JP2006079348A - Electronic watermark embedding target selection method and device - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To easily select a target suitable for embedding of an electronic watermark from a plurality of electronic watermark embedding targets. <P>SOLUTION: An electronic watermark evaluation target selection device 100 automatically selects a content satisfying conditions required by a user or a system from a plurality of contents, such as image quality, or a detection rate. An evaluation part 110 evaluates each static image constituting a moving image inputted from the outside by use of an index of a PSNR (Peak Signal-to-Noise Ratio) in time of watermark embedding, a WNR (Watermark-to-Noise Power Ratio) in time of the watermark embedding, a correlation coefficient between a watermark bit and a watermark detection value, or the like. A selection part 120 selects the static image satisfying a reference set by the user or the system from the moving image on the basis of an evaluation result by the evaluation part 110. <P>COPYRIGHT: (C)2006,JPO&NCIPI

Description

  The present invention relates to a digital watermark technique, and more particularly to a digital watermark embedding target selection method and apparatus for selecting a target for embedding a digital watermark from a plurality of targets.

  In recent years, a technique for embedding a digital watermark in content such as sound, an image, and a moving image has been put into practical use. Digital watermarks can be embedded in various contents in a form almost unrecognizable by human eyes and ears, and are mainly used for DRM (Digital Rights Management) and the like.

Digital watermarks are used not only for copyright management but also for other purposes. For example, with the widespread use and enhancement of functions of camera-equipped mobile phones, a service for photographing printed matter with a digital watermark and acquiring content on the Internet associated with the digital watermark is being put into practical use (for example, Patent Document 1). reference).
Japanese translation of PCT publication No. 2002-544637

  When a digital watermark is embedded in a series of contents, it may be embedded in some contents instead of all contents. For example, in the above service, in order to sell moving image contents such as movies, a catalog of printed materials is sent to the user by direct mail or the like. In the catalog, one or several still images cut out from the moving image content are posted for each title. A digital watermark indicating title identification information or the like is embedded in these still images.

  As described above, when selecting a content to be embedded with a digital watermark from a plurality of contents, it is general that a creator such as the printed matter arbitrarily selects the content. In this case, an embedded digital watermark with a low detection rate may be selected due to the influence of the frequency distribution of the selected content. In some cases, content with a high degree of deterioration in image quality and sound quality is selected.

  The present invention has been made in view of such circumstances, and an object of the present invention is to select a digital watermark embedding target selection method and apparatus that can easily select a target suitable for embedding a digital watermark from a plurality of digital watermark embedding targets. Is to provide.

  In order to solve the above-mentioned problem, a digital watermark embedding target selection method according to an aspect of the present invention includes a step of electronically inputting a plurality of digital watermark embedding targets, a step of reading a predetermined standard, and a reference of the read standard And selecting a candidate to embed the digital watermark. The “predetermined reference” may be, for example, any one of a digital watermark detection rate, a quality deterioration level after digital watermark embedding, a digital watermark embedding data amount, or a combination of two or more thereof.

  According to this aspect, by referring to a predetermined standard, a target suitable for embedding a digital watermark can be easily selected from a plurality of digital watermark embedded targets.

  Another aspect of the present invention is a digital watermark embedding target selection device. This apparatus includes an evaluation unit that evaluates a plurality of digital watermark embedding targets with reference to a predetermined standard, and a selection unit that selects candidates for embedding a digital watermark with reference to an evaluation result by the evaluation unit. The “predetermined criterion” may be any one of a digital watermark detection rate, a quality deterioration level after embedding a digital watermark, and a data amount that can be embedded with a digital watermark.

  According to this aspect, by referring to a predetermined standard, a target suitable for embedding a digital watermark can be easily selected from a plurality of digital watermark embedded targets.

  The evaluation unit may perform evaluation by referring to the degree of quality degradation after embedding the digital watermark and the detection rate of the digital watermark as predetermined criteria. According to this aspect, it is possible to easily select a digital watermark embedding target that satisfies the standards regarding the quality degradation level and the detection rate.

  The evaluation unit may fix either one of the quality degradation level and the detection rate at a predetermined value, and determine whether the other value satisfies a predetermined condition. According to this aspect, it is possible to select an electronic watermark embedding target with emphasis on a desired reference while achieving a balance between a plurality of references.

