Image processing method and device, image processing program and recording medium storing said program

FIELD: information technology.

SUBSTANCE: first band pass (BP) is determined based on initial image data; a matrix of filter coefficients (FC) is calculated to obtain frequency characteristics corresponding to limitation of frequency band (FB) using the first BP; data of the first filtered image are generated by filtering data of the initial image using the matrix of first FC; an estimate value of the objective image quality of data of the first filtered image is obtained and the distribution coefficient (DC) is calculated, which is used to determine the optimum BP based on the estimate value of objective image quality; the optimum BP corresponding to the calculated DC is determined using a table in which the corresponding relationship between DC and optimum BP is defined; a matrix of optimum FC is calculated to obtain frequency characteristics corresponding to limitation of FB using the optimum BP; and data of the optimally filtered image is generated by filtering data of the initial image using the matrix of optimum FC.

EFFECT: adaptive image filtering process for providing high-quality image.

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The technical field

The present invention relates to a method of processing image and the corresponding device used to perform simplified filtering operations to transform a source image into an image having a specific value of an objective evaluation of the image, and also relates to image processing application used to implement the method of image processing, and computer-readable storage medium that stores the program.

Priority is claimed on Japanese patent application No. 2006-182931, filed July 3, 2006, the contents of which are incorporated here by reference.

The level of technology

It is known that the pre-filter, which is often used in pre-processing video encoding is effective for reducing block distortion and mosquito noise and similar, followed by coding, thereby improving subjective image quality. Bandwidth (hereinafter referred to as "bandwidth") used pre-filter is limited in order to reduce the noise contained in the original image, and improve the coding efficiency. However, if the bandwidth is reduced, the image quality is greatly reduced.

7 shows a method of processing image is, including limitation of bandwidth.

As shown in Fig.7, in the method of image processing, including limitation of the frequency band, first enter the data B(1) of the original image and then converted into a frequency component I(1) (see step S100). Frequency component of I(1) is subject to the restriction on the bandwidth using the bandwidth r1 (0<r1<1), in order to obtain the frequency component I(r1) (see step S101). The frequency component I(r1) is transformed image, thereby forming the data B(r1) filtered image (see step S102).

When such image processing is applied to all frames of the video image by using the same bandwidth, the image quality of each filtered frame is not the same, since each frame has an individual frequency characteristics of the image. That is, an image having a large number of low-frequency components, has only a small difference from the original image and, thus, a decrease in subjective and objective image quality a little. However, in the image having many high-frequency components, edges or the like are smoothed and blurred, which greatly reduces the subjective and objective quality of the image.

As the value of objective assessment and image, for example, is often used PSNR (the Ratio of maximum signal to noise ratio). With a certain level (S) of the signal and the level (N) noise PSNR is expressed by the following formula:

PSNR=20·log10(S/N)

In the method of solving the above problem management objective and subjective image quality is performed by "cyclic" limit the bandwidth used for each image.

Fig shows the device structure 100 of forming optimum filtered image data generating optimum filtered image by performing the "circular" limitations strip.

As shown in Fig, the device 100 forming optimally filtered image includes unit 101 of the input original image data, the block 102 analysis of the frequency component, unit 103 manual selection of the bandwidth, block 104 limits the bandwidth of the block 105 forming the image data, the block 106 compute the PSNR, the block 107 evaluation image and the block 108 output image data, optimally limited bandwidth.

Fig.9 shows the method of processing image data generating optimum filtered image by performing the "circular" limit bandwidth, the method is performed in the device 100 of the formation of optimally Phil is trojanova image, with the above structure.

In the device 100 of forming optimal first filtered image data B(1) of the original image is entered in block 101 of the input original image data and then converted into a frequency component I(1) in block 102 analysis of the frequency component (see step S200).

Then in block 103 manual selection of the bandwidth is selected manually pre bandwidth r1 (see step S201). Then in block 104 limits the frequency band converted to the frequency component of I(1) subject to the limitation on the frequency band using the selected bandwidth r1 in order to obtain the frequency component I(r1) (see step S202).

Next, in block 105 forming the image data of the frequency component I(r1) is transformed image, thereby forming the data B(r1) of the image (see step S203). In block 106, the PSNR calculation data B(1) of the original image are compared with the data B(r1) of the image to calculate the RSNR(r1) (indicated by "P(r1)below) (see step S204).

In block 107 evaluation of the image is determined, the calculated P(r1) whether the desired quality of the image (see step S205). If it has the desired quality of the image, the block 108 issuance of image data, optimally a limited band of frequencies, outputs the data B(r1) image as image data, optimally limited is about the band (i.e. data optimally filtered image) (see step S206).

