CN115396743B - Video watermark removing method, device, equipment and storage medium - Google Patents
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- H—ELECTRICITY
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- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
- H04N21/80—Generation or processing of content or additional data by content creator independently of the distribution process; Content per se
- H04N21/83—Generation or processing of protective or descriptive data associated with content; Content structuring
- H04N21/835—Generation of protective data, e.g. certificates
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- H—ELECTRICITY
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- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
- H04N21/40—Client devices specifically adapted for the reception of or interaction with content, e.g. set-top-box [STB]; Operations thereof
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- H04N21/44008—Processing of video elementary streams, e.g. splicing a video clip retrieved from local storage with an incoming video stream or rendering scenes according to encoded video stream scene graphs involving operations for analysing video streams, e.g. detecting features or characteristics in the video stream
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Abstract
The application relates to a video water printing method, a device, equipment and a storage medium, wherein the method comprises the steps of carrying out framing treatment on a video to be processed to obtain an image frame set, carrying out preliminary restoration on the image frame set to obtain a preliminary image set, calculating a score difference value of each frame and a first frame in the preliminary image set, taking a frame corresponding to the score difference value exceeding a first preset threshold value as an image frame to be restored to obtain an image frame set to be restored, taking any one of the image frames to be restored in the image frame set to be a second reference image frame, carrying out depth restoration on the second reference image frame to obtain a depth restoration image frame, calculating the similarity between each frame in the image frame set to be restored and the second reference image frame, filling restoration content of the depth restoration image frame into the corresponding image frame if the similarity exceeds the second preset threshold value, and carrying out depth restoration on the corresponding image frame if the similarity is lower than the second preset threshold value. The application reduces the time for removing the watermark of the video and lightens the flickering condition of the video.
Description
Technical Field
The present application relates to the field of video processing, and in particular, to a video watermark removing method, apparatus, device and storage medium.
Background
With the widespread popularization of photographing devices (mobile phones, digital cameras, etc.) and the great reduction of photographing costs, and the rise of a short video content sharing platform, video plays an increasingly important spreading form in the life of users. The video is overlapped with various watermarks in the transmission process, so that the watching experience of a user is greatly reduced, and the problem of how to effectively remove watermark Logo in the video and improve the watching experience of the user is urgent to be solved in the field of video processing.
The current video watermarking technology can be divided into two major categories, namely a traditional algorithm and a deep learning algorithm, and specifically comprises the following steps: (1) And interpolating and filling the watermark area by using pixels around the watermark mask. For example, a delog function in ffmpeg adds a fuzzy filter to the mask position of the video watermark, and removes the logo through simple interpolation of surrounding pixels; (2) Directly blurring the watermark region so that the watermark cannot be seen clearly; (3) The repair algorithm based on the deep learning carries out repair filling of watermark areas, so that the whole achieves texture and structure consistency or semantic and visual credibility, and watermark removal of video is carried out from image repair application to video frames. Although the scheme of filling by interpolation technology based on surrounding pixels can remove watermarks, the generated interpolation effect is too obvious, color change generates great influence, while the scheme of blurring processing on watermark areas can remove watermarks to a certain extent and keep smooth transition of surrounding pictures, but due to blurring processing only, when the blurring processing degree is smaller, the pattern of the watermarks still can be thin and visible, the problem of incomplete watermark removal exists, and too large blurring processing degree can not ensure that the smooth transition has better repairing effect on a single frame than the traditional algorithm for a deep learning frame repairing algorithm, but the situation of flickering among frames occurs in the video, so that the video dehydration effect is poor. There is a need for a video watermarking method that is effective, fast and does not flicker in the repair area.
Disclosure of Invention
The embodiment of the application aims to provide a video watermarking method, a device, equipment and a storage medium, so as to realize video watermarking, reduce the video watermarking time and lighten the flicker of a repair area.
In order to solve the above technical problems, an embodiment of the present application provides a method for video watermarking, including:
the method comprises the steps of obtaining a video to be processed, and carrying out framing processing on the video to be processed to obtain an image frame set, wherein the video to be processed comprises a smearing area, and the position of the smearing area in each image frame of the image frame set is fixed;
performing preliminary restoration on the smearing area of each image frame in the image frame set through a preset algorithm to obtain a preliminary image set;
taking a first frame image frame in the preliminary image set as a first reference image frame, and calculating a score difference value between each image frame in the preliminary image set and the first reference image frame to obtain a difference value score set;
comparing each difference score in the difference score sets with a first preset threshold value, and taking an image frame corresponding to the difference score exceeding the first preset threshold value as an image frame to be repaired to obtain an image frame set to be repaired;
Taking any image frame in the image frame set to be repaired as a second reference image frame, and carrying out depth repair on the smeared area of the second reference image frame to obtain a depth repair image frame, wherein the depth repair image frame comprises repair contents of the smeared area;
calculating the similarity between each image frame to be repaired in the image frame set to be repaired and the second reference image frame;
judging whether the similarity exceeds a second preset threshold value, if so, taking an image frame to be repaired corresponding to the similarity exceeding the second preset threshold value as a first repair image, and filling the repair content of the second reference image frame into the smearing area of the first repair image;
and if the similarity is lower than the second preset threshold, taking the image frame to be repaired, corresponding to the similarity lower than the second preset threshold, as a second repair image, and carrying out the depth repair on the smearing area of the second repair image.