  You may further provide the input part which inputs at least one of a predetermined value and predetermined conditions from the outside. According to this aspect, it is possible to easily select a digital watermark embedding target that reflects a request of a user or a system.

  Yet another aspect of the present invention is a data structure. In this data structure, the watermark is linked to the selected candidate to be embedded. According to this aspect, since the digital watermark is linked to a candidate to be embedded such as a moving image, for example, content with a digital watermark such as a paper medium can be easily generated.

  It should be noted that any combination of the above-described constituent elements and a conversion of the expression of the present invention between a method, an apparatus, a system, a computer program, a recording medium, and the like are also effective as an aspect of the present invention.

  According to the present invention, it is possible to easily select a target suitable for embedding a digital watermark from a plurality of digital watermark embedded targets.

Embodiment 1
FIG. 1 is a configuration diagram of a digital watermark embedding target selection device 100 (hereinafter referred to as a selection device 100) according to the first embodiment. The configuration of the selection device 100 can be realized in hardware by a CPU, memory, or other LSI of an arbitrary computer, and is realized in software by a program having an encoding function loaded in the memory. Here, the functional blocks realized by the cooperation are depicted. Accordingly, those skilled in the art will understand that these functional blocks can be realized in various forms by hardware only, software only, or a combination thereof.

  The selection device 100 includes an evaluation unit 110, a selection unit 120, and an input unit 130, and automatically selects a plurality of contents satisfying conditions required by the user and the system, such as image quality and detection rate. FIG. 1 shows an example in which one or several still images are selected from a moving image that is a set of a plurality of still images to generate a pamphlet including a digest of the moving image.

  The evaluation unit 110 compares the pixel dispersion value, the PSNR (Peak Signal-to-Noise Ratio) at the time of watermark embedding, the WNR (Watermark-to-Noise Power Ratio) at the time of watermark embedding, or the phase between the watermark bit and the watermark detection value. Each still image constituting a moving image input from the outside is evaluated using an index such as the number of relations. These index parameters are associated with the embedding strength under the same visual degradation. If the embedding strength is high, there is a high possibility that a digital watermark will remain even after printing or capturing an image from a printed material, and the detection rate of the digital watermark is improved. Conversely, if the embedding strength is low, the detection rate decreases. Details of each index will be described later.

  Based on the evaluation result by the evaluation unit 110, the selection unit 120 selects, from the moving image, a still image that satisfies the criteria set by the user or the system. For example, an index parameter exceeding a certain threshold is automatically selected as a candidate still image.

  The input unit 130 inputs various parameters, various selection criteria, threshold values used in the evaluation unit 110 and / or the selection unit 120 when the evaluation unit 110 and the selection unit 120 evaluate and select each still image. . These parameters and the like may be arbitrarily set or selected from predetermined candidates by the user, or may be automatically set by the system according to the application. For example, in a system where detection errors must be reduced as much as possible, the threshold can be set strictly.

  FIG. 2 is a flowchart illustrating the overall operation of the selection device 100 according to the first embodiment. First, the user or system inputs a plurality of contents to be selected to the selection device 100 (S10). For example, a moving image or a set of still images obtained by capturing moving images at a predetermined interval is input. Here, N in the figure represents the number of contents. In addition, parameters such as image quality, that is, visual degradation level are input from the input unit 130 (S12). The degree of visual deterioration depends on the human visual model. The human visual model has a characteristic that even if a digital watermark is embedded with the same strength, a complex image such as a texture pattern, that is, an image containing a lot of high-frequency components is harder to recognize for human vision. Here, the input parameters are common parameters for the plurality of contents input as described above. In this example, the following processing is performed on the assumption that the visual deterioration degree is set to be constant.

  Next, the user or the system inputs selection criteria from the input unit 130 (S14). In this example, the pixel dispersion value, the PSNR at the time of embedding a watermark, the WNR at the time of embedding a watermark, or a correlation coefficient between a watermark bit and a watermark detection value can be used as a selection criterion. One of these selection criteria is selected. Alternatively, a plurality may be selected and used together. In that case, the content selected by any selection criterion can be finally selected.

