CN113706639A - Image compression method and device based on rectangular NAM, storage medium and computing equipment - Google Patents
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Abstract
The invention provides an image compression method, an image compression device, a storage medium and a computing device based on a rectangular NAM (network information model), wherein the method comprises the following steps: acquiring image data to be compressed, and segmenting the image data to be compressed through a rectangular NAM algorithm to obtain a segmentation gray image block; decomposing the segmentation gray scale image block into a binary image, carrying out XOR operation on the binary image to obtain a calculation result, and processing the calculation result through raster scanning to obtain an NAM (name information model) table corresponding to the segmentation gray scale image block; and acquiring a corresponding NAM table storage structure according to the NAM table, and optimizing the NAM table storage structure. The invention compresses the image data to be compressed by the rectangular NAM algorithm, can reduce the overlapping times of each sub-mode in the calculation process of the compressed image, and can reduce the image storage space without changing the compressed image data by changing the arrangement sequence of the NAM table, thereby being beneficial to improving the image compression efficiency.
Description
Technical Field
The invention relates to the technical field of image processing, in particular to an image compression method and device based on a rectangular NAM, a storage medium and a computing device.
Background
With the deep development of informatization, an image is applied more and more widely in many occasions as an effective means for information transmission, but with the increase of images, image data is more and more, and accordingly, the burden of image data to be processed on a system is more and more, and how to find a practical and effective method to improve the image processing efficiency and reduce the space occupied by the image is an urgent problem to be solved.
The prior art can only face a single type of image when compressing images, and although the method has high compression efficiency and good compression performance when compressing images, the system can not effectively and timely react to various images, and if too many images even cause system crash, an image compression method is urgently needed, which can improve the image compression efficiency while processing various image compression.
Disclosure of Invention
The invention aims to provide an image compression method, an image compression device, a storage medium and a computing device based on a rectangular NAM (network information model), and solves the technical problem that the prior art cannot process multiple kinds of image compression and simultaneously improve the image compression efficiency.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
in one aspect, the present invention provides an image compression method based on a rectangular NAM, including:
acquiring image data to be compressed, segmenting the image data to be compressed through a rectangular NAM algorithm, and acquiring the segmented image data to be compressed as a segmentation gray image block;
decomposing the segmentation gray scale image blocks into binary images, and calculating to obtain NAM tables of the segmentation gray scale image blocks based on the binary images;
and optimizing the NAM according to a defined NAM storage structure, wherein the optimized NAM corresponds to the compressed image data.
On the basis of the above technical solution, preferably, the segmenting the image data to be compressed by the rectangular NAM algorithm includes:
setting a matrix sub-mode of a rectangular NAM algorithm, wherein the matrix sub-mode is set as follows: a positive matrix and a diagonal matrix;
and dividing the image data to be compressed into a positive matrix compressed image and an oblique matrix compressed image according to the matrix sub-mode.
On the basis of the above technical solution, preferably, the dividing the image data to be compressed into a positive matrix compressed image and an oblique matrix compressed image according to the matrix sub-mode includes:
and searching a starting point of a matrix sub-mode in the image data to be compressed according to the raster scanning sequence, and scanning a positive matrix and an oblique matrix from the starting point to obtain a positive matrix compressed image and an oblique matrix compressed image.
On the basis of the above technical solution, preferably, in the raster scanning process, data of each positive matrix and each oblique matrix is recorded, and area data of a positive matrix compressed image and area data of an oblique matrix compressed image are further calculated.
On the basis of the foregoing technical solution, preferably, the acquiring the segmented image data to be compressed as the segmented gray-scale image block includes:
and respectively acquiring the area data of the positive matrix compressed image and the area data of the oblique matrix compressed image, and screening out the compressed image with the largest area data as a segmentation gray image block corresponding to the image data to be compressed.
On the basis of the foregoing technical solution, preferably, the obtaining of the NAM table of the divided grayscale image block based on binary image calculation includes:
decomposing the segmented gray scale image blocks into a plurality of binary images;
carrying out XOR operation on two adjacent binary images, and recording corresponding operation results;
and defining a positive matrix array and an oblique matrix array according to the operation result, and processing the operation result through raster scanning to obtain an NAM (name information model) table corresponding to the segmentation gray-scale image block.
On the basis of the above technical solution, preferably, the method further includes scanning the binary image by raster scanning to obtain a positive matrix compressed image and an oblique matrix compressed image with the largest area data;
adding the area data of the positive matrix compressed image and the area data of the oblique matrix compressed image into a positive matrix array and an oblique matrix array respectively;
and merging the added positive matrix array and the added oblique matrix array, and taking the merged array as an NAM (name information model) table corresponding to the segmentation gray scale image block.
