CN113763224A - Image processing method and device - Google Patents
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- CN113763224A CN113763224A CN202010494066.6A CN202010494066A CN113763224A CN 113763224 A CN113763224 A CN 113763224A CN 202010494066 A CN202010494066 A CN 202010494066A CN 113763224 A CN113763224 A CN 113763224A
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- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
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- G06T1/0021—Image watermarking
- G06T1/0092—Payload characteristic determination in a watermarking scheme, e.g. number of bits to be embedded
Abstract
The application discloses an image processing method and a device thereof, wherein the method comprises the following steps: acquiring a frequency domain image corresponding to an initial image to be processed; determining non-salient regions in the frequency domain image; determining an embedded block for performing watermark embedding from the insignificant region; and executing watermark embedding on the embedded block to generate a watermark-containing image. By the method and the device, the embedded block for executing watermark embedding is determined from the non-significant region, so that the consistency of the embedded end and the detection end can be improved.
Description
Technical Field
The present application relates to the field of computer technologies, and in particular, to an image processing method and an image processing apparatus.
Background
With the continuous development of digital media technology and networks, including the increasing popularity of 5G, digital forms of data, particularly media data such as digital images and videos, have shown explosive growth. As an advanced and effective security technology, digital watermarking technology has become an important research direction in the field of data security and intellectual property protection.
In the related art, in order to ensure synchronization between the watermark embedding end and the detection end, the watermark embedding end embeds a watermark image in a specified position (i.e., a watermark embedding position) of a video image and transmits position information of the watermark embedding position to the watermark detection end, but transmitting the watermark embedding position brings extra code rate to video encoding, increases bandwidth and network load, and thus reduces encoding efficiency. Therefore, there is a need in the related art for a solution that does not depend on the watermark embedding location and can ensure the synchronization between the horizontal embedding terminal and the detection section.
Disclosure of Invention
The embodiment of the application provides an image processing method and an image processing device, which at least solve the above-mentioned technical problems.
The embodiment of the present application further provides an image processing method, where the method includes: acquiring a frequency domain image corresponding to an initial image to be processed; determining non-salient regions in the frequency domain image; determining an embedded block for performing watermark embedding from the insignificant region; and executing watermark embedding on the embedded block to generate a watermark-containing image.
The embodiment of the present application further provides an image processing method, where the method includes: acquiring a frequency domain image corresponding to an initial image to be processed; determining non-salient regions in the frequency domain image; determining an embedded block for performing watermark embedding from the insignificant region; watermark information is extracted from the embedded block.
The embodiment of the present application further provides a computer-readable storage medium, on which computer instructions are stored, wherein the instructions, when executed, implement the above method.
An embodiment of the present application further provides an image processing apparatus, including: a processor; and a memory arranged to store computer executable instructions that, when executed, cause the processor to perform the above method.
The embodiment of the application adopts at least one technical scheme which can achieve the following beneficial effects:
according to the method, the embedded block for executing watermark embedding is determined from the non-salient region, so that the embedded block is selected in a self-adaptive manner, and the problem that the watermark is aligned in the salient region of the embedded end and the salient region of the detection end can be effectively solved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
FIG. 1 is a scene diagram of image processing according to an exemplary embodiment of the present application;
FIG. 2 is a flow chart of steps of an image processing method according to an exemplary embodiment of the present application;
fig. 3 is a frame diagram of an image processing method according to another exemplary embodiment of the present application;
fig. 4 is a diagram illustrating an embedding end embedding watermark information according to an image processing method of an exemplary embodiment of the present application;
fig. 5 is a diagram illustrating that a detecting end extracts watermark information according to an image processing method of an exemplary embodiment of the present application;
FIG. 6 is a comparison of the effect of an embedded end and a detection end on a saliency mask, according to an exemplary embodiment of the present application;
fig. 7 is a block diagram of an image processing apparatus according to an exemplary embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the technical solutions of the present application will be described in detail and completely with reference to the following specific embodiments of the present application and the accompanying drawings. It should be apparent that the described embodiments are only some of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The technical solutions provided by the embodiments of the present application are described in detail below with reference to the accompanying drawings.
