CN110909187B - Image storage method, image reading method, image memory and storage medium - Google Patents
Image storage method, image reading method, image memory and storage medium Download PDFInfo
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Abstract
The invention discloses an image storage method, which comprises the following steps: acquiring a target image, and decomposing the target image to obtain a target sub-image; matching the target sub-image with the metadata in the metadata storage area, and determining the target metadata corresponding to the target sub-image; acquiring target metadata information corresponding to the target metadata and attribute information of a target image; composing the combination information of the target image by using the attribute information and the target metadata information, and storing the combination information; the method combines and stores the target metadata information corresponding to the target metadata and the attribute information of the target image, which is equivalent to storing the target image, without storing a large number of same images and only storing the combined information corresponding to the target image, thereby reducing the waste of a storage unit; the invention also provides an image reading method, an image storage device, an image reading device, an image memory and a computer readable storage medium, and the image reading method, the image storage device, the image memory and the computer readable storage medium also have the beneficial effects.
Description
Technical Field
The present invention relates to the field of image storage technologies, and in particular, to an image storage method, an image reading method, an image memory, an image storage apparatus, an image reading apparatus, and a computer-readable storage medium.
Background
Today, electronic information technology is rapidly developed, and particularly with the development of artificial intelligence, big data analysis and AR technology, more and more images need to be stored. Because most of the images stored in a certain storage cluster are in the same field, such as face images or flower images, many images in the storage cluster are very similar, so that many similar or identical images are stored, waste of a large number of storage units is caused, the reuse rate of the storage units is not high, the storage efficiency is further low, and the storage cost is high.
Therefore, how to solve the problem that the storage cost is high due to the low storage efficiency of the existing image storage method is a technical problem to be solved by the technical personnel in the field.
Disclosure of Invention
In view of the above, the present invention provides an image storage method, an image reading method, an image memory, an image storage device, an image reading device, and a computer readable storage medium, which solve the problems of low storage efficiency and high storage cost of the conventional image storage method.
In order to solve the above technical problem, the present invention provides an image storage method, including:
acquiring a target image, and decomposing the target image to obtain a target sub-image;
matching the target sub-image with metadata in a metadata storage area, and determining target metadata corresponding to the target sub-image;
acquiring target metadata information corresponding to the target metadata and attribute information of the target image;
and composing combination information of the target image by using the attribute information and the target metadata information, and storing the combination information.
Optionally, the obtaining target metadata information corresponding to the target metadata includes:
obtaining a transformation coefficient between the target sub-image and the corresponding target metadata;
and acquiring a pre-stored address of the target metadata in the metadata storage area by using a metadata table, and forming target metadata information by using the transformation coefficient and the pre-stored address.
Optionally, the process of creating the metadata table includes:
storing each metadata into the metadata storage area, and acquiring the pre-stored address corresponding to each metadata;
and constructing the metadata table by utilizing each pre-stored address.
Optionally, after the matching the target sub-image with the metadata in the metadata storage area, the method further includes:
storing the special subimages into the metadata storage area, and acquiring prestored addresses corresponding to the special subimages; the special sub-image is a target sub-image which fails to be matched;
and updating the metadata table by using the prestored address corresponding to the special sub-image.
Optionally, the storing the special sub-image into the metadata storage area includes:
and compressing the special sub-image and storing the compressed special sub-image into the metadata storage area.
Optionally, the obtaining of the attribute information of the target image includes:
acquiring corresponding position information of the target sub-image in the target image;
and forming the attribute information by using the position information and the identification information of the target image.
The invention also provides an image reading method, which comprises the following steps:
acquiring a reading instruction for reading a target image, and determining combination information corresponding to the target image by using the reading instruction;
analyzing the combined information to obtain attribute information and target metadata information, and acquiring corresponding target metadata from a plurality of metadata in a metadata storage area by using the target metadata information;
and constructing the target metadata by using the attribute information and the target metadata information to obtain the target image.
Optionally, the acquiring, by using the target metadata information, corresponding target metadata from a metadata storage area includes:
analyzing the target metadata information to obtain a corresponding prestored address;
and acquiring the corresponding target metadata from the metadata storage area by using the prestored address.
Optionally, the constructing the target metadata by using the attribute information and the target metadata information to obtain the target image includes:
analyzing the target metadata information to obtain a transformation coefficient;
processing the corresponding target metadata by using the transformation coefficient to obtain a corresponding target sub-image;
analyzing the attribute information to obtain position information corresponding to the target subimage;
and constructing the target sub-image by using the position information to obtain the target image.
