CN117671044A - Image processing method, device, electronic equipment and medium - Google Patents

Abstract

The application is applicable to the technical field of image processing, and provides an image processing method, an image processing device, electronic equipment and a medium, wherein the method comprises the following steps: acquiring attribute information and an index matrix of a target image; singular value decomposition is carried out on the index matrix to obtain a left singular matrix, a right singular matrix and a diagonal matrix corresponding to the target image; wherein the values of the elements on the diagonal of the diagonal matrix are the singular values of the index matrix; determining a singular value quantity threshold according to the attribute information; and carrying out partial element zero setting operation on the diagonal matrix, the left singular matrix and the right singular matrix according to the singular value quantity threshold value to obtain an approximate matrix used for representing the data after the target image compression. Compared with the prior art, the method has the advantages that the singular value decomposition of the index matrix of the target image is performed, and the compression efficiency is improved. Meanwhile, the singular value quantity threshold value matched with the image can be accurately determined according to the attribute information of the image, so that the image quality is ensured when the target object is compressed.

Description

An image processing method, device, electronic equipment and medium

Technical field

The present application belongs to the field of image processing technology, and in particular, relates to an image processing method, device, electronic equipment and media.

Background technique

Lightweight embedded graphics library (Light and Versatile Graphics Library, LVGL) is an open source graphics library that can run on a real-time operating system (Real-Time Operating System, RTOS), supports a variety of displays and controllers, and provides Rich graphical user interface (GUI) components and animation effects. Lightweight embedded graphics libraries often use custom image formats to store and display images. However, the images in its custom image format take up a lot of storage space, which limits the number and types of images that the RTOS smart screen can store and display. Therefore, image compression is required for the images in the above formats.

The existing technology usually converts multiple identical index values in an image into a quantity value and an index value to reduce the storage space of the image, resulting in low image compression efficiency.

Contents of the invention

Embodiments of the present application provide an image processing method, device, electronic equipment and media, which improve image compression efficiency.

In a first aspect, embodiments of the present application provide an image processing method, including:

Obtain the attribute information and index matrix of the target image;

Perform singular value decomposition on the index matrix to obtain the left singular matrix, the right singular matrix and the diagonal matrix corresponding to the target image; wherein the value of each element on the diagonal of the diagonal matrix is the Singular values of the index matrix;

Determine a singular value number threshold based on the attribute information;

Perform a zero-setting operation on some elements of the diagonal matrix, the left singular matrix, and the right singular matrix according to the singular value number threshold to obtain an approximate matrix used to represent the compressed data of the target image.

Optionally, the diagonal matrix, the left singular matrix and the right singular matrix are zeroed on some elements according to the singular value number threshold to obtain a compressed representation of the target image. An approximate matrix of data, including:

Sort each singular value in the diagonal matrix according to size to obtain a singular value sequence table;

Obtain a number of target singular values equal to the singular value quantity threshold from the singular value sequence table in order from large to small;

According to the plurality of target singular values, a zero-setting operation is performed on some elements of the diagonal matrix, the left singular matrix and the right singular matrix to obtain the approximate matrix.

Optionally, based on the plurality of target singular values, performing zero-setting operations on some elements of the diagonal matrix, the left singular matrix and the right singular matrix to obtain the approximate matrix, including:

Set the remaining singular values in the diagonal matrix except the plurality of target singular values to zero to obtain a first matrix;

Set the singular vectors associated with the remaining singular values in the left singular matrix to zero to obtain a second matrix;

Set the singular vectors associated with the remaining singular values in the right singular matrix to zero to obtain a third matrix;

The first matrix, the second matrix and the third matrix are determined as the approximation matrices.

Optionally, the attribute information includes image resolution, and the image resolution is positively correlated with the singular value number threshold.

Optionally, the attribute information includes image resolution; and determining the singular value number threshold based on the attribute information includes:

Obtain the desired image compression ratio of the target image;

From multiple pre-constructed threshold determination tables, search for a target threshold determination table corresponding to the desired image compression ratio; wherein, the threshold determination table is used to describe the difference between different image resolutions and different values under any image compression ratio. Correspondence between singular value number thresholds;

From the target threshold determination table, a singular value number threshold corresponding to the image resolution of the target image is found.

Optionally, the target image includes a color palette; performing a zero-setting operation on some elements of the diagonal matrix, the left singular matrix and the right singular matrix according to the singular value number threshold, we obtain After the approximate matrix used to represent the compressed data of the target image, it also includes:

The color palette, the singular value number threshold, and the approximation matrix are determined as compressed image data corresponding to the target image, and the image data is stored.