To better illustrate the present application, a scene diagram of image processing will be described below in conjunction with fig. 1, and it should be noted that an image processed by the image processing method according to the exemplary embodiment of the present application is much larger than that shown in fig. 1, and fig. 1 describes main components only for illustrating a scene to which the present application is applied, and the present application is applicable to various scenes including fig. 1.
As shown in fig. 1, a user a creates a video image 10 using an electronic terminal 100, and then the user a as an author may request image protection from a server 101 providing image copyright protection, and then the server 101 may perform image processing on the image to provide copyright protection. That is, watermark images are embedded in the video image 10 to provide copyright protection.
Subsequently, the server 101 may store the image with the embedded digital watermark, and although the server 101 is used to store the image with the embedded digital watermark in fig. 1, in the implementation, the image may be stored by a server different from the server 101, for example, a user creates an image on an application (e.g., an animation website), the server corresponding to the application may apply copyright protection to the server 101 dedicated to implementing image copyright protection, the server 101 may embed the digital watermark in the image as described above, and then the server corresponding to the application may acquire the image with the embedded digital watermark from the server 101 and store the image, and it can be seen that, in this example, the server performing copyright protection may be different from the server storing the image.
Subsequently, the server 101 may provide the image 10 to other servers 200 according to the settings of the user a (e.g., the user a may set the image 10 to be presentable but not downloadable). In this case, when the user B views the image 20 like the image 10 through the electronic device 300, the server 101 may extract the image 20, and then may extract the digital watermark from the image 20 to determine whether the image 20 is an unauthorized image 10.
It should be noted that an electronic apparatus according to the present application (hereinafter referred to as an image processing apparatus performing an exemplary embodiment according to the present application) is a device including a display unit, and may include, but is not limited to, any of the following: personal Computers (PCs), mobile devices such as cellular phones, Personal Digital Assistants (PDAs), digital cameras, portable game consoles, MP3 players, portable/Personal Multimedia Players (PMPs), handheld electronic books, tablet PCs, portable laptop PCs, and Global Positioning System (GPS) navigators, smart TVs, and the like. Although the server 101 in fig. 1 is a specific server, in an implementation, a plurality of servers may be implemented together.
In the prior art, the way of embedding the watermark can be divided into blind processing and non-blind processing. The watermark embedding method using non-blind processing requires exact knowledge of the location of the embedded watermark, which increases the network load for transmitting the image. The watermark embedding method using blind processing generally performs watermark embedding on different regions according to different embedding strengths by using non-overlapping sliding windows after determining the region in which the watermark is embedded, but this method is prone to dislocation between the embedding end and the detection end.
Based on this, in the case of adopting blind processing, in the case of determining an insignificant region in the frequency domain image, the image processing method of the exemplary embodiment of the present application performs screening on the insignificant region, determines an embedded block that can perform watermark embedding, and performs watermark embedding on the embedded block, thereby improving consistency between the embedded end and the detection end.
The following will specifically describe the technical solutions involved in the following with reference to fig. 2 to 4. Fig. 2 shows a flow chart of steps of an image processing method according to an exemplary embodiment of the present application.
As shown in fig. 2, in step S210, a frequency domain image corresponding to the initial image to be processed is acquired. In implementation, the initial image to be processed may be a single-frame image, including and not limited to an image created by a user (e.g., a cartoon, an oil painting, etc.) or a document in various formats, and may also be a single-frame video image in a video.
Specifically, after the initial image is acquired, a channel image corresponding to luminance information may be acquired by extracting luminance information of the initial image. As an example, the initial image may be a YUV image, which is a color coding scheme mainly applied to analog video and television systems, where Y represents Luminance information (Luma or Luma) and U and V represent Chrominance (Chroma or Chroma). And under the condition that the initial image is a YUV image, extracting a Y channel in the initial image so as to obtain a channel image corresponding to the Y channel.