The invention also provides an image memory comprising a processor, a memory and an input-output component, wherein:
the input and output component is used for acquiring or outputting a target image;
the memory comprises a metadata memory area, a combined information memory area and a program memory area; wherein the program storage area is used for storing computer programs, the metadata storage area is used for storing metadata, and the combined information storage area is used for storing combined information;
the processor is configured to execute the computer program to implement the above-mentioned image storage method or the above-mentioned image reading method.
The present invention also provides an image storage apparatus comprising:
the decomposition module is used for acquiring a target image and decomposing the target image to obtain a target sub-image;
the matching module is used for matching the target sub-image with the metadata in the metadata storage area and determining the target metadata corresponding to the target sub-image;
the acquisition module is used for acquiring target metadata information corresponding to the target metadata and attribute information of the target image;
and the storage module is used for forming the combined information of the target image by using the attribute information and the target metadata information and storing the combined information.
The present invention also provides an image reading apparatus comprising:
the instruction acquisition module is used for acquiring a reading instruction for reading a target image and determining combination information corresponding to the target image by using the reading instruction;
the analysis module is used for analyzing the combined information to obtain attribute information and target metadata information, and acquiring corresponding target metadata from a plurality of metadata in a metadata storage area by using the target metadata information;
and the target image acquisition module is used for constructing the target metadata by using the attribute information and the target metadata information to obtain the target image.
The present invention also provides a computer-readable storage medium for storing a computer program, wherein the computer program, when executed by a processor, implements the above-described image storage method or the above-described image reading method.
The invention provides an image storage method, which is used for acquiring a target image and decomposing the target image to obtain a target sub-image. And matching the target sub-image with the metadata in the metadata storage area, and determining the target metadata corresponding to the target sub-image. And acquiring target metadata information corresponding to the target metadata and attribute information of the target image. And composing the combination information of the target image by using the attribute information and the target metadata information, and storing the combination information.
Therefore, the image storage method decomposes the target image to be stored to obtain the target sub-image, and matches the target sub-image in the metadata storage area to obtain the target metadata. The target metadata information corresponding to the target metadata and the attribute information of the target image are combined and stored, that is, the target image is stored. The image storage method does not need to store a large number of similar or identical images, and only needs to store the combination information corresponding to the target image, so that the waste of the storage unit is reduced, the reuse rate of the storage unit is improved, the storage efficiency is improved, and the storage cost is reduced.
The invention also provides an image reading method, corresponding to the image storage method, the reading instruction for reading the target image is obtained, and the combination information corresponding to the target image is determined by using the reading instruction. And analyzing the combined information to obtain attribute information and target metadata information, and acquiring corresponding target metadata from a plurality of metadata in the metadata storage area by using the target metadata information. And constructing the target metadata by using the attribute information and the target metadata information to obtain a target image.
Therefore, when the target image is read, the image reading method firstly acquires the combination information corresponding to the target image, acquires the target metadata from the metadata storage area by using the combination information, and combines the target metadata by using the attribute information and the target metadata information to obtain the target image. The image reading method does not need to store a large number of similar or identical images, only needs to store the combination information corresponding to the target image, and reads the combination information when the target image is read. Therefore, the waste of the storage unit is reduced, the reuse rate of the storage unit is improved, the storage efficiency is improved, and the storage cost is reduced.
In addition, the invention also provides an image memory, an image storage device, an image reading device and a computer readable storage medium, which also have the beneficial effects.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.
FIG. 1 is a flowchart of an image storage method according to an embodiment of the present invention;
FIG. 2 is a flowchart of an image reading method according to an embodiment of the present invention;
FIG. 3 is a flow chart of a specific construction process according to an embodiment of the present invention;
FIG. 4 is a schematic structural diagram of an image storage device according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram of an image reading apparatus according to an embodiment of the present invention;
fig. 6 is a schematic structural diagram of an image memory according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, belong to the protection scope of the present invention.
Referring to fig. 1, fig. 1 is a flowchart illustrating an image storage method according to an embodiment of the present invention. The method comprises the following steps:
s101: and acquiring a target image, and decomposing the target image to obtain a target sub-image.
The target image is an image to be stored, and may be in any picture format such as JPEG, TIFF, RAW, BMP, GIF, or PNG, and the shape and scale of the target image may be arbitrary, and may be, for example, a square, a rectangle with an aspect ratio of 4:3, or a rectangle with an aspect ratio of 16. The content of the target image is not limited in this embodiment, and may be a human face image or a flower image, for example.