Optionally, after determining the color palette, the singular value number threshold and the approximation matrix as compressed image data corresponding to the target image and storing the image data, it further includes: :

Obtain the image data;

Perform matrix reconstruction according to the singular value number threshold and the approximation matrix to obtain the index matrix of the target image;

The target image is generated according to the index matrix and the color palette, and the target image is displayed.

In a second aspect, embodiments of the present application provide an image processing device, including:

The first acquisition unit is used to acquire the attribute information and index matrix of the target image;

A decomposition unit configured to perform singular value decomposition on the index matrix to obtain the left singular matrix, the right singular matrix and the diagonal matrix corresponding to the target image; wherein, the values of each element on the diagonal line of the diagonal matrix The values are all singular values of the index matrix;

A first determination unit configured to determine a singular value quantity threshold based on the attribute information;

The first zero-setting unit is configured to perform a zero-setting operation on some elements of the diagonal matrix, the left singular matrix and the right singular matrix according to the singular value quantity threshold to obtain a compression representation of the target image. Approximate matrix of the subsequent data.

In a third aspect, embodiments of the present application provide an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program Implement the image processing method as described in any one of the above first aspects.

In a fourth aspect, embodiments of the present application provide a computer-readable storage medium that stores a computer program. When the computer program is executed by a processor, the computer program implements any of the above-described first aspects. The image processing method described above.

In a fifth aspect, embodiments of the present application provide a computer program product. When the computer program product is run on an electronic device, the electronic device can execute the image processing method described in any one of the above first aspects.

Compared with the prior art, the beneficial effects of the embodiments of the present application are:

An image processing method provided by an embodiment of the present application obtains the attribute information and index matrix of the target image; performs singular value decomposition on the index matrix to obtain the left singular matrix, right singular matrix and diagonal matrix corresponding to the target image; wherein, The value of each element on the diagonal of the diagonal matrix is the singular value of the index matrix; determine the singular value number threshold based on the attribute information; select some elements of the diagonal matrix, left singular matrix, and right singular matrix based on the singular value threshold. The zero-setting operation obtains an approximate matrix used to represent the compressed data of the target image. Compared with the existing technology, this method improves compression efficiency through singular value decomposition of the index matrix of the target image. At the same time, the threshold of the number of singular values matching the image can be accurately determined according to the attribute information of the image, so that the image quality can be guaranteed when compressing the target object.

Description of drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiments or description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only for the purpose of the present application. For some embodiments, for those of ordinary skill in the art, other drawings can be obtained based on these drawings without exerting creative efforts.

Figure 1 is an implementation flow chart of an image processing method provided by an embodiment of the present application;

Figure 2 is an implementation flow chart of an image processing method provided by another embodiment of the present application;

Figure 3 is an implementation flow chart of an image processing method provided by yet another embodiment of the present application;

Figure 4 is an implementation flow chart of an image processing method provided by yet another embodiment of the present application;

Figure 5 is a schematic structural diagram of an image processing device provided by an embodiment of the present application;

FIG. 6 is a schematic structural diagram of an electronic device provided by an embodiment of the present application.

Detailed ways

In the following description, for the purpose of explanation rather than limitation, specific details such as specific system structures and technologies are provided to provide a thorough understanding of the embodiments of the present application. However, it will be apparent to those skilled in the art that the present application may be practiced in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.

It will be understood that, when used in this specification and the appended claims, the term "comprising" indicates the presence of the described features, integers, steps, operations, elements and/or components but does not exclude one or more other The presence or addition of features, integers, steps, operations, elements, components and/or collections thereof.

It will also be understood that the term "and/or" as used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

As used in this specification and the appended claims, the term "if" may be interpreted as "when" or "once" or "in response to determining" or "in response to detecting" depending on the context. ". Similarly, the phrase "if determined" or "if [the described condition or event] is detected" may be interpreted, depending on the context, to mean "once determined" or "in response to a determination" or "once the [described condition or event] is detected ]" or "in response to detection of [the described condition or event]".

In addition, in the description of this application and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the description, and cannot be understood as indicating or implying relative importance.

Reference in this specification to "one embodiment" or "some embodiments" or the like means that a particular feature, structure or characteristic described in connection with the embodiment is included in one or more embodiments of the application. Therefore, the phrases "in one embodiment", "in some embodiments", "in other embodiments", "in other embodiments", etc. appearing in different places in this specification are not necessarily References are made to the same embodiment, but rather to "one or more but not all embodiments" unless specifically stated otherwise. The terms “including,” “includes,” “having,” and variations thereof all mean “including but not limited to,” unless otherwise specifically emphasized.