However, rarely it happens that P(r1)obtained in the first stage of treatment is the desired image quality. When it is not desired quality of the image, the operation returns to the process (at step S201), performed by the block 103 manual selection of the bandwidth and the bandwidth (r2) is selected again so that the corresponding image, limited frequency band, had a quality closer to the desired image quality. Then the restriction of the bandwidth of the image formation and the PSNR calculation is again performed similarly.

That is, the above operation is repeated N times until, until you get the desired image quality and bandwidth rN, which is finally obtained, is used as the optimal bandwidth for the data B(rN) image by block 108 output image data, optimally limited bandwidth. The generated data B(rN) images are available as image data, optimally limited by the bandwidth (i.e. data optimally filtered image) (see step S206).

However, in the above method, various video and all the frames that shape them, are subjected to filtering, is estimated subjective or the objective image quality of each received image signal, and the corresponding operation is repeated "loop" until then, until you get the same picture quality for all video frames. The above method is not acceptable and is not feasible from the point of view of estimation of time and cost when processing a lot of images.

To solve the above problems in the known method (see Patent document 1), image processing is performed by obtaining the optimal bandwidth based on the encoding data (video) images.

Figure 10 shows the structure of the device 200 of forming optimum filtered image data generating optimum filtered image using data encryption.

As shown in Figure 10, the device 200 of forming optimum filtered image includes unit 201 of the input original image data, the analysis unit 202 frequency component, unit 203 encoding image data, the block 204 to determine the optimal bandwidth, block 205 limits the bandwidth of the block 206 forming the image data and the block 207 output image data, optimally limited bandwidth.

11 shows a method of processing image data generating optimum filtered image by using the encoding data, when this method is executed in the device 200 of forming optimum filtered image having the above structure.

In the device 200 of forming the optimal first filtered image data B(1) of the original image is entered in block 201 of the input original image data and then converted into a frequency component I(1) in block 202 analysis of the frequency component (see step S300).

Then in block 203 encoding the image data of the input data B(1) of the original image is encoded (see spider S301 demonstration stage). Based on the number of codes obtained by the corresponding encoding is determined optimum bandwidth r1 in block 204 to determine the optimal bandwidth (see step S203).

In block 205 limits the frequency band converted to the frequency component of I(1) subject to the limitation on the frequency band using a specific bandwidth r1 in order to obtain the frequency component I(r1) (see step S203). In block 206 forming the image data of the frequency component I(r1) is transformed image, thereby forming the data B(r1) of the image (see step S304).

In conclusion, the data B(r1) images are available as image data, optimally limited bandwidth (i.e. data optimally filtered image), block 207 issuing the image data, optimally a limited band of frequencies (see step S305).

Accordingly, in the conventional device 200 of forming optimum filtered image is formed, as shown in Figure 10, after the encoding is performed, the optimal bandwidth is determined based on the encoding obtained by coding. So the data is optimally filtered images are obtained without performing repetitive operations required in the device 100 of forming optimum filtered image is formed, as shown in Fig.

Patent document 1: Japanese patent application, which has not passed the examination, first publication No. H06-225276.

Of course, in accordance with the conventional device 200 of forming optimum filtered image is formed, as shown in Figure 10, the data is optimally filtered image can be formed without performing repetitive operations required in the device 100 of forming optimum filtered image is formed, as shown in Fig.

However, in the device 200 of forming optimum filtered image figure 10 after encoding is performed, the optimal bandwidth based on the data encoding obtained through encoding.

In t the com method, using data coding, the process of limiting the bandwidth and the encoding process are inseparable. Therefore, even if the user wishes to perform only the pre-filtering process using the optimum bandwidth, it is also necessary to perform the encoding. If the encoding is also performed after pre-filtering, coding will be performed twice. In particular, if the image size is large, it takes a long time for processing.

In the discussion above to optimize bandwidth for the pre-filter is preferable to use a method that can facilitate appropriate treatment and which can freely be controlled using, for example, PSNR (as standard objective assessment of image quality), compared with the method using data encoding (e.g., the number of codes).

The invention

In the light of the above circumstances, an object of the present invention provides a new method of image processing, by which the adaptive filtering process for the image can be automatically performed without performing the encoding process in order to perform a simplified adaptive filtering process and all the frames in the video had a high subjective quality is zobrazenie and the same value of the objective assessment of image quality.

