Image fuzzy processing method and device, electronic equipment, chip and medium

Abstract

The invention relates to an image fuzzy processing method and device, electronic equipment, a chip and a medium, and belongs to the technical field of image processing. The method comprises: acquiring an original image; performing fuzzy processing on the first pixel point to obtain a pixel value of the first pixel point after fuzzy processing; updating the pixel value of the second pixel point based on the pixel value of the first pixel point after fuzzy processing; and obtaining a target image after the original image is blurred based on the pixel value after the first pixel point is blurred and the pixel value after the second pixel point is updated. Therefore, only the first pixel point needs to be subjected to fuzzy processing, the second pixel point does not need to be subjected to fuzzy processing, for example, the second pixel point does not need to be subjected to fuzzy processing by adopting a logic complex fuzzy algorithm, and the effect of carrying out fuzzy processing on the second pixel point can be indirectly achieved according to the pixel value obtained after the first pixel point is subjected to fuzzy processing; the calculation amount of fuzzy processing can be greatly reduced, and the fuzzy processing efficiency is improved.

Description

Technical Field

[0001] The present disclosure relates to the field of image processing technology, and in particular to a method, device, electronic device, chip, and storage medium for image blur processing. Background Art

[0002] Image blurring is currently widely used in scenarios such as driving, live streaming, smart homes, and film and television production to optimize the visual experience. However, current image blurring methods suffer from high computational complexity and low processing efficiency. Summary of the Invention

[0003] The present disclosure provides an image blur processing method, device, electronic device, chip, and storage medium to at least address the problems of large computational complexity and low processing efficiency in image blur processing methods in related technologies. The technical solutions of the present disclosure are as follows: According to a first aspect of an embodiment of the present disclosure, a method for blurring an image is provided, comprising: acquiring an original image, the original image comprising a first pixel and a second pixel; blurring the first pixel to obtain a pixel value of the blurred first pixel; updating the pixel value of the second pixel based on the blurred pixel value of the first pixel; and obtaining a target image after blurring the original image based on the blurred pixel value of the first pixel and the updated pixel value of the second pixel.

[0004] According to a second aspect of an embodiment of the present disclosure, a device for blurring an image is provided, comprising: an acquisition module configured to acquire an original image, the original image comprising a first pixel and a second pixel; a first processing module configured to blur the first pixel to obtain a pixel value of the first pixel after blurring; an update module configured to update the pixel value of the second pixel based on the pixel value of the first pixel after blurring; and a second processing module configured to obtain a target image after blurring the original image based on the pixel value of the first pixel and the updated pixel value of the second pixel.

[0005] According to a third aspect of an embodiment of the present disclosure, an electronic device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the steps of the image blurring method described in the first aspect of the embodiment of the present disclosure are implemented.

[0006] According to a fourth aspect of an embodiment of the present disclosure, a computer-readable storage medium is provided, on which computer program instructions are stored. When the program instructions are executed by a processor, the steps of the image blurring processing method described in the first aspect of the embodiment of the present disclosure are implemented.

[0007] According to the fifth aspect of the embodiment of the present disclosure, a chip is provided, which includes an interface circuit and a processing circuit coupled to each other, wherein the interface circuit is used to input or output signals, and the processing circuit is configured to implement the steps of the image blurring processing method described in the first aspect of the embodiment of the present disclosure.

[0008] According to a sixth aspect of an embodiment of the present disclosure, a computer program product is provided, comprising a computer program. When the computer program is executed by a processor, the computer program implements the steps of the image blurring processing method described in the first aspect of the embodiment of the present disclosure.

[0009] The technical solution provided by the embodiments of the present disclosure brings at least the following beneficial effects: obtaining an original image, the original image includes a first pixel and a second pixel, blurring the first pixel to obtain the pixel value of the blurred first pixel, updating the pixel value of the second pixel based on the pixel value of the blurred first pixel, and obtaining a target image after the blurred original image based on the pixel value of the blurred first pixel and the updated pixel value of the second pixel. Thus, it is only necessary to blur the first pixel and update the pixel value of the second pixel based on the pixel value of the blurred first pixel, that is, there is no need to blur the second pixel, for example, there is no need to use a logically complex fuzzy algorithm to blur the second pixel, and the effect of blurring the second pixel can be indirectly achieved based on the pixel value of the blurred first pixel, which can greatly reduce the computational complexity of the blurring process and improve the efficiency of the blurring process.

[0010] In addition, the present disclosure does not require downsampling of the image, and can directly blur the image of the original resolution, thereby avoiding the jagged problem in the blurred image and optimizing the visual experience.

[0011] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] The accompanying drawings herein are incorporated into and constitute a part of the specification, illustrate embodiments consistent with the present disclosure, and together with the description are used to explain the principles of the present disclosure, and do not constitute an improper limitation of the present disclosure.