On the basis of the above technical solution, preferably, the optimizing the NAM table according to the defined storage structure of the NAM table includes:
acquiring a corresponding NAM table storage structure according to the NAM table, wherein the NAM table storage structure comprises: a watch head and a watch body;
recording the serial numbers of each header and each table body, and arranging the serial numbers of each header and each table body according to the sequence of 'header-table body-header-table body';
and acquiring the final arrangement result as an optimized NAM table, wherein the optimized NAM table corresponds to the compressed image data.
On the basis of the above technical solution, preferably, the method further comprises,
scanning the compressed image data corresponding to each optimized NAM table to obtain corresponding image complexity values;
comparing the acquired image complexity value with a preset image complexity threshold value, and if the image complexity value is greater than the image complexity threshold value, re-optimizing compressed image data corresponding to the optimized NAM; and if the image complexity value is smaller than the image complexity threshold value, storing the compressed image data corresponding to the optimized NAM table.
In another aspect, the present invention provides an image compression apparatus based on a rectangular NAM, including:
the acquisition and segmentation module is used for acquiring image data to be compressed, segmenting the image data to be compressed through a rectangular NAM algorithm, and acquiring the segmented image data to be compressed as a segmentation gray image block;
the processing module is used for decomposing the segmentation gray level image blocks into binary images and calculating to obtain NAM tables of the segmentation gray level image blocks based on the binary images;
and the optimization module is used for optimizing the NAM according to a defined NAM storage structure, and the optimized NAM corresponds to the compressed image data.
In a third aspect, the invention provides a computing device comprising: the image compression method comprises a memory, a processor and a rectangular NAM-based image compression method program stored on the memory and capable of running on the processor, wherein the rectangular NAM-based image compression method program is configured to realize the steps of the rectangular NAM-based image compression method.
In a fourth aspect, the present invention provides a storage medium, which is a computer medium having a rectangular NAM-based image compression method program stored thereon, wherein the rectangular NAM-based image compression method program, when executed by a processor, implements the steps of the rectangular NAM-based image compression method as described above.
Compared with the prior art, the image compression method and device based on the rectangular NAM have the following beneficial effects:
(1) the image data to be compressed is divided by defining the sub-mode, so that the storage space occupied by the sub-mode can be reduced, namely, the storage space occupied by the whole compression process is reduced, and the compression efficiency is improved.
(2) By changing the arrangement sequence of the NAM table, the storage space occupied by the image compression can be reduced under the condition of not changing the original compressed image, and the whole image compression efficiency is improved.
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FIG. 1 is a flow chart of a rectangular NAM-based image compression method according to a first embodiment of the present invention;
FIG. 2 is a schematic diagram of an image compression apparatus based on rectangular NAM according to a second embodiment of the present invention;
fig. 3 is a schematic structural diagram of an electronic device according to a third embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to 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 any inventive step based on the embodiments of the present invention, are within the scope of the present invention.
Referring to fig. 1, fig. 1 is a flowchart illustrating a first embodiment of an image compression method based on rectangular NAM according to the present invention.
In this embodiment, the image compression method based on the rectangular NAM includes the following steps:
s1: acquiring image data to be compressed, segmenting the acquired image data to be compressed through a rectangular NAM algorithm, and acquiring the segmented image data to be compressed as a segmentation gray image block.
It should be understood that this step will first acquire the image data to be compressed, and set the matrix sub-mode of the rectangular NAM algorithm, which is set as: and the positive matrix and the oblique matrix divide the compressed image data into a positive matrix compressed image and an oblique matrix compressed image according to the matrix sub-mode, respectively acquire the area data of the positive matrix compressed image and the area data of the oblique matrix compressed image, and screen out the compressed image with the maximum value as a divided gray image block corresponding to the compressed image data.
It should be understood that when the compressed image data is divided, the system searches for a starting point of a sub-pattern in the compressed image data according to a raster scanning sequence, then finishes scanning of a positive matrix and an oblique matrix from the starting point, and finally obtains a positive matrix compressed image and an oblique matrix compressed image obtained by scanning.
S2: and decomposing the segmentation gray image block into a binary image, carrying out XOR operation on the binary image to obtain a calculation result, and processing the calculation result through raster scanning to obtain an NAM (name information model) table corresponding to the segmentation gray image block.