In order to solve the above technical problem, an embodiment of the present application provides a video watermarking apparatus, including:
the mask generation module is used for acquiring a video to be processed, carrying out frame division processing on the video to be processed to obtain an image frame set, wherein the acquired video to be processed comprises a smearing area, and the position of the smearing area in each image frame of the image frame set is fixed;
The preliminary restoration module is used for carrying out preliminary restoration on the smearing area of each image frame in the image frame set through a preset algorithm to obtain a preliminary image set;
the difference value calculation module is used for taking a first frame image frame in the preliminary image set as a first reference image frame, calculating the score difference value of each image frame in the preliminary image set and the first reference image frame, and obtaining a difference value score set;
the difference value comparison module is used for comparing each difference value score in the difference value score set with a first preset threshold value, and taking an image frame corresponding to the difference value exceeding the first preset threshold value as an image frame to be repaired to obtain an image frame set to be repaired;
the sample restoration module is used for taking any image frame in the image frame set to be restored as a second reference image frame, and carrying out depth restoration on the smearing area of the second reference image frame to obtain a depth restoration image frame, wherein the depth restoration image frame comprises restoration contents of the smearing area;
the similarity calculation module is used for calculating the similarity between each image frame to be repaired in the image frame set to be repaired and the second reference image frame;
The filling module is used for judging whether the similarity exceeds a second preset threshold value, if so, taking an image frame to be repaired corresponding to the similarity exceeding the second preset threshold value as a first repair image, and filling the repair content of the second reference image frame into the smearing area of the first repair image;
and the depth restoration module is used for taking an image frame to be restored, which corresponds to the similarity lower than the second preset threshold value, as a second restoration image if the similarity is lower than the second preset threshold value, and carrying out the depth restoration on the smearing area of the second restoration image.
In order to solve the technical problems, the invention adopts a technical scheme that: a computer device comprising a memory and a processor, the memory having a computer program stored therein, the processor implementing the video watermarking method of any of the above claims when executing the computer program.
In order to solve the technical problems, the invention adopts a technical scheme that: a computer readable storage medium having stored thereon a computer program which when executed by a processor implements the video watermarking method of any of the above.
The embodiment of the invention provides a video water-jet printing method, a device, equipment and a storage medium. According to the embodiment of the invention, the video to be processed is subjected to framing processing to obtain the image frame set, the image frame set is subjected to preliminary restoration to obtain the preliminary image set, the score difference value of each frame and the first frame in the preliminary image set is calculated, and the frame corresponding to the score difference value exceeding the first preset threshold value is used as the image frame to be restored to obtain the image frame set to be restored, so that the processing time of video watermarking removal is reduced; taking any image frame to be repaired in the image frame set to be repaired as a second reference image frame, carrying out depth repair on the second reference image frame to obtain a depth repair image frame, calculating the similarity between each frame in the image frame set to be repaired and the second reference image frame, filling the repair content of the depth repair image frame into the corresponding image frame if the similarity exceeds a second preset threshold, and carrying out depth repair on the corresponding image frame if the similarity is lower than the second preset threshold. According to the embodiment of the invention, the image to be repaired is initially repaired, then the image frames corresponding to the difference value of the scores exceeding the first preset threshold value are screened out by evaluating and scoring the image to be repaired, the similarity between the image frames corresponding to the difference value of the scores exceeding the first preset threshold value and the second repair image is calculated, the image frames corresponding to the similarity exceeding the second preset threshold value are used as the first repair image, the repair content of the second reference image frame is filled into the smearing area of the first repair image, the image frames corresponding to the similarity lower than the second preset threshold value are used as the second repair image, and the smearing area of the second repair image is subjected to depth repair, so that the repair of each frame of video is avoided, the time for removing watermark of video is reduced, the condition that the video repair area flickers is lightened, and the viewing experience of users is improved.
Drawings
In order to more clearly illustrate the solution of the present application, a brief description will be given below of the drawings required for the description of the embodiments of the present application, it being apparent that the drawings in the following description are some embodiments of the present application, and that other drawings may be obtained from these drawings without the exercise of inventive effort for a person of ordinary skill in the art.
FIG. 1 is a flowchart of a video watermarking method according to an embodiment of the present application;
FIG. 2 is a flow chart of another implementation of a sub-process in a video watermarking method provided by an embodiment of the present application;
FIG. 3 is a flowchart of a sub-process in a video watermarking method according to an embodiment of the present application;
FIG. 4 is a flowchart of a sub-process of a video watermarking method according to an embodiment of the present application;
FIG. 5 is a flowchart of another implementation of a sub-process in a video watermarking method provided by an embodiment of the present application;
FIG. 6 is a flowchart of another implementation of a sub-process in a video watermarking method provided by an embodiment of the present application;
FIG. 7 is a flowchart of a sub-process in a video watermarking method according to an embodiment of the present application;
fig. 8 is a schematic diagram of a video watermarking apparatus according to an embodiment of the present application;
Fig. 9 is a schematic diagram of a computer device according to an embodiment of the present application.
