLOB field is called subscriber's meter; Described reference frame table is by ImageID field, and KeyBLOB field and Reference field are formed; ImageID field is used for stored record sequence number, as the unique identification with reference to a line every in frame table; KeyBLOB field is used for storage of reference frames image information complete after frame data compression; Reference field is for recording by the number of times of subscriber's meter reference record;
S22, adds user images record;
S23, reads user images record;
S24, deletes user images record;
S25, amendment user images record.
5. large-scale database image storage optimization method according to claim 4, is characterized in that: described step S22 comprises:
S221, waits request to be added, input new images, selects first image in described user images record as the Article 1 record with reference to frame, is recorded in reference frame table; When preserving the new images inputted, the last item record read in described reference frame table and the new images inputted are compared; Judge that the new images inputted is reference frame or coded frame according to described decision function, the judgment threshold of described decision function is set to N, when inputted new images is with when being obtained residual error coefficient that predictive frame compares and be less than threshold value N by reference frame and absolute movement vector, the new images of input is judged as coded frame; When residual error coefficient with when being more than or equal to threshold value N, the new images of input is judged as reference frame;
S222, if the new images of input is judged as coded frame, so in subscriber's meter, add a new record, the ImageID information of reference frame associated with it and different information are combined, be recorded in the BLOB field of described subscriber's meter, upgrade the Reference field of the last item record in reference frame table simultaneously, make it add 1;
S223, if the new images of input is judged as reference frame, so adds a new record in reference frame table; Wherein, the ImageID of new images described in ImageID field record, its value is ImageID+1; KeyBLOB field store is through the binary image information of the complete described new images of JPEG compression process, and juxtaposition Reference field is 1; In subscriber's meter, increase a new record simultaneously, the ImageID of new images is kept in BLOB field.
6. large-scale database image storage optimization method according to claim 4, is characterized in that: described step S23 comprises:
S231, waits for read requests, according to the BLOB field recorded in the subscriber's meter of database, reads the value of ImageID and searches in reference frame table;
S232, according to KeyBLOB field and the different information reconstructed image of reference frame table; The different information later stored by the BLOB field in subscriber's meter 64 and the reference frame of KeyBLOB field store by coding/decoding method also original image, and export the image of reduction.
7. large-scale database image storage optimization method according to claim 4, is characterized in that: described step S24 comprises:
S241, waits for removal request, looks for ImageID, judge whether the Reference field of ImageID corresponding record in reference frame table is 1 in the BLOB field in subscriber's meter;
S242, judges whether the Reference field of ImageID corresponding record in reference frame table is 1, if so, from subscriber's meter and reference frame table, directly deletes this record; If not, then only from subscriber's meter, delete this record, and subtract 1 with reference to the Reference field of this record in frame table.
8. large-scale database image storage optimization method according to claim 4, is characterized in that: described step S25 comprises:
S251, waits request to be modified;
S252, judges that the user images be modified is recorded as reference frame or coded frame; If coded frame, then give tacit consent to reference frame associated with it constant, recalculate different information, and upgrade the different information in BLOB field; If reference frame, then in reference frame table, search respective record according to ImageID, judge whether the user images record that need revise is independent reference frames further, namely judge whether the value of the Reference of respective record in reference frame table is 1;
S253, judges whether the value of the Reference of respective record in reference frame table is 1, if Reference is 1, namely judges that the user images that need revise is recorded as independent reference frames, then directly revise the KeyBLOB field contents that in reference frame table, ImageID is corresponding with it; If Reference is not 1, then need increase new record, make the value of its ImageID be the ImageID+1 of the last item record in reference frame table, KeyBLOB field store is through the image information of JPEG compression process, and juxtaposition Reference field is 1; Upgrade the BLOB field of the user images record be modified in subscriber's meter simultaneously, be updated to the ImageID of new record.
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| CN104349174B (en) * | 2013-08-05 | 2017-12-19 | 浙江大华系统工程有限公司 | Audio, video data Transmission system, storage and generation method, device and equipment |
| CN105007094B (en) * | 2015-07-16 | 2017-05-31 | 北京中宸泓昌科技有限公司 | A kind of exponent pair spread spectrum coding coding/decoding method |
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