[0013] Figure 1 The figure is a flowchart of a method for blurring an image according to an exemplary embodiment.

[0014] Figure 2 The figure is a flowchart of a method for blurring an image according to another exemplary embodiment.

[0015] Figure 3 The figure is a flowchart of a method for blurring an image according to another exemplary embodiment.

[0016] Figure 4 The figure is a schematic diagram showing a method for blurring an image according to an exemplary embodiment.

[0017] Figure 5 The figure is a schematic structural diagram of a device for blurring an image according to an exemplary embodiment.

[0018] Figure 6 The figure is a schematic structural diagram of an electronic device according to an exemplary embodiment.

[0019] Figure 7 The figure is a schematic structural diagram of a chip according to an exemplary embodiment. DETAILED DESCRIPTION

[0020] In order to enable ordinary people in the art to better understand the technical solutions of the present disclosure, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below with reference to the accompanying drawings.

[0021] It should be noted that the terms "first," "second," and the like in the description of the present disclosure and the accompanying drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or precedence. It should be understood that the terms used in this manner are interchangeable where appropriate, so that the embodiments of the present disclosure described herein can be implemented in sequences other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present disclosure.

[0022] The following describes an image blur processing method, device, electronic device, chip, and storage medium according to embodiments of the present disclosure with reference to the accompanying drawings.

[0023] Figure 1 FIG. 1 is a flow chart showing a method for blurring an image according to an exemplary embodiment. Figure 1 As shown, the image blur processing method of the embodiment of the present disclosure includes the following steps.

[0024] S101, obtaining an original image, where the original image includes a first pixel and a second pixel.

[0025] It should be noted that the executors of the image blur processing method of the embodiment of the present disclosure are electronic devices, such as terminal devices, vehicles, cameras, servers, chips, etc. The terminal devices may include mobile phones, wearable devices (such as smart watches, smart glasses), laptops, etc. The vehicles may include on-board terminals, on-board controllers, etc. The chips may include ISPs (Image Signal Processors), etc.

[0026] The image blurring method of the embodiment of the present disclosure can be executed by the image blurring device of the embodiment of the present disclosure. The image blurring device of the embodiment of the present disclosure can be configured in any electronic device to execute the image blurring method of the embodiment of the present disclosure.

[0027] There are no specific restrictions on original images. For example, in an image editing scenario, original images may include images captured by the terminal device, such as facial images, landscape images, and building images. In a driving scenario, for example, original images may include images of the vehicle's exterior (such as the road ahead), interior areas (such as the driver's seat), and road monitoring images. In a live broadcast scenario, for example, original images may include at least one frame from a live broadcast video. In a smart home scenario, for example, original images may include at least one frame from a home surveillance video.

[0028] The original image can include RGB, HSV, HSL, YCbCr, Lab, YUV images, etc.

[0029] The number of the first pixel points and the number of the second pixel points are both at least one, and there is no excessive restriction on the size relationship between the number of the first pixel points and the number of the second pixel points, for example, the number of the first pixel points is the same as the number of the second pixel points.

[0030] There are no excessive restrictions on the distribution of the first pixel points and the second pixel points in the original image, such as continuous distribution, discontinuous distribution, uniform distribution, etc.

[0031] Optionally, at least one neighboring pixel of the second pixel is the first pixel. Neighboring pixel points refer to the surrounding pixel points of a certain pixel point, and there are no excessive restrictions on the neighboring pixel points. For example, it can include 4 neighborhoods, 8 neighborhoods, circular neighborhoods, rectangular neighborhoods, etc.

[0032] The 4-neighborhood refers to the 4 directly adjacent pixels above, below, left, and right of a pixel. For example, the 4-neighborhood pixels of the pixel in the x-th row and y-th column include the pixel in the (y-1)-th row and the (y+1)-th column, the pixel in the (x-1)-th row and the y-th column, and the pixel in the (x+1)-th row and the y-th column.

[0033] An 8-neighborhood refers to the 8 pixels above, below, left, right, and diagonally of a pixel. For example, the 8-neighborhood pixels of the pixel in the x-th row and y-th column include the pixel in the (y-1)-th row, the (y+1)-th row, the y-th column, the (x+1)-th row, the y-th column, the (x-1)-th row, the (y-1)-th column, the (x-1)-th row, the (y+1)-th column, the (x-1)-th row, the (y+1)-th column, the (x+1)-th row, the (y-1)-th column, and the (x+1)-th row, the (y+1)-th column.

[0034] A circular neighborhood refers to all pixels within a circular area that spreads outward from a certain pixel.

[0035] A rectangular neighborhood refers to all pixels within a rectangular area that spreads outward from a certain pixel.