  Next, the user or the system inputs a threshold value to be used according to the input selection criterion from the input unit 130 (S16). The number of selection candidates can be adjusted to some extent by setting the threshold and the parameters such as the image quality.

  The evaluation unit 110 evaluates the plurality of input contents using the input parameters and selection criteria, and the selection unit 120 determines the evaluation value of each content by the evaluation unit 110 and the input threshold value. And a candidate list is created (S18).

Next, a specific example of evaluation and selection using the above selection criteria will be described. First, as a first example, an example in which the content variance value is used as a selection criterion will be described. FIG. 3 is a flowchart illustrating an example of selection based on the content variance value. First, the evaluation unit 110 sets an initial value for the parameter s to be used (S20). Here, the parameter s is set to 0. Next, the evaluation unit 110 determines whether or not the parameter s is less than the input content number N (S22). When the parameter s is less than the number of contents N (Y in S22), the evaluation unit 110 calculates the variance value σ ² _s of the sth content (S24).

Hereinafter, an example of a method for calculating the variance value σ ² _s of each image will be described assuming that the content is an image. First, an image of size W × H is divided into K = W / k ₁ × H / k ₂ using blocks of size k ₁ × k ₂ . When a pixel value at coordinates (p, q) is v (p, q), a set of pixel values B _{i, j} in each block is defined as follows.

B _{i, j} = {v (ik ₁ + 1, jk ₂ + m)} _{l = 0, m = 0} ^{l = k1-1, m = k2-1}
i = 0, 1,. . . , W / k ₁ −1, j = 0, 1,. . . , H / k ₂ −1

The variance value σ ² _{i, j} of each B _{i, j} is expressed by the following equation.
_{^{σ 2 i, j = (1}} / k 1 × k 2) Σ l = 0 k1-1 Σ m = 0 k2-1 (v (ik 1 + l, jk 2 + m) -v - i, j) 2
_{^{v - i, j = (1}} / k 1 × k 2) Σ l = 0 k1-1 Σ m = 0 k2-1 v (ik 1 + l, jk 2 + m)
Here, v ⁻ _{i, j} represents the average of the pixel values of each block.

The block average value σ ² _v of the variance value σ ² _{i, j} of each B _{i, j} is expressed by the following equation.
σ ² _v = 1 / KΣ _{i = 0} ^{W / k1-1} Σ _{j = 0} ^{H / k2-1} σ ² _{i, j}
This block average value σ ² _v is set as a variance value of the entire image.

Returning to FIG. 3, the selection unit 120 compares the variance value σ ² _{s of the} sth content with the input threshold T (S26). When the variance value σ ² _s exceeds the threshold T (Y in S26), the s-th content is added to the candidate list (S28). Here, when a watermark is embedded under a certain visual deterioration, the watermark can be embedded more strongly as the variance value σ ² _s is larger. This is because human vision is insensitive to texture changes with a large variance value σ ² _s . Therefore, an image having a variance value σ ² _s larger than the input threshold value T is selected as a candidate. Then, the parameter s is incremented (S30), and the process proceeds to step S22.

In step S26, when the variance value σ ² _{s of the} sth content is equal to or smaller than the threshold T (N in S26), the parameter s is incremented without adding the sth content to the candidate list (S30), The process proceeds to step S22. In step S22, when the parameter s becomes equal to the number of contents N (N in S22), the evaluation selection process based on the variance value is terminated. Note that the evaluation selection process based on the variance value does not evaluate by embedding each content with a digital watermark. Therefore, in step S12 of the flowchart shown in FIG. 2, an image quality is input to assume a certain degree of visual deterioration. There is no need. Since this evaluation selection process can be evaluated without actually embedding a digital watermark in a digital watermark embedding target, a candidate for embedding can be selected with a simple process.

  Next, an example using PSNR as a selection criterion will be described as a second example. FIG. 4 is a flowchart showing an example of selection based on PSNR. The flowchart of FIG. 4 has the same basic structure as the flowchart of FIG. 3, and the same processes are denoted by the same reference numerals. Hereinafter, different processes will be described. When the parameter s is less than the number of contents N (Y in S22), the evaluation unit 110 embeds a randomly generated digital watermark in the s-th content s based on the input visual degradation degree, Content s ′ is acquired (S34). Then, the PSNR (p_s) between the content s before embedding and the content s ′ after embedding is calculated (S35).