And after the target image is obtained, decomposing the target image to obtain a target sub-image. The specific decomposition method is not limited in this embodiment, for example, the target image may be identified and marked by using a deep learning network, and the target image may be decomposed according to the mark of the deep learning network; or the target image may be decomposed according to a preset format. In order to make the decomposed target sub-images more similar to the metadata, it is preferable in this embodiment to identify and label the target images by using a deep learning network, and decompose the target images according to the labels. The deep learning network can be trained in advance, specifically, a plurality of deep learning networks can be trained so as to decompose the target image according to the content of the target image, for example, after the target image is obtained, the content of the target image can be recognized first, when the target image is a face image, the face recognition model is used for marking the target image, and then the target image is divided according to the mark; or when the target image is a flower image, marking the target image by using the flower recognition model, and dividing the target image according to the marks. The structure of the deep learning model, the training process and the content of the training image set are not limited in this embodiment, and can be adjusted or selected according to the actual situation.
The present embodiment does not limit the number of target sub-images, and the specific number of target sub-images is related to the decomposition method. Specifically, when the target image is decomposed according to the preset rule, the number of the target sub-images is fixed, for example, fixed to 5; when the target image is identified, marked and decomposed by using the deep learning network, the number of the target sub-images can be determined according to the result of identifying the mark, and the result of identifying the mark may be influenced by the training set image of the deep learning model and the structure of the model itself, for example, the target image can be decomposed into a plurality of target sub-images under certain circumstances, for example, when the target image is a face image, the target image can be decomposed into a plurality of target sub-images such as a mouth, a nose, eyes, a top of the head, a cheek and the like; or when the target image is a five-star red flag, the target image can be divided into two sub-images of five stars and red. Of course, the target image may also be decomposed into a sub-image, i.e. the target image is determined as a sub-image, e.g. if the target image is a red flag with pure red color, it may be determined as a target sub-image.
S102: and matching the target sub-image with the metadata in the metadata storage area, and determining the target metadata corresponding to the target sub-image.
And after the target sub-image is obtained, matching the target sub-image with the metadata in the metadata storage area, wherein the metadata is stored in the metadata storage area in advance for matching operation with the target sub-image and is used for constructing the target image in the corresponding reading process. The specific form of the metadata is not limited in this embodiment, and may be, for example, a common image; or the information may be information composed of some information elements, such as information composed of combinations of colors, sizes, rules, and the like, such as information composed of red, a pixel size, and 360 degrees of rotation around a certain point, that is, the information represents a red circle, and the radius of the circle is not limited; or may be yellow, one pixel size, shifted ten times forward from a certain point, i.e. the information represents a line segment of yellow of 10 pixels in length.
Specifically, after the target sub-image is matched with the metadata, the metadata passing through the matching is determined as the target metadata, that is, the target metadata corresponding to the target sub-image. The present embodiment does not limit the specific determination criterion for successful matching, for example, when the metadata is a normal image, the target sub-image may be matched with each metadata in the metadata storage area, and the confidence of each matching is determined, and when the confidence corresponding to a certain metadata is greater than a confidence threshold, for example, when the confidence is greater than 99%, the metadata is determined as the target metadata; or the target sub-image may be matched with each metadata in the metadata storage area, and the confidence of each matching may be determined, and when the confidence corresponding to a plurality of metadata is greater than a confidence threshold, for example, greater than 99%, the metadata with the highest confidence in the plurality of metadata may be determined as the target metadata. When the metadata is information composed of some information elements, the prior art may be referred to for a specific matching process, and this embodiment is not limited as long as matching can be achieved.
S103: and acquiring target metadata information corresponding to the target metadata and attribute information of the target image.
The target metadata information is information corresponding to the target metadata, and may include a transformation coefficient corresponding to the target sub-image and the target metadata, and a pre-stored address of the target metadata in the metadata storage area, and in addition, may include a number or id information of the target metadata. When there are multiple target sub-images, there will be multiple target metadata correspondingly, and each target metadata corresponds to one target metadata information, so there will be multiple target metadata information. The attribute information of the target image includes identification information of the target image, such as id information or number information, and also includes position information, where the position information is used to indicate the position of the target sub-image in the target image, and may also be referred to as a mapping factor, i.e., the mapping position of the target sub-image in the target image.