Detailed Description
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs; the terminology used in the description of the applications herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application; the terms "comprising" and "having" and any variations thereof in the description of the application and the claims and the description of the drawings above are intended to cover a non-exclusive inclusion. The terms first, second and the like in the description and in the claims or in the above-described figures, are used for distinguishing between different objects and not necessarily for describing a sequential or chronological order.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the application. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments.
In order to make the person skilled in the art better understand the solution of the present application, the technical solution of the embodiment of the present application will be clearly and completely described below with reference to the accompanying drawings.
The present application will be described in detail with reference to the drawings and embodiments.
It should be noted that the video watermarking method provided in the embodiments of the present application is generally executed by a server, and accordingly, the video watermarking apparatus is generally configured in the server.
Referring to fig. 1, fig. 1 illustrates one embodiment of a video watermarking method.
It should be noted that, if there are substantially the same results, the method of the present application is not limited to the flow sequence shown in fig. 1, and the method includes the following steps:
s1: and obtaining a video to be processed, and carrying out framing processing on the video to be processed to obtain an image frame set, wherein the video to be processed comprises an smearing area, and the position of the smearing area in each image frame of the image frame set is fixed.
Specifically, the user side performs manual smearing on the to-be-repaired area on the to-be-processed video to generate a smearing area comprising the to-be-repaired area, obtains the to-be-processed video obtained after manual smearing, and then frames the to-be-processed video, and because the watermark position is fixed on each frame of the to-be-processed video, the smearing area is fixed in each image frame of the image frame set, so that smearing on each frame is not needed. The video to be processed is obtained from a local database by the server, and it can be understood that the video to be processed can be obtained by other methods, for example, the user side sends the video to the server, and the data source of the video to be processed is not limited.
Referring to fig. 2, fig. 2 shows a specific embodiment of step S1, which is described in detail as follows:
s11: and acquiring an initial video, and returning the video to be processed to the user side so that the user side can smear the watermark area of the video to be processed to obtain the video to be processed.
S12: and carrying out framing treatment on the video to be treated to obtain an image frame set.
Specifically, the server acquires a video to be processed from the database and sends the video to the user side, the user side receives the video to be processed, then manually smears the video to be processed, then sends the smeared video to the server, frames the video to be processed, and sequentially stores each frame of image. The user side smears the watermark area of the video to be processed, on one hand, the watermark area is blurred, the visibility of the watermark is reduced, and on the other hand, the area of the video to be repaired is distinguished from other areas, so that the identification and the repair are facilitated.
S2: and performing preliminary restoration on the smearing area of each image frame in the image frame set through a preset algorithm to obtain a preliminary image set.
Specifically, the primary repair of this embodiment uses FFmpeg software, which provides a delog filter to remove the watermark by simply interpolating the pixels around the watermark area. It should be noted that, the repairing effect of the conventional algorithm FFmpeg delogo on a part of frames is better, and the processing speed of the FFmpeg delogo is greatly superior to that of other algorithms, and the FFmpeg delogo algorithm is cited, so that a certain processing time is reduced.
Referring to fig. 3, fig. 3 shows a specific embodiment of step S2, which is described in detail as follows:
s21: for any one of the image frames in the set of image frames, a smear region in each image frame is identified and a rectangular region is constructed based on the smear region, wherein the rectangular region includes the smear region.
Specifically, a rectangular frame including an application area is set according to the upper left corner vertex coordinates and the width and height of the rectangle. It should be noted that when the repair is completed, the rectangular frame is automatically deleted by default, but the rectangular frame can be reserved by changing the parameter setting.
S22: and acquiring pixels except the smearing area in the rectangular frame as pixels to be filled.
And S23, filling the pixels to be filled into the smearing area to obtain a preliminary restoration image.
S24, when all the image frames in the image frame set are repaired, a preliminary image set is obtained.
Specifically, pixels except the smearing area in the rectangular frame are obtained and used as pixels to be filled, the pixels to be filled are filled into the smearing area, and when each frame in the image frame set is repaired, a preliminary image set is obtained.
S3: and taking the first frame image frame in the preliminary image set as a first reference image frame, and calculating the score difference value of each image frame in the preliminary image set and the first reference image frame to obtain a difference value score set.
Specifically, a threshold reference is determined according to the image quality of the first frame, then a preset evaluation scoring algorithm is adopted to evaluate and score each image frame, each image frame obtains a corresponding score value, and the difference between the score value corresponding to each image frame and the score value corresponding to the first image frame is calculated to obtain a difference value score set.
Referring to fig. 4, fig. 4 shows a specific embodiment of step S3, which is described in detail as follows:
s31: the first frame image frame in the preliminary image set is taken as the first reference image frame.
And S32, performing evaluation scoring on the preliminary image set by adopting a preset evaluation scoring algorithm to obtain the score of each image frame.
S33: and calculating the difference between the score of each image frame and the score of the first reference image frame to obtain a score difference value.