[0036] The original image may include only two types of pixel points, namely, the first pixel points and the second pixel points, or, in addition to the first pixel points and the second pixel points, the original image may also include other pixel points, which is not limited here.

[0037] Optionally, the method further includes collecting the original image, or receiving the original image. Thus, the original image can be collected or received to achieve acquisition of the original image.

[0038] Optionally, before capturing the original image, the process further includes obtaining a shooting instruction. Thus, upon obtaining the shooting instruction, the original image can be captured. For example, if the execution subject is a terminal device equipped with a camera, the terminal device can capture the original image via the camera in response to the shooting instruction.

[0039] Optionally, receiving the original image includes receiving the original image sent by the terminal device. For example, taking the execution subject as a server as an example, the server can receive the original image sent by the terminal device.

[0040] S102: Perform blur processing on the first pixel to obtain a pixel value of the first pixel after blur processing.

[0041] It should be noted that the blurring process for the first pixel can be implemented by using any image blurring algorithm in the related art, and the image blurring algorithm may include Gaussian blurring, mean blurring, median filtering, bilateral filtering, etc.

[0042] S103: Update the pixel value of the second pixel based on the pixel value of the first pixel after blurring.

[0043] S104 , obtaining a target image after the original image is blurred based on the pixel value of the first pixel after the blurring process and the pixel value of the second pixel after the update process.

[0044] Image blurring is currently widely used in scenarios such as driving, live streaming, smart homes, and film and television production to optimize the visual experience. However, current image blurring methods suffer from high computational complexity and low processing efficiency.

[0045] For example, to reduce the computational effort of Gaussian blur, especially when the image resolution is high, most methods first downsample the image, apply Gaussian blur to the downsampled image, and then upsample the Gaussian blurred image to restore the image to its original resolution. However, the resulting blurred image is prone to aliasing, resulting in a poor visual experience. Aliasing refers to the phenomenon where previously smooth lines or color transitions become discontinuous, step-like, or rough at edges or details.

[0046] In the present disclosure, it is only necessary to blur the first pixel point and update the pixel value of the second pixel point based on the pixel value of the blurred first pixel point. That is, there is no need to blur the second pixel point. For example, there is no need to use a logically complex fuzzy algorithm to blur the second pixel point. The effect of blurring the second pixel point can be indirectly achieved based on the pixel value of the blurred first pixel point, which can greatly reduce the computational complexity of the blurring process and improve the efficiency of the blurring process.

[0047] In addition, the present disclosure does not require downsampling of the image, and can directly blur the image of the original resolution, thereby avoiding the jagged problem in the blurred image and optimizing the visual experience.

[0048] Optionally, the pixel value of the second pixel point is updated based on the pixel value after blurring of the first pixel point, including determining the reference pixel point corresponding to the second pixel point from each first pixel point, and updating the pixel value of the second pixel point based on the pixel value after blurring of each reference pixel point.

[0049] It is understandable that different second pixel points may correspond to different reference pixel points, and the number of reference pixel points corresponding to one second pixel point is at least one.

[0050] Optionally, a reference pixel point corresponding to the second pixel point is determined from each first pixel point, including determining a first pixel point located in the same row as the second pixel point from each first pixel point as a reference pixel point, and / or determining a first pixel point located in the same column as the second pixel point from each first pixel point as a reference pixel point.

[0051] For the relevant contents of updating the pixel value of the second pixel point based on the pixel value after blurring of each reference pixel point, reference may be made to the following embodiment, which will not be described in detail here.

[0052] Understandably, due to the ever-changing nature of Internet bandwidth, encoded videos are prone to issues, especially in low-bitrate scenarios. To improve video encoding quality, most video frames are first blurred and then encoded to produce the encoded video.

[0053] Optionally, the method further includes obtaining an original video to be encoded, and using each frame image in the original video as an original image.

[0054] After obtaining the target image after the original image is blurred, it also includes generating a target video after the original video is blurred based on the target image after the blurring of each frame image in the original video, encoding the target video to obtain the encoded video, storing the encoded video in a local storage space, or sending the encoded video to a display device, and the encoded video is used for visual display on the display device.

[0055] Therefore, the image blur processing method provided by the present disclosure can be applied to video coding scenarios, which can greatly reduce the computational complexity of video coding processing and improve video coding efficiency.

[0056] It should be noted that the present disclosure does not limit the execution sequence of steps S101 to S104. Figure 1 The steps S101 to S104 are merely executed in sequence for example.