In practical applications, the Lightweight Embedded Graphics Library (LVGL) is an open source graphics library that can run on a Real-Time Operating System (RTOS) and supports a variety of displays. and controller, providing rich graphical user interface (GUI) components and animation effects. Lightweight embedded graphics libraries often use custom image formats to store and display images. However, its custom image formats, such as LV_IMG_CF_INDEXED_8BIT format images, occupy more storage space and limit the number and types of images that the RTOS smart screen can store and display. Therefore, image compression of the above format images is required.

However, existing image compression methods based on singular value decomposition (SVD) are usually aimed at grayscale images or color images (RGB), and are not suitable for images in the LV_IMG_CF_INDEXED_8BIT format, because the images in the LV_IMG_CF_INDEXED_8BIT format contain modulation Color palette, singular value decomposition cannot be performed directly.

It should be noted that the LV_IMG_CF_INDEXED_8BIT format is an indexed color format supported by the LVGL graphics library. Each pixel occupies 8 bits, that is, one byte. The image in this format includes a palette and an index matrix. Among them, the palette refers to the palette matrix, which is the image color table.

Based on this, the embodiment of the present application provides an image compression method that is applied to image formats that cannot directly perform singular value decomposition, such as the LV_IMG_CF_INDEXED_8BIT format. For detailed description, please refer to the embodiment shown in the figure below, which will not be described again here.

Please refer to FIG. 1 , which is an implementation flow chart of an image processing method provided by an embodiment of the present application. In the embodiment of the present application, the execution subject of the image processing method is an electronic device. Among them, the electronic device may be an RTOS smart screen.

As shown in Figure 1, the image processing method provided by an embodiment of the present application may include S101 to S104, which are detailed as follows:

In S101, obtain the attribute information and index matrix of the target image.

In practical applications, in order to reduce the storage space occupied by images, users can trigger image processing requests for electronic devices.

In this embodiment of the present application, when the electronic device detects the above image processing request, it may be: detecting that the user triggers the first preset operation for the electronic device. The first preset operation can be determined according to actual needs and is not limited here. For example, the first preset operation may be clicking on the first preset control. That is, if the electronic device detects that the user clicks on the first preset control on the electronic device, it is considered that the first preset operation for the electronic device is detected; Of course, the first preset operation can also be a time-triggered operation. The electronic device can be configured with a corresponding workflow when running. The workflow includes trigger nodes for multiple key events. The key events include performing operations on the target image. Image compression event. In this case, if the electronic device detects that the trigger node associated with the event of image compression of the target image has been reached, the operations of S101 to S104 are performed to perform the compression operation of the target image. Among them, the target image refers to the image that needs to be compressed.

In some possible embodiments, the target image may be an image in the LV_IMG_CF_INDEXED_8BIT format.

In the embodiment of the present application, after detecting the above image processing request, the electronic device can obtain the target image and determine the attribute information and index matrix of the target image. The attribute information of the target image includes but is not limited to the resolution of the target image.

In S102, perform singular value decomposition on the index matrix to obtain the left singular matrix, the right singular matrix and the diagonal matrix corresponding to the target image; wherein, the values of each element on the diagonal of the diagonal matrix are all singular values of the index matrix.

In the embodiment of the present application, after obtaining the index matrix of the target image, the electronic device can perform singular value decomposition on the index matrix to obtain the left singular matrix, the right singular matrix and the diagonal matrix corresponding to the index matrix of the target image.

It should be noted that the values of each element on the diagonal of the diagonal matrix are singular values of the index matrix. Therefore, the diagonal matrix is also called a singular value matrix.

For example, taking the matrix A whose index matrix is m rows and n columns as an example, perform singular value decomposition on the matrix A to obtain the matrix U (i.e., left singular matrix), matrix S (i.e., diagonal matrix), and matrix V (i.e., Right singular matrix), so that A = USV ^H .

Please refer to Table 1, which is a detailed description of the left singular matrix, right singular matrix and diagonal matrix after decomposition of matrix A.

Table 1

In S103, a singular value number threshold is determined based on the attribute information.

It should be noted that the singular value number threshold is used to characterize the maximum number of singular values retained in the diagonal matrix, and can be set according to actual needs, and is not limited here.

Attribute information includes image resolution.

In one embodiment of the present application, the electronic device pre-stores the corresponding relationship between different image resolutions and singular value number thresholds. Therefore, after acquiring the image resolution of the target image, the electronic device can The resolution and the above correspondence relationship determine the singular value number threshold corresponding to the image resolution of the target image.