Specifically, in this embodiment, the image frames are evaluated and scored by using a MetaIQA algorithm, and the MetaIQA algorithm learns the shared quality prior model by using a series of NR-IQA tasks of known distortion types by using a meta learning technology, and then fine-adjusts the quality prior model by using the NR-IQA tasks of unknown distortion. Generalization was learned by a bilayer gradient descent method. In the first step, firstly, training data of NR-IQA is divided into a support set and a query set; secondly, calculating the gradient of the model parameters by using a support set, and attempting to update by using SGD; finally, the query set is used to verify whether the updated model is executing effectively. Training a quality priori model for a series of digital image quality evaluation tasks with specific distortions, fine-tuning the digital image quality evaluation tasks with unknown distortions by using the model as priori knowledge, finally obtaining a quality evaluation model, evaluating and scoring a preliminary image set by using the quality evaluation model to obtain the score of each image frame, and calculating the difference value between the score of each image frame and the score of the first image frame to obtain a difference value score set.
It should be noted that, in this embodiment, the MetaIQA algorithm is adopted to evaluate and score the image frame, or other conventional indexes or algorithms may be used instead, for example, the brique algorithm may also be adopted, where the brique algorithm principle is to extract the average subtraction comparison normalization coefficient MSCN from the image, then fit the average subtraction comparison normalization coefficient MSCN into the asymmetric generalized gaussian distribution AGGD, extract the characteristic of the fitted high-speed distribution, input the characteristic into the support vector machine SVM to make regression, so as to obtain the evaluation result of the image quality, and the specific method for evaluating and scoring the image frame is not limited herein.
S4: comparing each difference score in the difference score sets with a first preset threshold, and taking the image frames corresponding to the difference scores exceeding the first preset threshold as the image frames to be repaired to obtain an image frame set to be repaired.
Specifically, if the difference value of a certain image frame score and the first image frame score exceeds a first preset threshold, the delog restoration effect is poor and needs to be further restored, and if the difference value of a certain image frame score and the first image frame score does not exceed the threshold, the delog restoration effect is good and does not need to be further restored. The first preset threshold is set according to the actual situation, and the value of the first preset threshold is not limited here. In a specific embodiment, the first preset threshold is 0.3.
Furthermore, the picture repairing speed of the delog is higher, and the mode of combining MetaIQA and delog is adopted, so that the image frames with better effect after being repaired by the delog are not further repaired, and the time consumption of certain processing is reduced.
S5: and taking any image frame in the image frame set to be repaired as a second reference image frame, and carrying out depth repair on the smearing area of the second reference image frame to obtain a depth repair image frame, wherein the depth repair image frame comprises repair contents of the smearing area.
Specifically, the method adopted by the deep restoration of the embodiment is large mask restoration LaMa, the method has high receptive field and larger training mask, is used for sensing loss and releasing the potential of the front component, and uses a restoration network architecture of fast Fourier convolution FFC, the architecture has wider image receptive field, can be popularized to high-resolution images after being trained on low-resolution data, can capture and generate complex periodic structures, and has robustness to the large mask.
Referring to fig. 5, fig. 5 shows a specific embodiment of step S5, which is described in detail as follows:
s51: taking any image frame in the image frame set to be repaired as a second reference image frame, and carrying out inverse processing on the smearing area of the second reference image frame to obtain an inverse smearing image frame, wherein the inverse smearing image frame comprises the smearing area after the inverse smearing.
S52: and multiplying the inverted smear image frame with the second reference image frame to obtain a first target image, wherein the first target image comprises an inverted smear region.
S53: and superposing the first target image and the smearing area to obtain a second target image.
Specifically, a second reference image frame and an application area image of the second reference image frame are obtained, the application area image of the second reference image frame is subjected to inverse processing, namely, the application area image is subjected to gray processing to obtain a gray image, original black pixels in the gray image are replaced by white pixels, original white pixels are replaced by black pixels, the white pixels are multiplied by the second reference image frame image to obtain a second reference image frame image with an application area, and then the second reference image frame image with the application area and the application area image of the second reference image frame are subjected to superposition processing based on a channel to obtain a second target image.
S54: and repairing the second target image to obtain a depth repairing image frame.
Referring to fig. 6, fig. 6 shows a specific embodiment of step S54, which is described in detail below:
s541: and carrying out downsampling operation on the second target image to obtain a downsampled image.
S542: and performing fast Fourier convolution FFC processing on the downsampled image to obtain a convolution image.
S543: and performing up-sampling operation on the convolution image to obtain a depth restoration image frame.
Specifically, during the fast fourier convolution FFC processing, the second target image is divided into 2 parts based on the channel, and the two parts are respectively branched into 2 different branches. One branch is responsible for extracting local information, called the local branch. The other branch is responsible for extracting global information, called global branch. Global features are extracted using FFCs in global branches. And finally, carrying out cross fusion on the local information and the global information, and then splicing based on the channels to obtain the final depth restoration image frame of the output result.
S6: and calculating the similarity between each image frame to be repaired in the image frame set to be repaired and the second reference image frame.
Specifically, in this embodiment, the structural similarity measure SSIM is used to evaluate the image similarity, and measures the image similarity from three aspects of brightness, contrast and structure. It should be noted that, the effect of locally solving for SSIM index is better than global, because the statistical features of the image are often distributed unevenly in space, the distortion condition of the image is also changed in space and in normal viewing distance, people can only focus the line of sight in one area of the image, so the local processing is more in line with the characteristics of the human visual system.
