Image processing method and device

Abstract

This specification provides an image processing method and device, which includes: acquiring a processing target image, converting the processing target image into a grayscale image and performing a wavelet transform on the grayscale image to obtain high-frequency information of the processing target image, constructing a high-frequency image corresponding to the processing target image based on the high-frequency information, processing the high-frequency image according to a preset image information density calculation policy, and determining an image information density corresponding to each image region in the processing target image based on a processing result.

Description

[Technical field]

[0001] The present specification relates to the technical field of image processing, and in particular to an image processing method and apparatus. [Background technology]

[0002] With the development of Internet technology, image processing technology is increasingly applied in many fields. In particular, in scenes that require the embedding of visualization resources, such as live streaming scenes, teaching scenes, commentary scenes, etc., it is necessary to embed visualization resources according to the commentary subject into the display content to improve the experience effect of the viewer. In the conventional technology, each area in the display content is usually directly covered with one rectangular frame, and the visualization resource is embedded in this rectangular frame to achieve the purpose of commentary. However, in such a processing form, the density of information in the display content is not taken into consideration, so that information is easily occluded. Therefore, how to accurately calculate the density of information in the display content has become a problem that needs to be solved. Summary of the Invention [Problem to be solved by the invention]

[0003] In view of this, the embodiments of the present specification provide an image processing method, and also provide an image processing device, a display method, a display device, a computing device, and a computer-readable recording medium, which solve the drawback of the prior art of not taking image information density into consideration. [Means for solving the problem]

[0004] According to a first aspect of an embodiment of the present specification, an image processing method is provided, comprising: acquiring a target image to be processed; converting the target image to a gradient image and wavelet transforming the gradient image to obtain high-frequency information of the target image to be processed; constructing a high-frequency image corresponding to the target image to be processed based on the high-frequency information; processing the high-frequency image in accordance with a predetermined image information density calculation policy; and determining an image information density corresponding to each image region in the target image to be processed based on the processing result.

[0005] Preferably, the high frequency information includes at least one of horizontal high frequency information, vertical high frequency information and diagonal high frequency information.

[0006] Preferably, constructing a high frequency image corresponding to the image to be processed based on the high frequency information includes calculating pixel information corresponding to each pixel dot in the image to be processed based on the horizontal high frequency information, the vertical high frequency information and the diagonal high frequency information, mapping the value of each pixel dot in the image to be processed to a gradation range based on the pixel information, and constructing the high frequency image based on the mapping result.

[0007] Preferably, processing the high frequency image in accordance with the predetermined image information density calculation policy and determining the image information density corresponding to each image region in the processing target image based on the processing result includes analyzing the image information density calculation policy and determining a target sliding window and a target sliding step based on the analysis result, moving the target sliding window in the high frequency image according to the target sliding step and calculating the image information density corresponding to each high frequency image region in the high frequency image based on the movement result, and determining the image information density corresponding to each image region in the processing target image based on the image information density corresponding to each high frequency image region in the high frequency image.

[0008] Preferably, determining a target sliding window and a target sliding step based on the analysis result includes determining an initial sliding window and an initial sliding step based on the analysis result, and determining a transformation relationship between the image to be processed and the high frequency image, and adjusting the initial sliding window and the initial sliding step based on the transformation relationship to obtain the target sliding window and the target sliding step.

[0009] Preferably, the image information density corresponding to any one of the high frequency image regions is determined as follows: a pixel information density corresponding to each pixel dot in a target image region of the processing target image is read, a high frequency pixel information density corresponding to each pixel dot in a target high frequency image region of the high frequency image is calculated based on the pixel information density corresponding to each pixel dot in the target image region, an average value of the high frequency pixel information densities corresponding to each pixel dot in the target high frequency image region is calculated, and an image information density corresponding to the target high frequency image region is determined based on the calculation result.

[0010] Preferably, determining the image information density corresponding to each image region in the target image based on the image information density corresponding to each high frequency image region in the high frequency image includes establishing a mapping relationship between each high frequency image region in the high frequency image and each image region in the target image, and determining the image information density corresponding to each image region in the target image based on the mapping relationship and the image information density corresponding to each high frequency image region.

[0011] Preferably, the method further includes determining an image information density corresponding to each image region in the target image based on the processing result, selecting the image region corresponding to the lowest image information density as an additional image region, reading a visualization resource that matches the target image, and adding the visualization resource to the additional image region.

[0012] Preferably, adding the visualization resource to the additional image area includes determining a transparency level based on an image information density corresponding to the additional image area, applying a transparency process to the visualization resource according to the transparency level, and adding the visualization resource after the transparency process to the additional image area.

[0013] According to a second aspect of an embodiment of the present specification, an image processing device is provided, comprising: an acquisition module arranged to acquire a processing target image; a transformation module arranged to convert the processing target image into a gradation image and wavelet transform the gradation image to obtain high-frequency information of the processing target image; a construction module arranged to construct a high-frequency image corresponding to the processing target image based on the high-frequency information; and a processing module arranged to process the high-frequency image according to a predetermined image information density calculation policy and determine an image information density corresponding to each image region in the processing target image based on the processing result.

[0014] According to a third aspect of an embodiment of the present specification, a display method is provided, comprising: obtaining a target image to be processed, converting the target image to a grayscale image, wavelet transforming the grayscale image to obtain high-frequency information of the target image, and constructing a high-frequency image corresponding to the target image based on the high-frequency information, processing the high-frequency image according to a predetermined image information density calculation policy, and determining an image information density corresponding to an initial image area in the target image to be processed based on the processing result, screening a target image area from the initial image area based on the image information density, and adding and displaying a visualization resource to the target image area in the target image to be processed.

[0015] Preferably, a target task corresponding to the image to be processed is determined, an image region positioning policy corresponding to the target task is loaded, and the initial image region is determined in the image to be processed according to the image region positioning policy.

[0016] Preferably, screening target image regions from the initial image regions based on the image information density comprises: The method includes comparing the image information density corresponding to each initial image region, and selecting, based on a result of the comparison, the initial image region corresponding to the lowest image density information as the target image region.

[0017] Preferably, adding a visualization resource to the target image area in the image to be processed and displaying it includes determining a target image information density corresponding to the target image area, determining a target transparency level corresponding to the target image information density based on a correspondence between image information density and transparency level, applying transparency processing to the visualization resource in accordance with the target transparency level, and adding the visualization resource after transparency processing to the target image area in the processed image and displaying it.

[0018] According to a fourth aspect of an embodiment of the present specification, a display device is provided, comprising: an image acquisition module configured to acquire a target image and convert the target image into a grayscale image; an image construction module configured to wavelet transform the grayscale image to obtain high-frequency information of the target image and construct a high-frequency image corresponding to the target image based on the high-frequency information; an image processing module configured to process the high-frequency image according to a predetermined image information density calculation policy and determine an image information density corresponding to an initial image area in the target image based on the processing result; and an area screening module configured to screen a target image area from the initial image area based on the image information density and add and display a visualization resource to the target image area in the target image.

[0019] According to a fifth aspect of the present embodiment, a computing device is provided, comprising a memory and a processor, the memory storing computer executable instructions, the processor executing the computer executable instructions to implement steps of the image processing or display method.

[0020] According to a sixth aspect of the present invention, a computer-readable recording medium is provided, storing computer-executable instructions which, when executed by a processor, implement steps of the image processing or display method.