[0057] The embodiment of the present disclosure provides an image blurring method, which obtains an original image, wherein the original image includes a first pixel and a second pixel, blurs the first pixel to obtain a pixel value of the blurred first pixel, updates the pixel value of the second pixel based on the pixel value of the blurred first pixel, and obtains a target image of the blurred original image based on the pixel value of the blurred first pixel and the updated pixel value of the second pixel. Thus, it is only necessary to blur the first pixel and update the pixel value of the second pixel based on the pixel value of the blurred first pixel, that is, there is no need to blur the second pixel. For example, there is no need to use a logically complex blurring algorithm to blur the second pixel. The effect of blurring the second pixel can be indirectly achieved based on the pixel value of the blurred first pixel, which can greatly reduce the computational complexity of blurring and improve the efficiency of blurring.

[0058] In addition, the present disclosure does not require downsampling of the image, and can directly blur the image of the original resolution, thereby avoiding the jagged problem in the blurred image and optimizing the visual experience.

[0059] Figure 2is a flow chart showing a method for blurring an image according to another exemplary embodiment. Figure 2 As shown, the image blur processing method of the embodiment of the present disclosure includes the following steps.

[0060] S201, obtaining an original image.

[0061] The relevant contents of step S201 can be found in the above embodiment and will not be repeated here.

[0062] S202: extracting first pixel points from the original image according to a set interval, and using each pixel point not extracted from the original image as a second pixel point.

[0063] Optionally, extracting the first pixel from the original image at a set interval includes extracting the first pixel from each row or column of the original image at a set interval. Thus, the first pixel can be extracted row by row from the original image, or column by column from the original image.

[0064] It should be noted that there are no excessive restrictions on the set interval, for example, it can be one pixel.

[0065] Optionally, determining the set interval includes at least one of the following operations: Operation 1: Determine a set interval based on a distribution rule of black areas or white areas in a checkerboard image.

[0066] It can be understood that the checkerboard image includes black areas and white areas, for example, an image in which black rectangular areas and white rectangular areas are alternately arranged.

[0067] For example, the number A of white areas between two closest black areas in any row of the checkerboard image may be obtained, and the interval may be set to A pixels.

[0068] For example, the number B of white areas between the two closest black areas in any column of the checkerboard image may be obtained, and the interval may be set to B pixels.

[0069] For example, the number C of black areas between two white areas closest to each other in any row of the checkerboard image may be obtained, and the interval may be set to C pixels.

[0070] For example, the number D of black areas between two white areas closest to each other in any column of the checkerboard image may be obtained, and the interval may be set to D pixels.

[0071] For example, Figure 4As shown, the black areas and white areas in any row and any column of the checkerboard image are alternately distributed, and the set interval can be determined to be one pixel.

[0072] The original image includes pixel points numbered 1 to 16. The first pixel point can be extracted from each row of pixel points in the original image at intervals of one pixel. For example, pixel points numbered 2, 4, 5, 7, 10, 12, 13, and 15 are extracted from the original image as the first pixel points, and pixel points numbered 1, 3, 6, 8, 9, 11, 14, and 16 are extracted as the second pixel points.

[0073] Operation 2: Determine a set interval based on a preset constraint condition, where the constraint condition includes that at least one neighboring pixel point of any pixel point in the original image is extracted.

[0074] Optionally, the constraint condition includes that at least one pixel point among four neighboring pixels of any pixel point in the original image is extracted.

[0075] For example, Figure 4 As shown, the pixel points numbered 2, 4, 5, 7, 10, 12, 13, and 15 are extracted from the original image as the first pixel points, and the pixel points numbered 1, 3, 6, 8, 9, 11, 14, and 16 are extracted as the second pixel points.

[0076] Taking the pixel numbered 1 as an example, the four neighboring pixels of the pixel numbered 1 include the pixel points numbered 2 and 5, and the pixel points numbered 2 and 5 are both extracted.

[0077] Taking pixel number 3 as an example, the four neighboring pixel points of pixel number 3 include pixel points numbered 2, 4, and 7, and pixel points numbered 2, 4, and 7 are all extracted.

[0078] Taking pixel number 6 as an example, the four neighboring pixel points of pixel number 6 include pixel points numbered 2, 5, 7, and 10, and the pixel points numbered 2, 5, 7, and 10 are all extracted.

[0079] S203: Perform blur processing on the first pixel to obtain a pixel value of the first pixel after blur processing.

[0080] S204: Update the pixel value of the second pixel based on the pixel value of the first pixel after blurring.

[0081] S205 , obtaining a target image after the original image is blurred based on the pixel value of the first pixel after the blurring process and the pixel value of the second pixel after the update process.

[0082] The relevant contents of steps S203-S205 can be found in the above embodiment and will not be repeated here.

[0083] It should be noted that the present disclosure does not limit the execution sequence of steps S201 to S205. Figure 2 The steps S201 to S205 are merely executed in sequence for example.