In the embodiment of the present invention, when obtaining target metadata information corresponding to target metadata, a transformation coefficient between a target sub-image and the corresponding target metadata may be obtained, where the transformation coefficient may include other multiple coefficients, such as a scaling coefficient, a rotation coefficient, a length coefficient, a size coefficient, or a flipping coefficient, and the target metadata is transformed or operated according to the scaling coefficient, so as to obtain the target sub-image. After the transformation coefficients are acquired, the pre-stored addresses of the target metadata in the metadata storage area are acquired by using a metadata table, the pre-stored addresses are the storage addresses of the target metadata, and the metadata table is used for storing the pre-stored addresses of the metadata so as to generate target metadata information when a target image is stored or providing the pre-stored addresses in the corresponding target image reading process so as to acquire the target metadata. And forming target metadata information by using the transformation coefficient and the prestored address.
When the attribute information of the target image is obtained, corresponding position information of the target sub-image in the target image may be obtained, where the position information may be represented by a position of a specific pixel in the target sub-image, or by positions of multiple pixels in the target sub-image, for example, a center point of the target sub-image may be selected as the specific pixel, and coordinate information of the center point in the target image may be used as the position information of the target sub-image; or a plurality of pixels on the boundary of the target sub-image may be selected, and the coordinate information of these pixels in the target image is used as the position information of the target sub-image. After the position information is acquired, the attribute information is composed using the position information and the identification information of the target image.
Further, before the pre-stored address is obtained by using the metadata table, the metadata table is required to be constructed. Specifically, each metadata is stored in the metadata storage area, a pre-stored address corresponding to each metadata is obtained, and a metadata table is constructed by using each pre-stored address. The metadata table may also include the number or id of each metadata.
S104: and composing composition information of the target image using the attribute information and the target metadata information, and storing the composition information.
After acquiring the attribute information and the target metadata information, the attribute information and the target metadata information are used to form combined information corresponding to the target image, and a specific combined information generation method is not limited in this embodiment, for example, the combined information of the target image may be formed in the order of the attribute information being before and the target metadata information being after; or when a plurality of target metadata information exist, the target metadata information is sequenced, and the attribute information is placed behind the target metadata information to form combined information. After the combination information is generated, the combination information may be stored in a combination information storage area.
By applying the image storage method provided by the embodiment of the invention, the target image to be stored is decomposed to obtain the target sub-image, and the target sub-image is matched in the metadata storage area to obtain the target metadata. The target metadata information corresponding to the target metadata and the attribute information of the target image are combined and stored, that is, the target image is stored. The image storage method does not need to store a large number of similar or identical images, and only needs to store the combination information corresponding to the target image, so that the waste of the storage unit is reduced, the reuse rate of the storage unit is improved, the storage efficiency is improved, and the storage cost is reduced.
Based on the foregoing embodiment of the present invention, in a possible implementation manner, the decomposing the target image to obtain the target sub-image, and the target sub-image is not matched with the metadata in the metadata storage area, and to solve the foregoing problem, after the matching the target sub-image and the metadata in the metadata storage area, the method may further include:
and storing the special subimage into a metadata storage area, and acquiring a prestored address corresponding to the special subimage.
It should be noted that the special sub-image is the target sub-image with failed matching. In the embodiment of the present invention, the determination criterion of the matching failure corresponds to the determination criterion of the matching success in the embodiment of the present invention, for example, when the determination criterion of the matching success is that the confidence between the target sub-image and the target metadata is greater than 99%, the determination criterion of the matching failure may be that the confidence between the target sub-image and any metadata is less than 99%. And after the matching fails, determining the target subimage which fails to be matched as a special subimage, storing the special subimage into the metadata storage area, and simultaneously acquiring a prestored address corresponding to the special subimage. It should be noted that, this embodiment does not limit the storage method for storing the special sub-image in the metadata storage area, for example, the special sub-image may be directly stored as metadata in the metadata storage area; or the special sub-image may be parsed to obtain information corresponding to the special sub-image, and the information may be stored as metadata in the metadata storage area.
Further, in order to reduce the storage space of the metadata storage area occupied by the special sub-image, so as to store more metadata in the metadata storage area, it is preferable in an embodiment of the present invention that the special sub-image is compressed and then stored in the metadata storage area.
And updating the metadata table by using the pre-stored address corresponding to the special sub-image.
And storing the special sub-image into a metadata storage area, and updating a metadata table by using a pre-stored address after acquiring the corresponding pre-stored address. In the process of updating the metadata table, it may be necessary to number the special sub-image to obtain an image number corresponding to the special sub-image, and add the image number to the metadata table, and the specific updating process is not limited in this embodiment.