[0021] In the image processing method according to the present specification, after obtaining a target image, the target image is converted into a grayscale image, and the grayscale image is subjected to wavelet transformation to obtain high-frequency information of the target image, so as to accurately complete the calculation of the image information density of each region in the target image. Then, a high-frequency image corresponding to the target image is constructed based on the high-frequency information, and the image information density corresponding to each image region in the target image in the high-frequency dimension is obtained, thereby effectively ensuring the accuracy of the calculation of the image information density of each image region, and subsequently facilitating the embedding of visualization resources based on the image information density, and preventing information occlusion in the target image. [Brief description of the drawings]

[0022] [Figure 1] 1 is a flowchart of an image processing method according to an embodiment of the present specification. [Diagram 2] 1 is a diagram illustrating an image conversion process according to an embodiment of the present specification. [Diagram 3] FIG. 2 is a diagram showing an image to be displayed according to an embodiment of the present specification. [Figure 4] FIG. 2 illustrates a visualization image layout according to one embodiment of the present disclosure. [Diagram 5] FIG. 2 illustrates a visualization image according to an embodiment of the present disclosure. [Figure 6] FIG. 2 illustrates a high frequency image according to an embodiment of the present specification. [Figure 7] FIG. 2 is a diagram illustrating a first type of image information density according to an embodiment of the present specification. [Figure 8] FIG. 11 is a diagram illustrating a second type of image information density according to an embodiment of the present specification. [Figure 9]FIG. 2 illustrates embedding of a first type of visualization resource according to an embodiment of the present specification. [Figure 10] FIG. 1 is a diagram illustrating a configuration of an image processing device according to an embodiment of the present specification. [Figure 11] 1 is a flowchart of a display method according to an embodiment of the present specification. [Figure 12] FIG. 13 illustrates a third type of image information density according to an embodiment of the present specification. [Figure 13] FIG. 2 illustrates embedding of a second type of visualization resource according to an embodiment of the present specification. [Figure 14] FIG. 1 is a diagram showing a configuration of a display device according to an embodiment of the present specification. [Figure 15] FIG. 1 is a block diagram of a configuration of a computing device according to an embodiment of the present specification. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0023] In the following, numerous specific details are described to allow the present specification to be fully understood, but the present specification is not limited to the specific implementations disclosed below, since the present specification can be implemented in many other forms different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the present specification.

[0024] The terms used in one or more examples herein are not intended to limit one or more examples herein, but are intended only to describe a particular example. The singular forms "a," "the," and "the" used in one or more examples herein and in the appended claims are intended to include the plural forms unless the context clearly indicates otherwise. Furthermore, the term "and / or" used in one or more examples herein may be understood to refer to any or all possible combinations of the associated one or more listed items.

[0025] Although one or more embodiments of the present specification use terms such as first, second, etc. to describe various types of information, it will be understood that such information is not limited to these terms. These terms are merely used to distinguish between the same types of information. For example, a first can also be referred to as a second, and similarly, a second can also be referred to as a first, without departing from the scope of one or more embodiments of the present specification. Depending on the context, the word "if" used may be interpreted as "when..." or "in the case of..." or "depending on what is specified."

[0026] The present specification provides an image processing method, and the present specification also relates to an image processing device, a display method, a display device, a computing device and a computer-readable recording medium, which will be described in detail one by one in the following embodiments.

[0027] FIG. 1 is a flowchart of an image processing method according to an embodiment of this specification, which specifically includes the following steps: Step S102: The image to be processed is acquired.

[0028] Specifically, the processing target image refers to an image that requires calculation of information density. For an image whose information density has been calculated, the image information density of each image region in the processing target image can be determined, which makes it easy to determine the region with the highest image information density and the region with the lowest image information density in the processing target image. Note that the image information density refers to the area covered by information in the image region, and the larger the covered area, the higher the image information density, and conversely, the smaller the area covered by information, the lower the image information density. Note that the size of the image region may be set as necessary, but is not limited to this in this embodiment.

[0029] In practice, in a live streaming scene, a teaching scene or a commentary scene, the image information density of each image area in the video frame image can be determined by calculating the image information density of the displayed video frame image, and the image area with the lowest image information density can be selected to embed the visualization resource, which can be a newscaster's avatar screen, a teacher's lecture screen, a director's station screen, etc., and the amount of information in the video frame image covered by the visualization resource can be reduced, preventing the blocking of information in the video frame image and improving the user's viewing experience.

[0030] In this way, the processing target image may be a video frame image at a certain time in a live distribution scene, or a PPT of a certain page in a lesson scene. Furthermore, the processing target image may be a video frame image at a certain time in a commentary scene, but is not limited to these in this embodiment.

[0031] Furthermore, when the image to be processed is laid out, for example, when a new element is embedded in the image to be processed, the image information density of each image region may be calculated by the image processing method according to this embodiment in order to prevent mutual shielding between the elements. This allows the image region with the smallest image information density to be easily selected to add the new element and complete the layout arrangement of the image to be processed. For specific details, the corresponding description in this embodiment may be referred to, and detailed description will be omitted in this embodiment.

[0032] In this embodiment, a lesson scene is taken as an example, and the processing target image is a PPT page that needs to be displayed to a user who is taking a lesson in the lesson scene. By calculating the information density of each image area in the PPT of this page, the image area with the lowest image information density is selected and an explanation screen is placed to prevent the contents of the PPT from being obscured.

[0033] Step S104: The image to be processed is converted into a grayscale image, and the grayscale image is subjected to wavelet transformation to obtain high frequency information of the image to be processed.

[0034] Specifically, based on obtaining the target image, the information density of each image region in the target image can be calculated with high accuracy by converting the target image into a grayscale image, and then performing a wavelet transform on the grayscale image to obtain high-frequency information of the target image, which then facilitates the calculation of the image information density of each image region in the high-frequency dimension.

[0035] The grayscale image specifically refers to an image corresponding to one channel, which is converted from a three-channel (RGB) processing target image. Correspondingly, the high-frequency information specifically refers to information corresponding to the processing target image in the high-frequency dimension obtained after performing a wavelet transform, and includes, but is not limited to, horizontal high-frequency information, vertical high-frequency information, and diagonal high-frequency information corresponding to the processing target image. The horizontal high-frequency information represents details in the horizontal dimension of the processing target image, the vertical high-frequency information represents details in the vertical dimension of the processing target image, and the diagonal high-frequency information represents details in the diagonal dimension of the processing target image.

[0036] In other words, in the image processing method according to this embodiment, high frequency features of the image to be processed are extracted, the fluctuation values ​​of partial changes in the image information in the image to be processed are reflected, and based on this, the details of the information distribution of the image to be processed are further calculated, thereby making it possible to accurately calculate the image information density of each image region in the image to be processed.

[0037] Furthermore, the wavelet transformed image to be processed can provide corresponding low-frequency information and high-frequency information. The low-frequency information reflects the gradual change of the image to be processed in response to the determination of the average value, and the high-frequency information reflects the fluctuation value of the transformation of the image to be processed in response to the determination of the difference. The high-frequency information can reflect the amount of information of each image region in the image to be processed, which makes it easier to calculate the image information density afterwards.

[0038] Furthermore, as shown in FIG. 2, when a grayscale image is subjected to wavelet transform, in order to improve the processing efficiency, the grayscale image is then filtered by a high-pass filter h high and the low-pass filter h low and filter it with a high-pass filter h high The vertical high-frequency information V and the diagonal high-frequency information D corresponding to the image to be processed are generated by the low-pass filter h low The horizontal high frequency information H and low frequency information A corresponding to the image to be processed are generated by the high pass filter h high allows high frequency information to pass, while the low pass filter h low allows low frequency information to pass.