Therefore, the present invention provides an imaging device, comprising: (1) a means of determining the first bandwidth based on the size of the input image data of the original image; (2) a means of calculating the first matrix of filter coefficients for implementing frequency characteristics corresponding to the restriction on the frequency band using the first bandwidth; (3) the means of forming the data of the first filtered image by performing the filtering process of the original image data using a matrix of first coefficients of the filter; (4) means for obtaining values of the objective assessment of image quality data of the first filtered image and calculate the distribution factor used to determine the optimal bandwidth based on the values of the objective assessment of image quality; (5) a means of determining the optimum bandwidth corresponding to the calculated distribution coefficient, by reference to the table determine the optimal bandwidth, which defines a corresponding relationship between the distribution coefficient and the optimum bandwidth; (6) a means of calculating the matrix of optimal filter coefficients for implementing frequency characteristics, appropriate to ejstvujuschij to limit bandwidth, using certain optimal bandwidth; and (7) the tool data generating optimum filtered image by performing the filtering process of the original image data using a matrix of optimal filter coefficients.

The above structure can use a table determining the first bandwidth, which defines a corresponding relationship between the image size and the first bandwidth. In this case, the means of determining the first bandwidth, the first bandwidth corresponding to the image size of the original image data, by reference to the table of determining the first bandwidth.

Additionally, many of the tables identify the optimal bandwidths can be provided in accordance with the image size and the value of a given objective image quality. In this case, the means of determining the optimal bandwidth selects the table to determine the optimal bandwidth, which corresponds to the image size of the original image data and a specific value of a given objective image quality, and determines the optimum bandwidth corresponding to the distribution coefficient (calculated medium spans the PTO calculation of the distribution coefficient) by reference to the selected table to determine the optimal bandwidth.

The method of image processing according to the present invention, which is used when the above-described device can also be implemented using a computer program. Such a computer program may be provided by storing it in the appropriate machine-readable storage media or over the network. When the present invention is implemented, the program can be installed in the control device, such as a CPU.

In the imaging device, implemented as described above, when data is entered in the source image, the first bandwidth in accordance with the image size of the original image data is determined by, for example, access to the table determining the first bandwidth.

Then we can calculate the matrix of the first filter coefficients for implementing frequency characteristics corresponding to the restriction on the frequency band, using some of the first bandwidth, and performs a filtering process of the original image data using the computed matrix of the first filter coefficients, thereby forming the data of the first filtered image.

Then, in the example where the PSNR is used as the value of the objective assessment of image quality, PSNR obtained sformirovannyh the first filtered image to the original image data and based on the PSNR, which is obtained when no restriction is applied on the frequency band to data of the original image can be obtained based on the obtained PSNR in order to calculate the distribution factor used to determine the optimal bandwidth.

Then select the table to determine the optimum bandwidth corresponding to the image size of the original image data, and certain specified PSNR, and the optimum bandwidth corresponding to the calculated distribution coefficient, determined by reference to the selected table to determine the optimal bandwidth.

Then we can calculate the matrix of optimal filter coefficients for implementing frequency characteristics corresponding to the restriction on the frequency band, using a certain bandwidth, and performs a filtering process of the original image data using a matrix of coefficients of the optimal filter, thereby forming the data optimally filtered data.

In accordance with the present invention, the filtering process for converting the original image into an image having a specific value of the objective assessment of image quality, can be automatically performed without performing the encoding process.

Therefore according to the present invention, it is possible to automatically perform the process of the adaptive filter through which all frames of the video are highly subjective image quality and the same value of the objective assessment of image quality without performing the encoding process. Therefore, it is possible to perform a simplified and rational limit bandwidth.

Brief description of drawings

Figure 1 shows the forming device optimally filtered image as a variant implementation of the present invention.

Figure 2 - block diagram used for explanation of tables determining the first bandwidth.

Figure 3 - block diagram used for explanation of tables determine the optimal bandwidth.

4 is the block diagram used for explanation of tables determine the optimal bandwidth.

Figure 5 - algorithm performed by the forming device optimally filtered image option implementation.

6 is a block diagram used to explain the results of experiments to obtain the corresponding relationship between the bandwidth and PSNR.

7 is a block diagram used for explanation of a method of image processing, including the limited bandwidth.

Fig shows the device structure formation optimally Phi is trojanova image data generating optimum filtered image by performing the "circular" restrictions on the frequency band.

Fig.9 - the algorithm performed by the device forming the optimum filtered image data generating optimum filtered image by performing the "circular" restrictions on the frequency band.