Referring to fig. 7, fig. 7 shows a specific embodiment of step S6, which is described in detail as follows:
s61: and performing blocking processing on any image frame to be repaired and the second image frame to obtain a first blocking and a second blocking.
And S62, adopting a Gaussian weighting algorithm to respectively perform calculation processing on the first block and the second block to obtain a first calculation result and a second calculation result, wherein the first calculation result comprises the mean value, the variance and the covariance of the first block, and the second calculation result comprises the mean value, the variance and the covariance of the second block.
S63: calculating the structural similarity of the first block according to the first calculation result to obtain the first block similarity; and calculating the structural similarity of the second block according to the second calculation result to obtain the second block similarity.
S64: and calculating the average value of the structural similarity values of the first block and the second block to obtain the similarity of any image frame to be repaired and the second image frame.
Specifically, the image in the image frame set to be repaired is segmented by utilizing Gaussian sliding windows, then the mean value, variance and covariance of each window are calculated by adopting a Gaussian weighting algorithm, then the structural similarity SSIM of the corresponding block is calculated, and finally the mean value is used as the structural similarity measurement of the two images. The calculation formula of the structural similarity SSIM is as follows: Wherein X and Y are images, and X is i ,y j For the location of the local SSIM index in the map, MN is the number of local windows.
It should be noted that, in this embodiment, the structural similarity SSIM is used to evaluate the image similarity, or other image similarity evaluation indexes may be used to measure the similarity between frames, for example, the mean square error MSE is used to evaluate the similarity of the images, which is not limited by the specific method for evaluating the image similarity.
S7: judging whether the similarity exceeds a second preset threshold value, if so, taking the image frame to be repaired corresponding to the similarity exceeding the second preset threshold value as a first repair image, and filling the repair content of the second reference image frame into the smearing area of the first repair image.
Specifically, if the similarity exceeds a second preset threshold, it is determined that the similarity of the two frame repair areas is high, and frame skipping processing can be performed, that is, the frame to be repaired is directly filled with the repair content of the current frame, without using a deep repair Lama algorithm to perform image repair. The second preset threshold is set according to the actual situation, and the value of the second preset threshold is not limited here. In one embodiment, the second predetermined threshold is 0.05.
Furthermore, the frame skipping processing avoids the deep restoration of each frame, so that the processing time of video watermarking is greatly reduced, and the flickering condition of a video restoration area is reduced to a certain extent, thereby greatly improving the watching experience of users.
S8: and if the similarity is lower than a second preset threshold, taking the image frame to be repaired, which corresponds to the similarity lower than the second preset threshold, as a second repair image, and performing depth repair on the smearing area of the second repair image.
Specifically, if the similarity is lower than the second preset threshold, it is determined that the similarity of the two frame repair areas is low, and depth repair needs to be performed on the current frame, where the depth repair adopts large mask repair LaMa, and the large mask repair LaMa is already explained in the above steps and is not described herein again.
In the embodiment, a video to be processed is obtained, and framing processing is carried out on the video to be processed to obtain an image frame set, wherein the video to be processed comprises an smearing area, and the position of the smearing area in each image frame of the image frame set is fixed; performing preliminary restoration on the smearing area of each image frame in the image frame set through a preset algorithm to obtain a preliminary image set; taking a first frame image frame in the preliminary image set as a first reference image frame, calculating a score difference value between each image frame in the preliminary image set and the first reference image frame, and obtaining a difference value score set; comparing each difference score in the difference score sets with a first preset threshold value, and taking an image frame corresponding to the difference score exceeding the first preset threshold value as an image frame to be repaired to obtain an image frame set to be repaired; taking any image frame in the image frame set to be repaired as a second reference image frame, and carrying out depth repair on the smearing area of the second reference image frame to obtain a depth repair image frame, wherein the depth repair image frame comprises repair contents of the smearing area; calculating the similarity between each image frame to be repaired in the image frame set to be repaired and the second reference image frame; judging whether the similarity exceeds a second preset threshold value, if so, taking the image frame to be repaired corresponding to the similarity exceeding the second preset threshold value as a first repair image, and filling the repair content of the second reference image frame into the smearing area of the first repair image; and if the similarity is lower than a second preset threshold, taking the image frame to be repaired, which corresponds to the similarity lower than the second preset threshold, as a second repair image, and performing depth repair on the smearing area of the second repair image. According to the embodiment of the invention, the frames with good primary repair effects are not subjected to depth repair, and in addition, on the basis of the depth repair, the frame skipping strategy is adopted for frames with high similarity, so that each frame is prevented from being processed, and the processing time of video watermarking is greatly reduced; meanwhile, the frame skipping strategy avoids repairing each frame, and reduces the flickering condition of the video repairing area to a certain extent, so that the watching experience of a user is greatly improved.
Referring to fig. 8, as an implementation of the method shown in fig. 1, the present application provides an embodiment of a video watermarking apparatus, which corresponds to the embodiment of the method shown in fig. 1, and the apparatus may be applied to various electronic devices.