[0039] The high-frequency information of the image to be processed is capable of constructing a high-frequency image for calculating image information density. The construction of the high-frequency image is controlled by high-frequency information (horizontal high-frequency information, vertical high-frequency information, diagonal high-frequency information). Therefore, in different dimensions (low-frequency dimension, horizontal dimension, vertical dimension, and diagonal dimension), there are visualized images corresponding to the dimensions. In order to facilitate the subsequent calculation of image density information, the visualized images corresponding to the different dimensions map the pixel dot values ​​within the range of 0 to 255 to construct a visualized image of gradation, and align the visualized images in the high-frequency dimension to obtain a high-frequency image of the image to be processed, which facilitates the subsequent calculation of image information density.

[0040] For example, Fig. 3 shows a one-page PPT that needs to be displayed to a user, and in order to improve the effect of the PPT commentary, the business platform provides a commentary screen for the commentary teacher to be added during the PPT commentary process. In order to prevent the area corresponding to the commentary screen from obscuring the content of the PPT, it is necessary to calculate the image information density of each image area in the current PPT page.

[0041] Based on this, the image to be processed corresponding to the PPT of this page is first converted into a grayscale image, and then the grayscale image is wavelet transformed to obtain low-frequency information A, horizontal high-frequency information H, vertical high-frequency information V and diagonal high-frequency information D corresponding to the image to be processed, respectively. The layout of the visualized image corresponding to the information is shown in FIG. 4. Note that the visualized image corresponding to the low-frequency information A is shown in FIG. 5(a), the visualized image corresponding to the horizontal high-frequency information H is shown in FIG. 5(b), the visualized image corresponding to the vertical high-frequency information V is shown in FIG. 5(c), and the visualized image corresponding to the diagonal high-frequency information D is shown in FIG. 5(d). Then, the horizontal high-frequency information H of the horizontal dimension, the vertical high-frequency information V of the vertical dimension, and the diagonal high-frequency information D of the diagonal dimension are combined to facilitate the construction of a visualized high-frequency image corresponding to the image to be processed, which is used to calculate the image information density.

[0042] In summary, the conversion to a grayscale image obtains high-frequency information, which not only prepares the subsequent calculation of the image information density of the image region, but also effectively improves the accuracy of the calculation of the image information density, thereby enabling the distribution details of the image information density in the processed image to be analyzed more quickly.

[0043] Step S106: Based on the high frequency information, a high frequency image corresponding to the processing target image is constructed.

[0044] Specifically, based on obtaining the high frequency information of the above-mentioned processing target image, and furthermore, in order to accurately calculate the image information density of each image region, the high frequency information is synthesized to construct a high frequency image corresponding to the processing target image, and the image information density of each image region is calculated in the high frequency dimension and mapped to the processing target image, thereby obtaining the image information density of each image region in the processing target image, and effectively improving the calculation accuracy and calculation efficiency. Note that the high frequency image specifically refers to the image obtained after mapping the processing target image to the high frequency dimension, and the value of each pixel dot in the image is mapped within the range of 0 to 255 and displayed by a tone diagram. After that, the calculation of the image information density of each image region can be completed by analyzing the value of the pixel dot.

[0045] Furthermore, when constructing a high-frequency image corresponding to the image to be processed, the high-frequency information of the image to be processed includes horizontal high-frequency information, vertical high-frequency information and diagonal high-frequency information, so that only by synthesizing the high-frequency information of three dimensions, a high-frequency image that synthesizes the features of three dimensions can be constructed, which makes it easy to calculate the image information density afterwards. In this embodiment, the specific implementation form is as follows: Pixel information corresponding to each pixel dot in the processing target image is calculated based on the horizontal high frequency information, the vertical high frequency information, and the diagonal high frequency information. The value of each pixel dot in the processing target image is mapped to a tone interval based on the pixel information, and the high frequency image is constructed based on the mapping result.

[0046] Specifically, the pixel information specifically refers to the value in the high frequency dimension of each pixel dot calculated after combining the horizontal high frequency information, the vertical high frequency information, and the diagonal high frequency information. Correspondingly, the gradation range specifically refers to the range of 0 to 255.

[0047] Based on this, the horizontal high-frequency information, vertical high-frequency information and diagonal high-frequency information of the image to be processed are obtained, and then the horizontal high-frequency information, vertical high-frequency information and diagonal high-frequency information are combined to calculate the value of each pixel dot in the high-frequency dimension of the image to be processed, and then the value of each pixel dot is mapped within the range of 0 to 255, so that the mapping value of each pixel dot in the high-frequency dimension can be obtained. A high-frequency image corresponding to the high-frequency dimension can be constructed based on the position of each pixel dot, and the horizontal high-frequency information, vertical high-frequency information and diagonal high-frequency information are combined with the high-frequency image, so that the calculation accuracy of the image information density can be effectively ensured. In this process, when calculating the pixel information of each pixel dot in the image to be processed based on the horizontal high-frequency information, vertical high-frequency information and diagonal high-frequency information, the horizontal high-frequency information, vertical high-frequency information and diagonal high-frequency information corresponding to each pixel dot are respectively squared to obtain the average value, and finally the square root is taken to obtain the value of each pixel dot and mapped to the range of 0 to 255, so that the mapping value of each pixel dot in the high-frequency dimension can be obtained, and finally the high-frequency image of the image to be processed can be stitched together by the mapping value of each pixel dot.

[0048] According to the above example, after obtaining the high frequency information {horizontal high frequency information H, vertical high frequency information V, and diagonal high frequency information D} of the image to be processed, the horizontal high frequency information Hn, vertical high frequency information Vn, and diagonal high frequency information Dn of each pixel dot in the image to be processed can be respectively taken, and the value in the high frequency dimension of each pixel dot can be calculated by formula (1). Finally, by mapping the value in the high frequency dimension of each pixel dot within the range of 0 to 255, the mapping value in the high frequency dimension of each pixel dot can be obtained. By constructing a high frequency image of the image to be processed with the mapping value of each pixel dot, the high frequency image shown in FIG. 6 can be obtained. The high frequency image is a combination of the high frequency information {horizontal high frequency information H, vertical high frequency information V, and diagonal high frequency information D} of the image to be processed, and then, it is easy to calculate the image information density of each image region based on this image. JPEG0007673257000001.jpg9170

[0049] To summarise the above, by combining high frequency information to construct a high frequency image, it is not only possible to ensure that the high frequency information of the image to be processed is synthesized into the high frequency image, but also possible to ensure that the subsequent calculation of image information density is completed based on the gradation image, thereby effectively improving the calculation accuracy of image information density.

[0050] Step S108: The high frequency image is processed according to a preset image information density calculation policy, and an image information density corresponding to each image region in the processing target image is determined based on the processing result.

[0051] Specifically, after obtaining the high frequency image as described above, further process the high frequency image corresponding to the processing target image according to a preset image information density calculation policy, calculate the image information density of each image area in the high frequency dimension and map it to the processing target image, then obtain the image information density of each image area in the processing target image.Note that, the image information density calculation policy specifically refers to move in the high frequency image according to a preset sliding window and a preset step, and after each move, calculate the image information density of the area corresponding to the sliding window at this time in the high frequency image, and after the sliding window moves, obtain the image information density of the area corresponding to n sliding windows in the high frequency image, and then map the image information density of the n areas in the high frequency image to the processing target image, then obtain the image information density of each image area in the processing target image.

[0052] Furthermore, when calculating the image information density of each image region in the processing target image, in order to ensure the calculation accuracy, the calculation of the image information density may be completed by performing processing in a high frequency dimension and then mapping it to the processing target image. In this embodiment, the specific implementation is as follows. Step S1082: Analyze the image information density calculation policy, and determine a target sliding window and a target sliding step based on the solution result. Specifically, the target sliding window specifically refers to a rectangular frame for setting the size, and the image information density of each image region is obtained by mapping the image information density of the rectangular frame corresponding to the frame region selected in the high frequency image. Correspondingly, the target sliding step specifically refers to the pixel value that the target sliding window moves to each time in the high frequency image.