[0084] The embodiment of the present disclosure provides an image blur processing method, which extracts a first pixel point from the original image according to a set interval, and uses each pixel point not extracted from the original image as a second pixel point to achieve the determination of the first pixel point and the second pixel point.

[0085] Figure 3 is a flow chart showing a method for blurring an image according to another exemplary embodiment. Figure 3 As shown, the image blur processing method of the embodiment of the present disclosure includes the following steps.

[0086] S301, obtaining an original image, where the original image includes a first pixel and a second pixel.

[0087] S302: Perform blur processing on the first pixel to obtain a pixel value of the first pixel after blur processing.

[0088] The relevant contents of steps S301-S302 can be found in the above embodiment and will not be repeated here.

[0089] S303: Determine neighboring pixels of the second pixel from each first pixel as reference pixels.

[0090] S304: Update the pixel value of the second pixel based on the pixel value of each reference pixel after blurring.

[0091] In this embodiment, the pixel value of the second pixel point can be updated by taking into account the pixel values ​​of each of the neighboring pixel points after blurring processing, so that the transition between the updated pixel value of the second pixel point and the pixel values ​​of its neighboring pixel points after blurring processing is smoother, thereby indirectly achieving the effect of blurring the second pixel point.

[0092] Optionally, updating the pixel value of the second pixel based on the pixel values ​​of each reference pixel after blurring includes determining an i-th adjustment parameter based on a difference parameter between the pixel value of the i-th reference pixel before blurring and the pixel value of the i-th reference pixel after blurring, where i is a positive integer, and updating the pixel value of the second pixel based on each adjustment parameter. Thus, each adjustment parameter can be determined to update the pixel value of the second pixel by taking into account the difference parameter between the pixel values ​​of the reference pixel before and after blurring.

[0093] Optionally, based on the pixel value of the i-th reference pixel before blurring and the difference parameter between the pixel values ​​of the i-th reference pixel after blurring, the i-th adjustment parameter is determined, including subtracting the difference parameter between the pixel value of the i-th reference pixel before blurring from the pixel value of the i-th reference pixel after blurring, as the i-th adjustment parameter.

[0094] Optionally, updating the pixel value of the second pixel based on the adjustment parameters includes obtaining weights of the reference pixels and updating the pixel value of the second pixel based on the adjustment parameters and the weights of the reference pixels. Thus, the pixel value of the second pixel can be updated taking into account the adjustment parameters and the weights of the reference pixels.

[0095] It is understandable that a weight may be set for each reference pixel, and different reference pixels may have different corresponding weights.

[0096] Optionally, updating the pixel value of the second pixel based on the adjustment parameters and the weights of the reference pixels includes obtaining the product of the weight of the i-th reference pixel and the i-th adjustment parameter as the i-th product, and updating the pixel value of the second pixel based on the sum of the products. Thus, the adjustment parameters can be weighted summed, taking into account the weights of the reference pixels, to update the pixel value of the second pixel.

[0097] For example, updating the pixel value of the second pixel can be achieved by the following formula:

[0098] in, is the updated pixel value of the second pixel, is the pixel value of the second pixel before updating, For the i The pixel value after blurring of the reference pixel point, For the i The pixel value of the reference pixel before blurring, For the i The weight of the reference pixel, is the coefficient, N is the number of reference pixels.

[0099] for example, is a coefficient ranging from 0 to 1.

[0100] Optionally, based on each adjustment parameter, the pixel value of the second pixel point is updated, including obtaining the average value of each adjustment parameter, and obtaining the sum of the pixel value of the second pixel point and the average value of each adjustment parameter as the updated pixel value of the second pixel point.

[0101] S305 , obtaining a target image after the original image is blurred based on the pixel value of the first pixel after the blurring process and the pixel value of the second pixel after the update process.

[0102] The relevant contents of step S305 can be found in the above embodiment and will not be repeated here.

[0103] It should be noted that the present disclosure does not limit the execution sequence of steps S301 to S305. Figure 3 The steps S301 to S305 are merely executed in sequence for example.

[0104] The image blurring method provided by the embodiments of the present disclosure determines neighboring pixels of a second pixel from each first pixel as reference pixels, and updates the pixel value of the second pixel based on the blurred pixel values ​​of each reference pixel. Thus, the pixel value of the second pixel can be updated by taking into account the blurred pixel values ​​of each of the second pixel's neighboring pixels, resulting in a smoother transition between the updated pixel value of the second pixel and the blurred pixel values ​​of its neighboring pixels, thereby indirectly achieving the effect of blurring the second pixel.

[0105] Figure 5 The figure is a schematic structural diagram of a device for blurring an image according to an exemplary embodiment.

[0106] Reference Figure 5 The image blur processing device 500 according to the embodiment of the present disclosure includes: an acquisition module 501, a first processing module 502, an update module 503 and a second processing module 504.