It should be understood that, in this step, the divided gray-scale image block is decomposed into a plurality of binary images, an exclusive or operation is performed on two adjacent binary images, and corresponding operation results are recorded, a positive matrix array and an oblique matrix array are defined according to the operation results, and the positive matrix array and the oblique matrix array are processed through raster scanning, so as to obtain the NAM table corresponding to the divided gray-scale image block.
It should be understood that, when the binary image is scanned by raster scanning, a positive matrix compressed image and an oblique matrix compressed image with the largest area data can be obtained, the area data of the positive matrix compressed image and the area data of the oblique matrix compressed image are added to the positive matrix array and the oblique matrix array, respectively, the added positive matrix array and oblique matrix array are merged, and the merged array is used as the NAM table corresponding to the split grayscale image block.
S3: and acquiring a corresponding NAM table storage structure according to the NAM table, acquiring a local optimization rule, and optimizing the NAM table storage structure according to the local optimization rule.
It should be understood that this step will obtain a corresponding NAM table storage structure according to the obtained NAM table, where the NAM table storage structure includes: and the header and the body record the number of each header and body, the header-body-header-body sequence is arranged according to the number of each header and body, the final arrangement result is obtained as an optimized NAM, and the optimized NAM corresponds to the compressed image data.
It should be understood that, in order to detect the compression optimization result, the method further includes scanning the compressed image data corresponding to each optimized NAM table, obtaining a corresponding image complexity value, obtaining an image complexity threshold, comparing the image complexity value with the image complexity threshold, and when the image complexity value is greater than the image complexity threshold, re-optimizing the compressed image data corresponding to the optimized NAM table; and when the image complexity value is smaller than the image complexity threshold value, storing the compressed image data corresponding to the optimized NAM table.
The above description is only for illustrative purposes and does not limit the technical solutions of the present application in any way.
As can be easily found from the above description, in the present embodiment, by acquiring image data to be compressed, segmenting the image data to be compressed through a rectangular NAM algorithm, and acquiring the segmented image data to be compressed as a segmentation gray image block; decomposing the segmentation gray scale image block into a binary image, carrying out XOR operation on the binary image to obtain a calculation result, and processing the calculation result through raster scanning to obtain an NAM (name information model) table corresponding to the segmentation gray scale image block; and acquiring a corresponding NAM table storage structure according to the NAM table, acquiring a local optimization rule, and optimizing the NAM table storage structure according to the local optimization rule. According to the method and the device, the image data to be compressed is compressed through the rectangular NAM algorithm, the overlapping times of each sub-mode of the compressed image in the calculation process can be reduced, and meanwhile, the image storage space can be reduced under the condition that the compressed image data is not changed through changing the arrangement sequence of the NAM tables, and the image compression efficiency can be improved.
A second embodiment of the present invention proposes an image compression apparatus based on a rectangular NAM. As shown in fig. 2, the rectangular NAM-based image compression apparatus includes: a segmentation module 10, a processing module 20 and an optimization module 30 are obtained.
The acquisition and segmentation module 10 is used for acquiring image data to be compressed, segmenting the image data to be compressed through a rectangular NAM algorithm, and acquiring the segmented image data to be compressed as a segmentation gray image block;
the processing module 20 is configured to decompose the split grayscale image block into a binary image, perform an exclusive or operation on the binary image to obtain a calculation result, and process the calculation result through raster scanning to obtain an NAM table corresponding to the split grayscale image block;
and the optimization module 30 is configured to obtain a corresponding NAM table storage structure according to the NAM table, obtain a local optimization rule, and optimize the NAM table storage structure according to the local optimization rule.
In addition, it should be noted that the above-described embodiments of the apparatus are merely illustrative, and do not limit the scope of the present invention, and in practical applications, a person skilled in the art may select some or all of the modules to implement the purpose of the embodiments according to actual needs, and the present invention is not limited herein.
In addition, the technical details that are not described in detail in this embodiment may refer to the image compression method based on the rectangular NAM provided in any embodiment of the present invention, and are not described herein again.
A third embodiment of the present invention is directed to a computing device. As shown in fig. 3, the apparatus may include: a processor 1001, such as a Central Processing Unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. Wherein a communication bus 1002 is used to enable connective communication between these components. The user interface 1003 may include a Display screen (Display), an input unit such as a Keyboard (Keyboard), and the optional user interface 1003 may also include a standard wired interface, a wireless interface. The network interface 1004 may optionally include a standard wired interface, a WIreless interface (e.g., a WIreless-FIdelity (WI-FI) interface). The Memory 1005 may be a Random Access Memory (RAM) Memory, or may be a Non-Volatile Memory (NVM), such as a disk Memory. The memory 1005 may alternatively be a storage device separate from the processor 1001.