[0053] In addition, the image information density is calculated in the high frequency dimension and then mapped to the target image to obtain the image information density of each image region, but the sliding window and sliding step are usually preset for the target image, and therefore cannot be applied to the high frequency image. In addition, since the high frequency image is the feature representation of the target image in the high frequency dimension and its size is half that of the target image, a conversion is required after obtaining the sliding window and sliding step corresponding to the target image. In this way, the target sliding window and target sliding step to be applied to the high frequency image can be obtained. In this embodiment, the specific implementation is as follows: An initial sliding window and an initial sliding step are determined based on the analysis result, and a transformation relationship between the processing target image and the high frequency image is determined. The initial sliding window and the initial sliding step are adjusted based on the transformation relationship to obtain the target sliding window and the sliding step.

[0054] Specifically, the initial sliding window refers to a sliding window preset for the image to be processed, and the initial sliding step refers to a sliding step preset for the image to be processed. Correspondingly, the transformation relationship specifically refers to a proportional relationship between the image to be processed and the high frequency image, and changes depending on parameters according to the wavelet transformation.

[0055] Based on this, when it is necessary to calculate image information density, first, the image information density calculation policy is analyzed to obtain an initial sliding window and an initial sliding step to be applied to the image to be processed, and the transformation relationship between the image to be processed and the high-frequency image is determined. Furthermore, the initial sliding window and the initial sliding step are adjusted based on the transformation relationship, so that a target sliding window and an initial sliding step to be applied to the high-frequency image can be obtained. Then, the image information density of each region can be easily calculated by the target sliding window and the target sliding step.

[0056] The size of the sliding window and the sliding step can be set according to the actual scene, and the unit is pixels, but there is no limit to this in this embodiment.

[0057] To sum up, by adjusting the initial sliding step and the initial sliding window according to the transformation relationship, a target sliding window and a target sliding step applied to the high-frequency image can be obtained, which facilitates the subsequent calculation of image information density in the high-frequency image, and ensures that the calculation is more comprehensive and accurate.

[0058] Step S1084: Move the target sliding window in the high frequency image according to the target sliding step, and calculate image information density corresponding to each high frequency pixel in the high frequency image according to the moving result. Specifically, after determining a target sliding window and an initial sliding step to be applied to the high frequency image, the target sliding window is moved in the high frequency image according to the target sliding step, and the image information density of each high frequency image region in the high frequency image is calculated for each moving result, and then mapped to the processing target image to obtain the image information density of each image region in the processing target image.

[0059] Based on this, each high frequency image region in the high frequency image specifically refers to the frame region selected after the target sliding window moves each time.The image information density of each high frequency image region refers to the ratio of the information coverage area in the selected frame region, the larger the ratio, the higher the image information density, and conversely, the smaller the ratio, the lower the image information density.The target sliding window corresponds to one high frequency image region after each movement, and each high frequency image region corresponds to one image information density.

[0060] In addition, when calculating the image information density of each high frequency image region in the high frequency image, in order to ensure the calculation of image information density in the high frequency dimension, the pixel information density of the pixel dot in the processing target image needs to be mapped to the high frequency dimension, so as to realize the calculation of image information density in the gray chart, and not only improve the calculation accuracy but also ensure the calculation efficiency. In this embodiment, the calculation process of the image information density of any high frequency image region is as follows: A pixel information density corresponding to each pixel dot in a target image region of the image to be processed is read. A high frequency pixel information density corresponding to each pixel dot in a target high frequency image region of the high frequency image is calculated based on the pixel information density corresponding to each pixel dot in the target image region. An average value of the high frequency pixel information densities corresponding to each pixel dot in the target high frequency image region is calculated, and an image information density corresponding to the target high frequency image region is determined based on the calculation result.

[0061] Specifically, the target image area refers to any image area of ​​the image to be processed for which calculation of image information density is required. The pixel information density specifically refers to the information density corresponding to each pixel dot in the target image area. Correspondingly, the target high frequency image area refers to the high frequency image area in the high frequency image that corresponds to the target image area. The high frequency pixel information density specifically refers to the information density corresponding to each pixel dot in the target high frequency image area. The pixel information density corresponding to each pixel dot is determined by the ratio of the information occupation area of ​​the pixel dot to the total area of ​​the pixel dot.

[0062] Based on this, when the target sliding window moves in the high frequency image according to the target sliding step, the image information density of any high frequency image region is calculated in the following manner. First, read the pixel information density of each pixel dot in the target image region of the processing target image. Since the frame region selected by the target sliding window in the high frequency image is the target high frequency image region, and the region corresponds to the target image region after being mapped to the processing target image region, the pixel information density corresponding to each pixel dot in the target image region may be determined, and then the high frequency pixel information density corresponding to each pixel dot in the target high frequency image region, that is, the similar information density corresponding to each pixel dot after being mapped to the high frequency dimension may be calculated based on this mapping relationship. Furthermore, since the target high frequency image region includes a plurality of pixel dots, and the high frequency pixel information density corresponding to each pixel dot is different, in order to ensure the calculation accuracy, the average value may be selected to determine the image information density corresponding to the target high frequency image region, that is, the average value of the high frequency pixel information density corresponding to each pixel dot in the target high frequency image region is calculated, and the image information density corresponding to the target high frequency image region is obtained and mapped to the processing target image, so that the image information density of the target image region can be obtained. Thereafter, the image information density of each image region in the processing target image can be obtained in the same manner. In a specific implementation, when calculating the high-frequency pixel information density corresponding to each pixel dot in the target high-frequency image region of the high-frequency image based on the pixel information density corresponding to each pixel dot in the target image region, it may be realized in the following form. First, the pixel information density corresponding to any pixel dot in the processing target image is determined, then the ratio between the pixel information density corresponding to the pixel dot and the pixel information density max (i.e., the highest pixel dot information density) corresponding to the pixel dot in the processing target image is calculated, and then the ratio is multiplied by 255 (because the value of each pixel dot in the high-frequency image is within the range of 0 to 255), and finally, based on the calculation result, the high-frequency pixel information density of the pixel dot in the target high-frequency image region corresponding to any pixel dot in the processing target image can be obtained.Thereafter, by similarly calculating each pixel dot in the above manner, it is possible to obtain the high frequency pixel information density corresponding to each pixel dot in the target high frequency image region.

[0063] Since the high frequency image is a gradation image corresponding to the processing target image in the high frequency dimension, as the value of each pixel dot in the high frequency image approaches 255, this pixel dot approaches white and furthermore, this pixel dot has a higher image information density. Conversely, as the value of the pixel dot approaches 0, this pixel dot approaches black and furthermore, this pixel dot has a lower image information density.

[0064] In summary, by calculating the image information density of the target high frequency image region on a pixel dot basis, the calculation accuracy of the image information density of each high frequency image region can be improved, and calculation efficiency can be ensured, which makes it easier to subsequently quickly map the image information density of each image region in the image to be processed.

[0065] Step S1086: The image information density corresponding to each image region in the processing target image is determined based on the image information density corresponding to each high frequency image region in the high frequency image.

[0066] Specifically, after the calculation of the image information density of each high frequency image region in the high frequency image is completed, because there is a transformation relationship between the target image and the high frequency image, the image information density corresponding to each image region in the target image is determined based on the image information density corresponding to each high frequency image region in the high frequency image, so as to complete the high frequency mapping to the target image and realize the calculation of the image information density of each image region.