[0107] An acquisition module 501 is configured to acquire an original image, where the original image includes a first pixel and a second pixel; A first processing module 502 is configured to perform blur processing on the first pixel point to obtain a pixel value of the first pixel point after blur processing; An updating module 503 is configured to update the pixel value of the second pixel point based on the pixel value of the first pixel point after blurring; The second processing module 504 is configured to obtain a target image after the original image is blurred based on the pixel value of the first pixel after the blurring process and the pixel value of the second pixel after the update process.

[0108] In some possible implementations, the first processing module 502 is further configured to: extract the first pixel points from the original image according to a set interval, and use the unextracted pixel points in the original image as the second pixel points; The first processing module 502 is further configured to perform at least one of the following operations: Determining the set interval based on a distribution rule of black pixels or white pixels in the checkerboard image; The set interval is determined based on a preset constraint condition, where the constraint condition includes that at least one neighboring pixel point of any pixel point in the original image is extracted.

[0109] In some possible implementations, the first processing module 502 is further configured to extract the first pixel points from pixel points in each row or each column of the original image according to the set interval.

[0110] In some possible implementations, the update module 503 is further configured to: determine the neighboring pixel points of the second pixel point from each first pixel point as a reference pixel point; and update the pixel value of the second pixel point based on the pixel value of each reference pixel point after blurring.

[0111] In some possible implementations, the update module 503 is further configured to: determine the i-th adjustment parameter based on the difference parameter between the pixel value of the i-th reference pixel point before blurring and the pixel value of the i-th reference pixel point after blurring, where i is a positive integer; and update the pixel value of the second pixel point based on each adjustment parameter.

[0112] In some possible implementations, the updating module 503 is further configured to: obtain a weight of each reference pixel; and update the pixel value of the second pixel based on each adjustment parameter and the weight of each reference pixel.

[0113] In some possible implementations, the acquisition module 501 is further configured to: acquire an original video to be encoded, and use each frame image in the original video as the original image.

[0114] After obtaining the target image after the original image is blurred, the second processing module 504 is further configured to: generate a target video after the original video is blurred based on the target image after the blurring of each frame image in the original video; encode the target video to obtain an encoded video; store the encoded video in a local storage space, or send the encoded video to a display device, and the encoded video is used for visual display on the display device.

[0115] In some possible implementations, the acquisition module 501 is further configured to: capture the original image; or receive the original image.

[0116] Regarding the apparatus in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0117] The image blurring device provided by the embodiment of the present disclosure obtains an original image, which includes a first pixel and a second pixel. The first pixel is blurred to obtain a pixel value of the blurred first pixel. The pixel value of the second pixel is updated based on the pixel value of the blurred first pixel. The target image of the blurred original image is obtained based on the pixel value of the blurred first pixel and the updated pixel value of the second pixel. Therefore, it is only necessary to blur the first pixel and update the pixel value of the second pixel based on the pixel value of the blurred first pixel. That is, there is no need to blur the second pixel. For example, there is no need to use a logically complex fuzzy algorithm to blur the second pixel. The effect of blurring the second pixel can be indirectly achieved based on the pixel value of the blurred first pixel, which can greatly reduce the computational complexity of blurring and improve the efficiency of blurring.

[0118] In addition, the present disclosure does not require downsampling of the image, and can directly blur the image of the original resolution, thereby avoiding the jagged problem in the blurred image and optimizing the visual experience.

[0119] In order to implement the above embodiments, the present disclosure also proposes an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the steps of the image blurring method provided by the present disclosure are implemented.

[0120] Figure 6 6 is a schematic diagram illustrating the structure of an electronic device according to an exemplary embodiment. For example, the electronic device 600 may be a vehicle, a mobile phone, a computer, a digital broadcast terminal, a messaging device, a game console, a tablet device, a medical device, a fitness device, a personal digital assistant, etc.

[0121] Reference Figure 6 The electronic device 600 may include one or more of the following components: a processing component 602 , a memory 604 , a power component 606 , a multimedia component 608 , an audio component 610 , an input / output (I / O) interface 612 , a sensor component 614 , and a communication component 616 .

[0122] The processing component 602 generally controls the overall operation of the electronic device 600, such as operations associated with display, phone calls, data communications, camera operation, and recording operations. The processing component 602 may include one or more processors 620 to execute instructions to perform all or part of the steps of the above-described image blurring method. In addition, the processing component 602 may include one or more modules to facilitate interaction between the processing component 602 and other components. For example, the processing component 602 may include a multimedia module to facilitate interaction between the multimedia component 608 and the processing component 602.

