阅读说明：本技术 一种基于直方图移动的对比度增强rdh方法及系统 (Histogram movement-based contrast enhancement RDH method and system ) 是由 王春兴 王德艳 万文博 于 2021-08-02 设计创作，主要内容包括：本发明属于图像处理技术领域,提供了一种基于直方图移动的对比度增强RDH方法及系统。该方法包括,基线嵌入过程和扩展嵌入过程；所述基线嵌入过程：通过合并直方图单元处理灰度图像,得到预留空间；将迭代的灰度图像边信息和附加数据嵌入预留空间,得到增强图像；所述扩展嵌入过程：将增强图像与采用PEH变换提取的原始图像的附加数据进行拼接,覆盖原始图像,得到灰度图像恢复后的原始图像。(The invention belongs to the technical field of image processing, and provides a contrast enhancement RDH method and system based on histogram movement. The method includes a baseline embedding process and an extended embedding process; the baseline embedding process: processing the gray level image by a merging histogram unit to obtain a reserved space; embedding iterative grayscale image side information and additional data into a reserved space to obtain an enhanced image; the extension embedding process: and splicing the enhanced image and the additional data of the original image extracted by adopting PEH transformation to cover the original image to obtain the original image with the restored gray level image.)

1. A histogram shifting-based contrast enhanced RDH method, comprising: a baseline embedding process and an extended embedding process;

the baseline embedding process: processing the gray level image by a merging histogram unit to obtain a reserved space; embedding iterative grayscale image side information and additional data into a reserved space to obtain an enhanced image;

the extension embedding process: and splicing the enhanced image and the additional data of the original image extracted by adopting PEH transformation to cover the original image to obtain the original image with the restored gray level image.

2. The histogram shifting-based contrast-enhanced RDH method according to claim 1, wherein the process of acquiring the reserved space comprises:

constructing an image histogram based on the gray scale of the gray scale image;

merging the non-empty least significant bits in the histogram, and removing the empty part of the histogram to obtain a new histogram;

and inserting the new histogram iteration into the image histogram to obtain a reserved space.

3. The histogram shifting-based contrast-enhanced RDH method according to claim 1, wherein said embedding the iterative grayscale image side-information and additional data into the reserved space comprises: data hiding and side information generation.

4. The histogram shifting-based contrast-enhanced RDH method according to claim 3, wherein the data hiding comprises: dividing the gray image into a plurality of blocks, and replacing the pixel values in the blocks with the possibility of the pixel values in the blocks until all the pixel values of the blocks are embedded to obtain a plurality of new blocks.

5. The histogram shifting-based contrast-enhanced RDH method according to claim 4, wherein the side-information generation comprises:

and acquiring side information of a plurality of new blocks, and embedding the additional data into the reserved space in the sequence of the front side information and the rear side information to obtain an enhanced image.

6. The histogram shifting-based contrast-enhanced RDH method according to claim 1, wherein the additional data comprises: text, images, sound and video.

7. A histogram shifting based contrast enhanced RDH system, comprising: a baseline embedding module and an extended embedding module;

the baseline embedding module configured to: processing the gray level image by a merging histogram unit to obtain a reserved space; embedding iterative grayscale image side information and additional data into a reserved space to obtain an enhanced image;

the extension embedding module configured to: and splicing the enhanced image and the additional data of the original image extracted by adopting PEH transformation to cover the original image to obtain the original image with the restored gray level image.

8. The histogram shifting-based contrast-enhanced RDH system according to claim 7, wherein the process of acquiring the reserved space comprises:

constructing an image histogram based on the gray scale of the gray scale image;

merging the non-empty least significant bits in the histogram, and removing the empty part of the histogram to obtain a new histogram;

and inserting the new histogram iteration into the image histogram to obtain a reserved space.

9. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps in the histogram shift based contrast enhanced RDH method as claimed in any one of claims 1 to 6.

10. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor when executing the program implements the steps in the histogram shift based contrast enhanced RDH method as claimed in any one of claims 1 to 6.

Technical Field

The invention belongs to the technical field of image processing, and particularly relates to a contrast enhancement RDH method and system based on histogram movement.

Background

The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.

Reversible Data Hiding (RDH) is a technique for reversibly hiding data into digital media. The RDH technology can be adopted to accurately extract the hidden information and restore the original image without loss. RDH is useful in digital image tagging applications. In cloud storage, the server may embed additional data such as timestamps, tags, user information, notes, and upload pictures by the user. The server may achieve better management and save storage overhead as messages are attached to the image. Furthermore, the original content can be accurately restored before the user downloads. Over the past two decades, much research work on RDH has been based on eliminating MSE distortion. There are three kinds of conventional RDH, which are RDH based on Lossless Compression (LC), RDH based on Differential Expansion (DE), and RDH based on Histogram Shift (HS). In early RDH methods, digital images were compressed to make room for additional data. Some content of the image, such as insignificant bit planes, is compressed and concatenated with additional bits. At the receiving end, the hidden bits can be read directly from the end of the image and the original image can be restored by decompression. The LC-based RDH has the characteristics of high efficiency and easy realization. However, LC-based RDH is not good enough for the trade-off between embedding rate and image distortion. Conventional Reversible Data Hiding (RDH) focuses on enlarging the embedded payload while minimizing distortion using the Mean Square Error (MSE) criterion.

Obviously, although the existing RDH method in the literature is effective in reversible data hiding, there are some limitations. First, the visual quality retention after enhancement based on embedded images is to be improved. Second, the histogram library is not efficient. Third, some schemes may introduce salt and pepper noise during the data embedding process.

Disclosure of Invention

In order to solve the technical problems in the background art, the invention provides a contrast enhancement RDH method and system based on histogram movement, which have better invisibility, robustness and analysis resistance, obviously improved embedding capacity, capability of resisting common image processing and attack and capability of realizing lossless information hiding.

In order to achieve the purpose, the invention adopts the following technical scheme:

a first aspect of the invention provides a histogram shifting based contrast enhanced RDH method.

A histogram shifting-based contrast enhanced RDH method, comprising: a baseline embedding process and an extended embedding process;

the baseline embedding process: processing the gray level image by a merging histogram unit to obtain a reserved space; embedding iterative grayscale image side information and additional data into a reserved space to obtain an enhanced image;

the extension embedding process: and splicing the enhanced image and the additional data of the original image extracted by adopting PEH transformation to cover the original image to obtain the original image with the restored gray level image.

Further, the process of obtaining the reserved space includes:

constructing an image histogram based on the gray scale of the gray scale image;

merging the non-empty least significant bits in the histogram, and removing the empty part of the histogram to obtain a new histogram;

and inserting the new histogram iteration into the image histogram to obtain a reserved space.

Further, the embedding the iterative grayscale image side information and the additional data into the reserved space includes: data hiding and side information generation.

Further, the data hiding comprises: dividing the gray image into a plurality of blocks, and replacing the pixel values in the blocks with the possibility of the pixel values in the blocks until all the pixel values of the blocks are embedded to obtain a plurality of new blocks.

Further, the side information generation includes:

and acquiring side information of a plurality of new blocks, and embedding the additional data into the reserved space in the sequence of the front side information and the rear side information to obtain an enhanced image.

Further, the additional data includes: text, images, sound and video. Where the embedded additional data are any of a number of relatively simple texts, images, sounds and videos. These data are, for example, for an image, color images of three channels converted into grayscale images. A gray-scale digital image is an image with only one sample color per pixel.

A histogram shifting based contrast enhanced RDH system comprising: a baseline embedding module and an extended embedding module;

the baseline embedding module configured to: processing the gray level image by a merging histogram unit to obtain a reserved space; embedding iterative grayscale image side information and additional data into a reserved space to obtain an enhanced image;

the extension embedding module configured to: and splicing the enhanced image and the additional data of the original image extracted by adopting PEH transformation to cover the original image to obtain the original image with the restored gray level image.