[0067] Furthermore, when determining the image information density of each image region in the processing target image, the mapping relationship between the processing target image and the high frequency image may be also completed. In this embodiment, the specific implementation form is as follows.

[0068] A mapping relationship is established between each high frequency image region in the high frequency image and each image region in the processing target image, and an image information density corresponding to each image region in the processing target image is determined based on the mapping relationship and the image information density corresponding to each high frequency image region.

[0069] Specifically, a mapping relationship between each pixel dot in the high frequency image and each pixel dot in the processing target image may be determined based on this mapping relationship, the mapping relationship between the pixel dots makes it easy to establish a mapping relationship between the image region and the high frequency image region, and the image information density of each image region in the processing target image is determined based on this mapping relationship, i.e., the image information density of the high frequency image region is the image information density of the image region having the mapping relationship.

[0070] Based on this, the image information density of each high frequency image region in the high frequency image is calculated, and then a mapping relationship is established between each high frequency image region in the high frequency image and each image region in the image to be processed. Then, based on this mapping relationship, the image information density of each high frequency image region is added to its corresponding image region, thereby obtaining the image information density of each image region in the image to be processed.

[0071] To summarize the above, after completing the calculation of the image information density of each high-frequency image region in the high-frequency dimension, the image information density of each image region in the image to be processed is determined based on the mapping relationship between the image to be processed and the high-frequency image, so as to effectively ensure the accuracy of the image information density.

[0072] According to the above example, after obtaining the high-frequency image corresponding to the image to be processed, analyze the preset image information density calculation policy at this time, obtain the initial sliding window square frame of 256×256 pixels corresponding to the image to be processed, and the initial sliding step of 32 pixels. Then, adjust the initial sliding window and the initial sliding step according to the transformation relationship between the image to be processed and the high-frequency image, and obtain the target sliding window of 128×128 pixels and the target sliding step of 16 pixels.

[0073] Furthermore, according to the target sliding step of 16 pixels, the target sliding window of 128×128 pixels is moved in the high frequency image, and after each sliding, the image information density of the high frequency image area corresponding to the target sliding window in the high frequency image is obtained, and after the target sliding window is moved, the image information density corresponding to each high frequency image area in the high frequency image is obtained. Furthermore, based on the mapping relationship between the processing target image and the high frequency image, the image information density of each image area in the processing target image can be obtained, and it is determined that the image information density of the S1th image area in the processing target image is the lowest, which is 0.0166, and the image information density of the S2th image area in the processing target image is the highest, which is 0.1088. After the calculation of the image information density is completed, the one shown in FIG. 7 is generated, and the two areas, which are the highest density area and the lowest density area, are represented by rectangular frames representing the image information density.

[0074] In addition, in business scenarios, the image information density needs to be recalculated every time the display content changes, and since the image information density changes depending on the display content, the area corresponding to the highest / lowest image information density also changes. If it is necessary to adjust the placement location of the visualization resource every time a change is made, the visualization resource position will change arbitrarily, which will have a great impact on the user's viewing experience. Therefore, in order to prevent the arbitrary change of the visualization resource position, the image area with the lowest image information density is selected as the additional image area from the specified image areas, and then the visualization resource embedding process is performed. In this embodiment, the specific implementation form is as follows.

[0075] The image region corresponding to the lowest image information density is selected as an additional image region, A visualization resource that matches the image to be processed is read and the visualization resource is added to the additional image region.

[0076] Specifically, the additional image area refers to the image area with the lowest image information density in one or more specified image areas of the image to be processed. Correspondingly, the visualization resource refers to the necessary image, video, animation, and other resources to be added to the additional image area, but is not limited in this embodiment.

[0077] In a specific implementation, since the image to be processed changes from time to time, it is necessary to recalculate the image information density of each image region in the current image to be processed whenever the image to be processed is changed. If the image region with the lowest image information density is directly selected as the additional image region, there is a problem that the position of the visualization resource changes with the change of the image to be processed, which greatly affects the user's viewing experience. Therefore, one or more image regions may be designated as image region candidates, and the image information density of each image region may be calculated, and then the image information density of the image region candidates may be directly determined, and then, based on the image information density of each image region in the designated one or more image regions, the region with the lowest image information density is selected as the additional image region from the current image to be processed, thereby realizing the embedding processing operation of the visualization resource.

[0078] In the above example, the bottom left and bottom right of the PPT display page are designated as candidate image areas, and the image information density is calculated for each image area of ​​the image to be processed. After that, as shown in FIG. 8, it is determined that the image information density of the candidate image area corresponding to the bottom left is 0.0319, and the image information density of the candidate image area corresponding to the bottom right is 0.0225. Furthermore, it is determined that the image information density of the candidate image area corresponding to the bottom right is the lowest. Then, this area is selected as an additional image area, and the avatar of the teaching user who will explain the PPT is added to this position as a visualization resource and embedded in the current PPT page. Based on the embedding result, the one shown in FIG. 9(a) is generated, thereby realizing the completion of the explanation of the PPT content even when the least amount of occluded content is present.

[0079] To sum up, by comparing the image information density to determine the additional image area, the position with the least amount of information content covered by the visualization resource is selected for embedding, which can avoid the problem of information occlusion affecting the user's viewing experience and further improve the user's participation experience.

[0080] Furthermore, when the information content of the image to be processed is large, even if the image area with the lowest image information density in the image to be processed is selected to embed the visualization resource, a certain amount of information will be covered. Since the user can easily view all the information, the visualization resource may be subjected to a transparency process. In this embodiment, the specific implementation form is as follows: A transparency level is determined based on the image information density corresponding to the added image area; A transparency process is performed on the visualization resource according to the transparency level, and the visualization resource after the transparency process is added to the added image area.

[0081] Specifically, the transparency level refers to a level that represents the transparency of the visualization resource. The higher the transparency level, the higher the transparency of the visualization resource, and conversely, the lower the transparency level, the lower the transparency of the visualization resource. Furthermore, the transparency level corresponds to the image information density, and the higher the image information density, the more the information content of the image region, and the higher the corresponding transparency level, while the lower the image information density, the less the information content of the image region, and the lower the corresponding transparency level. Based on this, after the additional image region is determined, it is possible to prevent the visualization resource from obscuring information in the processing target image, so at that time, it is only necessary to determine the transparency level corresponding to the image information density of the additional image region based on the correspondence between the image information density and the transparency level, and then perform a transparency process on the visualization resource according to this level, and finally embed the visualization resource after the transparency process into the additional image region.

[0082] Using the above example, in order to prevent the avatar of the class user explaining the PPT from obscuring the PPT contents, the transparency level is then determined based on the image information density of the candidate image area corresponding to the bottom right, and then a transparency process is applied to the class user's avatar according to this level. Finally, the avatar of the class user after the transparency process is added to the additional image area, thereby obtaining the result shown in Figure 9(b), thereby preventing the obscuration of the PPT contents.

[0083] In summary, processing the visualization resource in the form of transparency processing can prevent information occlusion in the processed image, and can also embed the visualization resource to further enhance the user's viewing experience.

[0084] In the image processing method according to the present specification, after obtaining a target image, the target image is converted into a grayscale image, and the grayscale image is subjected to wavelet transformation to obtain high-frequency information of the target image, so as to accurately complete the calculation of the image information density of each region in the target image. Then, a high-frequency image corresponding to the target image is constructed based on the high-frequency information, and the image information density corresponding to each image region in the target image in the high-frequency dimension is obtained, thereby effectively ensuring the accuracy of the calculation of the image information density of each image region, and subsequently facilitating the embedding of visualization resources based on the image information density, and preventing information occlusion in the target image.