[0123] The memory 604 is configured to store various types of data to support operations on the electronic device 600. Examples of such data include instructions for any application or method operating on the electronic device 600, contact data, phone book data, messages, pictures, videos, etc. The memory 604 can be implemented by any type of volatile or non-volatile memory device, or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk, or optical disk.

[0124] The power component 606 provides power to the various components of the electronic device 600. The power component 606 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to the electronic device 600.

[0125] The multimedia component 608 includes a screen that provides an output interface between the electronic device 600 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, it may be implemented as a touch screen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touches, slides, and gestures on the touch panel. The touch sensors can detect not only the boundaries of a touch or slide action, but also the duration and pressure associated with the touch or slide action. In some embodiments, the multimedia component 608 includes a front-facing camera and / or a rear-facing camera. When the electronic device 600 is in an operating mode, such as a capture mode or a video mode, the front-facing camera and / or the rear-facing camera can receive external multimedia data. Each front-facing camera and the rear-facing camera can have a fixed optical lens system or have a variable focal length and optical zoom capability.

[0126] The audio component 610 is configured to output and / or input audio signals. For example, the audio component 610 includes a microphone (MIC) that is configured to receive external audio signals when the electronic device 600 is in an operating mode, such as a call mode, a recording mode, and a voice recognition mode. The received audio signals may be further stored in the memory 604 or transmitted via the communication component 616. In some embodiments, the audio component 610 also includes a speaker for outputting audio signals.

[0127] I / O interface 612 provides an interface between processing component 602 and peripheral interface modules, such as a keyboard, click wheel, buttons, etc. These buttons may include but are not limited to: a home button, volume buttons, a start button, and a lock button.

[0128] Sensor assembly 614 includes one or more sensors for providing various status assessments of electronic device 600. For example, sensor assembly 614 can detect the open / closed state of electronic device 600, the relative positioning of components, such as the display and keypad of electronic device 600. Sensor assembly 614 can also detect changes in the position of electronic device 600 or a component thereof, the presence or absence of user contact with electronic device 600, the orientation or acceleration / deceleration of electronic device 600, and changes in the temperature of electronic device 600. Sensor assembly 614 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. Sensor assembly 614 may also include an optical sensor, such as a complementary metal-oxide-semiconductor (CMOS) or charge-coupled device (CCD) image sensor, for use in imaging applications. In some embodiments, sensor assembly 614 may also include an accelerometer, a gyroscope, a magnetic sensor, a pressure sensor, or a temperature sensor.

[0129] The communication component 616 is configured to facilitate wired or wireless communication between the electronic device 600 and other devices. The electronic device 600 can access a wireless network based on a communication standard, such as WiFi, 4G or 5G, or a combination thereof. In an exemplary embodiment, the communication component 616 receives a broadcast signal or broadcast-related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 616 also includes a near field communication (NFC) module to facilitate short-range communication. For example, the NFC module can be implemented based on radio frequency identification (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology and other technologies.

[0130] In an exemplary embodiment, the electronic device 600 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the steps of the above-mentioned image blur processing method.

[0131] In an exemplary embodiment, a non-transitory computer-readable storage medium including instructions is also provided, such as a memory 604 including instructions. The instructions may be executed by a processor 620 of an electronic device 600 to implement the above-described image blurring method. For example, the non-transitory computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a compact disc (CD-ROM), a magnetic tape, a floppy disk, or an optical data storage device.

[0132] In order to implement the above embodiments, the present disclosure further proposes a computer-readable storage medium having computer program instructions stored thereon, which implement the steps of the image blur processing method provided by the present disclosure when the program instructions are executed by a processor.

[0133] Alternatively, the computer-readable storage medium may be a ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, and the like.

[0134] In order to implement the above embodiments, the present disclosure further proposes a chip, which includes an interface circuit and a processing circuit coupled to each other, the interface circuit is used to input or output signals, and the processing circuit is configured to implement the steps of the image blur processing method provided by the present disclosure.

[0135] Figure 7 FIG. 1 is a schematic diagram showing the structure of a chip according to an exemplary embodiment. Figure 7 The structure of the chip 700 is shown, but is not limited thereto.

[0136] The chip 700 includes a processing circuit 701 , which is configured to execute the steps of any of the above image blur processing methods.

[0137] In some embodiments, chip 700 further includes one or more interface circuits 702. Optionally, interface circuit 702 is connected to memory 703. Interface circuit 702 can be used to receive signals from memory 703 or other devices, and interface circuit 702 can be used to send signals to memory 703 or other devices. For example, interface circuit 702 can read instructions stored in memory 703 and send the instructions to processing circuit 701.

[0138] In some embodiments, the interface circuit 702 performs at least one of the communication steps such as sending and / or receiving in the above method, and the processing circuit 701 performs the other steps.