In this embodiment, the singular value number thresholds corresponding to different image resolutions are not exactly the same.

In some possible embodiments, the image resolution is positively correlated with the singular value number threshold, that is, the larger the image resolution, the larger the singular value number threshold, and the smaller the image resolution, the smaller the singular value number threshold.

In another embodiment of the present application, after obtaining the image resolution of the target image, the electronic device can determine the image level according to the size of the image resolution. The image levels include but are not limited to first level, second level and third level.

The value range of the first level of image resolution is (0, first resolution threshold), the value range of the second level of image resolution is [first resolution threshold, second resolution threshold), and the value range of the third level of image resolution is [first resolution threshold, second resolution threshold] The value range of the image resolution of the level is [second resolution threshold, +∞).

In this embodiment, when the electronic device detects that the image resolution of the target image is smaller than the first resolution threshold, it can determine that the image level of the target image is the first level; when the electronic device detects that the image resolution of the target image is smaller than the first resolution threshold, When the size is greater than or equal to the first resolution threshold and less than the second resolution threshold, the image level of the target image can be determined to be the second level; when the electronic device detects that the size of the image resolution of the target image is greater than or equal to the second resolution When the rate threshold is set, it can be determined that the image level of the target image is the third level.

In this embodiment, the singular value quantity thresholds include but are not limited to: a first quantity threshold, a second quantity threshold, and a third quantity threshold. Wherein, the first quantity threshold is smaller than the second quantity threshold, and the second quantity threshold is smaller than the third quantity threshold.

In an implementation manner of this embodiment, the electronic device can set the number threshold of singular values corresponding to the first level as the first number threshold, and the electronic device can set the number threshold of singular values corresponding to the second level as the second number threshold. The device can set the singular value quantity threshold corresponding to the third level to the third quantity threshold, thereby obtaining the corresponding relationship between different image levels and the singular value quantity threshold.

Based on this, in this embodiment, after determining the image level of the target image, the electronic device can determine the singular values corresponding to the target image based on the image level of the target image and the correspondence between different image levels and the singular value number threshold. Quantity threshold.

In yet another embodiment of the present application, in order to further improve the accuracy of determining the singular value threshold, the electronic device can specifically determine the singular value threshold through steps S201 to S203 as shown in Figure 2, as detailed below:

In S201, obtain the desired image compression ratio of the target image.

In this embodiment, the image compression ratio refers to the compression ratio between the size of the image data compressed by the encoder and the size of the original image data. Among them, the original image refers to the image without any processing.

In an implementation manner of this embodiment, the electronic device can obtain the desired image compression ratio required by the user in real time through the terminal device connected to the electronic device through wireless communication. Among them, the terminal device may be a smartphone, notebook, computer or other device used by the user.

In another implementation of this embodiment, the user can input the desired image compression ratio of the target image on the display interface of the electronic device, so that the electronic device obtains the desired image compression ratio.

In S202, search for a target threshold determination table corresponding to the desired image compression ratio from a plurality of pre-constructed threshold determination tables; wherein the threshold determination table is used to describe different images under any image compression ratio. Correspondence between resolution and different singular value number thresholds.

In this embodiment, after obtaining the desired image compression ratio of the target image, the electronic device can search for a target threshold determination table corresponding to the desired image compression ratio from a plurality of pre-constructed threshold determination tables.

In some possible embodiments, different threshold determination tables have different corresponding image compression ratio ranges. Therefore, the electronic device can determine the image compression ratio range in which the desired image compression ratio is located, and set the threshold value corresponding to the image compression ratio range. The determination table is determined as a target threshold determination table.

For example, assume that the threshold determination table includes a first determination table, a second determination table, and a third determination table. The image compression ratio range corresponding to the first determination table is (0, 2], and the image compression ratio corresponding to the second determination table is The range is (2, 4], the image compression ratio range corresponding to the first determination table is (4, 6], and the expected image compression ratio is 3.5, then the electronic device can determine that the image compression ratio range corresponding to the expected image compression ratio is (2 , 4], therefore, the electronic device can determine the second determination table as the target threshold determination table.

In S203, search for the singular value number threshold corresponding to the image resolution of the target image from the target threshold determination table.

In this embodiment, after obtaining the target threshold determination table, the electronic device can search the singular value number threshold corresponding to the image resolution of the target image from the target threshold determination table according to the image resolution of the target image.

In S104, a zero-setting operation is performed on some elements of the diagonal matrix, the left singular matrix, and the right singular matrix according to the singular value number threshold to obtain a value representing the compressed data of the target image. Approximate matrix.