[0085] Corresponding to the above method embodiment, this specification further provides an embodiment of an image processing device. Figure 10 is a diagram showing the configuration of an image processing device according to an embodiment of this specification. As shown in Figure 10, the device includes: an acquisition module 1002 arranged to acquire a processing target image; a transformation module 1004 arranged to convert the processing target image into a grayscale image and perform wavelet transformation on the grayscale image to obtain high-frequency information of the processing target image; a construction module 1006 arranged to construct a high-frequency image corresponding to the processing target image based on the high-frequency information; and a processing module 1008 arranged to process the high-frequency image according to a preset image information density calculation policy, and determine image information density corresponding to each image region in the processing target image based on the processing result.

[0086] In one preferred embodiment, the high frequency information includes at least one of horizontal high frequency information, vertical high frequency information and diagonal high frequency information.

[0087] In a preferred embodiment, the construction module 1006 is further configured to calculate pixel information corresponding to each pixel dot in the target image based on the horizontal high frequency information, the vertical high frequency information and the diagonal high frequency information, map values ​​of each pixel dot in the target image to a gradation range based on the pixel information, and construct the high frequency image based on the mapping result.

[0088] In one preferred embodiment, the processing module 1008 is further configured to analyze the image information density calculation policy, determine a target sliding window and a target sliding step based on the analysis result, move the target sliding window in the high frequency image according to the target sliding step, calculate an image information density corresponding to each high frequency image region in the high frequency image based on the movement result, and determine an image information density corresponding to each image region in the processing target image based on the image information density corresponding to each high frequency image region in the high frequency image.

[0089] In one preferred embodiment, the processing module 1008 is further configured to determine an initial sliding window and an initial sliding step based on the analysis result, determine a transformation relationship between the processing target image and the high frequency image, and adjust the initial sliding window and the initial sliding step based on the transformation relationship to obtain the target sliding window and the target sliding step.

[0090] In one preferred embodiment, the processing module 1008 is further configured to read a pixel information density corresponding to each pixel dot in a target image area of ​​the processing target image, calculate a high frequency pixel information density corresponding to each pixel dot in a target high frequency image area of ​​the high frequency image based on the pixel information density corresponding to each pixel dot in the target image area, calculate an average value of the high frequency pixel information densities corresponding to each pixel dot in the target high frequency image area, and determine an image information density corresponding to the target high frequency image area based on the calculation result.

[0091] In a preferred embodiment, the processing module 1008 is further arranged to establish a mapping relationship between each high frequency image region in the high frequency image and each image region in the target image, and to determine an image information density corresponding to each image region in the target image based on said mapping relationship and the image information density corresponding to each high frequency image region.

[0092] In a preferred embodiment, the image display device further comprises an addition module, which is arranged to select an image area corresponding to the lowest image information density as an additional image area, to read a visualization resource matching the image to be processed and to add said visualization resource to said additional image area.

[0093] In one preferred embodiment, the additional module is further configured to determine a transparency level based on an image information density corresponding to the additional image area, to apply a transparency process to the visualization resource depending on the transparency level, and to add the visualization resource after the transparency process to the additional image area.

[0094] In the image processing device according to the present specification, after obtaining a target image, the image processing device converts the target image into a grayscale image, and then performs wavelet transform on the grayscale image to obtain high-frequency information of the target image, and then constructs a high-frequency image corresponding to the target image based on the high-frequency information, and obtains image information density corresponding to each image region in the target image in the high-frequency dimension, thereby effectively ensuring the accuracy of the image information density calculation of each image region, and subsequently facilitating the embedding of visualization resources based on the image information density, and preventing information occlusion in the target image.

[0095] The above is a schematic solution of an image processing device in this embodiment. Note that the technical solution of the image processing device belongs to the same concept as the technical solution of the above image processing method, and the technical solution of the image processing device will not be described in detail, but the description of the technical solution of the above image processing method may be referred to.

[0096] FIG. 11 is a flowchart of a display method according to an embodiment of the present specification, which specifically includes the following steps: Step S1102: The image to be processed is obtained, and the image to be processed is converted into a gray scale image.

[0097] Step S1104: The gray scale image is subjected to a wavelet transform to obtain high frequency information of the processing target image, and a high frequency image corresponding to the processing target image is constructed based on the high frequency information.

[0098] Step S1106: The high frequency image is processed according to a preset image information density calculation policy, and an image information density corresponding to an initial image region in the processing target image is determined based on the processing result.

[0099] The relevant explanatory contents in the display method of this embodiment are similar to the explanatory contents in the image display method described above, and for the same or similar explanatory contents, reference may be made to the corresponding explanatory contents in the image display method described above, and for ease of explanation, the details are omitted in this embodiment.

[0100] Specifically, an initial image region refers to one or more image regions in the image to be processed, and by calculating the image information density of the initial image region, it is possible to quickly determine the regions in which visualization resources need to be added, and then facilitate embedding the visualization resources on the basis of not occluding information in the image to be processed.

[0101] In addition, since the added position of the visualization resource differs depending on the business scene, in order to avoid the problem of the position of the visualization resource being changed arbitrarily, the initial image area may be directly positioned from the processing target image according to the business scene. In this embodiment, the specific implementation form is as follows. A target task corresponding to the to-be-processed image is determined. An image region positioning policy corresponding to the target task is loaded. The initial image region is determined in the to-be-processed image according to the image region positioning policy.

[0102] For example, when explaining a PPT, in order to prevent the avatar of the teaching user explaining the PPT from obscuring the PPT content, the bottom left and bottom right of the PPT are selected at that time as the initial image area of ​​the current PPT page, and then visualization resources can be easily added and displayed at this position.

[0103] Step S1108: Screen a target image area from the initial image area based on the image information density, and add a visualization resource to the target image area in the processing target image for display.

[0104] Specifically, after determining the image information density of a specified initial image area in the image to be processed, the target image area may be screened from the initial image area based on the image information density so as to further reduce the amount of information covered by the visualization resource, and then the visualization resource may be added to the target image area in the image to be processed and displayed to the user.

[0105] In addition, the image information density of each image region is calculated from pixel dots. Therefore, even if the visualization resource is a random region, a visualization resource with a random appearance can be added to the target image region, so as to realize embedding of the visualization resource in the target image region in the target image, and displaying a more effective content to the user. In addition, the image information density can be calculated for the random image region, that is, when the initial image region in the target image is random, the calculation of the image information density of each image region can be completed by the above image information density calculation method, which makes it easier to add random visualization resources later.

[0106] In addition, in order to complete the addition of visualization resources and prevent information occlusion in the target image, the target image area may be determined in the following manner: Compare the image information densities corresponding to each initial image area, and select the initial image area corresponding to the lowest image density information according to the comparison result as the target image area.

[0107] Furthermore, when the information content of the processing target image is large, even if the visualization resource is embedded by selecting an image area in the processing target image with the lowest image information density, a certain amount of information will be covered. In order to allow the user to easily view all information, a transparency process may be applied to the visualization resource. In this embodiment, a specific implementation form is as follows. A target image information density corresponding to the target image area is determined. A target transparency level corresponding to the target image information density is determined based on the correspondence between the image information density and the transparency level. A transparency process is applied to the visualization resource according to the target transparency level, and the visualization resource after the transparency process is added to the target image area in the processing image and displayed.

[0108] In the above example, as shown in Fig. 12, if the image information density of the lower-left initial image region is 0.0213 and the image information density of the lower-right initial image region is 0.0136, the lower-right initial image region is selected as the target image region, and then, based on the correspondence between the image information density and the transparency level, the transparency level corresponding to the image information density of 0.0136 of the target image region is determined to be X. At that time, the transparency process is performed on the visualization resource according to the transparency level X. Then, the visualization resource after the transparency process is added to the target image region to generate the one shown in Fig. 13.