An image reading method corresponding to the image storage method will be described in an embodiment of the present invention, specifically referring to fig. 2, where fig. 2 is a flowchart of an image reading method provided in an embodiment of the present invention, including:
s201: and acquiring a reading instruction for reading the target image, and determining the combination information corresponding to the target image by using the reading instruction.
The reading instruction is used to specify a target image to be read, and may include identification information of the target image, or may include number information of combination information, or the like. After the reading instruction is acquired, the combination information corresponding to the target image is determined by using the reading instruction. The combination information may include attribute information and target metadata information, the attribute information may include identification information of the target image, and the identification information and the reading instruction may be used to determine the combination information corresponding to the target image.
S202: and analyzing the combined information to obtain attribute information and target metadata information, and acquiring corresponding target metadata from a plurality of metadata in the metadata storage area by using the target metadata information.
And analyzing the combined information to obtain attribute information and target metadata information. The attribute information of the target image includes identification information of the target image, such as id information or number information, and also includes position information indicating the position of the target sub-image in the target image. The target metadata information is information corresponding to the target metadata, and may include a transformation coefficient corresponding to the target sub-image and the target metadata, and a pre-stored address of the target metadata in the metadata storage area, and in addition, may also include a number or id information of the target metadata.
After the target metadata information is acquired, corresponding target metadata is acquired from the plurality of metadata in the metadata storage area using the target metadata information. The present embodiment does not limit the number of target metadata information in one combination information, for example, one combination information may include only one target metadata information, or one combination information may include a plurality of target metadata information. The target metadata information corresponds to the target metadata, and thus the target metadata corresponding to the target metadata information can be determined and acquired from among the plurality of metadata in the metadata storage area using the target metadata information.
Specifically, when the target metadata information is used to obtain the corresponding target metadata from the metadata storage area, the target metadata information may be analyzed to obtain a pre-stored address corresponding to the target metadata information, that is, a pre-stored address of the target metadata, and the corresponding target metadata is obtained from the metadata storage area by using the pre-stored address.
S203: and constructing the target metadata by using the attribute information and the target metadata information to obtain a target image.
And constructing the target image by using the attribute information and the target metadata information to obtain the target image. The construction processing may specifically include transformation processing and combination processing, and a specific process of the construction processing is not limited in this embodiment, for example, when there are a plurality of pieces of target metadata information, the target metadata are respectively transformed by using the target metadata information, and after the transformation processing, the transformed target metadata are combined by using the attribute information, so that the target image can be obtained. After the target image is obtained, other operations may be performed, for example, the target image may be output, or the target image may be presented.
By applying the image reading method provided by the embodiment of the invention, when the target image is read, the combination information corresponding to the target image is firstly obtained, the combination information is utilized to obtain the target metadata from the metadata storage area, and the attribute information and the target metadata information are utilized to combine the target metadata to obtain the target image. The image reading method does not need to store a large number of similar or identical images, only needs to store the combination information corresponding to the target image, and reads the combination information when the target image is read. Therefore, the waste of the storage unit is reduced, the reuse rate of the storage unit is improved, the storage efficiency is improved, and the storage cost is reduced.
Based on the above embodiments of the invention, the embodiments of the invention will describe a specific construction process flow, that is, a specific description is given to the step S203. Referring to fig. 3, fig. 3 is a specific flow chart of a construction process according to an embodiment of the present invention, including:
s301: and analyzing the target metadata information to obtain a transformation coefficient.
In the embodiment of the present invention, the target metadata information includes the transformation coefficient and the prestored address of the target metadata in the metadata storage area. The transform coefficients are used to transform the target metadata into corresponding target sub-images, which are decomposed from the target image.
S302: and processing the corresponding target metadata by using the transformation coefficient to obtain a corresponding target sub-image.
After the transform coefficient is obtained, the corresponding target metadata is processed by using the transform coefficient to obtain a corresponding target sub-image, which is not limited to a specific processing procedure in this embodiment. For example, the transformation coefficient may include an f parameter and an m parameter, and when the target metadata is { a-s-m }, the f parameter and the m parameter are used to perform { a-s-m }. M.f calculation on the target metadata to obtain the target sub-image. In the embodiment of the present invention, the operator only represents operation, and the specific operation method and content are not limited in this embodiment.
S303: and analyzing the attribute information to obtain the position information corresponding to the target sub-image.
In the embodiment of the present invention, the attribute information includes position information of the target sub-image in the target image and identification information of the target image. The position information is used to indicate the position of the target sub-image in the target image.
S304: and combining the target sub-images by using the position information to obtain a target image.