As shown in fig. 8, the video watermarking apparatus of the present embodiment includes: mask generation module 81, preliminary restoration module 82, difference calculation module 83, difference comparison module 84, sample restoration module 85, similarity calculation module 86, padding module 87, and depth restoration module 88, wherein:
the mask generation module is used for acquiring a video to be processed, and carrying out framing treatment on the video to be processed to obtain an image frame set, wherein the acquired video to be processed comprises an smearing area, and the position of the smearing area in each image frame of the image frame set is fixed;
the primary restoration module is used for carrying out primary restoration on the smearing area of each image frame in the image frame set through a preset algorithm to obtain a primary image set;
the difference value calculation module is used for taking a first frame image frame in the preliminary image set as a first reference image frame, calculating the score difference value of each image frame in the preliminary image set and the first reference image frame, and obtaining a difference value score set;
The difference value comparison module is used for comparing each difference value score in the difference value score set with a first preset threshold value, and taking an image frame corresponding to the difference value score exceeding the first preset threshold value as an image frame to be repaired to obtain an image frame set to be repaired;
the sample restoration module is used for taking any image frame in the image frame set to be restored as a second reference image frame, and carrying out depth restoration on the smearing area of the second reference image frame to obtain a depth restoration image frame, wherein the depth restoration image frame comprises restoration contents of the smearing area;
the similarity calculation module is used for calculating the similarity between each image frame to be repaired in the image frame set to be repaired and the second reference image frame;
the filling module is used for judging whether the similarity exceeds a second preset threshold value, if so, taking the image frame to be repaired corresponding to the similarity exceeding the second preset threshold value as a first repair image, and filling the repair content of the second reference image frame into the smearing area of the first repair image;
and the depth restoration module is used for taking the image frame to be restored, which corresponds to the similarity lower than the second preset threshold value, as a second restoration image and carrying out depth restoration on the smearing area of the second restoration image if the similarity is lower than the second preset threshold value.
Further, the mask generating module 81 includes:
the video acquisition unit is used for acquiring an initial video and returning the video to be processed to the user side so that the user side can smear the watermark area of the video to be processed to obtain the video to be processed;
the framing unit is used for framing the video to be processed to obtain an image frame set.
Further, the preliminary repair module 82 includes:
a rectangular construction unit for identifying a smear region in each image frame for any one of the image frames in the image frame set, and constructing a rectangular region based on the smear region, wherein the rectangular region includes the smear region;
the pixel obtaining unit is used for obtaining pixels except the smearing area in the rectangular frame and used as pixels to be filled;
the pixel filling unit is used for filling the pixels to be filled into the smearing area to obtain a preliminary repair image;
and the preliminary image set unit is used for obtaining a preliminary image set when the restoration of all the image frames in the image frame set is completed.
Further, the difference calculating module 83 includes:
a first frame reference unit configured to use a first frame image frame in the preliminary image set as a first reference image frame;
the evaluation scoring unit is used for evaluating and scoring the preliminary image set by adopting a preset evaluation scoring algorithm to obtain the score of each image frame;
And the score difference unit is used for calculating the difference between the score of each image frame and the score of the first reference image frame to obtain a score difference.
Further, the difference comparison module 84 includes:
the comparison unit is used for comparing each difference score in the difference score set with a first preset threshold value;
the to-be-repaired unit is used for taking the image frames corresponding to the difference values exceeding the first preset threshold value as to-be-repaired image frames to obtain a to-be-repaired image frame set.
Further, the sample repair module 85 includes:
the image processing unit is used for processing the image frames to be repaired to obtain a second reference image frame, and processing the smearing area of the second reference image frame to obtain a reversely smeared image frame, wherein the reversely smeared image frame comprises the smeared area after being reversely smeared;
the multiplication unit is used for carrying out multiplication processing on the inverted smearing image frame and the second reference image frame to obtain a first target image, wherein the first target image comprises an inverted smearing area;
the superposition unit is used for carrying out superposition processing on the first target image and the smearing area to obtain a second target image;
and the restoration unit is used for restoring the second target image to obtain a depth restoration image frame.
Further, the similarity calculation module 86 includes:
the blocking unit is used for blocking any image frame to be repaired and a second image frame to obtain a first blocking and a second blocking;
the computing unit is used for respectively carrying out computing processing on the first block and the second block by adopting a Gaussian weighting algorithm to obtain a first computing result and a second computing result, wherein the first computing result comprises the mean value, the variance and the covariance of the first block, and the second computing result comprises the mean value, the variance and the covariance of the second block;
the block similarity unit is used for calculating the structural similarity of the first block according to the first calculation result to obtain the first block similarity; calculating the structural similarity of the second block according to the second calculation result to obtain second block similarity;
and the average similarity unit is used for calculating the average value of the structural similarity values of the first block and the second block to obtain the similarity between any image frame to be repaired and the second image frame.
Further, the padding module 87 includes:
the judging unit is used for judging whether the similarity exceeds a second preset threshold value, and if the similarity exceeds the second preset threshold value, the image frame to be repaired corresponding to the similarity exceeding the second preset threshold value is used as a first repair image;
And the filling repair unit is used for filling the repair content of the second reference image frame into the smearing area of the first repair image.