The channel image may then be frequency domain converted. In an implementation, a discrete wavelet transform may be performed on the channel image, generating a plurality of subgraphs corresponding to the initial image. The wavelet transform (DWT) is a series of wavelets with different scales to decompose an original function, and the transformed original function is a coefficient of the original function under the wavelets with different scales.
In an exemplary embodiment of the present application, the wavelet transform may be performed on the channel image as follows: firstly, performing discrete wavelet transform on each row of the channel image to obtain a low-frequency component L and a high-frequency component H of the channel image in the horizontal direction, then performing discrete wavelet transform on each column of data obtained by the transform, and finally obtaining a plurality of subgraphs of the channel image, wherein the subgraphs comprise: a low-frequency component sub-graph (i.e., a thick sub-graph) LL in the horizontal and vertical directions, a low-frequency sub-graph (i.e., a horizontal-direction detail sub-graph) LH in the horizontal and vertical directions, a high-frequency sub-graph (i.e., a vertical-direction detail sub-graph) HL in the horizontal and vertical directions, and a high-frequency component sub-graph (i.e., a diagonal-direction detail sub-graph) HH in the horizontal and vertical directions.
In an embodiment of the present application, a sub-graph including detail information may be selected from the sub-graphs as a frequency domain image, and preferably, a horizontal detail sub-graph may be selected from the plurality of sub-graphs as a frequency domain image. Optionally, both the horizontal direction detail subgraph and the diagonal direction detail subgraph can be taken as frequency domain images.
Step S220 is performed to determine a non-significant region in the frequency domain image.
Specifically, attention area segmentation may be performed on the initial image by using a visual attention model, which refers to simulating an attention mechanism of a human to acquire an image of interest in the image, to acquire a saliency mask corresponding to the saliency area.
In addition, in implementations, other ways may be used to determine non-salient regions in the frequency domain image. For example, the image name may be utilized to determine non-salient regions in the frequency domain image. Specifically, after the image name of the image is recognized, a region corresponding to the image name may be recognized by using an image recognition technique, and the region may be determined as a salient region, and a region other than the region in the image may be a non-salient region. Alternatively, the text region in the image may be identified, the text region may be determined as a non-salient region, and a region other than the text region in the image may be a salient region.
In an exemplary embodiment of the present application, a visual attention model based on texture and brightness may be employed to generate the attention area of the initial image. Subsequently, a binarized Otsu threshold segmentation process may be performed on the attention area to acquire the saliency mask.
The mask mentioned here refers to the binary marking of the pixel points on the processed image to control the area of image processing. For example, by using a preset region-of-interest mask and corresponding image to correspond to each pixel point one by one, it can be known whether each pixel point on the image is in the region-of-interest.
In an exemplary embodiment of the present application, in order to acquire the saliency mask, processing may be performed on the attention region using the maximum between-class variance method, thereby acquiring the saliency mask. The maximum inter-class variance method may also be called as an atrazine algorithm (i.e., OTSU), which is an algorithm for determining an image segmentation threshold, in short, an image is divided into a background and an object according to a gray level distribution of the image, so that the inter-class variance between the background and the object is maximum, wherein the definition of the inter-class variance is shown in formula 1:
wherein σ2(k) For inter-class variance, k is the optimal classification threshold that maximizes inter-class variance. Omega0And ω1Are two kinds of probabilities, mu0And mu1Are two types of mathematical expectations. The Suzuki method is applied to the segmentation of the attention area, so that the difference between the segmented non-significant area and the significant area is maximum, and the non-significant area is less prominent.
The non-salient regions are then acquired by combining a saliency mask with the frequency domain image. Specifically, the saliency mask is subjected to down-sampling, the down-sampled saliency mask is obtained, the resolution of the down-sampled saliency mask is enabled to be the same as that of the frequency domain image, then the down-sampled saliency mask and the frequency domain image are in one-to-one correspondence on each pixel point, and the non-salient region is obtained.
