Video noise estimation method, video noise estimation device and computer storage medium

Abstract

The invention discloses a video noise estimation method, a video noise estimation device and a computer storage medium. The video noise estimation method comprises the following steps: calculating an inter-frame difference result between corresponding pixel points in adjacent frame images; performing image edge calculation on each pixel point in each frame image to obtain an edge detection feature value corresponding to each pixel point in each frame image; determining a static edge point in each frame image according to the edge detection feature value corresponding to each pixel point in each frame image; and determining a video noise estimation value according to an inter-frame difference result between the static edge point in each frame image and each corresponding pixel point in the adjacent frame image. By adopting the method, the influence of image texture details and inter-frame motion on the video noise estimation result can be avoided, so that the video noise estimation result can better reflect the real noise level.

Description

Technical Field

[0001] The present invention relates to the technical field of video noise estimation, and in particular, to a video noise estimation method, a video noise estimation device, and a computer storage medium. Background Art

[0002] In related technologies, in the existing video noise estimation methods, the first method is to use the local image block variance in the spatial domain for each frame of the video as the noise estimation result. However, this method only relies on the spatial domain to calculate the local image block variance, so that the noise estimation result is greatly affected by the image texture details and the image content. When the entire frame of the image is texture details, the noise estimation result will be too large. Therefore, the noise estimation result calculated by this method is inaccurate. At the same time, the noise estimation result is affected by the video image coding compression. When a small amount of local image block variance is used as the noise estimation result, it cannot directly reflect the true noise level of the video. In the second method, the absolute value difference between two frames is used as the basis for noise estimation, and the variance of the local region with the smallest difference between the two frames is used as the noise estimation result. However, the noise estimation result of this method is inaccurate due to the influence of the noise form and the like. Summary of the Invention

[0003] The present invention aims to at least solve one of the technical problems existing in the prior art. For this reason, an object of the present invention is to provide a video noise estimation method. By using this method, the influence of image texture details and inter-frame motion on the video noise estimation result can be avoided, so that the video noise estimation result can better reflect the true noise level.

[0004] A second object of the present invention is to provide a video noise estimation device.

[0005] A third object of the present invention is to provide a computer storage medium.

[0006] To solve the above problems, an embodiment of the first aspect of the present invention provides a video noise estimation method, including: calculating an inter-frame difference result between each corresponding pixel point in adjacent frame images; performing image edge calculation on each pixel point in each frame image to obtain an edge detection feature value corresponding to each pixel point in each frame image; determining stationary edge points in each frame image according to the edge detection feature value corresponding to each pixel point in each frame image; and determining a video noise estimation value according to the stationary edge points in each frame image and the inter-frame difference result between each corresponding pixel point in adjacent frame images.

[0007] According to the video noise estimation method of the embodiments of the present invention, the static edge points in the inter-frame static area of each frame image are used as the pixel points for statistically calculating the video noise result, and then the video noise level is estimated through the inter-frame difference result between each corresponding static edge point in adjacent frame images. Thus, compared with estimating the noise by using the variance of the intra-frame local image block and the variance of the minimum local area of the inter-frame difference, in this application, the video noise level is estimated through the inter-frame difference result between each corresponding static edge point in adjacent frame images, so as to avoid the influence of image texture details and inter-frame motion on the video noise estimation result, so that the video noise estimation result can better reflect the true noise level.

[0008] In some embodiments, calculating the inter-frame difference result between each corresponding pixel point in adjacent frame images includes: obtaining the luminance component value of each pixel point in each frame image; calculating the luminance difference between each corresponding pixel point in adjacent frame images according to the luminance component value of each pixel point in each frame image; performing an absolute value operation on the luminance difference to obtain the inter-frame difference result between each corresponding pixel point in adjacent frame images.