Further, the depth restoration module includes:
the similarity judging unit is used for taking the image frame to be repaired corresponding to the similarity lower than the second preset threshold value as a second repair image if the similarity is lower than the second preset threshold value;
and the second restoration unit is used for carrying out the depth restoration on the smeared area of the second restoration image.
In order to solve the technical problems, the embodiment of the application also provides computer equipment. Referring specifically to fig. 9, fig. 9 is a basic structural block diagram of a computer device according to the present embodiment.
The computer device 9 comprises a memory 91, a processor 92, a network interface 93 communicatively connected to each other via a system bus. It is noted that only a computer device 9 having three components memory 91, a processor 92, a network interface 93 is shown, but it should be understood that not all of the illustrated components are required to be implemented and that more or fewer components may be implemented instead. It will be appreciated by those skilled in the art that the computer device herein is a device capable of automatically performing numerical calculations and/or information processing in accordance with predetermined or stored instructions, the hardware of which includes, but is not limited to, microprocessors, application specific integrated circuits (Application Specific Integrated Circuit, ASICs), programmable gate arrays (fields-Programmable Gate Array, FPGAs), digital processors (Digital Signal Processor, DSPs), embedded devices, etc.
The computer device may be a desktop computer, a notebook computer, a palm computer, a cloud server, or the like. The computer device can perform man-machine interaction with a user through a keyboard, a mouse, a remote controller, a touch pad or voice control equipment and the like.
The memory 91 includes at least one type of readable storage medium including flash memory, hard disk, multimedia card, card memory (e.g., SD or DX memory, etc.), random Access Memory (RAM), static Random Access Memory (SRAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), programmable Read Only Memory (PROM), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 91 may be an internal storage unit of the computer device 9, such as a hard disk or a memory of the computer device 9. In other embodiments, the memory 91 may also be an external storage device of the computer device 9, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card) or the like, which are provided on the computer device 9. Of course, the memory 91 may also comprise both an internal memory unit of the computer device 9 and an external memory device. In this embodiment, the memory 91 is typically used to store an operating system installed on the computer device 9 and various types of application software, such as program codes of a video watermarking method. Further, the memory 91 may be used to temporarily store various types of data that have been output or are to be output.
The processor 92 may be a central processing unit (Central Processing Unit, CPU), controller, microcontroller, microprocessor, or other data processing chip in some embodiments. The processor 92 is typically used to control the overall operation of the computer device 9. In this embodiment, the processor 92 is configured to execute the program code stored in the memory 91 or process data, for example, execute the program code of the video watermarking method described above, to implement various embodiments of video watermarking.
The network interface 93 may comprise a wireless network interface or a wired network interface, which network interface 93 is typically used to establish a communication connection between the computer device 9 and other electronic devices.
The present application also provides another embodiment, namely, a computer readable storage medium storing a computer program executable by at least one processor to cause the at least one processor to perform the steps of a video watermarking method as described above.
From the above description of the embodiments, it will be clear to those skilled in the art that the above-described embodiment method may be implemented by means of software plus a necessary general hardware platform, but of course may also be implemented by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (e.g. ROM/RAM, magnetic disk, optical disk) comprising several instructions for causing a terminal device (which may be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to perform the method of the embodiments of the present application.
It is apparent that the above-described embodiments are only some embodiments of the present application, but not all embodiments, and the preferred embodiments of the present application are shown in the drawings, which do not limit the scope of the patent claims. This application may be embodied in many different forms, but rather, embodiments are provided in order to provide a thorough and complete understanding of the present disclosure. Although the application has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that modifications may be made to the embodiments described in the foregoing description, or equivalents may be substituted for elements thereof. All equivalent structures made by the content of the specification and the drawings of the application are directly or indirectly applied to other related technical fields, and are also within the scope of the application.
Claims (10)
1. A method of video watermarking comprising:
the method comprises the steps of obtaining a video to be processed, and carrying out framing processing on the video to be processed to obtain an image frame set, wherein the video to be processed comprises a smearing area, and the position of the smearing area in each image frame of the image frame set is fixed;
Performing preliminary restoration on the smearing area of each image frame in the image frame set through a preset algorithm to obtain a preliminary image set;
taking a first frame image frame in the preliminary image set as a first reference image frame, and calculating a score difference value between each image frame in the preliminary image set and the first reference image frame to obtain a difference value score set;
comparing each difference score in the difference score sets with a first preset threshold value, and taking an image frame corresponding to the difference score exceeding the first preset threshold value as an image frame to be repaired to obtain an image frame set to be repaired;
taking any image frame in the image frame set to be repaired as a second reference image frame, and carrying out depth repair on the smeared area of the second reference image frame to obtain a depth repair image frame, wherein the depth repair image frame comprises repair contents of the smeared area;
calculating the similarity between each image frame to be repaired in the image frame set to be repaired and the second reference image frame;
judging whether the similarity exceeds a second preset threshold value, if so, taking an image frame to be repaired corresponding to the similarity exceeding the second preset threshold value as a first repair image, and filling repair contents of the second reference image frame into the smearing area of the first repair image;
And if the similarity is lower than the second preset threshold, taking the image frame to be repaired, corresponding to the similarity lower than the second preset threshold, as a second repair image, and carrying out the depth repair on the smearing area of the second repair image.