Those skilled in the art will appreciate that the configuration shown in fig. 3 is not intended to be limiting of the apparatus, and in actual implementations the apparatus may include more or fewer components than those shown, or some components may be combined, or a different arrangement of components.
As shown in fig. 3, the memory 1005, which is a medium, may include therein an operating system, a network communication module, a user interface module, and a rectangular NAM-based image compression method program.
In the device shown in fig. 3, the network interface 1004 is mainly used to establish a communication connection between the device and a server that stores all data required in the rectangular NAM-based image compression method program; the user interface 1003 is mainly used for data interaction with a user. The electronic device calls the rectangular NAM-based image compression method program stored in the memory 1005 through the processor 1001 and executes the rectangular NAM-based image compression method provided by the first embodiment of the present invention.
The fourth embodiment of the present invention further proposes a storage medium, which is a computer medium having a rectangular NAM-based image compression method program stored thereon, and when being executed by a processor, the rectangular NAM-based image compression method program implements the following operations:
s1, acquiring image data to be compressed, segmenting the image data to be compressed through a rectangular NAM algorithm, and acquiring the segmented image data to be compressed as a segmentation gray image block;
s2, decomposing the segmentation gray scale image block into a binary image, carrying out XOR operation on the binary image to obtain a calculation result, and processing the calculation result through raster scanning to obtain an NAM (name information model) table corresponding to the segmentation gray scale image block;
and S3, acquiring a corresponding NAM table storage structure according to the NAM table, acquiring a local optimization rule, and optimizing the NAM table storage structure according to the local optimization rule.
Further, the rectangular NAM-based image compression method program further realizes the following operations when executed by a processor:
acquiring image data to be compressed, and setting a matrix sub-mode of a rectangular NAM algorithm, wherein the matrix sub-mode is set as follows: and the positive matrix and the oblique matrix divide the compressed image data into a positive matrix compressed image and an oblique matrix compressed image according to the matrix sub-mode, and determine a divided gray image block corresponding to the compressed image data according to the positive matrix compressed image and the oblique matrix compressed image.
Further, the rectangular NAM-based image compression method program further realizes the following operations when executed by a processor:
and respectively acquiring area data of the positive matrix compressed image and area data of the oblique matrix compressed image, and screening out the compressed image with the maximum value from the area data as a segmentation gray image block corresponding to the compressed image data.
Further, the rectangular NAM-based image compression method program further realizes the following operations when executed by a processor:
decomposing the segmentation gray image block into a plurality of binary images, carrying out XOR operation on two adjacent binary images, recording corresponding operation results, defining a positive matrix array and an oblique matrix array according to the operation results, and processing the positive matrix array and the oblique matrix array through raster scanning to obtain an NAM (name information model) table corresponding to the segmentation gray image block.
Further, the rectangular NAM-based image compression method program further realizes the following operations when executed by a processor:
scanning the binary image through raster scanning to obtain a positive matrix compressed image and an oblique matrix compressed image with the largest area data, respectively adding the area data of the positive matrix compressed image and the area data of the oblique matrix compressed image into a positive matrix array and an oblique matrix array, merging the added positive matrix array and the added oblique matrix array, and taking the merged array as an NAM (name information model) table corresponding to the segmentation gray-scale image block.
Further, the rectangular NAM-based image compression method program further realizes the following operations when executed by a processor:
acquiring a corresponding NAM table storage structure according to the NAM table, wherein the NAM table storage structure comprises: and the header and the body record the number of each header and body, the header-body-header-body sequence is arranged according to the number of each header and body, the final arrangement result is obtained as an optimized NAM, and the optimized NAM corresponds to the compressed image data.
Further, the rectangular NAM-based image compression method program further realizes the following operations when executed by a processor:
scanning the compressed image data corresponding to each optimized NAM table to obtain a corresponding image complexity value and an image complexity threshold value, comparing the image complexity value with the image complexity threshold value, and when the image complexity value is greater than the image complexity threshold value, re-optimizing the compressed image data corresponding to the optimized NAM table; and when the image complexity value is smaller than the image complexity threshold value, storing the compressed image data corresponding to the optimized NAM table.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (12)
1. An image compression method based on rectangular NAM, comprising:
acquiring image data to be compressed, segmenting the image data to be compressed through a rectangular NAM algorithm, and acquiring the segmented image data to be compressed as a segmentation gray image block;
decomposing the segmentation gray scale image blocks into binary images, and calculating to obtain NAM tables of the segmentation gray scale image blocks based on the binary images;
and optimizing the NAM according to a defined NAM storage structure, wherein the optimized NAM corresponds to the compressed image data.