[0009] In some embodiments, determining the static edge points in each frame image according to the edge detection feature value corresponding to each pixel point in each frame image includes: if the edge detection feature value corresponding to any pixel point in the current frame image is greater than a preset edge detection threshold, and the edge detection feature value corresponding to the target pixel point in the previous frame image is greater than the preset edge detection threshold, then determining the any pixel point as the static edge point in the current frame image, where the target pixel point is the pixel point corresponding to the any pixel point in the previous frame image.

[0010] In some embodiments, determining the video noise estimation value according to the static edge points in each frame image and the inter-frame difference result between each corresponding pixel point in adjacent frame images includes: constructing a difference matrix, the length and width of the difference matrix are the same as those of the frame image; storing the inter-frame difference result corresponding to each static edge point in each frame image into the difference matrix correspondingly; determining the video noise estimation value according to the element feature value of the difference matrix.

[0011] In some embodiments, the element feature value of the difference matrix includes at least one or more of the mode, the average value, and the median.

[0012] In some embodiments, determining the video noise estimation value according to the difference matrix includes: obtaining the mode in the difference matrix; calculating the average value of all the inter-frame difference results in the difference matrix; determining the video noise estimation value according to the mode and the average value.

[0013] In some embodiments, the video noise estimation value is obtained by calculating according to the following formula:

[0014] N = (1 - W) * Avg + W * P

[0015] Where N is the video noise estimation value, W is the weight coefficient, Avg is the average value, and P is the mode.

[0016] In some embodiments, image edge calculation is performed on each pixel point in each frame image, including: performing image edge calculation on each pixel point in each frame image by using an edge detection operator or a filtering operator.

[0017] An embodiment of the second aspect of the present invention provides a video noise estimation device, including: at least one processor; a memory communicatively connected to at least one of the processors; wherein, a computer program executable by at least one of the processors is stored in the memory, and when the at least one processor executes the computer program, the video noise estimation method described in the above embodiment is implemented.

[0018] According to the video noise estimation device of the embodiment of the present invention, the static edge points in the inter-frame static region in each frame image are used as the pixel points for statistically calculating the video noise result, and then the video noise level is estimated by the inter-frame difference result between each corresponding static edge point in adjacent frame images. Thus, compared with estimating the noise by using the variance of the intra-frame local image block and the variance of the minimum local region of the inter-frame difference, in this application, the video noise level is estimated by the inter-frame difference result between each corresponding static edge point in adjacent frame images, so as to avoid the influence of image texture details and inter-frame motion on the video noise estimation result, so that the video noise estimation result can better reflect the true noise level.

[0019] An embodiment of the third aspect of the present invention provides a computer storage medium, on which a computer program is stored, wherein when the computer program is executed by a processor, the video noise estimation method described in the above embodiment is implemented.

[0020] The additional aspects and advantages of the present invention will be partially given in the following description, partially become apparent from the following description, or be understood through the practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The above and / or additional aspects and advantages of the present invention will become apparent and be readily understood from the description of the embodiments in conjunction with the following drawings, wherein:

[0022] Figure 1 is a flowchart of a video noise estimation method according to an embodiment of the present invention;

[0023] Figure 2It is a flowchart of a video noise estimation method according to another embodiment of the present invention;

[0024] Figure 3 It is a structural block diagram of a video noise estimation device according to an embodiment of the present invention.

[0025] Reference numerals:

[0026] Video noise estimation device 10;

[0027] Processor 1; Memory 2. Detailed implementation manners

[0028] The embodiments of the present invention will be described in detail below. The embodiments described with reference to the accompanying drawings are exemplary. The embodiments of the present invention will be described in detail below.

[0029] To solve the above problems, an embodiment of the first aspect of the present invention provides a video noise estimation method. Using this method can avoid the influence of image texture details and inter-frame motion on the video noise estimation result, so that the video noise estimation result can better reflect the true noise level.

[0030] Next, refer to Figure 1 Describe the video noise estimation method according to the embodiments of the present invention. As Figure 1 shown, the method at least includes: step S1 to step S4.