Further, the process of obtaining the reserved space includes:

constructing an image histogram based on the gray scale of the gray scale image;

merging the non-empty least significant bits in the histogram, and removing the empty part of the histogram to obtain a new histogram;

and inserting the new histogram iteration into the image histogram to obtain a reserved space.

A third aspect of the invention provides a computer-readable storage medium.

A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the histogram shift based contrast enhanced RDH method as defined in the first aspect above.

A fourth aspect of the invention provides a computer apparatus.

A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor when executing the program implementing the steps in the histogram shift based contrast enhanced RDH method as described in the first aspect above.

Compared with the prior art, the invention has the beneficial effects that:

the two stages of data embedding described in the present invention perform histogram migration on two different histograms of an image, respectively. The process of contrast enhancement and data embedding in the baseline embedding portion may greatly reduce the strong correlation between neighboring pixels, causing the bell-shaped prediction error histogram to become coarser. Therefore, the method has better invisibility, robustness and analysis resistance, the embedding capacity is obviously improved, common image processing and attack can be resisted, and lossless information hiding is realized. At the receiving end, the hidden data can be accurately extracted, and the original image can be correctly recovered. Compared with the existing RDH method, the method can obtain better embedding effect while maintaining the visual quality of the marked image, but cannot judge whether the information is attacked or not before extracting the secret information.

Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, are included to provide a further understanding of the invention, and are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the invention and together with the description serve to explain the invention and not to limit the invention.

Fig. 1(a) is a histogram of an example of a vacancy reservation in an embodiment of the present invention;

FIG. 1(b) is a diagram of an original histogram in an embodiment of the present invention

FIG. 1(c) is a merged vacancy distribution histogram in an embodiment of the present invention;

FIG. 2 is a diagram illustrating an iterative data embedding process according to an embodiment of the present invention;

FIG. 3 is a flow chart of data extraction recovery in an embodiment of the present invention;

FIG. 4(a) is a diagram of a baseline embedding framework in an embodiment of the invention;

FIG. 4(b) is a diagram of an extended embedded framework in an embodiment of the present invention;

wherein HS is histogram translation, SI is side information, AD is additional data, PEH is prediction error histogram.

Detailed Description

The invention is further described with reference to the following figures and examples.

It is to be understood that the following detailed description is exemplary and is intended to provide further explanation of the invention as claimed. 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 invention belongs.

It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the invention. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.

It is noted that the flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of methods and systems according to various embodiments of the present disclosure. It should be noted that each block in the flowchart or block diagrams may represent a module, a segment, or a portion of code, which may comprise one or more executable instructions for implementing the logical function specified in the respective embodiment. It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

Example one

As shown in fig. 1, the embodiment provides a contrast-enhanced RDH method based on histogram movement, and the embodiment is illustrated by applying the method to a server, it is understood that the method may also be applied to a terminal, and may also be applied to a system including a terminal and a server, and implemented by interaction between the terminal and the server. The server may be an independent physical server, a server cluster or a distributed system formed by a plurality of physical servers, or a cloud server providing basic cloud computing services such as a cloud service, a cloud database, cloud computing, a cloud function, cloud storage, a network server, cloud communication, middleware service, a domain name service, a security service CDN, a big data and artificial intelligence platform, and the like. The terminal may be, but is not limited to, a smart phone, a tablet computer, a laptop computer, a desktop computer, a smart speaker, a smart watch, and the like. The terminal and the server may be directly or indirectly connected through wired or wireless communication, and the application is not limited herein. In this embodiment, the method includes the steps of:

a baseline embedding process and an extended embedding process;

the baseline embedding process: processing the gray level image by a merging histogram unit to obtain a reserved space; embedding iterative grayscale image side information and additional data into a reserved space to obtain an enhanced image;

the extension embedding process: and splicing the enhanced image and the additional data of the original image extracted by adopting PEH transformation to cover the original image to obtain the original image with the restored gray level image.