2. The rectangular NAM-based image compression method as claimed in claim 1, wherein the segmenting the image data to be compressed by the rectangular NAM algorithm comprises:
setting a matrix sub-mode of a rectangular NAM algorithm, wherein the matrix sub-mode is set as follows: a positive matrix and a diagonal matrix;
and dividing the image data to be compressed into a positive matrix compressed image and an oblique matrix compressed image according to the matrix sub-mode.
3. The rectangular NAM-based image compression method as claimed in claim 2, wherein the dividing of the image data to be compressed into the positive matrix compressed image and the oblique matrix compressed image according to the matrix sub-mode comprises:
and searching a starting point of a matrix sub-mode in the image data to be compressed according to the raster scanning sequence, and scanning a positive matrix and an oblique matrix from the starting point to obtain a positive matrix compressed image and an oblique matrix compressed image.
4. The rectangular NAM-based image compression method as claimed in claim 3, wherein in the raster scanning process, the data of each of the positive matrix and the diagonal matrix is recorded, and further the area data of the positive matrix compressed image and the area data of the diagonal matrix compressed image are calculated.
5. The rectangular NAM-based image compression method as claimed in claim 2, wherein the obtaining of the segmented image data to be compressed as the segmented gray-scale image blocks comprises:
and respectively acquiring the area data of the positive matrix compressed image and the area data of the oblique matrix compressed image, and screening out the compressed image with the largest area data as a segmentation gray image block corresponding to the image data to be compressed.
6. The rectangular NAM-based image compression method as claimed in claim 1, wherein said calculating based on binary image to obtain NAM table of said split gray image blocks comprises:
decomposing the segmented gray scale image blocks into a plurality of binary images;
carrying out XOR operation on two adjacent binary images, and recording corresponding operation results;
and defining a positive matrix array and an oblique matrix array according to the operation result, and processing the operation result through raster scanning to obtain an NAM (name information model) table corresponding to the segmentation gray-scale image block.
7. The rectangular NAM-based image compression method of claim 6, wherein,
scanning the binary image through raster scanning to obtain a positive matrix compressed image and an oblique matrix compressed image with the largest area data;
adding the area data of the positive matrix compressed image and the area data of the oblique matrix compressed image into a positive matrix array and an oblique matrix array respectively;
and merging the added positive matrix array and the added oblique matrix array, and taking the merged array as an NAM (name information model) table corresponding to the segmentation gray scale image block.
8. The rectangular NAM-based image compression method as claimed in claim 1, wherein said optimizing the NAM table according to the defined NAM table storage structure comprises:
acquiring a corresponding NAM table storage structure according to the NAM table, wherein the NAM table storage structure comprises: a watch head and a watch body;
recording the serial numbers of each header and each table body, and arranging the serial numbers of each header and each table body according to the sequence of 'header-table body-header-table body';
and acquiring the final arrangement result as an optimized NAM table, wherein the optimized NAM table corresponds to the compressed image data.
9. The rectangular NAM-based image compression method of claim 1, further comprising,
scanning the compressed image data corresponding to each optimized NAM table to obtain corresponding image complexity values;
comparing the acquired image complexity value with a preset image complexity threshold value, and if the image complexity value is greater than the image complexity threshold value, re-optimizing compressed image data corresponding to the optimized NAM; and if the image complexity value is smaller than the image complexity threshold value, storing the compressed image data corresponding to the optimized NAM table.
10. An image compression apparatus based on rectangular NAM, comprising:
the acquisition and segmentation module is used for acquiring image data to be compressed, segmenting the image data to be compressed through a rectangular NAM algorithm, and acquiring the segmented image data to be compressed as a segmentation gray image block;
the processing module is used for decomposing the segmentation gray level image blocks into binary images and calculating to obtain NAM tables of the segmentation gray level image blocks based on the binary images;
and the optimization module is used for optimizing the NAM according to a defined NAM storage structure, and the optimized NAM corresponds to the compressed image data.
11. A computing device, comprising: a memory, a processor and a rectangular NAM based image compression method program stored on the memory and executable on the processor, the rectangular NAM based image compression method program being configured to implement the steps of the rectangular NAM based image compression method as claimed in any one of claims 1 to 9.
12. A storage medium, characterized in that the storage medium is a computer medium having stored thereon a rectangular NAM-based image compression method program, which when executed by a processor implements the steps of the rectangular NAM-based image compression method according to any one of claims 1 to 9.
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