[0031] Step S1, calculate the inter-frame difference result between each corresponding pixel point in adjacent frame images.

[0032] Specifically, calculate the inter-frame difference result between each corresponding pixel point in adjacent frame images, that is, perform a difference operation on each corresponding pixel point in adjacent frame images to obtain the inter-frame difference result between each corresponding pixel point in adjacent frame images, that is, obtain the difference between each corresponding pixel point in adjacent frame images.

[0033] Step S2, perform image edge calculation on each pixel point in each frame image to obtain the edge detection feature value corresponding to each pixel point in each frame image.

[0034] In the embodiment, the image edge calculation can be performed on each pixel point in each frame image through an edge detection algorithm to obtain the edge detection feature value corresponding to each pixel point in each frame image. Among them, the edge detection feature value of each pixel point can be understood as a value that can reflect whether the pixel point is located at the edge of the frame image. The edge detection feature value of each pixel point is the gray value and color change situation of the pixels around each pixel point.

[0035] Step S3, determine the static edge points in each frame image according to the edge detection feature value corresponding to each pixel point in each frame image.

[0036] Specifically, according to the edge detection feature values corresponding to each pixel point in each frame image, for example, by comparing the edge detection feature values of each pixel point in each frame image with the edge detection feature values of the corresponding pixel points in the previous frame image, the stationary edge points in each frame image are determined, that is, the pixel points with very little change or no obvious change in each frame image are determined as stationary edge points. In addition, the stationary edge points in each frame image can also be determined by the gradient values of each pixel point.

[0037] Step S4: Determine the video noise estimation value according to the stationary edge points in each frame image and the inter-frame difference results between each corresponding pixel point in adjacent frame images.

[0038] Specifically, the noise estimation results of existing video noise estimation methods are inaccurate. To solve this problem, in this application, the video noise estimation value is determined by the inter-frame difference results between the static edge points in each frame image and each corresponding pixel point in the adjacent frame image. That is to say, since the video noise estimation result has a direct correlation with video denoising, that is, in the process of denoising the video, it is necessary to estimate the noise in the video. If the video noise estimation result, that is, the video noise estimation value, is inaccurate, it will lead to over-processing of non-noise regions in the video or insufficient processing of noise regions, resulting in a poor video denoising effect. Noise will occur in the video during video acquisition, video codec, and video transmission, and the form and intensity of the noise generated in the video vary greatly in different processes, resulting in inaccurate noise estimation results. Therefore, when estimating the noise in the video, since the inter-frame static region refers to the region without obvious movement or change between consecutive frames, and the noise intensity in the inter-frame static region is low and the noise form is relatively average, the video noise can be estimated more accurately. Therefore, in this application, the video noise level is estimated by the static edge points in each frame image, where the static edge points are in the inter-frame static region. The static edge points in each frame image that are in the inter-frame static region are used as the pixel points for statistically calculating the video noise result, and then the video noise estimation value, that is, the video noise estimation result, is determined by the inter-frame difference results between each corresponding pixel point in the adjacent frame image, that is, the video noise estimation value is determined by the inter-frame difference results between each corresponding static edge point in the adjacent frame image, that is, the noise level of the video is evaluated by the pixel value difference between each corresponding static edge point in the adjacent frame image, so that the video noise estimation result level is average and ideal. Thus, compared with using the variance of the intra-frame local image block and the variance of the minimum local region of the inter-frame difference for noise estimation, in this application, the static edge points in each frame image are used as the pixel points for statistically calculating the video noise result, and the inter-frame difference results between each corresponding pixel point in the adjacent frame image are used as the video noise estimation result, thereby avoiding the influence of image texture details and inter-frame motion on the video noise estimation result, and still having a good noise estimation result when the noise form in the frame image is irregular or varies greatly, improving the strong robustness of the video noise estimation, so that the video noise estimation result can better reflect the true noise level, and the accuracy of the video noise estimation result is relatively high, thereby improving the video denoising effect.