In the embodiment of the present application, after obtaining the singular value quantity threshold, the electronic device can filter all elements in the diagonal matrix, the left singular matrix, and the right singular matrix according to the singular value quantity threshold, and retain a number equal to the singular value quantity threshold. The target element, and all other elements except the target element are set to zero, thereby achieving dimensionality reduction and compression of the target image, and obtaining an approximate matrix used to represent the compressed data of the target image.

In one embodiment of the present application, the electronic device can number all elements in the diagonal matrix, the left singular matrix, and the right singular matrix respectively, such as number 1, number 2,... number n, etc., and then the electronic device The target number that needs to be retained can be determined from the above multiple numbers based on a random function until the number of target numbers equals the singular value number threshold. Among them, the random function can be the rand() function.

In another embodiment of the present application, in order to ensure the image compression quality while ensuring the image compression rate of the target image, the electronic device can specifically compress the target image through steps S301 to S303 as shown in Figure 3 to obtain an approximate matrix, detailed below:

In S301, each singular value in the diagonal matrix is sorted according to size to obtain a singular value sequence table.

In S302, obtain a plurality of target singular values equal to the singular value quantity threshold from the singular value sequence table in order from large to small.

In S303, based on the plurality of target singular values, a zero-setting operation is performed on some elements of the diagonal matrix, the left singular matrix, and the right singular matrix to obtain the approximate matrix.

In this embodiment, after obtaining the singular value sequence table, the electronic device can sequentially obtain multiple target singular values from the singular value sequence table in order from large to small, until the number of target singular values is equal to the singular value quantity threshold, That is to say, the multiple target singular values acquired by the electronic device are the singular values ranked in order from large to small in the singular value sequence table with the singular value number threshold in front.

In one embodiment of the present application, the electronic device can obtain the approximate matrix through the following steps, as detailed below:

Set the remaining singular values in the diagonal matrix except the plurality of target singular values to zero to obtain a first matrix;

Set the singular vectors associated with the remaining singular values in the left singular matrix to zero to obtain a second matrix;

Set the singular vectors associated with the remaining singular values in the right singular matrix to zero to obtain a third matrix;

The first matrix, the second matrix and the third matrix are determined as the approximation matrices.

In this embodiment, the electronic device can retain multiple target singular values in the diagonal matrix, and set other singular values except the multiple target singular values to zero to obtain the first matrix corresponding to the diagonal matrix. .

The electronic device can retain the singular vectors in the left singular matrix that are respectively associated with each target singular value, and set all other singular vectors except the multiple target singular vectors to zero, that is, the singular vectors associated with the remaining singular values are all zeroed. Set to zero to obtain the second matrix corresponding to the left singular matrix.

The electronic device can retain the singular vectors in the right singular matrix that are respectively associated with each target singular value, and set all other singular vectors except the multiple target singular vectors to zero, that is, the singular vectors associated with the remaining singular values are all equal to zero. Set to zero to obtain the third matrix corresponding to the right singular matrix.

For example, taking the matrix A whose index matrix is m rows and n columns, assume that the expansion form of matrix A through singular value decomposition is:

Among them, A represents the index matrix, u _i represents the i-th m-dimensional singular vector in the left singular matrix, σ _i represents the i-th singular value in the diagonal matrix, Represents the i-th n-dimensional singular vector in the right singular matrix.

Assuming that the threshold of the number of singular values is K, the approximate matrix is:

Among them, A represents the index matrix, u _i represents the j-th m-dimensional singular vector in the left singular matrix, σ _i represents the j-th singular value in the diagonal matrix, and v _i ^H represents the j-th n in the right singular matrix. Dimensional singular vector, K<r.

Based on this, the electronic device may determine the first matrix, the second matrix, and the third matrix as approximate matrices.

As can be seen from the above, the image processing method provided by the embodiment of the present application obtains the attribute information and index matrix of the target image; performs singular value decomposition on the index matrix to obtain the left singular matrix, right singular matrix and pair corresponding to the target image. Angular matrix; where the value of each element on the diagonal of the diagonal matrix is the singular value of the index matrix; determine the singular value number threshold based on the attribute information; calculate the diagonal matrix, left singular matrix, and right singular matrix based on the singular value threshold. The singular matrix performs a zero-setting operation on some elements to obtain an approximate matrix used to represent the compressed data of the target image. Compared with the existing technology, this method improves compression efficiency through singular value decomposition of the index matrix of the target image. At the same time, the threshold of the number of singular values matching the image can be accurately determined according to the attribute information of the image, so that the image quality can be guaranteed when compressing the target object.