Since invisibility can also be achieved by image processing, the present embodiment proposes a contrast-enhanced RDH method based on histogram shifting. The method does not minimize Mean Square Error (MSE), but rather utilizes structural similarity to generate good quality marker images. The method comprises a baseline embedding part and a wide embedding part. In the baseline part, the least significant blocks are first merged to preserve spare blocks, and then additional data is embedded by using the arithmetic coded histogram shifting method. When the histogram moves, it is proposed to construct a histogram of the transfer matrix with maximum entropy. After embedding, the marked image containing the extra data is more contrasting than the original image. In the extended embedding part, it is further proposed to connect baseline embedding with mean square error based embedding. At the receiving side, the additional data can be accurately extracted, and the original image can be restored without loss. Compared with the existing RDH method, the method can obtain better embedded payload.

2.1 preserving Embedded space

For a given sheet size of m_r×m_cI, we first generate a histogram h ═ h₀，h₁，......h₂₅₅}. We point out that there are M original histograms empty in the original image, satisfying h_i0(M may be equal to zero). Since only the desired embedding rate can be guaranteed with the original histogram bins in the original image, we merge the least significant histogram cells (i.e. the cells containing the least pixels) in h to produce more histogram bins. Here "merge hi to hj" means all pixels that modify the value i to the value j.

Fig. 1 is provided to explain the above-described example. For simplicity we use a 4-bit grayscale image, containing 16 total grays. The image histogram is represented as { h₀，h₁，......h₁₅}. We set N-4 and M-1. In the merging phase, the non-empty least significant bit h₀、h₈And h₁₅And h₁、h₉And h₁₄Merging, removing histogram vacancy and forming new histogram h^seq1.. 5 }. After applying the iteration step, the reserved histogram gaps are iteratively inserted therein to ensure accurate recovery at the receiving end, some side information being collected.

2.2 data embedding

Once the transmission matrix T has been generated, we iteratively embed additional data M into the image Ic, where I represents the original image I_cRepresenting the overlay image. We will take the image I_cDivided into L blocks of equal size. At each block B_k(1. ltoreq. k. ltoreq.L), a piece of additional data is embedded by an arithmetic decoding algorithm. Fig. 2 provides a schematic diagram of the embedding process, where the solid line represents embedding and the dashed line represents the change in the Least Significant Bit (LSB). The embedding process includes two steps: data hiding and side information generation. Is arranged at B_KThere are s pixel values b in the block_k，1，b_k，2，........b_k,sAre { n (b) } respectively_k，1)，n(_bk，2)，.......，n(_bk，s)}。

We will b_k，uIs denoted by b_k，u＝{b_k，u，b_k，u，.....b_k，u}^TA vector of where b_k，u|＝n(b_k，u) And u is more than or equal to 1 and less than or equal to s. From the transfer matrix T, the likelihood of modifying a pixel value from i to j can be calculated as:

each b_k，u(1. ltoreq. u. ltoreq.s) according to the transmission probability p_bk,u(j) J is 0, the. By P_bk,u＝[p_bk,u(0)，.....，p_bk,u(255)]By using the formula (2) to add an extra bit m_k,uSegment conversion to n_(bk，u)An integer number.

Next, we useReplacement block B_kN (b) in (1)_k,u) Pixel b_k,u. In B_kAfter the data embedding process is performed on all pixels of the block, we get a new block B containing extra bits_k'。

Next, we are at B_k' generating side information A for pixel recovery_BkFor receiving B_k' conversion back to original B_K. The side information is used to distinguish which pixels in x are counted and which pixels are not. Let B_k' there are t kinds of pixel valuesWherein the number is { n (C)_K,1),n(C_k,2),.......n(C_k,t)}。