[0039] According to the video noise estimation method of an embodiment of the present invention, the static edge points in the inter-frame static region of each frame image are used as the pixel points for statistically calculating the video noise result, and then the video noise level is estimated through the inter-frame difference result between each corresponding static edge point in adjacent frame images. Thus, compared with estimating the noise by using the variance of the intra-frame local image block and the variance of the minimum local region of the inter-frame difference, in this application, the video noise level is estimated by the inter-frame difference result between each corresponding static edge point in adjacent frame images, so as to avoid the influence of image texture details and inter-frame motion on the video noise estimation result, so that the video noise estimation result can better reflect the true noise level.

[0040] In some embodiments, the inter-frame difference result between each corresponding pixel point in adjacent frame images is calculated through the following steps. That is, the luminance component value of each pixel point in each frame image is obtained. Among them, the luminance component value is a coordinate value of the pixel point in the YUV color space. In the YUV color space, the coordinate value of each pixel point can be expressed as (Y, U, V), where Y represents the luminance component value of the pixel point. The luminance component value of each pixel point in each frame image can also be calculated by the three component values of each pixel point in the RGB (RGB color mode) color space. Among them, the luminance component value of each pixel point in each frame image can be the average value of the three component values in the RGB color space.

[0041] The luminance difference between each corresponding pixel point in adjacent frame images is calculated according to the luminance component value of each pixel point in each frame image, that is, the luminance difference between the luminance component value of each pixel point in the i-th frame image and the luminance component value of each corresponding pixel point in the (i - 1)-th frame image is calculated, where i≥2. Then, an absolute value operation is performed on the luminance difference, and then the absolute value of the luminance difference between each corresponding pixel point in adjacent frame images is used as the inter-frame difference result between each corresponding pixel point in adjacent frame images, so as to obtain the inter-frame difference result between each corresponding pixel point in adjacent frame images.

[0042] In some embodiments, determining the static edge points in each frame image according to the edge detection feature value corresponding to each pixel point in each frame image includes: if the edge detection feature value corresponding to any pixel point in the current frame image is greater than the preset edge detection threshold, and the edge detection feature value corresponding to the target pixel point in the previous frame image is greater than the preset edge detection threshold, then it is determined that any pixel point is a static edge point in the current frame image, where the target pixel point is the pixel point corresponding to any pixel point in the previous frame image.

[0043] Among them, the preset edge detection threshold can be understood as the detection threshold of the edge detection eigenvalue preset for detecting whether a pixel point is located at the edge in the frame image, and the preset edge detection threshold can be adjusted according to the actual situation.

[0044] Specifically, if the edge detection eigenvalue corresponding to any pixel point in the current frame image is greater than the preset edge detection threshold, it means that any pixel point in the current frame image is located at the edge of the current frame image, and the edge detection eigenvalue corresponding to the target pixel point in the previous frame image is greater than the preset edge detection threshold. That is to say, the pixel point corresponding to any pixel point in the previous frame image is still located at the edge of the current frame image. At this time, it means that the change between any pixel point in the current frame image and the pixel point corresponding to any pixel point in the previous frame image is very small or there is no obvious change. Then, it is determined that any pixel point is a stationary edge point in the current frame image. Exemplarily, if the edge detection eigenvalue of the pixel point p'(i, j) in the current frame image, that is, the edge detection result, is greater than the preset edge detection threshold Edge_Th, and it is compared that the edge detection eigenvalue of the pixel point p'(i, j) in the previous frame image is greater than the preset edge detection threshold Edge_Th, then the pixel point p'(i, j) in the current frame image is a stationary edge point in the current frame image.

[0045] In the embodiment, the edge detection eigenvalue corresponding to each pixel point in each frame image can be obtained by calculating the following content.