After the position information is acquired, the target sub-images may be combined using the position to obtain the target image. It should be noted that the embodiment of the present invention only describes a specific construction process, and other similar methods can also be used in the image reading method provided by the present invention.
In the following, the image storage apparatus provided by the embodiment of the present invention is described, and the image storage apparatus described below and the image storage method described above may be referred to correspondingly.
Referring to fig. 4, fig. 4 is a schematic structural diagram of an image storage device according to an embodiment of the present invention, including:
the decomposition module 410 is configured to obtain a target image, and decompose the target image to obtain a target sub-image;
the matching module 420 is configured to match the target sub-image with the metadata in the metadata storage area, and determine target metadata corresponding to the target sub-image;
an obtaining module 430, configured to obtain target metadata information corresponding to the target metadata and attribute information of the target image;
and a storage module 440 for composing the combination information of the target image using the attribute information and the target metadata information, and storing the combination information.
Optionally, the obtaining module 430 includes:
a transform coefficient acquisition unit for acquiring a transform coefficient between a target sub-image and corresponding target metadata;
and the pre-stored address acquisition unit is used for acquiring the pre-stored address of the target metadata in the metadata storage area by using the metadata table, and the target metadata information is formed by using the transformation coefficient and the pre-stored address.
Optionally, the method includes:
the template storage module is used for storing each metadata into a metadata storage area and acquiring a pre-stored address corresponding to each metadata;
and the metadata table building module is used for building a metadata table by utilizing each prestored address.
Optionally, the method further includes:
the special storage module is used for storing the special subimages into the metadata storage area and acquiring the pre-stored addresses corresponding to the special subimages; wherein, the special sub-image is a target sub-image which fails to be matched;
and the metadata table updating module is used for updating the metadata table by using the prestored address corresponding to the special sub-image.
Optionally, the special storage module includes:
and the compression unit is used for compressing the special sub-image and storing the special sub-image into the metadata storage area.
Optionally, the obtaining module 430 includes:
the position information acquisition unit is used for acquiring the corresponding position information of the target sub-image in the target image;
and a composing unit for composing the attribute information using the position information and the identification information of the target image.
In the following, the image reading apparatus provided by the embodiment of the present invention is described, and the image reading apparatus described below and the image reading method described above may be referred to correspondingly.
Referring to fig. 5, fig. 5 is a schematic structural diagram of an image reading apparatus according to an embodiment of the present invention, including:
an instruction obtaining module 510, configured to obtain a reading instruction for reading a target image, and determine combination information corresponding to the target image by using the reading instruction;
the analyzing module 520 is configured to analyze the combined information to obtain attribute information and target metadata information, and obtain corresponding target metadata from a plurality of metadata in the metadata storage area by using the target metadata information;
and a target image obtaining module 530, configured to construct the target metadata by using the attribute information and the target metadata information, so as to obtain a target image.
Optionally, the parsing module 520 includes:
the pre-stored address analysis unit is used for analyzing the target metadata information to obtain a corresponding pre-stored address;
and a target metadata acquiring unit for acquiring corresponding target metadata from the metadata storage area by using the pre-stored address.
Optionally, the target image acquiring module 530 includes:
the transformation coefficient analysis unit is used for analyzing the target metadata information to obtain a transformation coefficient;
the transformation processing unit is used for processing the corresponding target metadata by utilizing the transformation coefficient to obtain a corresponding target sub-image;
the position information analyzing unit is used for analyzing the attribute information to obtain position information corresponding to the target sub-image;
and the acquisition unit is used for constructing the target sub-image by using the position information to obtain the target image.
In the following, the image storage device provided by the embodiment of the present invention is described, and the image storage device described below and the image storage method or the image reading method described above may be referred to correspondingly.
Referring to fig. 6, fig. 6 is a schematic structural diagram of an image memory according to an embodiment of the present invention, where the image memory includes an input/output unit 610, a memory 630, and a processor 620, where:
an input-output section 610 for acquiring or outputting a target image;
a memory 630 including a metadata storage area, a combined information storage area, and a program storage area; the system comprises a program storage area, a metadata storage area and a combined information storage area, wherein the program storage area is used for storing computer programs, the metadata storage area is used for storing metadata, and the combined information storage area is used for storing combined information;
the processor 620 is configured to execute a computer program to implement the image storage method or the image reading method.
In the following, the computer-readable storage medium provided by the embodiment of the present invention is introduced, and the computer-readable storage medium described below and the image storage method or the image reading method described above may be referred to correspondingly.