We will want toIs defined asWherein | C_k,u|＝n(C_k,u) And u is more than or equal to 1 and less than or equal to s. From the transfer matrix, we have the possibility to modify the pixel values:

we obtain the vector C_k,uIt hasThe original value of the middle pixel. We encode n (C) by arithmetic_k,u) A integer being converted into side bits A_k,uA segment of (1), probability is P'_bk,u＝[p_bk,u(0)，p_bk,u(1)，.......p_bk,u(255)]Wherein:

to B_kAfter data embedding of the values of all pixels in a block, we have A_Bk＝[A_k，1，A_k,2,......A_k,t]As B_kDue to side information of A_BkNeeds to be embedded in the cover image, so we will A_BkIs placed in front of the additional data M and then the next block is embedded. In the last block, we use A_BLAnd A_MThe Least Significant Bit (LSB) of the first block is changed. In practical applications, the LSB length of a single block is always sufficient to hold side information. Then, the first block A is divided into_LSBThe LSB of (1) is also embedded in the image. When we complete the data hiding in the first block, we turn A_LSBPlaced before M and using the binary sequence A ═ A_BL,A_M]A＝[ABL，AM]LSBs were altered. A. the_BLAnd A_MIs important side information for starting data extraction and can be directly extracted from the LSB.

2.3 data extraction and image restoration

When the receiver gets the marked image I^mIn time, the hidden bits can be extracted and the original image can be accurately restored. Fig. 3 shows a flow chart of data extraction and image restoration. The recovery process of the baseline part comprises iterative extraction and inversion of vacancy reservation.

In iterative extraction, the recipient first segments the image L blocks, extracting a from the LSBs of the first block_MAnd A_BL. Utilizing side data A_MGenerating a diagonal matrix T⁽⁰⁾The transfer matrix T is iteratively generated, as in the step of transfer matrix construction. Now recipient starts from B'_LWherein, first, he is obtained as B'_LMiddle t pixel valuesIn (1)And the transfer matrix gives P'_L,u＝[p_L,u(0),p_L,u(1),.......,p_L,u(255)]The possibility of (a).

For eachA is to be_BLA fragment of (a)_L,uConversion to n (c)_k,u) An integer, decompressed by arithmetic:

wherein A is_L＝[A_L,1,A_L,2,.....A_L,t]，A_L,uIs dependent on n (c)_k,u) And possibly a code word. For other use C_L,uAlternative B_LN (c) in a block_L,u) PixelIn pair B'_LAfter all the pixels in the block have performed the data recovery process, he can get the original block B_L。

To extract additional data he gets B_LMiddle t pixel values { b_L,1,b_L,2,........b_L,t{ b } of_L,1，b_L,2，..........，b^L,t}. He also gets the vectorIt has b_k,uModified value of the middle pixel. He will n (b)_L,u) A whole number is converted into a single bit m_k,uBy arithmetic coding:

to B_LAfter the data extraction is carried out on the block, additional data m is obtained_L＝[m_L,1,m_L,2,......m_L,t]. At m_LHe can obtain side data A_BL-1And using the data to recover B_L-1In B₂He obtains a at the front end of the embedded sequence at the end of the image recovery and data extraction of_LSBAnd replacing the modified LSB with A_LSB. Finally, the data embedded in the first block is obtained to form hidden data M ═ M₁,m₂,......,m_LRecovery of B₁While, at the same time, restoring the enhanced image I without distortion^E。

Thereafter, the present embodiment is subjected to vacancy-preserving inversion. First, side information A preceding the extracted additional data is obtained_C＝[A_O,A_E]. A is to be_ODecompressed into the original histogram sequence of the post-merged image I'. According to the histogram sequence, I^EThe non-empty blocks in (a) map to their original positions in the merged image I. I is^EInitial position of the ith non-empty block, noted as h_NiThe position corresponding to the ith "1" in the sequence is denoted as M_i. Thus, h_NiThe middle-counted pixel should be modified to the gray level M_i. Then to I^EThe pixels of (a) are modified accordingly, and the merged image I' can be restored accurately. Then, carrying out inverse operation on the merging operation to obtain an original image, and then carrying out A_EDecompression to a location error map E_m. Finally, we restore the original image I by adding a position map of I', i.e. I ═ E_m+I'。

