Method for transmitting additional information during combined usage of vector quantization and fractal encoding of images

FIELD: electric communications, namely, area connected to reduction of excessiveness of information being transmitted.

SUBSTANCE: in accordance to the invention, during encoding of image by means of combined usage of vector quantization and fractal compression method, additional information is integrated into lower bits of domain indexes or code book blocks. Testing inversion procedure is applied to remaining bits of domain indexes of original image or code book blocks. After integration of additional information and optimization of domain indexes and code book blocks, these data together with information about indexes of their orientation, brightness and contrast coefficients are transferred through a communication channel. At receiving side, additional information is extracted and original image is restored.

EFFECT: transmission of image with additional information (concealed information, another image or other useful message) without increasing the volume of transferred data.

3 dwg, 1 tbl

 

The invention relates to telecommunications, and in particular to the field associated with the reduction of the redundancy of the transmitted information. The reason for this is essentially that, with the help of modern technology coding and data compression can significantly reduce the frequency range required for image transfer. The technical result of the invention is to develop a method for the transmission of additional information when combined vector quantization and fractal image coding for the transmission of additional information without increasing the amount of transmitted data.

In digital video technology to increase the transmitted amount of information has already been proposed to replace the digital signal scan data contained in the interval blanking additional information, such as digital audio signals, and to transmit additional information (Ulrich Schmidt, Digital video equipment, Publishing Franzis Verlag, Feldkirchen, 1996) [1].

The closest in technical essence to the claimed method for the transmission of additional information in the image coding method is described in patent No. 2212769, IPC704N 7/08 [2]. Prototype method eliminates non-essential part of image information, i.e. the physical is logically barely or not at all perceive the fine structure image, due to the irreversible compaction and subsequent decompression of the image signal, i.e. data reduction and thus obtained the free space inserts the useful and control information.

However, the total bandwidth occupied in the transmission of the main image and additional information, remains the same as before irreversible compaction of the source image.

The purpose of this invention to provide a method which allows for the coding of any type of images to convey more useful information while maintaining the transmission rate and the length of the frame format. This objective is achieved in that during compression of the original image through the joint use of a vector quantization and fractal coding in the low-order bits of the index of the domain or blocks of code books introduce additional information that reduces the list of used domains and blocks of code book that leads to a significant decrease in the total encoding time with a slight deterioration in the quality of the reconstructed image. The remaining bits of the applied procedure test inversion.

Consider the algorithm for the transmission of additional information when combined vector quantization and fractal, codiovan the images (figure 1).

Previously the original image is divided into nonoverlapping rank blocks, and then cover with a sequence of domain blocks, possibly overlapping. Domains can be of different sizes. Also in the encoder and the decoder pre-form is identical to the code book. After that, in the low-order bits of the index of the domain or blocks of code books embed additional information.

Next to the original image is the basic algorithm for fractal image coding [3]. The main computational step in fractal coding is a comparison of the domain and rank areas. For each rank of the block, find the domain and the corresponding transformation that best covers the ranking unit. This is usually an affine transformation

where αi- a constant that expands or narrows the range of values of the function f (controls the contrast of the image);

βi- a constant that increases or reduces the value of the grayscale (controls the brightness of the image);

wi- affine transform;

- spatial component of the transformation wi;

f(x,y) is the pixel value of the image with coordinates (x,y).

An affine transformation consists of three steps. The PE is o, to the selected domain is one of the eight basic rotations/reflections (four 90-degree rotation and mirror reflection in each orientation). Secondly, rotating domain region is compressed to fit the size of the rank field. And, finally, by the method of least squares is calculated contrast and brightness corresponding to the optimal values that minimize the expression

where n and m are the number of rows and columns in rank processed block;

Rijand Dij- the pixel values of rank and domain fields.

Continue this process until, until you get an acceptable match or the size ranking of blocks reaches a predetermined limit.

In addition, the search for the required domain is from the previously generated code book including additional embedded information in the low-order bits of the fragments in the code book. Forming a codebook is an iterative clustering algorithm, also known as the k-means algorithm described in [4]. In accordance with this algorithm selects a sequence of images belonging to different classes. Then this sequence is converted into a sequence of input vectors or training pic is egovernance, which form a code book. The input vector must have a large dynamic range, as the code book must contain units representing a sharp edge between portions of the image, because in such cases there might be problems with finding a suitable domain. This situation usually occurs because of a limitation on the dynamic range of the search domain, which, as a rule, must be greater than the dynamic range of the rank field. In areas with sharp boundaries, the dynamic range is very large, and it often turns out that it reaches its maximum value. In this case there is no domain with strictly high dynamic range, and rank region is replaced with the most similar block of code book.

If rank better block displays the domain block, the decoder is passed the index of the corresponding domain, the reception of which the decoder is fractal decoding the source block. When choosing for rank block fragments from the codebook to the decoder is passed to the index at which the character from the books in the decoder selects the required fragment, which after the fractal decoding is filled with restored image.