[0046]

[0047] A is each frame of image, G x is the convolution of A in the horizontal direction x, G y is the convolution of A in the vertical direction y. Then, the edge detection result after convolution is: |G| = |G x | + |G y |, that is, the edge detection eigenvalue corresponding to each pixel point in each frame image is |G|.

[0048] In some embodiments, determining a video noise estimation value based on the inter-frame difference results between the static edge points in each frame image and each corresponding pixel point in the adjacent frame image includes: constructing a difference matrix, denoted as Diff_static_edge_Image, where the length and width of the difference matrix are the same as those of the frame image. Initially, the difference matrix is a zero matrix with the same length and width as the frame image. Traverse all frame images, and store the inter-frame difference results corresponding to each static edge point in each frame image into the difference matrix. That is, the difference matrix is the accumulated inter-frame difference results corresponding to all static edge points, and record the number of static edge points. For example, store the inter-frame difference result between the pixel point p'(i, j) in the current frame image and the pixel point p'(i, j) in the previous frame image into the difference matrix. Then, determine the video noise estimation value according to the element eigenvalues of the difference matrix. That is to say, the inter-frame difference result corresponding to the static edge points between two frames that remain relatively static should be 0 under ideal conditions. Therefore, the non-zero values of the inter-frame difference results corresponding to the static edge points in each frame image are all noise. That is, the element eigenvalues of the difference matrix that are not 0 are all noise. Then, determine the video noise estimation value through these noises. Thus, in this application, the video noise estimation value is determined by the element eigenvalues of the difference matrix, thereby avoiding the influence of image texture details and inter-frame motion on the video noise estimation result, so that the video noise estimation result can better reflect the true noise level.

[0049] In some embodiments, the element eigenvalues of the difference matrix include at least one or more of the mode, average value, and median.

[0050] In some embodiments, determine the video noise estimation value according to the difference matrix. That is, obtain the mode in the difference matrix, then calculate the average value of all inter-frame difference results in the difference matrix, and then determine the video noise estimation value according to the mode and the average value. Among them, the average value can reflect the overall level of noise, and the mode can reflect the concentration degree of noise. Thus, in this application, the video noise estimation value is determined by the element eigenvalues of the difference matrix, that is, the inter-frame difference results corresponding to each static edge point in each frame image are used to determine the video noise estimation value, thereby avoiding the influence of image texture details and inter-frame motion on the video noise estimation result, so that the video noise estimation result can better reflect the true noise level.

[0051] In some embodiments, the video noise estimation value is calculated by the following formula:

[0052] N = (1 - W) * Avg + W * P

[0053] Where, N is the video noise estimation value, W is the weight coefficient, Avg is the average value, and P is the mode.

[0054] Specifically, substitute the weight coefficient W, the average value Avg, and the mode P into the above formula. The weight coefficient W can be 0.5 to calculate the video noise estimation value N. Thus, in this application, the video noise estimation value is determined through the eigenvalue of the element of the difference matrix, that is, the frame difference result corresponding to each static edge point in each frame image is used to determine the video noise estimation value, so as to avoid the influence of image texture details and inter-frame motion on the video noise estimation result, so that the video noise estimation result can better reflect the true noise level.

[0055] In some embodiments, for each pixel point in each frame image, image edge calculation is performed. An edge detection operator or a filtering operator can be used to perform image edge calculation on each pixel point in each frame image to obtain the edge detection eigenvalue corresponding to each pixel point in each frame image. Among them, the edge detection operator can be Soble operator, Canny operator, Prewitt operator, Krisch operator, etc. For example, the edge detection operator usually calculates the difference value of the gray value or color value of the pixels around each pixel point in each frame image for image edge calculation to obtain the edge detection eigenvalue corresponding to each pixel point in each frame image, and the edge detection operator will calculate the edge detection eigenvalue according to different principles and parameters. In addition, calculating the edge detection eigenvalue through the filtering operator can enhance the edge information of the frame image to facilitate better detection of the frame image edge.