Step S230 is performed to determine an embedded block for performing watermark embedding from the insignificant area. As an example, the insignificant area may be divided into sub-blocks of a predetermined size, and then an embedded block in which watermark embedding is performed may be screened out from the sub-blocks.
Specifically, the insignificant region may be divided into multiple sub-blocks of 8 × 8, and in practice, if the length or width of the insignificant region cannot be divided by 8, pixels in the significant region need to be included, so that the insignificant region can be completely partitioned.
For each sub-block, the number of significant pixel points in the significance mask corresponding to each sub-block can be determined, and the sub-blocks of which the number is less than or equal to a preset threshold value are determined as embedded blocks for executing watermark embedding. Specifically, as shown in the following formula 2:
wherein MaskmRefers to the significance mask corresponding to the mth sub-block, and the value f after the summation of 64 pixelsmAnd L is a preset threshold value and represents the size of the intersection of the subblock region and the saliency region. Can be according to fmAnd determining the sub-block in which the watermark is embedded, namely if the number of significant pixels contained in 64 pixels corresponding to the sub-block is less than or equal to L, determining that the sub-block is an embedded block in which the watermark is embedded, otherwise, determining that the current sub-block is located in the significant sub-block, and not embedding the watermark.
And step S240 is executed to perform watermark embedding on the embedded block, and generate a watermark-containing image.
Watermark information corresponding to the initial image may be generated prior to performing watermark embedding. Identification information of an original image may be generally used as watermark information, and in order to allow other users to intuitively know whether the original image is infringed, identification information of an author of the original image (for example, information such as an author name and an identification number) may be used as the watermark information. Then, the identification information may be converted into a binary image, where each pixel on the image has only two possible values, for example, a black-and-white image, and then an image scrambling process may be performed on the binary image to generate a scrambled binary image as watermark information, where the image scrambling process may be arnold scrambling, and furthermore, the scrambled binary image may be configured to generate redundant information according to a redundancy mechanism, and then embed the redundant information into the image by using a subsequent algorithm, for example, in a case where the watermark information is 00001111, the redundant information is 000011110000111100001111. This makes the true watermark information unavailable to other users.
In the case where watermark information has been acquired, watermark embedding may be performed on the embedded block. Discrete cosine transform is performed for each embedded block, a matrix of discrete cosine coefficients is obtained, and then pairs of discrete cosine coefficients, preferably three pairs of discrete cosine coefficients, are selected from the matrix of discrete cosine coefficients. In an 8 by 8 matrix, the individual coefficients may be ordered in a predetermined manner, for example, in terms of energy size, and then the coefficients of the three pairs of energy intermediate frequencies may be selected and their positions in the matrix recorded.
And then, performing watermark embedding on the embedded block by using the discrete cosine coefficient pair to generate a watermark-containing image. Taking three pairs of discrete cosine coefficients as an example, the three pairs of coefficients can be arranged into one-dimensional vectors (C0, C1, C2, C3, C4, C5), and the 6 vectors are respectively even-numbered coefficients C0, C2, C4, and odd-numbered coefficients C1, C3, C5.
If the watermark information is 1, the absolute values of the coefficients C0, C2, and C4 are set to be larger than the absolute values of C1, C3, and C5. That is, according to the positive and negative of the even number coefficient, adding or subtracting an offset T on the original value to make the absolute value become larger; if the watermark is 0, the absolute values of the coefficients C0, C2, and C4 are set smaller than the absolute values of C1, C3, and C5. The offset T represents the watermark embedding strength.
Through the above processing, the frequency domain image with the embedded watermark can be obtained, and then the frequency domain image with the embedded watermark can be subjected to frequency domain-to-time domain conversion processing to obtain the watermark-containing image. Specifically, the embedded block in which the watermark is embedded may be subjected to inverse discrete cosine transform, and then, the frequency domain image in which the watermark is embedded may be subjected to inverse wavelet transform to obtain the watermarked image.