Figure 10 shows the structure of a conventional device forming optimally filtered image.

11 - the algorithm, performed the usual forming device optimally filtered image.

Best mode for carrying out the invention

Below, the present invention will be explained in detail in accordance with the embodiment.

Figure 1 shows the device 1 forming the optimum filtered image as a variant implementation of the present invention.

In accordance with the device 1 forming the optimum filtered image of this version of the implementation process adaptive filtering of images can be automatically performed without performing the encoding process so as to realize a simplified process adaptive filtering and all the frames in the video had a high subjective image quality and the same PSNR. Therefore, the device 1 forming the optimum filtered image includes a table 10 determining the first bandwidth (factual and, a memory unit for storing a table determining the first bandwidth); table 11 determination of the optimal bandwidth (actually, a memory unit for storing tables determine the optimal bandwidth); block 12 of the input image; block 13 determining the first bandwidth; block 14 calculation of the matrix of the first filter coefficients; unit 15 of the data generation of the first filtered image; block 16 calculation of distribution coefficients; block 17 selection table determine the optimal bandwidth; block 18 to determine the optimal bandwidth; block 19 calculation of the matrix of optimal filter coefficients; block 20, the data generating optimum filtered image; and a unit 21 data output optimally filtered image.

As shown in figure 2, the table 10 determining the first bandwidth manages the data values of the first bandwidth r1 in accordance with each size of the image, with the first bandwidth r1 is used for image processing corresponding to the image size. For example, the corresponding relationship between the image size and the first bandwidth r1 is controlled so that the first bandwidth r1 is equal to C1 for the image having the image size of 4096×2048 pixels, and r1 is equal to C for the image, having the image size of 1920×1080 pixels.

Here the table is set so that a large image size, small first bandwidth r1. Therefore, the values Ci of the first bandwidth r1, shown in figure 2, have the following relationship:

0<C1<C2<C3<C4<C5<C6<C7< ... <1

Table 10 determining the first bandwidth in figure 2 has the structure of a table that shows the value of the first bandwidth r1 for each image size. However, the other structure of the table can be used, which shows the value of the first bandwidth r1 for each range of image sizes.

As shown in Figure 3 in table 11 to determine the optimal bandwidth, many given PSNR is assigned to each image size and offers plenty of tables for all combinations. As shown in figure 4, each table for each given PSNR assigned to the image size, manages the data values of the optimum bandwidth r2 (used for execution of a given PSNR)assigned to each coefficient of the X distribution (explained later) in its range of values.

For example, the corresponding relationship between the range of the coefficient of the X distribution and the optimum bandwidth r2 (used to implement a given PSNR) upravlaet is thus the optimum bandwidth r2 is: B1for each distribution coefficient of X in the range X<A1; B2for each coefficient of X in the range A1≤X<A2; and B3for each distribution coefficient in the range A2≤X<A2.

Ai(i=1 to n-1) has the following relationship:

0<A1<A2<A3< ... <An-2<An-1

In accordance with the mission of such a larger coefficient of X distribution, the larger the optimum bandwidth r2 is obtained the following relation:

0<B1<B2<B3< ... <Bn-2<Bn-1<Bn<1

The block 12 of the input original image data receives the data B(1) of the original image, for which to derive optimum filtered image, and determines the size V of the image data B(1) of the source image.

Unit 13 determining the first bandwidth refers to table 10 determining the first bandwidth using size V image (defined by the block 12 of the input original image data) as the key in order to determine the first bandwidth r1, defined in accordance with the size V of the image.

Unit 14 the calculation of a matrix of first coefficients of the filter computes the matrix of the first filter coefficients to implement frequency is the features, relevant limitation on the frequency band using the first bandwidth r1 defined by the block 13 determining the first bandwidth.

Unit 15 of the data generation of the first filtered image performs the filtering process data B(1) of the original image, using the first matrix of filter coefficients, which is calculated by the calculation unit 14 of the first matrix of filter coefficients so as to generate data B(r1) of the first filtered image.

Block 16 calculation of the distribution coefficient compares the data B(1) of the original image data B(1) of the first filtered image, generated by the block 15 of the formation of the first filtered image, in order to measure P(r1), which is equal PSNR data B(r1) of the first filtered image. Block 16 calculation of the distribution coefficient calculates the coefficient of X distribution based on P(r1).