[0056] The following refers to Figure 2 the following to illustrate the video noise estimation method of the embodiment of the present invention by way of example. The specific content is as follows.

[0057] Step S5, obtain the i-th frame image, and execute Step S7 and Step S9.

[0058] Step S6, obtain the (i - 1)-th frame image, and execute Step S9 and Step S10.

[0059] Step S7, perform image edge calculation on each pixel point in the i-th frame image to obtain the edge detection eigenvalue corresponding to each pixel point in the i-th frame image.

[0060] Step S8, calculate the frame difference result between each corresponding pixel point in adjacent frame images, that is, calculate the frame difference result between each corresponding pixel point in the i-th frame image and the (i - 1)-th frame image.

[0061] Step S9, perform image edge calculation on each pixel point in the (i - 1)-th frame image to obtain the edge detection eigenvalue corresponding to each pixel point in the (i - 1)-th frame image.

[0062] Step S10, if the edge detection eigenvalue corresponding to any pixel point in the i-th frame image is greater than the preset edge detection threshold, and the edge detection eigenvalue corresponding to the target pixel point in the (i - 1)-th frame image is greater than the preset edge detection threshold, then determine the stationary edge point in the i-th frame image as any pixel point in the current frame image.

[0063] Step S11, then determine the video noise estimation value through the inter-frame difference result between the stationary edge points in the i-th frame image and each corresponding pixel point in the adjacent frame image, i.e., the (i - 1)-th frame image.

[0064] An embodiment of the second aspect of the present invention provides a video noise estimation device, as Figure 3 shown, the video noise estimation device 10 includes: at least one processor 1 and a memory 2 communicatively connected to the at least one processor 1.

[0065] Wherein, the memory stores a computer program executable by the at least one processor, and when the at least one processor executes the computer program, the video noise estimation method of the above embodiment is implemented.

[0066] According to the video noise estimation device of the embodiment of the present invention, the stationary edge points in the inter-frame stationary region of each frame image are used as the pixel points for statistically calculating the video noise result, and then the video noise level is estimated through the inter-frame difference result between each corresponding stationary edge point in the adjacent frame images. Thus, compared with using the variance of the intra-frame local image block and the variance of the minimum local region of the inter-frame difference for noise estimation, in this application, the video noise level is estimated through the inter-frame difference result between each corresponding stationary edge point in the adjacent frame images, thereby avoiding the influence of image texture details and inter-frame motion on the video noise estimation result, so that the video noise estimation result can better reflect the true noise level.

[0067] An embodiment of the third aspect of the present invention provides a computer storage medium, on which a computer program is stored, wherein when the computer program is executed by a processor, the video noise estimation method of the above embodiment is implemented.

[0068] In the description of this specification, any process or method description in the flowchart or described in other ways herein can be understood as representing a module, segment, or part of code including one or more executable instructions for implementing a customized logical function or process, and the scope of the preferred embodiments of the present invention includes additional implementations, where the functions can be executed in a substantially simultaneous manner or in the reverse order according to the involved functions, rather than in the order shown or discussed, which should be understood by those skilled in the technical field to which the embodiments of the present invention belong.

[0069] The logic and / or steps represented in the flowchart or otherwise described herein can, for example, be considered a definitional sequence list of executable instructions for implementing logical functions, which can be embodied specifically in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch and execute instructions from the instruction execution system, apparatus, or device), or used in conjunction with these instruction execution systems, apparatuses, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by or in conjunction with an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection portion with one or more wirings (electronic device), a portable computer diskette (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpretation, or otherwise processing as appropriate, and then storing it in a computer memory.

[0070] It should be understood that the various parts of the present invention can be implemented using hardware, software, firmware, or a combination thereof. In the above-described embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one or a combination of the following techniques well known in the art can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application-specific integrated circuits having appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0071] Those of ordinary skill in the art of this technology can understand that all or part of the steps carried by the methods of the above embodiments can be completed by instructing relevant hardware through a program, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiments.