In one embodiment of the present application, after the electronic device obtains the approximation matrix, the electronic device can determine the palette of the target image, the singular value number threshold, and the approximation matrix as compressed image data corresponding to the target image, and Store image data for subsequent operations on the target image.

In this embodiment, the electronic device can store the above image data in its own memory.

Based on this, please refer to Figure 4, which is an image processing method provided by another embodiment of the present application. In this embodiment, after storing the image data, in order to display the target image, this embodiment may also include S401 to S403, which are detailed as follows:

In S401, the image data is obtained.

In S402, matrix reconstruction is performed according to the singular value number threshold and the approximation matrix to obtain an index matrix of the target image.

In S403, the target image is generated according to the index matrix and the palette, and the target image is displayed.

In practical applications, when the user needs the electronic device to display a target image, the user can trigger an image display request for the electronic device.

In this embodiment, the electronic device detecting the above image display request may be: detecting that the user triggers a second preset operation for the electronic device. The second preset operation can be determined according to actual needs and is not limited here. For example, the second preset operation may be clicking on the second preset control. That is, if the electronic device detects that the user clicks on the second preset control on the electronic device, it is considered to have detected the second preset operation for the electronic device. That is, an image display request is detected.

After the electronic device detects the image display request, since the target image has been compressed, in order to ensure that the electronic device can completely display the target image, the electronic device can obtain the compressed image data corresponding to the target image from its own memory.

The electronic device can perform matrix reconstruction based on the singular value number threshold and the approximate matrix in the image data to obtain an approximate representation of the index matrix of the target image.

Afterwards, the electronic device may generate a target image based on the color palette in the image data and an approximate representation of the index matrix of the target image, and display the target image.

As can be seen from the above, the image processing method provided by this embodiment obtains image data; performs matrix reconstruction according to the singular value number threshold and the approximation matrix to obtain the index matrix of the target image; generates the target image according to the index matrix and palette, and Output the target image. After compressing the target image, the method provided in this embodiment can restore the target image before compression based on the target image's palette, singular value threshold, and approximation matrix, and display the target image to avoid distortion of the displayed target image.

It should be understood that the sequence number of each step in the above embodiment does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present application.

Corresponding to an image processing method described in the above embodiment, FIG. 5 shows a schematic structural diagram of an image processing device provided by an embodiment of the present application. For convenience of explanation, only the components related to the embodiment of the present application are shown. part. Referring to FIG. 5 , the image processing device 500 includes: a first acquisition unit 51 , a decomposition unit 52 , a first determination unit 53 and a first zero-setting unit 54 . in:

The first acquisition unit 51 is used to acquire the attribute information and index matrix of the target image.

The decomposition unit 52 is used to perform singular value decomposition on the index matrix to obtain the left singular matrix, the right singular matrix and the diagonal matrix corresponding to the target image; wherein, the The values are all singular values of the index matrix.

The first determining unit 53 is configured to determine a singular value number threshold according to the attribute information.

The first zero-setting unit 54 is configured to perform a zero-setting operation on some elements of the diagonal matrix, the left singular matrix, and the right singular matrix according to the singular value quantity threshold to obtain a compression method for representing the target image. Approximate matrix of the subsequent data.

In one embodiment of the present application, the first zero-setting unit 54 specifically includes: a sorting unit, a second acquisition unit, and a second zero-setting unit. in:

The sorting unit is used to sort each singular value in the diagonal matrix according to size to obtain a singular value sequence table.

The second acquisition unit is configured to acquire a plurality of target singular values equal to the singular value quantity threshold from the singular value sequence table in order from large to small.

The second zero-setting unit is configured to perform a zero-setting operation on some elements of the diagonal matrix, the left singular matrix, and the right singular matrix according to the plurality of target singular values to obtain the approximate matrix.

In one embodiment of the present application, the second zero-setting unit specifically includes: a third zero-setting unit, a fourth zero-setting unit, a fifth zero-setting unit, and a second determination unit. in:

The third zero-setting unit is used to set the remaining singular values in the diagonal matrix to zero except for the plurality of target singular values to obtain the first matrix.

The fourth zero-setting unit is used to set the singular vectors associated with the remaining singular values in the left singular matrix to zero to obtain the second matrix.

The fifth zero-setting unit is used to set the singular vectors associated with the remaining singular values in the right singular matrix to zero to obtain a third matrix.

The second determination unit is used to determine the first matrix, the second matrix and the third matrix as the approximate matrix

In one embodiment of the present application, the attribute information includes image resolution, and the image resolution is positively correlated with the singular value number threshold.