In summary, according to the image processing method of the exemplary embodiment of the present application, the non-significant region can be determined from the frequency domain image corresponding to the initial image, and the embedded block for performing watermark embedding is determined from the non-significant region under the condition that the quality of the main pipe of the image is not affected, so that the embedded block is adaptively selected without knowing the position information of the embedded block, and the problem of alignment of the watermark in the significant region of the embedded end and the detection end can be effectively reduced.
In addition, the above description is given by taking a single frame image as an example, in the implementation, the above processing may be performed on each frame image in the video, so as to achieve the purpose of performing copyright protection on the video.
To better describe the image processing method of the exemplary embodiment of the present application, a preferred embodiment will be described below with reference to fig. 3. Fig. 3 is a block diagram of an image processing method according to another exemplary embodiment of the present application.
As shown in fig. 3, first, a Y channel is extracted from the initial image, and an image corresponding to the Y channel is acquired, and it should be noted that this step may be omitted when the initial image is a grayscale image.
Subsequently, the Y-channel image is subjected to DWT transform. Wavelet decomposition can reduce the dimensionality of a high-resolution video, plays a role in concentrating signal energy, enables the invisibility of the watermark to be better by embedding the watermark in a high-frequency sub-band, and enables the video to still have good objective quality after the watermark is embedded. Considering objective quality of image containing watermark and performance of detecting watermark comprehensively, LH is selected as frequency domain image embedded with watermark.
At this time, based on the visual attention model, a saliency mask corresponding to the initial image may be acquired, and as shown in fig. 3, a non-salient region in the frequency domain image may be acquired using the saliency mask and the frequency domain image. Subsequently, embedded blocks that can be used for embedding the watermark can be determined from the insignificant areas. And finally, embedding the watermark into the embedded block and inversely transforming the frequency domain image embedded with the watermark information to obtain the image with the watermark information.
Processing will be performed on a given initial image in the steps shown in fig. 4, that is, watermark embedding is performed on the initial image 410 at the embedding end in accordance with the image processing method of the exemplary embodiment of the present application.
Fig. 4 is a diagram illustrating an embedding end embedding watermark information according to an image processing method of an exemplary embodiment of the present application. As described in FIG. 4, the Y channel may be read for initial image 410, resulting in image 420. A wavelet transform may be performed on image 420 to generate sub-image LL, sub-image HL, sub-image LH, and sub-image HH, and as shown in fig. 4, LH may be selected as image 430 for subsequent watermark embedding.
Further, processing may also be performed on the initial image 410 using the visual attention model already described above, generating an image 440 from which salient regions may be identified 440. Subsequently, in order to obtain the mask of the salient region, the image 440 may be subjected to a large threshold segmentation and downsampled to the size of the image 430 to obtain the salient mask 450, and then the non-salient region sub-blocks in the image 430 may be filtered in combination with the above mentioned formula 2 and the salient mask 450 to determine the embedded block for performing watermark embedding.
Having determined the embedded watermark image, Arnold may be scrambled to generate watermark information as described above, which is then embedded in the image 460 at the corresponding location.
The image 460 with embedded watermark information is then subjected to IDCT and IDWT transforms to obtain a watermarked image 470, and finally, the watermarked image 470 may be restored to the same image format as the original image 480.
Having described the watermark embedding process performed by the embedding side on the original image 410, the watermark extraction process performed by the detection side on the watermarked image 510 will be described below with reference to fig. 5. Fig. 5 is a diagram illustrating that a detecting end extracts watermark information according to an image processing method of an exemplary embodiment of the present application.
As shown in fig. 5, an embedded block for performing watermark embedding can be obtained for the watermarked image 510 according to the steps shown in fig. 4. Watermark information may then be extracted for the embedded block using discrete cosine coefficient pairs as mentioned above.