In some images with complex contents, the pure embedding rate of the base line is high enough to overcome the defect. The reason is that: in this framework, two stages of data embedding histogram-shift two different histograms of the image, respectively. The process of contrast enhancement and data embedding in the baseline portion may greatly reduce the strong correlation between neighboring pixels, causing the bell-shaped prediction error histogram to become coarser. Therefore, the method has better invisibility, robustness and analysis resistance, the embedding capacity is obviously improved, common image processing and attack can be resisted, and lossless information hiding is realized. At the receiving end, the hidden data can be accurately extracted, and the original image can be correctly recovered. Compared with the existing RDH method, the method can obtain better embedding effect while maintaining the visual quality of the marked image, but cannot judge whether the information is attacked or not before extracting the secret information. Therefore, in the next research work, the carrier is analyzed by using algorithms such as an artificial neural network and the like, the efficiency of extracting secret information is improved, and the attacked carrier image is classified by using a support vector machine to identify the attack type.

The proposed framework of this embodiment is shown in fig. 4, and includes a base line portion and an extension portion. The baseline portion provides a new RDH scheme with contrast enhancement, while the extended portion provides another scheme to extend the baseline. In the figure, the right flow represents the process of data embedding, and the left flow represents data extraction and image restoration. Fig. 4(a) depicts the baseline portion. In the case of a given image, the embedded room is first vacated using a vacancy reservation algorithm, generating an intermediate image. On the basis, a matrix construction algorithm is proposed to generate the embedded transfer matrix. The matrix is related to the features of the intermediate image. Subsequently, we iteratively embed the side information and the additional data into the intermediate image to generate a labeled image. The process of data extraction and image restoration is the reverse of the embedding process. A new RDH framework is further proposed, as shown in fig. 4 (b). The baseline portion is connected to the prediction-based histogram offset to embed more additional bits, where a transfer matrix construction of the Prediction Error Histogram (PEH) is provided. An iterative embedding scheme is then employed that approximates the baseline portion.

Example two

The embodiment provides a contrast enhanced RDH system based on histogram movement.

A histogram shifting based contrast enhanced RDH system comprising: a baseline embedding module and an extended embedding module;

the baseline embedding module configured to: processing the gray level image by a merging histogram unit to obtain a reserved space; embedding iterative grayscale image side information and additional data into a reserved space to obtain an enhanced image;

the extension embedding module configured to: and splicing the enhanced image and the additional data of the original image extracted by adopting PEH transformation to cover the original image to obtain the original image with the restored gray level image.

This embodiment proposes an RDH frame with contrast enhancement. The framework consists of a base line embedding part and an extension embedding part. In the baseline portion, histogram holes are first preserved by merging histogram cells. After maximizing the entropy of the histogram, a transfer matrix is generated. A histogram shift algorithm is used to embed a large amount of additional data in the image. In the extended embedding part, baseline embedding is combined with MSE-based embedding. Higher payloads are obtained using a prediction error based histogram shift. The recipient can extract the extra data and restore the original image without any errors.

It should be noted that the modules described above are the same as those of the first embodiment and the application scenarios, but are not limited to the disclosure of the first embodiment. It should be noted that the modules described above as part of a system may be implemented in a computer system such as a set of computer-executable instructions.

EXAMPLE III

The present embodiment provides a computer-readable storage medium, on which a computer program is stored, which when executed by a processor implements the steps in the histogram shift-based contrast-enhanced RDH method as described in the first embodiment above.

Example four

The present embodiment provides a computer device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor executes the computer program to implement the steps in the histogram shift-based contrast enhancement RDH method as described in the first embodiment.

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 a hardware embodiment, a 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, 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.

It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), or the like.

The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.