The present invention also provides a computer-readable storage medium, on which a computer program is stored, which, when executed by a processor, implements the steps of the above-mentioned image storing method or the steps of the above-mentioned image reading method.
The computer-readable storage medium may include: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
The embodiments are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same or similar parts among the embodiments are referred to each other. The device disclosed by the embodiment corresponds to the method disclosed by the embodiment, so that the description is simple, and the relevant points can be referred to the method part for description.
Those of skill would further appreciate that the various illustrative components and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the components and steps of the various examples have been described above generally in terms of their functionality in order to clearly illustrate this interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may reside in Random Access Memory (RAM), memory, read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.
Finally, it should also be noted that, herein, relationships such as first and second, etc., are intended only to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
The foregoing detailed description is directed to an image storage method, an image reading method, an image memory, an image storage device, an image reading device, and a computer-readable storage medium, which are provided by the present invention, and specific examples are applied herein to illustrate the principles and embodiments of the present invention, and the above descriptions of the embodiments are only used to help understand the method and the core idea of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present invention.
Claims (10)
1. An image storage method, comprising:
acquiring a target image, and decomposing the target image to obtain a target sub-image;
matching the target sub-image with metadata in a metadata storage area, and determining target metadata corresponding to the target sub-image;
acquiring target metadata information corresponding to the target metadata and attribute information of the target image;
composing combination information of the target image using the attribute information and the target metadata information, and storing the combination information;
wherein the decomposing the target image comprises:
identifying and marking the target image by utilizing a deep learning network;
decomposing the target image according to the mark of the deep learning network and the content of the identified target image;
the obtaining of the target metadata information corresponding to the target metadata includes:
obtaining a transformation coefficient between the target sub-image and the corresponding target metadata, wherein the transformation coefficient comprises a coefficient of one or more combinations of a scaling coefficient, a rotation coefficient, a length coefficient, a size coefficient, and a flipping coefficient;
and acquiring a pre-stored address of the target metadata in the metadata storage area by using a metadata table, and forming target metadata information by using the transformation coefficient and the pre-stored address.
2. The image storage method according to claim 1, wherein the creation process of the metadata table includes:
storing each metadata into the metadata storage area, and acquiring the pre-stored address corresponding to each metadata;
and constructing the metadata table by utilizing each pre-stored address.
3. The image storage method according to claim 2, further comprising, after said matching the target sub-image with the metadata in the metadata storage area:
storing the special subimages into the metadata storage area, and acquiring prestored addresses corresponding to the special subimages; the special sub-image is a target sub-image which fails to be matched;
and updating the metadata table by using the prestored address corresponding to the special sub-image.
4. The image storage method according to claim 3, wherein said storing a special sub-image in said metadata storage area comprises:
and compressing the special sub-image and storing the special sub-image into the metadata storage area.
5. The image storage method according to claim 1, wherein the acquiring of the attribute information of the target image includes:
acquiring corresponding position information of the target sub-image in the target image;
and forming the attribute information by using the position information and the identification information of the target image.
6. An image reading method, characterized by comprising:
acquiring a reading instruction for reading a target image, and determining combination information corresponding to the target image by using the reading instruction;
analyzing the combined information to obtain attribute information and target metadata information, and acquiring corresponding target metadata from a plurality of metadata in a metadata storage area by using the target metadata information;
constructing the target metadata by using the attribute information and the target metadata information to obtain the target image;
wherein the acquiring of the corresponding target metadata from the metadata storage area by using the target metadata information includes:
analyzing the target metadata information to obtain a corresponding prestored address;
acquiring the corresponding target metadata from the metadata storage area by using the prestored address;
the constructing the target metadata by using the attribute information and the target metadata information to obtain the target image includes:
analyzing the target metadata information to obtain a transformation coefficient, wherein the transformation coefficient comprises one or more combined coefficients of a scaling coefficient, a rotation coefficient, a length coefficient, a size coefficient and a turning coefficient;
processing the corresponding target metadata by using the transformation coefficient to obtain a corresponding target sub-image;
analyzing the attribute information to obtain position information corresponding to the target subimage;
the position information is utilized to construct the target sub-image to obtain the target image, the target sub-image is obtained by decomposing the target image, and the method specifically comprises the following steps:
identifying and marking the target image by utilizing a deep learning network;
and decomposing the target image according to the mark of the deep learning network and the identified content of the target image to obtain the target sub-image.