[0109] To sum up, after obtaining the target image, the calculation of the image information density of each region in the target image can be completed accurately, the target image is converted into a grayscale image, and the grayscale image is subjected to wavelet transformation to obtain high-frequency information of the target image. Then, a high-frequency image corresponding to the target image is constructed according to the high-frequency information, and the image information density corresponding to each initial image region in the target image in the high-frequency dimension is obtained. This effectively ensures the accuracy of the calculation of the image information density of each initial image region. Finally, the target image region is screened according to the image information density to add and display a visualization resource, which can prevent information occlusion in the target image resource and improve the user's viewing experience. Corresponding to the above method embodiment, the present specification further provides an embodiment of a display device. FIG. 14 is a diagram showing the configuration of a display device according to an embodiment of the present specification. As shown in FIG. 14, the device includes: an image acquisition module 1402 configured to acquire a processing target image and convert the processing target image into a grayscale image; an image construction module 1404 configured to perform wavelet transformation on the grayscale image to obtain high-frequency information of the processing target image and construct a high-frequency image corresponding to the processing target image based on the high-frequency information; an image processing module 1406 configured to process the high-frequency image according to a preset image information density calculation policy and determine an image information density corresponding to an initial image region in the processing target image based on a processing result; and an area screening module 1408 configured to screen a target image region from the initial image region based on the image information density, and add and display a visualization resource to the target image region in the processing target image.

[0110] In a preferred embodiment, the initial image region is determined by: determining a target task corresponding to the image to be processed; loading an image region positioning policy corresponding to the target task; and determining the initial image region in the image to be processed according to the image region positioning policy.

[0111] In one preferred embodiment, the region screening module 1408 is further configured to compare the image information density corresponding to each initial image region and select the initial image region corresponding to the lowest image density information as the target image region based on the comparison result.

[0112] In a preferred embodiment, the region screening module 1408 is further configured to determine a target image information density corresponding to the target image region, determine a target transparency level corresponding to the target image information density based on a correspondence between image information density and transparency level, apply transparency processing to the visualization resource according to the target transparency level, and add and display the visualization resource after transparency processing to the target image region in the processed image.

[0113] To sum up, after obtaining the target image, the calculation of the image information density of each region in the target image can be completed accurately, so that the target image is converted into a grayscale image, and the grayscale image is subjected to wavelet transformation to obtain high-frequency information of the target image. Then, a high-frequency image corresponding to the target image is constructed based on the high-frequency information, and the image information density corresponding to each initial image region in the target image in the high-frequency dimension is obtained. This effectively ensures the accuracy of the calculation of the image information density of each initial image region, and finally, the target image region is screened based on the image information density to add and display a visualization resource, thereby preventing the information shielding in the target image resource, and improving the user's viewing experience. The above is a schematic scheme of a display device in this embodiment. It should be noted that the technical scheme of the display device belongs to the same concept as the technical scheme of the above display method, and the technical scheme of the display device will not be described in detail, but the description of the technical scheme of the above display method may be referred to.

[0114] 15 is a block diagram showing a configuration of a computing device 1500 according to an embodiment of the present specification. Components of the computing device 1500 include, but are not limited to, a memory 1510 and a processor 1520. The processor 1520 and the memory 1510 are connected by a bus 1530, and a database 1550 is used to store data.

[0115] Computing device 1500 includes an access device 1540 that enables computing device 1500 to communicate over one or more networks 1560. Examples of these networks include a public switched telephone network (PSTN), a local area network (LAN), a wide area network (WAN), a personal area network (PAN), or a combination of communication networks such as the Internet. Access device 1540 may include one or more of any type of network interface, wired or wireless (e.g., a network interface card (NIC)), such as an IEEE 802.11 wireless local area network (WLAN) wireless interface, a global microwave interconnect access (Wi-MAX) interface, an Ethernet interface, a universal serial bus (USB) interface, a cellular network interface, a Bluetooth interface, a near field communication (NFC) interface, etc.

[0116] In one embodiment of the present specification, the above components of the computing device 1500 and other components not shown in Fig. 15 may also be connected to each other, for example, via a bus. It should be understood that the configuration block diagram of the computing device shown in Fig. 15 is merely an example and does not limit the scope of the present specification. Those skilled in the art may add or replace other components as necessary.

[0117] Computing device 1500 may be any type of fixed or mobile computing device, such as a mobile computer or device (e.g., a tablet computer, a personal digital assistant, a laptop computer, a notebook computer, a netbook, etc.), a mobile phone (e.g., a smartphone), a wearable computing device (e.g., a smart watch, smart glasses, etc.) or other type of mobile device, or a fixed computing device, such as a desktop computer or PC. Computing device 1500 may also be a mobile or fixed server.

[0118] Additionally, the processor 1520 executes computer-executable commands for image processing or display methods.

[0119] The above is a schematic solution of one computing device in this embodiment, and the technical solution of the computing device belongs to the same concept as the technical solution of the above-mentioned image processing method or display method, so the technical solution of the computing device will not be described in detail, but the description of the above-mentioned technical solution of the image processing method or display method may be referred to.

[0120] An embodiment herein further provides a computer readable storage medium having stored thereon computer instructions which, when executed by a processor, implement an image processing or display method.

[0121] The above is a schematic solution of a computer-readable storage medium in this embodiment, and the technical solution of the storage medium belongs to the same concept as the technical solution of the above-mentioned image processing method or display method, so the technical solution of the storage medium will not be described in detail, but you may refer to the description of the technical solution of the above-mentioned image processing method or display method.

[0122] Specific embodiments of the present specification have been described above. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims can be performed in a different order than in the examples and still achieve desirable results. It should be noted that the processes depicted in the figures do not require that the desired results be achieved in only the particular order or sequential order shown. Multi-task processing and parallel processing may also be possible or advantageous in some embodiments.

[0123] The computer commands include computer program code, which may be in the form of source code, object code, executable file, or some intermediate form, etc. The computer readable medium may include any entity or device capable of carrying the computer program code, a recording medium, a USB memory, a portable hard disk, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telegraph signal, and a software distribution medium, etc. It should be noted that the content of the computer readable medium may be increased or decreased as required by the legislation and patent practice in a jurisdiction, for example, in some jurisdictions, the computer readable medium may not include electrical carrier signals and telegraph signals as required by the legislation and patent practice in a jurisdiction.

[0124] Although the above-mentioned method embodiments are described as a series of combinations of operations for ease of explanation, those skilled in the art will appreciate that the present application is not limited by the order of operations described, because some steps may be performed in other orders or simultaneously according to the present application. Secondly, those skilled in the art will appreciate that the embodiments described in the specification are preferred embodiments, and the operations and modules involved are not necessarily required by the present application.

[0125] In the above embodiments, the description is focused on each embodiment, but for parts of an embodiment that are not described in detail, reference can be made to the relevant descriptions of other embodiments.

[0126] The preferred embodiments of the present application disclosed above are intended to clearly illustrate the present application. The selectable embodiments are not described in detail in full detail, and the present application is not limited only to the above specific embodiments. Obviously, many modifications and variations can be made based on the contents of the present application. The present application selects and specifically describes these embodiments in order to better interpret the principles and practical applications of the present application, so that those skilled in the art can better understand and utilize the present application. The present application is limited only by the claims and their full scope and equivalents.