In the proposed method, each vector index of the domain or block of code CED and, consisting of n bits, are input m bits of additional information instead of the least significant bits of this vector (figure 2). As a result of this introduction of a list of domains and blocks of code books to handle the specific rank of the unit is significantly reduced, resulting in less time searching for suitable domains and fragments of code books, and the total time coding in General. The remaining k=n-m digits of the procedure test inversion. In this procedure, with the aim of reducing computational complexity in comparison with the method of complete enumeration is proposed to use the well-known method of Gauss-Seidel. Procedure test inversion performs optimization of the elements of domain indexesby test inversion for each element. The process of finding the optimal vector elements in the claimed method are proposed to be implemented in the form of the following sequence of actions:

1. Put t=1;

2. To form the vector index of the domain or block of code book including additional information

3. Put r=1;

4. To choose the appropriate orientation of the domain or block of code books, to calculate the conversion settings, such as contrast and brightness and to determine the expression (2) for vector;

. To perform the inversion of the r-th element of the vector

where xr∈{0,1},

6. To form a vector:

1. To choose the appropriate orientation of the domain or block of code books, to customize the conversion settings, such as contrast and brightness, to minimize the expression (2) in the inversion of the r-th element of the vector

8. Calculate;

9. Run: if u>0, then the value of e2assign the value ofand the value of xrto assign; if u<0, then the values of e2and xrremain unchanged;

10. To do: if r<k, then increase r by one and go to step 4; if r=k, then put t=t+1 and go to step 2 to optimize the following indexes domain or blocks of code book.

After embedding additional information and optimizing indexes, domains and blocks of code books, these data, together with information about the indexes of their orientation, the coefficients of brightness and contrast are transmitted over the communication channel. The decoder is the allocation of additional information and restore the original image. The decoding of the image is performed by an iterative application of the affine transformation of the project to an arbitrary initial image. In accordance with theorem on contractive mappings iteration will converge regardless of the choice of the initial image. Compressive mapping is defined as a separate transformation for each rank of the block. Each rank unit has associated with it transformation and domain. The contents of this rank of the block is calculated by applying a transform to the domain block. One iteration is completed when you are ranking the blocks.

In the case of encoding the rank of a block of code books this snippet of code book with the index of its orientation, the ratios of brightness and contrast fills in the appropriate area of the reconstructed image.

The claimed method is illustrated by drawings:

- Figure 1 represents the encoding algorithm of the image when combined vector quantization and fractal compression method taking into account additional embedded information;

- Figure 2 - procedure record additional information;

- Figure 3 - diagram of the transmission of additional information when combined vector quantization and fractal encoding of images.

Figure 1 presents the algorithm for encoding image when combined vector quantization and fractal compression method taking into account the embedded additional information. Source the image is divided into nonoverlapping rank and domain blocks. Then in the domain blocks embedded additional information. As you can see, after you insert additional information to the remaining source categories used procedure test inversion, which use a known method Gauss-Seidel for solving optimization problems. Then, for each rank of the block, find the domain and the corresponding transformation that best covers the ranking unit. Usually this is an affine transformation. As a domain can be a domain area of the original image or blocks of code book. Coding ends when coating each rank of domain block area with a given error.

Figure 2 shows the procedure to record additional information. In the proposed method, each vector index domains or blocks of code book consisting of n bits, instead of the least significant bits are input m bits of additional information. Because these data are not added in addition to the original signal, and replace part of the original signal, the bandwidth of the signal does not increase by adding this information.

Figure 3 presents a scheme for the transmission of additional information when combined vector quantization and fractal image coding. Previously in the encoder and decoder is formed identical to the code book. After that, in the low-order bits of the index of the domain or blocks of code books embed additional information. Following is the fractal encoding rank blocks taking into account additional embedded information, and finding the best matching block of the previously generated code book. If rank better block displays the domain block, the decoder is passed the index of the corresponding domain, the reception of which the decoder is fractal decoding the source block. When choosing for rank block fragments from the codebook to the decoder is passed to the index at which the character from the books in the decoder selects the required fragment, which after the fractal decoding is filled with restored image.

Table 1 shows the simulation results for the insertion of additional information in the test image "Lena" of size 256×256 pixels. The size rank of the block was 8×8 pixels, while their total number in the image is 1024. The peak signal-to-noise ratio (PSNR) to insert additional information was 29 dB.

Table 1
The number of bits embedded in the index domain and blocks of code to the IGR
1 bit2 bits3 bits4 bits5-bit6-bit7-bit8-bit9-bit
PSNR for the image "Lena", [DB]28.928.427.827.326.525.624.523.423
The maximum amount of supplementary information [bit]102420483072409651206144716881929216

This way the transmission of additional information when combined vector quantization and fractal image coding can be implemented on modern processors signal processing. The method may find its application in the transmission of additional data over a slow communication channels. As additional information can be hidden information, another image or any other useful information.

Literature

1. Ulrich Schmidt, Digital video equipment. Publisher Franzis Verlag, Feldkirchen, 1996.

2. Patent No. 2212769, IPC7N 04 N 7/08, bull. No. 26 from 20.09.2003,

3. Walsted S. Fractals and wavelets for image compression in action. The tutorial. - M.: publishing house the construction Triumph, 2003 - 320 C.

4. Satellite television. New methods of transmission. / Negartive, Eighmy, VuePoint, Oguiza. Ed. Negartive. - M.: Radio and communication, 1993.

Method for the transmission of additional information when encoding images with stage a) digitization of the original image, (b) compression using irreversible compression method, (C) inserting a digital useful information, d) decompressing the compressed image, (e) the allocation of the digital useful information, characterized in that after compression of the original image through the joint use of a vector quantization and fractal coding, insert m bits of additional information instead of the least significant bits in each vector index of the domain or block of code book consisting of n bits, the remaining k=n-m source discharges apply the procedure for trial of inversion, in which use the method of Gauss-Seidel, after which they, together with the additional information is transferred over the communication channel.



 

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