  Hereinafter, the calculation method of PSNR is shown. If the pixel value of the original image is v (p, q) and the pixel value of the digital watermarked image is w (p, q), the PSNR can be calculated as follows.

PSNR = ₁₀ log ₁₀ (t ² / σ ² _w )
σ ² _w = (1 / W × H) Σ _{p = 0} ^W−1 Σ _{q = 0} ^H−1 (v (p, q) −w (p, q)) ²
Here, t is a value of the number of gradations-1 of the image. For example, in an image in which one pixel is expressed by 8 bits, t = 255.

  Returning to FIG. 4, the selection unit 120 compares the PSNR of the sth content with the input threshold T (S36). If the PSNR is less than the threshold T (Y in S36), the s-th content is added to the candidate list (S28). The subsequent processing is the same as that of the flowchart of FIG. Here, the smaller the PSNR, the larger the difference between the original image and the watermarked image, that is, the watermark is strongly buried under a certain visual deterioration. Therefore, a candidate whose PSNR is smaller than the input threshold value T is selected as a candidate. The digital watermark embedded in the image selected by the above processing has strong resistance to geometric changes caused by shooting and image compression such as JPEG (Joint Photographic Coding Experts Group) and MPEG (Moving Picture Experts Group). Can do.

  Next, an example using WNR as a selection criterion will be described as a third example. FIG. 5 is a flowchart illustrating an example of selection based on WNR. The flowchart in FIG. 5 has the same basic structure as the flowchart in FIG. 3, and the same processes are denoted by the same reference numerals. Hereinafter, different processes will be described. When the parameter s is less than the number of contents N (Y in S22), the evaluation unit 110 embeds a randomly generated digital watermark in the s-th content s based on the input visual degradation degree, Content s ′ is acquired (S44). Then, the WNR (W_s) of the sth content is calculated (S45).

  Hereinafter, a method for calculating WNR will be described. WNR is an index indicating the strength of the digital watermark with respect to assumed noise. The calculation method depends on a digital watermark embedding method and assumed noise. Hereinafter, a blind watermark model in which an original image cannot be obtained at the time of detection is targeted.

  FIG. 6 is a block diagram showing a blind watermark model. The blind watermark model can be expressed equivalently to a communication path in which the watermark bit x deteriorates due to the influence of the original image v and noise d. This model can also model, for example, noise when going from analog data by printing to digital data by imaging with a digital camera or the like.

  The message m is converted into a watermark bit x by the encoder 200 and then embedded in the original image v. The watermarked image w becomes a degraded image w ′ under the influence of the noise d. The decoder 300 extracts the embedded message m ′ from the degraded image w ′. In the blind watermark model, since the original image v cannot be used at the time of decoding, v is regarded as noise with respect to the watermark bit x like d.

Assuming _x to N (0, σ ² _x ), _{v to} N (0, σ ² _v ), and _{d to} N (0, σ ² _d ) in which the watermark bit x, the original image v, and the noise d are standardized, respectively. To do. Here, N (u, σ ² ) is a Gaussian distribution having an average value u and a variance value σ ² . Using these σ ² _x , σ ² _v , and σ ² _d , the WNR can be defined as follows.

WNR = ₁₀ log ₁₀ (σ ² _x / (σ ² _v + σ ² _d )) (A)
When the influence of the noise d can be ignored (σ ² _d << σ ² _v )
WNRo ₁₀ log ₁₀ (σ ² _x / σ ² _v ) (B)
It becomes.

As an example, an n-bit watermark x _i I {1, −1}, i = 0, 1,. . . Consider a case where n-1 is embedded using a patchwork method. Details of the patchwork method are described in the document `` W. Bender, D. Gruhl, N. Morimoto, and A. Lu. Techniques for data hiding. IBM Syst. J., 35 (3-4), 1996. '' ing.

In this case, the random 2n-number of selected points in ^{_{V + = (v + 0,}} v + 1, ···, v + n-1) and ^{_{V - = (v - 0,}} v - 1, ··· , V ⁻ _n−1 ) is an embedding target. Samples w + i and wi after embedding can be expressed as follows.

w + i = v ⁺ _i + α + i · xi
_{^{w-i = v - i -α}} -i · xi
Here, α + i and α-i30, which are set under constant visual deterioration.