[0072] In addition, in each embodiment of the present invention, each functional unit can be integrated into a processing module, or each unit can exist physically alone, or two or more units can be integrated into one module. The above-mentioned integrated module can be implemented in the form of hardware or in the form of a software functional module. When the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0073] The above-mentioned storage medium can be a read-only memory, a magnetic disk, an optical disc, etc. Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present invention.

[0074] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "illustrative embodiments", "examples", "specific examples", or "some examples", etc. means that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example.

[0075] Although the embodiments of the present invention have been shown and described, those of ordinary skill in the art can understand that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the claims and their equivalents.

Claims

1. A video noise estimation method, characterized in that, it includes: Calculating the inter-frame difference result between each corresponding pixel point in adjacent frame images; Performing image edge calculation on each pixel point in each frame image to obtain the edge detection feature value corresponding to each pixel point in each frame image; Determining the static edge points in each frame image according to the edge detection feature value corresponding to each pixel point in each frame image; Determining the video noise estimation value according to the static edge points in each frame image and the inter-frame difference result between each corresponding pixel point in adjacent frame images.

2. The video noise estimation method according to claim 1, characterized in that, Calculating the inter-frame difference result between each corresponding pixel point in adjacent frame images includes: Obtaining the luminance component value of each pixel point in each frame image; Calculating the luminance difference between each corresponding pixel point in adjacent frame images according to the luminance component value of each pixel point in each frame image; Performing an absolute value operation on the luminance difference to obtain the inter-frame difference result between each corresponding pixel point in adjacent frame images.

3. The video noise estimation method according to claim 1, characterized in that, Determining the static edge points in each frame image according to the edge detection feature value corresponding to each pixel point in each frame image includes: If the edge detection feature value corresponding to any pixel point in the current frame image is greater than the preset edge detection threshold, and the edge detection feature value corresponding to the target pixel point in the previous frame image is greater than the preset edge detection threshold, then determining that the any pixel point is the static edge point in the current frame image, where the target pixel point is the pixel point corresponding to the any pixel point in the previous frame image.

4. The video noise estimation method according to claim 1, characterized in that, Determining the video noise estimation value according to the static edge points in each frame image and the inter-frame difference result between each corresponding pixel point in adjacent frame images includes: Constructing a difference matrix, the length and width of the difference matrix are the same as those of the frame image; Correspondingly storing the inter-frame difference result corresponding to each static edge point in each frame image into the difference matrix; Determining the video noise estimation value according to the element feature value of the difference matrix.

5. The video noise estimation method according to claim 4, characterized in that, The element feature value of the difference matrix includes at least one or more of mode, average value and median.

6. The video noise estimation method according to claim 4, characterized in that, Determining the video noise estimation value according to the difference matrix includes: Obtaining the mode in the difference matrix; Calculating the average value of all inter-frame difference results in the difference matrix; Determining the video noise estimation value according to the mode and the average value.

7. The video noise estimation method according to claim 6, characterized in that, The video noise estimation value is calculated and obtained through the following formula: N = (1 - W) * Avg + W * P where N is the video noise estimation value, W is the weight coefficient, Avg is the average value, and P is the mode.

8. The video noise estimation method according to claim 1, characterized in that, Perform image edge calculation on each pixel point in each frame of the frame image, including: Perform image edge calculation on each pixel point in each frame of the frame image by using an edge detection operator or a filtering operator.

9. A video noise estimation device, characterized in that, it includes: at least one processor; a memory communicatively connected to at least one of the processors; wherein, a computer program executable by at least one of the processors is stored in the memory, and when the at least one processor executes the computer program, the video noise estimation method according to any one of claims 1-8 is implemented.

10. A computer storage medium, on which a computer program is stored, characterized in that, when the computer program is executed by a processor, the video noise estimation method according to any one of claims 1-8 is implemented.

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