Specifically, 3 pairs of coefficients are selected in the middle frequency portion of the embedded block in the 8 × 8DCT block, and the 3 pairs of coefficients are arranged into one-dimensional vectors (C0, C1, C2, C3, C4, C5), where the 6 vectors are the even-numbered coefficients C0, C2, C4, and the odd-numbered coefficients C1, C3, C5, respectively. Then judging C in each sub-blockkAnd Ck+1Absolute value of (C) ifkAbsolute value greater than Ck+1The embedded watermark is 1, otherwise it is 0. And finally, acquiring the watermark image by utilizing Arnold inverse mapping.
An effect of the embedded terminal and the detection terminal for the saliency mask according to an exemplary embodiment of the present application will be described below with reference to fig. 6. Fig. 6 (a) is a saliency mask acquired at the embedding end, fig. 6 (b) is a saliency mask acquired at the detecting end, and fig. 6 (c) is a difference therebetween. Therefore, after the watermark is inserted into the initial image according to the image processing method of the exemplary embodiment of the application, the synchronism of the watermark containing positions of the embedded end and the detection end is effectively improved.
In addition, in the exemplary embodiment of the present application, the threshold value preset for determining the embedded block may have a significant influence on the synchronicity of the embedded terminal and the detection terminal. Different thresholds may affect the quality of the host containing the printed image. In implementation, it can be determined that as the threshold value increases, the error of the detected watermark-containing image gradually decreases, and as the threshold value increases, the difference between the embedded end and the detected end in the position of the embedded block is reduced, which improves the robustness.
Fig. 7 in order to more clearly understand the inventive concept of the exemplary embodiment of the present application, a block diagram of a data processing apparatus of the exemplary embodiment of the present application will be described below with reference to fig. 7. Those of ordinary skill in the art will understand that: the apparatus in fig. 7 shows only components related to the present exemplary embodiment, and common components other than those shown in fig. 7 are also included in the apparatus.
Fig. 7 shows a block diagram of an image processing apparatus of an exemplary embodiment of the present application. It should be noted that the image processing apparatus may be a server that executes the image processing method shown in fig. 1.
Referring to fig. 7, the apparatus includes, at a hardware level, a processor, an internal bus, and a computer-readable storage medium, wherein the computer-readable storage medium includes a volatile memory and a non-volatile memory. The processor reads the corresponding computer program from the non-volatile memory and then runs it. Of course, besides the software implementation, the present application does not exclude other implementations, such as logic devices or a combination of software and hardware, and the like, that is, the execution subject of the following processing flow is not limited to each logic unit, and may also be hardware or logic devices.
Specifically, the processor performs the following operations: acquiring a frequency domain image corresponding to an initial image to be processed; determining non-salient regions in the frequency domain image; determining an embedded block for performing watermark embedding from the insignificant region; and executing watermark embedding on the embedded block to generate a watermark-containing image.
Optionally, the step of acquiring, by the processor, a frequency domain image corresponding to the initial image to be processed includes: acquiring a channel image corresponding to the brightness information by extracting the brightness information of the initial image; performing wavelet transformation on the channel image to generate a plurality of subgraphs corresponding to the initial image; determining the frequency domain image from the plurality of subgraphs.
Optionally, the processor in performing step determining the frequency domain image from the plurality of subgraphs comprises: selecting a vertical-direction detail subgraph from the plurality of subgraphs as the frequency-domain image.
Optionally, the processor in performing the step of determining the non-significant region in the frequency domain image comprises: performing attention area segmentation on the initial image by using a visual attention model to obtain a saliency mask corresponding to a saliency area; and combining the saliency mask and the frequency domain image to obtain the non-salient region.
Optionally, the processor in performing the step of obtaining the non-significant region by combining the significance mask with the frequency domain image comprises: performing down-sampling on the significance mask to obtain the down-sampled significance mask, so that the resolution of the down-sampled significance mask is the same as that of the frequency domain image; and corresponding the downsampled significance mask and the frequency domain image on each pixel point one by one to obtain the non-significant region.
Optionally, the performing, by the processor, attention region segmentation on the initial image by using a visual attention model in the performing step to obtain a saliency mask corresponding to a saliency region includes: generating an attention area of the initial image by using the visual attention model; and based on a maximum inter-class variance method, segmenting the attention area to obtain the significance mask.