When it is assumed that the influence of the noise d is small, the WNR corresponding to the above equation (B) can be defined as follows.
WNR = P / σ2
P = 1 / nΣ _{i = 0} ⁿ⁻¹ | (w + i−w−i) | ²
σ2 = 1 / nΣi _{= 0} ⁿ⁻¹ | (w + i−w−i) −√P · x _i | ²

When it is affected by the noise d, it deteriorates to w ′ _i = Attack (w _i ). At this time, the WNR corresponding to the formula (A) can be defined as follows.
WNR = P / σ2
P = 1 / nΣ _{i = 0} ⁿ⁻¹ | (w ′ + i−w′−i) | ²
σ2 = 1 / nΣ _{i = 0} ⁿ⁻¹ | (w ′ + i−w′−i) −√P · x _i | ²

  Returning to FIG. 5, the selection unit 120 compares the WNR of the sth content with the input threshold value T (S46). If the WNR exceeds the threshold T (Y in S46), the s-th content is added to the candidate list (S28). The subsequent processing is the same as that of the flowchart of FIG. Here, the greater the WNR, the greater the strength of the digital watermark relative to the strength of the image, so that the digital watermark is more easily detected. In addition, since the blind watermark model does not require an original image on the decoding side, the blind watermark model is not limited to an application and is highly versatile.

  Next, an example using a correlation coefficient as a selection criterion will be described as a fourth example. FIG. 7 is a flowchart showing an example of selection based on the correlation coefficient. The basic structure of the flowchart of FIG. 7 is the same as that of the flowchart of FIG. 3, and the same reference numerals are given to the same processes. Hereinafter, different processes will be described. When the parameter s is less than the number of contents N (Y in S22), the evaluation unit 110 embeds a randomly generated digital watermark in the s-th content s based on the input visual degradation degree, Content s ′ is acquired (S54). Then, a correlation coefficient ρ_s between the watermark bit string in the s-th content s and the detected value of the bit string is calculated (S55).

Hereinafter, an example of a method for calculating the correlation coefficient ρ_s will be described. Assume that the n-bit watermark is x = (x ₀ ,..., x _n−1 ), and the watermark detection value is z = (z ₀ ,..., z _n−1 ).

At this time, the correlation coefficient ρ between the watermark bit x and the detected value z is calculated as follows.
ρ = (x, z)
Here, (x, z) is an inner product of x and z.

Further, the standardized correlation coefficient ρ _s is calculated as follows.
ρ _s = (x, z) / (‖x‖‖z‖)

Returning to FIG. 7, the selection unit 120 compares the correlation coefficient ρ_s of the sth content with the input threshold T (S56). When the correlation coefficient ρ_s exceeds the threshold T (Y in S56), the s-th content is added to the candidate list (S28). The subsequent processing is the same as that of the flowchart of FIG. Here, the larger the correlation coefficient ρ and the standardized correlation coefficient ρ _s , the higher the possibility that a detected value having a high correlation with the watermark x will be obtained from the degraded image w ′. Therefore, a content having a correlation coefficient ρ or a standardized correlation coefficient ρ _s larger than the input threshold value T is selected as a candidate content. According to the evaluation selection process based on this correlation coefficient, it is possible to select content with a high detection rate.

  As described above, according to the first embodiment, an object suitable for embedding a digital watermark can be easily selected from a plurality of digital watermark embedding objects. Thereby, for example, it is possible to reduce the labor of generating a digital watermarked brochure from a set of scene images of a moving image.

Embodiment 2
Embodiment 2 relates to a content acquisition system using digital watermark technology. FIG. 8 shows an example of a content acquisition system using a digital watermarked printed material according to the second embodiment. The brochure 500 is a paper medium on which one or more digest images of various contents are printed. In FIG. 8, four digests 502, 504, 506, and 508 are printed. The content includes movies, game software, commercial videos of various products, music, ringtones, and the like. A special image such as a promotion video of a singer, a live video, or a wallpaper on a standby screen of a mobile phone may be added to the music or the incoming melody.