In one embodiment of the present application, the attribute information includes image resolution; the first determination unit 53 specifically includes: a third acquisition unit, a first search unit and a second search unit. in:

The third obtaining unit is used to obtain the desired image compression ratio of the target image.

The first search unit is used to search for a target threshold determination table corresponding to the desired image compression ratio from a plurality of pre-constructed threshold determination tables; wherein the threshold determination table is used to describe under any image compression ratio, Correspondence between different image resolutions and different singular value number thresholds.

The second search unit is configured to search the singular value number threshold corresponding to the image resolution of the target image from the target threshold determination table.

In one embodiment of the present application, the target image includes a color palette; the image processing device 500 further includes: a storage unit.

The storage unit is configured to determine the color palette, the singular value number threshold and the approximation matrix as compressed image data corresponding to the target image, and store the image data.

In one embodiment of the present application, the image processing device 500 further includes: a fourth acquisition unit, a reconstruction unit and a display unit. in:

The fourth acquisition unit is used to acquire the image data.

The reconstruction unit is configured to perform matrix reconstruction according to the singular value number threshold and the approximation matrix to obtain an index matrix of the target image.

The display unit is configured to generate the target image according to the index matrix and the palette, and display the target image.

It should be noted that the information interaction, execution process, etc. between the above-mentioned devices/units are based on the same concept as the method embodiments of the present application. For details of their specific functions and technical effects, please refer to the method embodiments section. No further details will be given.

Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above functional units and modules is used as an example. In actual applications, the above functions can be allocated to different functional units and modules according to needs. Module completion means dividing the internal structure of the device into different functional units or modules to complete all or part of the functions described above. Each functional unit and module in the embodiment can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be hardware-based. It can also be implemented in the form of software functional units. In addition, the specific names of each functional unit and module are only for the convenience of distinguishing each other and are not used to limit the scope of protection of the present application. For the specific working processes of the units and modules in the above system, please refer to the corresponding processes in the foregoing method embodiments, and will not be described again here.

FIG. 6 is a schematic structural diagram of an electronic device provided by an embodiment of the present application. As shown in Figure 6, the electronic device 6 of this embodiment includes: at least one processor 60 (only one is shown in Figure 6), a memory 61, and a processor stored in the memory 61 and capable of processing in the at least one processor 60. The computer program 62 runs on the processor 60. When the processor 60 executes the computer program 62, the steps in any of the above image processing method embodiments are implemented.

The electronic device may include, but is not limited to, a processor 60 and a memory 61 . Those skilled in the art can understand that FIG. 6 is only an example of the electronic device 6 and does not constitute a limitation on the electronic device 6. It may include more or fewer components than shown in the figure, or some components may be combined, or different components may be used. , for example, it may also include input and output devices, network access devices, etc.

The processor 60 may be a central processing unit (Central Processing Unit, CPU). The processor 60 may also be other general-purpose processors, digital signal processors (Digital Signal Processor, DSP), or application specific integrated circuits (Application Specific Integrated Circuit). , ASIC), off-the-shelf programmable gate array (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or the processor may be any conventional processor, etc.

The memory 61 may be an internal storage unit of the electronic device 6 in some embodiments, such as the memory of the electronic device 6 . In other embodiments, the memory 61 may also be an external storage device of the electronic device 6 , such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), or a secure digital device equipped on the electronic device 6 . (Secure Digital, SD) card, Flash Card, etc. Further, the memory 61 may also include both an internal storage unit of the electronic device 6 and an external storage device. The memory 61 is used to store operating systems, application programs, boot loaders, data and other programs, such as program codes of the computer programs. The memory 61 can also be used to temporarily store data that has been output or is to be output.

Embodiments of the present application also provide a computer-readable storage medium. The computer-readable storage medium stores a computer program. When the computer program is executed by a processor, the steps in each of the above method embodiments can be implemented.

Embodiments of the present application provide a computer program product. When the computer program product is run on an electronic device, the steps in each of the above method embodiments can be implemented when the electronic device is executed.

If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it may be stored in a computer-readable storage medium. Based on this understanding, this application can implement all or part of the processes in the methods of the above embodiments by instructing relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. The computer program can be stored in a computer-readable storage medium. When executed by the processor, the steps of each of the above method embodiments can be implemented. Wherein, the computer program includes computer program code, which may be in the form of source code, object code, executable file or some intermediate form. The computer-readable medium may at least include: any entity or device capable of carrying computer program code to an electronic device, a recording medium, a computer memory, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), electrical carrier signals, telecommunications signals, and software distribution media. For example, U disk, mobile hard disk, magnetic disk or CD, etc. In some jurisdictions, subject to legislation and patent practice, computer-readable media may not be electrical carrier signals and telecommunications signals.