Optionally, the visual attention model comprises a texture and brightness based visual attention model.
Optionally, the processor, in the step of performing, determining from the insignificant area an embedded block in which to perform watermark embedding, comprises: dividing the insignificant area into a plurality of sub-blocks of a predetermined size; and screening out an embedded block for executing watermark embedding from the plurality of sub-blocks.
Optionally, the processor in the step of performing, sifts out an embedded block for performing watermark embedding from the plurality of sub-blocks comprises: aiming at each sub-block, acquiring the number of significant pixel points corresponding to each sub-block; and determining the sub-blocks with the number less than or equal to a preset threshold value as embedded blocks for executing watermark embedding.
Optionally, the processor performing watermark embedding on the embedded block in the performing step to generate a watermarked image comprises: performing discrete cosine transform on each embedded block to obtain a discrete cosine coefficient matrix; selecting discrete cosine coefficient pairs from the discrete cosine coefficient matrix; and performing watermark embedding on the embedded block by utilizing the discrete cosine coefficient pair to generate a watermark-containing image.
Optionally, the processor in performing step performing watermark embedding on the embedded block using the transform coefficients to generate a watermarked image comprises: performing watermark embedding on the embedded block by using the transformation coefficient pair to obtain a frequency domain image embedded with the watermark; and (3) performing frequency domain to time domain conversion processing on the frequency domain image embedded with the watermark to obtain the watermark-containing image.
Optionally, the processor may further perform the steps of: determining identification information corresponding to the initial image; converting the identification information into a binary image; and performing image scrambling processing on the binary image to generate a scrambled binary image as watermark information.
Further, according to an exemplary embodiment of the present application, the processor may further perform the steps of: acquiring a frequency domain image corresponding to an initial image to be processed; determining non-salient regions in the frequency domain image; determining an embedded block for performing watermark embedding from the insignificant region; watermark information is extracted from the embedded block.
Optionally, the processor, in the step of extracting watermark information from the embedded block, includes: extracting a watermark matrix from the embedded block by using a discrete cosine transform coefficient pair mode; and converting the watermark matrix into a watermark image in an inverse mapping mode.
In summary, the image processing apparatus according to the exemplary embodiment of the present application may determine the non-significant region from the frequency domain image corresponding to the initial image, and determine the embedded block for performing watermark embedding from the non-significant region without affecting the quality of the main image, thereby implementing adaptive selection of the embedded block without knowing the position information of the embedded block, and thus effectively reducing the problem of alignment of the watermark in the significant region of the embedded end and the detection end.
It should be noted that the execution subjects of the steps of the method provided in embodiment 1 may be the same device, or different devices may be used as the execution subjects of the method. For example, the execution subject of steps 21 and 22 may be device 1, and the execution subject of step 23 may be device 2; for another example, the execution subject of step 21 may be device 1, and the execution subjects of steps 22 and 23 may be device 2; and so on.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.
Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The above description is only an example of the present application and is not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.
Claims (16)
1. An image processing method, comprising:
acquiring a frequency domain image corresponding to an initial image to be processed;
determining non-salient regions in the frequency domain image;
determining an embedded block for performing watermark embedding from the insignificant region;
and executing watermark embedding on the embedded block to generate a watermark-containing image.
2. The method of claim 1, wherein obtaining a frequency domain image corresponding to the initial image to be processed comprises:
acquiring a channel image corresponding to the brightness information by extracting the brightness information of the initial image;
performing wavelet transformation on the channel image to generate a plurality of subgraphs corresponding to the initial image;
determining the frequency domain image from the plurality of subgraphs.
3. The method of claim 2, wherein determining the frequency domain image from the plurality of subgraphs comprises:
selecting a vertical-direction detail subgraph from the plurality of subgraphs as the frequency-domain image.
4. The method of claim 1, wherein determining non-salient regions in the frequency domain image comprises:
performing attention area segmentation on the initial image by using a visual attention model to obtain a saliency mask corresponding to a saliency area;
and combining the saliency mask and the frequency domain image to obtain the non-salient region.