2. The method for video watermarking according to claim 1, wherein the obtaining the video to be processed, and performing framing processing on the video to be processed to obtain the image frame set, includes:
acquiring an initial video, and returning the video to be processed to a user side so that the user side can smear a watermark area of the video to be processed to obtain the video to be processed;
and carrying out framing treatment on the video to be treated to obtain the image frame set.
3. The method according to claim 1, wherein said performing preliminary repair on said smear region of each image frame in said set of image frames by a predetermined algorithm to obtain a preliminary image set comprises:
identifying the smear region in each image frame for any image frame in the image frame set, and constructing a rectangular region based on the smear region, wherein the rectangular region comprises the smear region;
Acquiring pixels except the smearing area in the rectangular frame as pixels to be filled;
filling the pixels to be filled into the smearing area to obtain a preliminary restoration image;
and when the repair of all the image frames in the image frame set is completed, obtaining the preliminary image set.
4. The method according to claim 1, wherein the calculating a score difference between each image frame in the preliminary image set and the first reference image frame by using the first frame image frame in the preliminary image set as the first reference image frame, to obtain a difference score set includes:
taking a first frame image frame in the preliminary image set as a first reference image frame;
performing evaluation scoring on the preliminary image set by adopting a preset evaluation scoring algorithm to obtain the score of each image frame;
and calculating the difference between the score of each image frame and the score of the first reference image frame to obtain the score difference.
5. The method according to claim 1, wherein the step of taking any one of the image frames in the set of image frames to be repaired as a second reference image frame and performing depth repair on the smeared area of the second reference image frame to obtain a depth repaired image frame comprises:
Taking any image frame in the image frame set to be repaired as a second reference image frame, and performing inverse processing on the smearing area of the second reference image frame to obtain an inverse smearing image frame, wherein the inverse smearing image frame comprises a smearing area after the inverse smearing;
multiplying the inverted smear image frame with the second reference image frame to obtain a first target image, wherein the first target image comprises the inverted smear region;
superposing the first target image and the smearing area to obtain a second target image;
and repairing the second target image to obtain the depth repairing image frame.
6. The method of claim 5, wherein the repairing the second target image to obtain the depth-repaired image frame comprises:
performing downsampling operation on the second target image to obtain a downsampled image;
performing fast Fourier convolution processing on the downsampled image to obtain a convolution image;
and performing up-sampling operation on the convolution image to obtain the depth restoration image frame.
7. The method of claim 1, wherein calculating the similarity of any one of the set of image frames to be repaired to a second image frame comprises:
performing blocking processing on any image frame to be repaired and the second image frame to obtain a first block and a second block;
respectively carrying out calculation processing on the first block and the second block by adopting a Gaussian weighting algorithm to obtain a first calculation result and a second calculation result, wherein the first calculation result comprises the mean value, the variance and the covariance of the first block, and the second calculation result comprises the mean value, the variance and the covariance of the second block;
calculating the structural similarity of the first block according to the first calculation result to obtain first block similarity; calculating the structural similarity of the second block according to the second calculation result to obtain second block similarity;
and calculating the average value of the structural similarity values of the first block and the second block to obtain the similarity between any image frame to be repaired and the second image frame.
8. An apparatus for watermarking video, comprising:
The mask generation module is used for acquiring a video to be processed, carrying out frame division processing on the video to be processed to obtain an image frame set, wherein the acquired video to be processed comprises a smearing area, and the position of the smearing area in each image frame of the image frame set is fixed;
the preliminary restoration module is used for carrying out preliminary restoration on the smearing area of each image frame in the image frame set through a preset algorithm to obtain a preliminary image set;
the difference value calculation module is used for taking a first frame image frame in the preliminary image set as a first reference image frame, calculating the score difference value of each image frame in the preliminary image set and the first reference image frame, and obtaining a difference value score set;
the difference value comparison module is used for comparing each difference value score in the difference value score set with a first preset threshold value, and taking an image frame corresponding to the difference value exceeding the first preset threshold value as an image frame to be repaired to obtain an image frame set to be repaired;
the sample restoration module is used for taking any image frame in the image frame set to be restored as a second reference image frame, and carrying out depth restoration on the smearing area of the second reference image frame to obtain a depth restoration image frame, wherein the depth restoration image frame comprises restoration contents of the smearing area;
The similarity calculation module is used for calculating the similarity between each image frame to be repaired in the image frame set to be repaired and the second reference image frame;
the filling module is used for judging whether the similarity exceeds a second preset threshold value, if so, taking an image frame to be repaired corresponding to the similarity exceeding the second preset threshold value as a first repair image, and filling the repair content of the second reference image frame into the smearing area of the first repair image;
and the depth restoration module is used for taking an image frame to be restored, which corresponds to the similarity lower than the second preset threshold value, as a second restoration image if the similarity is lower than the second preset threshold value, and carrying out the depth restoration on the smearing area of the second restoration image.
9. A computer device comprising a memory and a processor, the memory having stored therein a computer program, the processor implementing the video watermarking method according to any of claims 1 to 7 when executing the computer program.
10. A computer readable storage medium, characterized in that the computer readable storage medium has stored thereon a computer program which, when executed by a processor, implements the video watermarking method according to any of claims 1 to 7.
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