  A digital watermark is embedded in the digests 502 to 508. The creator of the pamphlet 500 can select an image to be digested from various moving image contents using the selection device 100 described in the first embodiment. Further, the reduced image of the package may be digests 502-508.

  The portable terminal 400 includes a communication unit 410 and an imaging unit 420. As the mobile terminal 400, a camera-equipped mobile phone, a camera-equipped PDA (Personal Digital Assistant), a camera-equipped remote control, or the like can be used. A PDA, a remote controller, or the like can communicate with an external device using a PHS (Personal Handyphone System) line or a wireless LAN (Local Area Network). The portable terminal 400 images one of the digests in the pamphlet 500 from the imaging unit 420 in accordance with a user operation. Then, the captured image data is transmitted from the communication unit 410 to the digital watermark detection apparatus 600 via the network.

  The digital watermark detection apparatus 600 includes a communication unit 610 and a conversion table 620. It consists mainly of servers. The digital watermark detection apparatus 600 receives image data transmitted from the portable terminal 400 from the communication unit 610 and detects a digital watermark from the image data. The digital watermark may be composed of a character string, that is, a bit string capable of ASCI conversion.

  The conversion table 620 associates the character string acquired from the digital watermark with information including the location of the content management apparatus 700 that manages the content corresponding to the image and the identification information of the content. The digital watermark detection apparatus 600 refers to the conversion table 620 and converts the bit string into information including the location and the identification information. Then, the digital watermark detection apparatus 600 returns the converted information from the communication unit 610 to the mobile terminal 400 via the network. When the Internet is used as a network, a URL (Uniform Resource Locator) may be used as information indicating the location of the content management apparatus 700.

  The portable terminal 400 transmits the identification information to the content management apparatus 700 that is returned from the digital watermark detection apparatus 600 via the network. At this time, when it is desired to distribute the content to devices other than the portable terminal 400 such as a personal computer, a video player, and a game machine, information indicating the location of these devices, for example, an IP address is also added and transmitted. . In addition, when mailing on a recording medium such as a DVD-ROM is desired, the address is added to the home or company.

  For the above processing, the URL information can be expressed by a 32-bit IP address and directly embedded in the digests 502 to 508 of the pamphlet 500. In that case, if the portable terminal 400 is equipped with a digital watermark detection function, the digital watermark detection apparatus 600 is not necessary, and the content management apparatus 700 can be directly accessed.

  The content management apparatus 700 includes a communication unit 710 and a content storage unit 720. It consists mainly of servers. The content storage unit 720 stores the types of content as described above, and corresponds to the identification information. The content management apparatus 700 receives the identification information transmitted from the mobile terminal 400 from the communication unit 710 and refers to the content storage unit 720 to identify the content to be provided. Then, the content is provided to the user by a designated method. At this time, the content file may be downloaded or stream-reproduced. At that time, a billing process may be performed.

  In the above, the example in which the digital watermark of the image photographed by the user is detected and the content is provided has been described. Digests 502 to 508 indicating the appearance of various products may be posted on the pamphlet 500, and product identification information may be transmitted to a product management device corresponding to the content management device 700 using the same method as described above. Thereafter, the ordered product is delivered to the user's home or company.

  As described above, according to the second embodiment, the user can easily obtain desired content and products simply by taking an image printed on a paper medium. On the other hand, in catalog mail order that is generally performed, as a means for purchasing products listed in the catalog, work for copying product numbers and the like to postcards has occurred. In this regard, according to the second embodiment, it is only necessary to take an image, and such trouble can be saved. There is no need to search the Internet. Furthermore, the sales side can urge the customer to purchase the product by creating a direct mail in which an image with a digital watermark matching the customer is printed.

  The present invention has been described based on the embodiments. It is to be understood by those skilled in the art that the embodiments are exemplifications, and that various modifications can be made to combinations of the respective components and processing processes, and such modifications are within the scope of the present invention. Such a modification is shown below.

  In the first embodiment, four examples of evaluation selection processing from a plurality of contents are shown. Hereinafter, an example of a combination method of these processes will be described. First, evaluation selection processing based on the dispersion value of each content is used for pre-checking, and evaluation selection targets are narrowed down to some extent, and then evaluation selection based on other PSNRs and WNRs is performed. For example, the upper half content having a large variance value is selected, and the final candidate is selected from these contents using other criteria. Since the selection evaluation process based on the variance value does not need to actually embed a digital watermark and the calculation amount is relatively small, the calculation amount can be reduced as compared with the case where all contents are evaluated by PSNR or the like.