In the above embodiments, each embodiment is described with its own emphasis. For parts that are not detailed or documented in a certain embodiment, please refer to the relevant descriptions of other embodiments.

The above-described embodiments are only used to illustrate the technical solutions of the present application, but not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still implement the above-mentioned implementations. The technical solutions described in the examples are modified, or some of the technical features are equivalently replaced; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions in the embodiments of this application, and should be included in within the protection scope of this application.

Claims (10)

1. An image processing method, characterized by comprising: Obtain the attribute information and index matrix of the target image; Perform singular value decomposition on the index matrix to obtain the left singular matrix, the right singular matrix and the diagonal matrix corresponding to the target image; wherein the value of each element on the diagonal of the diagonal matrix is the Singular values of the index matrix; Determine a singular value number threshold based on the attribute information; Perform a zero-setting operation on some elements of the diagonal matrix, the left singular matrix, and the right singular matrix according to the singular value number threshold to obtain an approximate matrix used to represent the compressed data of the target image. 2. The image processing method according to claim 1, characterized in that, the diagonal matrix, the left singular matrix and the right singular matrix are reset to zero according to the singular value number threshold. Operation to obtain an approximate matrix used to represent the compressed data of the target image, including: Sort each singular value in the diagonal matrix according to size to obtain a singular value sequence table; Obtain a number of target singular values equal to the singular value quantity threshold from the singular value sequence table in order from large to small; According to the plurality of target singular values, a zero-setting operation is performed on some elements of the diagonal matrix, the left singular matrix and the right singular matrix to obtain the approximate matrix. 3. The image processing method according to claim 2, wherein the diagonal matrix, the left singular matrix and the right singular matrix are subjected to partial element transformation according to the plurality of target singular values. Zero setting operation to obtain the approximate matrix, including: Set the remaining singular values in the diagonal matrix except the plurality of target singular values to zero to obtain a first matrix; Set the singular vectors associated with the remaining singular values in the left singular matrix to zero to obtain a second matrix; Set the singular vectors associated with the remaining singular values in the right singular matrix to zero to obtain a third matrix; The first matrix, the second matrix and the third matrix are determined as the approximation matrices. 4. The image processing method according to claim 1, wherein the attribute information includes image resolution, and the image resolution is positively correlated with the singular value number threshold. 5. The image processing method according to claim 1, wherein the attribute information includes image resolution; and determining the singular value number threshold according to the attribute information includes: Obtain the desired image compression ratio of the target image; From multiple pre-constructed threshold determination tables, search for a target threshold determination table corresponding to the desired image compression ratio; wherein, the threshold determination table is used to describe the difference between different image resolutions and different values under any image compression ratio. Correspondence between singular value number thresholds; From the target threshold determination table, a singular value number threshold corresponding to the image resolution of the target image is found. 6. The image processing method according to any one of claims 1 to 5, wherein the target image includes a color palette; in the pairing of the diagonal matrix, the The left singular matrix and the right singular matrix perform a zero-setting operation on some elements, and after obtaining an approximate matrix used to represent the compressed data of the target image, it also includes: The color palette, the singular value number threshold, and the approximation matrix are determined as compressed image data corresponding to the target image, and the image data is stored. 7. The image processing method according to claim 6, wherein the palette, the singular value number threshold and the approximation matrix are determined as compressed values corresponding to the target image. Image data, and after storing the image data, also includes: Obtain the image data; Perform matrix reconstruction according to the singular value number threshold and the approximation matrix to obtain the index matrix of the target image; The target image is generated according to the index matrix and the color palette, and the target image is displayed. 8. An image processing device, characterized in that it includes: The first acquisition unit is used to acquire the attribute information and index matrix of the target image; A decomposition unit configured to perform singular value decomposition on the index matrix to obtain the left singular matrix, the right singular matrix and the diagonal matrix corresponding to the target image; wherein, the values of each element on the diagonal line of the diagonal matrix The values are all singular values of the index matrix; A first determination unit configured to determine a singular value quantity threshold based on the attribute information; The first zero-setting unit is configured to perform a zero-setting operation on some elements of the diagonal matrix, the left singular matrix and the right singular matrix according to the singular value quantity threshold to obtain a compression representation of the target image. Approximate matrix of the subsequent data. 9. An electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that when the processor executes the computer program, the processor implements the claims as claimed in The image processing method described in any one of 1 to 7. 10. A computer-readable storage medium, the computer-readable storage medium stores a computer program, characterized in that, when the computer program is executed by a processor, the image processing according to any one of claims 1 to 7 is implemented. method.