[0139] In some embodiments, terms such as interface circuit, interface, transceiver pin, and transceiver may be used interchangeably.

[0140] In some embodiments, the chip 700 further includes one or more memories 703 for storing instructions. Alternatively, all or part of the memories 703 may be located outside the chip 700.

[0141] In order to implement the above embodiments, the present disclosure further proposes a computer program product, including a computer program. When the computer program is executed by a processor, the steps of the image blur processing method provided by the present disclosure are implemented.

[0142] Other embodiments of the present disclosure will readily occur to those skilled in the art after considering the specification and practicing the invention disclosed herein. This disclosure is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or customary techniques in the art not disclosed herein. The description and examples are to be considered as exemplary only.

[0143] It will be understood that the present disclosure is not limited to the exact construction that has been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof.

Claims

1. A method for blurring an image, characterized in that: include: Acquire an original image, where the original image includes a first pixel and a second pixel; Performing a blur process on the first pixel to obtain a pixel value of the first pixel after blurring; Based on the pixel value of the first pixel after blurring, updating the pixel value of the second pixel; Obtaining a target image after blurring the original image based on the pixel value of the first pixel after blurring and the pixel value of the second pixel after update; Extracting the first pixel points from the original image according to a set interval, and using the unextracted pixel points in the original image as the second pixel points; Determining the set interval includes at least one of the following operations: determining the set interval based on a distribution rule of black areas or white areas in the checkerboard image; The set interval is determined based on a preset constraint condition, where the constraint condition includes that at least one neighboring pixel point of any pixel point in the original image is extracted.

2. The method according to claim 1, characterized in that The extracting the first pixel point from the original image according to a set interval includes: The first pixel point is extracted from pixel points in each row or each column of the original image according to the set interval.

3. The method according to claim 1, characterized in that The updating of the pixel value of the second pixel point based on the pixel value of the first pixel point after blurring includes: Determine, from each first pixel point, a neighboring pixel point of the second pixel point as a reference pixel point; Based on the pixel values ​​of each reference pixel after blurring, the pixel value of the second pixel is updated.

4. The method according to claim 3, characterized in that The updating of the pixel value of the second pixel point based on the pixel value of each reference pixel point after blurring includes: Determine an i-th adjustment parameter based on a difference parameter between a pixel value of an i-th reference pixel before blurring and a pixel value of the i-th reference pixel after blurring, where i is a positive integer; Based on the adjustment parameters, the pixel value of the second pixel is updated.

5. The method according to claim 4, characterized in that The updating of the pixel value of the second pixel point based on the adjustment parameters includes: Get the weight of each reference pixel; Based on the adjustment parameters and the weights of the reference pixels, the pixel value of the second pixel is updated.

6. The method according to any one of claims 1 to 5, characterized in that The method further comprises: Obtaining an original video to be encoded, and using each frame image in the original video as the original image; After obtaining the target image after blurring the original image, the method further includes: Based on the target image after blurring each frame image in the original video, generating the target video after blurring the original video; Performing encoding processing on the target video to obtain an encoded video; The encoded video is stored in a local storage space, or the encoded video is sent to a display device, where the encoded video is used for visual display by the display device.

7. The method according to any one of claims 1 to 5, characterized in that The method further comprises: Acquire the original image; or, The original image is received.

8. An image blur processing device, characterized in that: include: An acquisition module is configured to acquire an original image, where the original image includes a first pixel and a second pixel; A first processing module is configured to perform blur processing on the first pixel point to obtain a pixel value of the first pixel point after blur processing; an updating module configured to update the pixel value of the second pixel point based on the pixel value of the first pixel point after blurring; The second processing module is configured to obtain a target image after the original image is blurred based on the pixel value of the first pixel point after blurring and the pixel value of the second pixel point after update.

9. The device according to claim 8, characterized in that The first processing module is further configured to: Extracting the first pixel points from the original image according to a set interval, and using the unextracted pixel points in the original image as the second pixel points; The first processing module is further configured to perform at least one of the following operations: determining the set interval based on a distribution rule of black areas or white areas in the checkerboard image; The set interval is determined based on a preset constraint condition, where the constraint condition includes that at least one neighboring pixel point of any pixel point in the original image is extracted.

10. The device according to claim 9, characterized in that The first processing module is further configured to: The first pixel point is extracted from pixel points in each row or each column of the original image according to the set interval.

11. An electronic device, characterized in that: The method comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps of the method according to any one of claims 1 to 7 are implemented.

12. A computer-readable storage medium having computer program instructions stored thereon, characterized in that: When the program instructions are executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

13. A chip, characterized in that: The chip includes an interface circuit and a processing circuit coupled to each other, the interface circuit is used to input or output signals, and the processing circuit is configured to implement the steps of the method according to any one of claims 1 to 7.

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