  In the first embodiment, each evaluation selection process creates a candidate list in comparison with the input threshold value T. In this regard, a plurality of contents may be sorted in the order of higher or lower index such as a variance value. A plurality of contents can be easily sorted by a general algorithm. And you may select several of the upper ranks. According to this, a plurality of contents can be evaluated more finely.

  In the first embodiment, the content to be embedded in the digital watermark is selected from the viewpoint of the digital watermark detection rate and the visual degradation level in the content evaluation selection process. In this regard, in the case of a movie or commercial video, it is desirable to select a characteristic scene as much as possible. Therefore, an image including a specified object such as a logo or a person may be used in combination with a method of extracting by pattern matching or the like. According to this, it is possible to select a content to be embedded with a digital watermark from various viewpoints.

  In the first exemplary embodiment, the example in which content with a high digital watermark detection rate is selected when the image quality degradation level is constant has been described. In this regard, an example in which the detection rate is constant and content having a low image quality deterioration level is selected may be used. Also in this case, evaluation selection can be performed by various algorithms.

  In the first embodiment, an example in which an image is mainly selected has been described. In this respect, the present invention can also be applied to the case where a digital watermark is embedded in audio. For example, the speech may be divided into predetermined sections, the variance value of each section may be calculated, and the section in which the digital watermark is to be embedded may be selected.

  Furthermore, in Embodiment 2, the URL and content identification information, which are digital watermark information embedded in an image, may be embedded separately. Thus, noise tolerance can be improved by using a double watermark. In addition, the technology according to the second embodiment can be applied as an input terminal to a system in which an existing DRM is performed.

It is a figure which shows the structure of the electronic watermark embedding target selection apparatus which concerns on Embodiment 1. FIG. 3 is a flowchart illustrating an overall operation of the selection device according to the first embodiment. It is a flowchart which shows the example which selects on the basis of a dispersion value. It is a flowchart which shows the example which selects on the basis of PSNR. It is a flowchart which shows the example which selects on the basis of WNR. It is a block diagram which shows a blind watermark model. It is a flowchart which shows the example which selects on the basis of a correlation coefficient. It is a figure which shows an example of the content acquisition system using the printed matter with a digital watermark concerning Embodiment 2. FIG.

Explanation of symbols

  100 digital watermark embedding target selection device, 110 evaluation unit, 120 selection unit, 130 input unit, 200 encoder, 300 decoder, 400 portable terminal, 410 communication unit, 420 imaging unit, 500 brochure, 502, 504, 506, 508 digest, 600 electronic watermark detection device, 610 communication unit, 620 conversion table, 700 content management device, 710 communication unit, 720 content storage unit.

Claims (7)

Electronically inputting a plurality of watermark embedding targets;
Reading predetermined criteria;
Selecting a candidate to embed a digital watermark with reference to the read criteria;
A method for selecting a digital watermark embedding target, comprising: An evaluation unit that evaluates a plurality of watermark embedding targets with reference to a predetermined standard;
A selection unit that selects a candidate to embed a digital watermark with reference to an evaluation result by the evaluation unit;
An electronic watermark embedding target selection device comprising:   3. The digital watermark embedding according to claim 2, wherein the evaluation unit performs evaluation by referring to the degree of quality degradation after embedding the digital watermark and the detection rate of the digital watermark as the predetermined criterion. Target selection device.   The evaluation unit fixes any one of the quality degradation degree and the detection rate at a predetermined value, and determines whether the other value satisfies a predetermined condition. The electronic watermark embedding target selecting device according to claim 1.   5. The digital watermark embedding target selection device according to claim 4, further comprising an input unit for inputting at least one of the predetermined value and the predetermined condition from the outside. An evaluation process for evaluating a plurality of watermark embedding targets with reference to a predetermined standard;
A selection process for selecting a candidate to embed a digital watermark with reference to an evaluation result by the evaluation process;
A computer program for causing a computer to execute.   A data structure characterized in that a digital watermark is linked to a selected candidate to be embedded.

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