5. The method of claim 4, wherein obtaining the non-salient region by combining a saliency mask with the frequency domain image comprises:
performing down-sampling on the significance mask to obtain the down-sampled significance mask, so that the resolution of the down-sampled significance mask is the same as that of the frequency domain image;
and corresponding the downsampled significance mask and the frequency domain image on each pixel point one by one to obtain the non-significant region.
6. The method of claim 4, wherein performing attention region segmentation on the initial image using a visual attention model to obtain a saliency mask corresponding to a saliency region comprises:
generating an attention area of the initial image by using the visual attention model;
and based on a maximum inter-class variance method, segmenting the attention area to obtain the significance mask.
7. The method of claim 4, wherein the visual attention model comprises a texture and brightness based visual attention model.
8. The method of claim 1, wherein determining an embedded block from the insignificant area where watermark embedding is performed comprises:
dividing the insignificant area into a plurality of sub-blocks of a predetermined size;
and screening out embedded blocks for executing watermark embedding from the plurality of sub-blocks.
9. The method of claim 1, wherein screening the plurality of sub-blocks for an embedded block that performs watermark embedding comprises:
for each sub-block, determining the number of significant pixel points of each sub-block in the significance mask;
and determining the sub-blocks with the number less than or equal to a preset threshold value as embedded blocks for executing watermark embedding.
10. The method of claim 1, wherein performing watermark embedding on the embedded blocks to generate a watermarked image comprises:
performing discrete cosine transform on each embedded block to obtain a discrete cosine coefficient matrix;
selecting discrete cosine coefficient pairs from the discrete cosine coefficient matrix;
and performing watermark embedding on the embedded block by utilizing the discrete cosine coefficient pair to generate a watermark-containing image.
11. The method of claim 10, wherein performing watermark embedding on the embedded blocks using the transform coefficient pairs to generate a watermarked image comprises:
performing watermark embedding on the embedded block by using the transformation coefficient pair to obtain a frequency domain image embedded with the watermark;
and (3) performing frequency domain to time domain conversion processing on the frequency domain image embedded with the watermark to obtain the watermark-containing image.
12. The method of claim 1, further comprising:
determining identification information corresponding to the initial image;
converting the identification information into a binary image;
and performing image scrambling processing on the binary image to generate a scrambled binary image as watermark information.
13. An image processing method, comprising:
acquiring a frequency domain image corresponding to an initial image to be processed;
determining non-salient regions in the frequency domain image;
determining an embedded block for performing watermark embedding from the insignificant region;
watermark information is extracted from the embedded block.
14. The method of claim 13, wherein extracting watermark information from the embedded block comprises:
extracting a watermark matrix from the embedded block by using a discrete cosine transform coefficient pair mode;
and converting the watermark matrix into a watermark image in an inverse mapping mode.
15. A computer readable storage medium having computer instructions stored thereon that, when executed, implement the method of any of claims 1 to 14.
16. An image processing apparatus characterized by comprising:
a processor; and
a memory arranged to store computer executable instructions that, when executed, cause the processor to perform the method of any of claims 1 to 14.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113989096A (en) * | 2021-12-27 | 2022-01-28 | 山东大学 | Robust image watermarking method and system based on deep learning and attention network |
| CN114666619A (en) * | 2022-03-11 | 2022-06-24 | 平安国际智慧城市科技股份有限公司 | Video file watermarking method, device, equipment and storage medium |
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2020
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113989096A (en) * | 2021-12-27 | 2022-01-28 | 山东大学 | Robust image watermarking method and system based on deep learning and attention network |
| CN113989096B (en) * | 2021-12-27 | 2022-04-12 | 山东大学 | Robust image watermarking method and system based on deep learning and attention network |
| CN114666619A (en) * | 2022-03-11 | 2022-06-24 | 平安国际智慧城市科技股份有限公司 | Video file watermarking method, device, equipment and storage medium |
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