Claims

1. 1. An image processing method, comprising: Obtaining an image to be processed; converting the processing target image into a gradation image, and performing a wavelet transform on the gradation image to obtain high frequency information of the processing target image; constructing a high frequency image corresponding to the target image based on the high frequency information; an image information density calculation policy is set in advance, the image information density calculation policy being a policy relating to the determination of a target sliding window, which is a rectangular frame that determines a set of pixel dots in a high frequency image that are the subject of image information density calculation, and a target sliding step, which is a movement amount of the target sliding window in the high frequency image; determining a target sliding window and a target sliding step in accordance with the image information density calculation policy; moving the target sliding window in the high frequency image by the target sliding step, defining an area surrounded by the target sliding window at the destination as a high frequency image area; performing a process of calculating the image information density of each high frequency image area; and determining the image information density of each image area corresponding to each high frequency image area in the image to be processed based on a processing result of calculating the image information density of each high frequency image area.

2. 2. The method of claim 1, wherein the high frequency information includes at least one of horizontal high frequency information, vertical high frequency information, and diagonal high frequency information.

3. Constructing a high frequency image corresponding to the processing target image based on the high frequency information includes: calculating pixel information corresponding to each pixel dot in the processing target image based on the horizontal high frequency information, the vertical high frequency information, and the diagonal high frequency information; 3. The image processing method according to claim 2, further comprising: mapping a value of each pixel dot in the processing target image to a gradation interval based on the pixel information; and constructing the high frequency image based on a mapping result.

4. Processing the high frequency image according to the preset image information density calculation policy, and determining an image information density corresponding to each image region in the processing target image based on the processing result, Analyzing the image information density calculation policy and determining a target sliding window and a target sliding step based on the analysis result; moving the target sliding window in the high frequency image according to the target sliding step, and calculating an image information density corresponding to each high frequency image region in the high frequency image based on a moving result; 2. The image processing method according to claim 1, further comprising determining an image information density corresponding to each image region in the processing target image based on an image information density corresponding to each high frequency image region in the high frequency image.

5. Determining a target sliding window and a target sliding step based on the analysis result includes: determining an initial sliding window and an initial sliding step based on an analysis result, and determining a transformation relationship between the processing target image and the high frequency image; 5. The image processing method according to claim 4, further comprising: adjusting the initial sliding window and the initial sliding step based on the transformation relationship to obtain the target sliding window and the target sliding step.

6. The image information density corresponding to any high frequency image region is determined as follows: reading pixel information density corresponding to each pixel dot in a target image area of ​​the image to be processed; calculating a high frequency pixel information density corresponding to each pixel dot in a target high frequency image region of the high frequency image based on a pixel information density corresponding to each pixel dot in the target image region; 5. The image processing method according to claim 4, further comprising the steps of: calculating an average value of high-frequency pixel information density corresponding to each pixel dot in the target high-frequency image region; and determining an image information density corresponding to the target high-frequency image region based on the calculation result.

7. Determining an image information density corresponding to each image region in the processing target image based on an image information density corresponding to each high frequency image region in the high frequency image, establishing a mapping relationship between each high frequency image region in the high frequency image and each image region in the processing target image; and determining an image information density corresponding to each image region in the target image based on the mapping relationship and the image information density corresponding to each high frequency image region.

8. determining an image information density corresponding to each image region in the processing target image based on the processing result; selecting an image region corresponding to the lowest image information density as the additional image region; The image processing method according to any one of claims 1 to 7, further comprising: reading a visualization resource that matches the image to be processed and adding the visualization resource to the additional image area.

9. Adding the visualization resource to the additional image region includes: determining a transparency level based on an image information density corresponding to the additional image region; The image processing method according to claim 8 , further comprising: performing a transparency process on the visualization resource in accordance with the transparency level; and adding the visualization resource after the transparency process to the additional image area.

10. An image processing device, an acquisition module arranged to acquire an image to be processed; a transformation module arranged to transform the target image into a gradient image and to perform a wavelet transformation on the gradient image to obtain high frequency information of the target image; a construction module arranged to construct a high frequency image corresponding to the target image based on the high frequency information; an image processing apparatus comprising: a processing module arranged to determine a target sliding window and a target sliding step in accordance with the image information density calculation policy, the target sliding window being a rectangular frame that determines a set of pixel dots in a high frequency image that are the subject of image information density calculation, the target sliding window being a rectangular frame that determines a set of pixel dots in a high frequency image that are the subject of image information density calculation, and a target sliding step being a movement amount of the target sliding window in the high frequency image, the processing module arranged to perform a process of calculating the image information density of each high frequency image area in the high frequency image based on a processing result of calculating the image information density of each high frequency image area.

11. A display method comprising: obtaining a processing target image and converting the processing target image into a grayscale image; performing a wavelet transform on the gradation image to obtain high frequency information of the processing target image, and constructing a high frequency image corresponding to the processing target image based on the high frequency information; an image information density calculation policy is set in advance, the image information density calculation policy being a policy relating to the determination of a target sliding window, which is a rectangular frame that determines a set of pixel dots that are the subject of image information density calculation in the high frequency image, and a target sliding step, which is a movement amount of the target sliding window in the high frequency image; a target sliding window and a target sliding step are determined in accordance with the image information density calculation policy; the target sliding window is moved in the high frequency image by the target sliding step, and an area surrounded by the target sliding window at the destination is set as a high frequency image area; a process of calculating the image information density of each high frequency image area is performed; and based on a process result of calculating the image information density of each high frequency image area, an image information density of each initial image area corresponding to each high frequency image area in the processing target image is determined; A display method comprising: screening a target image area from the initial image area based on the image information density of the initial image area; and adding and displaying a visualization resource in the target image area in the processing target image.

12. determining a target task corresponding to the image to be processed; loading an image region positioning policy corresponding to the target task; The method of claim 11 , further comprising determining the initial image region in the target image according to the image region positioning policy.

13. Screening a target image region from the initial image region based on the image information density includes: comparing image information densities corresponding to each initial image region; 12. The method of claim 11, further comprising: selecting, based on the comparison result, the initial image area corresponding to the lowest image density information as the target image area.

14. Adding and displaying a visualization resource to the target image region in the processing target image includes: determining a target image information density corresponding to the target image region; determining a target transparency level corresponding to the target image information density based on a correspondence relationship between image information density and transparency level; A display method according to any one of claims 11 to 13, characterized in that it includes performing a transparency process on the visualization resource in accordance with the target transparency level, and adding and displaying the visualization resource after the transparency process to the target image area in the processing target image.

15. A display device, comprising: an image acquisition module arranged to acquire a target image and convert the target image into a gradient image; an image construction module arranged to perform a wavelet transform on the grayscale image to obtain high frequency information of the processing target image, and to construct a high frequency image corresponding to the processing target image based on the high frequency information; an image processing module that is configured to: determine a target sliding window and a target sliding step according to the image information density calculation policy; move the target sliding window in the high frequency image by the target sliding step; define an area surrounded by the target sliding window at the destination as a high frequency image area; calculate the image information density of each high frequency image area; and determine the image information density of each initial image area corresponding to each high frequency image area in the processing target image based on a result of the calculation of the image information density of each high frequency image area; and a region screening module that screens a target image region from the initial image region based on the image information density of the initial image region, and adds visualization resources to the target image region in the processing target image to display the target image region.

16. 1. A computing device, comprising: A memory and a processor are included. The memory stores computer executable instructions; A computing device, characterized in that said processor executes said computer-executable instructions to implement the steps of the method according to any one of claims 1 to 9 or claims 11 to 14.

17. A computer-readable recording medium having computer instructions stored thereon, The instructions, when executed by a processor, implement the steps of the method according to any one of claims 1 to 9 or 11 to 14.

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