7. An image memory comprising a processor, a memory, and an input-output component, wherein:
the input and output component is used for acquiring or outputting a target image;
the memory comprises a metadata memory area, a combined information memory area and a program memory area; wherein the program storage area is used for storing computer programs, the metadata storage area is used for storing metadata, and the combined information storage area is used for storing combined information;
the processor is configured to execute the computer program to implement the image storage method according to any one of claims 1 to 5 or the image reading method according to claim 6.
8. An image storage apparatus, comprising:
the decomposition module is used for acquiring a target image and decomposing the target image to obtain a target sub-image;
the matching module is used for matching the target sub-image with the metadata in the metadata storage area and determining the target metadata corresponding to the target sub-image;
the acquisition module is used for acquiring target metadata information corresponding to the target metadata and attribute information of the target image;
the storage module is used for forming the combined information of the target image by using the attribute information and the target metadata information and storing the combined information;
wherein the decomposing the target image comprises:
identifying and marking the target image by utilizing a deep learning network;
decomposing the target image according to the mark of the deep learning network and the content of the identified target image;
the obtaining of the target metadata information corresponding to the target metadata includes:
obtaining a transformation coefficient between the target sub-image and the corresponding target metadata, wherein the transformation coefficient comprises a coefficient of one or more combinations of a scaling coefficient, a rotation coefficient, a length coefficient, a size coefficient, and a flipping coefficient;
and acquiring a pre-stored address of the target metadata in the metadata storage area by using a metadata table, and forming target metadata information by using the transformation coefficient and the pre-stored address.
9. An image reading apparatus, characterized by comprising:
the instruction acquisition module is used for acquiring a reading instruction for reading a target image and determining combination information corresponding to the target image by using the reading instruction;
the analysis module is used for analyzing the combined information to obtain attribute information and target metadata information, and acquiring corresponding target metadata from a plurality of metadata in a metadata storage area by using the target metadata information;
the target image acquisition module is used for constructing the target metadata by using the attribute information and the target metadata information to obtain the target image;
wherein the acquiring of the corresponding target metadata from the metadata storage area by using the target metadata information includes:
analyzing the target metadata information to obtain a corresponding prestored address;
acquiring the corresponding target metadata from the metadata storage area by using the prestored address;
the constructing the target metadata by using the attribute information and the target metadata information to obtain the target image includes:
analyzing the target metadata information to obtain a transformation coefficient, wherein the transformation coefficient comprises one or more combined coefficients of a scaling coefficient, a rotation coefficient, a length coefficient, a size coefficient and a turning coefficient;
processing the corresponding target metadata by using the transformation coefficient to obtain a corresponding target sub-image;
analyzing the attribute information to obtain position information corresponding to the target subimage;
the constructing processing is carried out on the target sub-image by utilizing the position information to obtain the target image, the target sub-image is obtained by decomposing the target image, and the method specifically comprises the following steps:
identifying and marking the target image by utilizing a deep learning network;
and decomposing the target image according to the mark of the deep learning network and the identified content of the target image to obtain the target sub-image.
10. A computer-readable storage medium for storing a computer program, wherein the computer program when executed by a processor implements the image storage method according to any one of claims 1 to 5 or the image reading method according to claim 6.
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| CN201911082402.XA CN110909187B (en) | 2019-11-07 | 2019-11-07 | Image storage method, image reading method, image memory and storage medium |
| PCT/CN2020/087161 WO2021088333A1 (en) | 2019-11-07 | 2020-04-27 | Image storage method, image reading method, image memory and storage medium |
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| JP2001043239A (en) * | 1999-07-30 | 2001-02-16 | Canon Inc | Method and device for storing image and storage medium |
| WO2019028700A1 (en) * | 2017-08-09 | 2019-02-14 | 深圳市大疆创新科技有限公司 | Image processing method, device and computer readable storage medium |
| CN109992200A (en) * | 2017-12-30 | 2019-07-09 | 苏龙田 | One kind makes information store the smallest method of occupied space |
| CN109543062A (en) * | 2018-09-29 | 2019-03-29 | 中国平安人寿保险股份有限公司 | Image processing method, system, computer installation and readable storage medium storing program for executing |
| CN110909187B (en) * | 2019-11-07 | 2023-01-10 | 苏州浪潮智能科技有限公司 | Image storage method, image reading method, image memory and storage medium |
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| CN103873438A (en) * | 2012-12-12 | 2014-06-18 | 鸿富锦精密工业（深圳）有限公司 | Compression packet uploading and duplication-removing system and method |
| CN103873504A (en) * | 2012-12-12 | 2014-06-18 | 鸿富锦精密工业（深圳）有限公司 | System enabling data blocks to be stored in distributed server and method thereof |
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