Unit 17 selection table determine the optimal bandwidth selects one of the tables 11 determine the optimal bandwidths that are associated with the image size and a given PSNR, where the selected table corresponds to the size V of the image (defined by the block 12 of the input original image data) and a given PSNR, which is assigned by the user. Unit 17 selection table determine the optimal bands of the bandwidth issues ID number, assigned to the selected table.

The block 18 to determine the optimal bandwidth determines the optimum bandwidth r2 by reference to table 11 to determine the optimal bandwidth, which is selected by the block 17 selection table determine the optimal bandwidth and specify the ID number through the use of the coefficient of the X distribution (calculated by the block 16 calculation of the distribution coefficient) as the key.

Unit 19 the calculation of the matrix of optimal filter coefficients computes the matrix of optimal filter coefficients for implementing frequency characteristics corresponding to the restriction on the frequency band, using the optimum bandwidth r2, which is determined by the block 18 to determine the optimal bandwidth.

Unit 20 of the data generating optimum filtered image performs the filtering process data B(1) of the original image, using the matrix of optimal filter coefficients calculated by the block 19 calculation of the matrix of optimal filter coefficients in order to generate data B(r2) is optimum filtered image.

Unit 21 of the data output optimally filtered image gives you the optimum filtered image generated by the unit 20 generate the data optimally filter the bathing of the image.

Figure 5 shows the algorithm executed by the device 1 forming the optimum filtered image of this variant implementation, formed as described above.

In accordance with the algorithm of the process performed by the device 1 forming the optimum filtered image of this option exercise, will be explained in detail.

As shown in the algorithm of figure 5, when the device 1 forming the optimum filtered image receives the request for the formation of data optimally filtered image data B(1) of the original image for which data are formed optimally filtered image is entered into the device, and determines the size V image entered in the original image B(1) (see step S10).

In the next step S11 refer to table 10 determining the first bandwidth using a certain size V image as a key to determine the first bandwidth, which is determined in accordance with the size V of the image.

If the size V of the image data B(1) of the original image manipulated in the device 1 forming the optimum filtered image of this variant implementation, is set in a pre-determined size, is not required the camping table 10 determining the first bandwidth, and is determined by the first bandwidth r1, which is defined in advance in accordance with a fixed size.

In the next step S12 calculates the matrix of the first filter coefficients for implementing frequency characteristics corresponding to the restriction on the frequency band, using some of the first bandwidth r1.

In the next step S13, the data B(1) of the original image subjected to the filtering process using the computed matrix of the first filter coefficients in order to generate data B(r1) of the first filtered image.

In the next step S14, the data B(1) of the original image are compared with the generated data B(r1) of the first filtered image in order to measure P(r1), which is equal PSNR data B(r1) of the first filtered image. On the basis of P(R1) calculated the coefficient of X distribution.

For example, the coefficient of the X distribution is calculated using P(r1) as follows:

X=51,2/P(r1) of Formula (1)

Fig.6 shows the results of experiments to obtain the corresponding relationship between the bandwidth r and P(r) (PSNR), where five different images 1-5, each has an image size of 1920×1080 pixels, were used as image data for experiments, and was filtering applied to selected components of the images 1-5, use the I matrix of filter coefficients, for implementing frequency characteristics corresponding to the same bandwidth r(0,3<r<1) in the horizontal and vertical directions.

As shown by the results of the experiments, the value of 51.2" in the Formula 1 indicates the PSNR value, which is obtained when the data of the original image are not subject to the limitation on the frequency band.

In the next step S15 selects one of the tables 11 determine the optimal bandwidth that is provided in accordance with the size V image and a given PSNR, and the table corresponds to the size V of the image (defined by the block 12 of the input original image data) and a given PSNR, which is assigned by the user.

If the size V of the image data B(1) of the original image manipulated in the device 1 forming the optimum filtered image of this variant implementation, is set in a pre-defined size, then you must provide table 11 determination of optimal bandwidths in accordance with the image size and a given PSNR and provide many tables 11 determine the optimal bandwidths in accordance with the values given PSNR.

In addition, if the size V of the image data B(1) of the original image manipulated in the device 1 forming the optimal is but filtered image, fixed in a predefined size and a given PSNR, which is controlled by the device 1 forming the optimum filtered image, is also set at a predetermined value, then you must provide the table 11 to determine the optimal bandwidth in accordance with the image size and a given PSNR, and table 11 to determine the optimal bandwidth.

In the next step S16, the optimum bandwidth r2 is determined by reference to the selected table 11 determination of the optimal bandwidth using ratio X of the distribution as the key.

In the next step S17 is calculated matrix of the optimal filter coefficients for implementing frequency characteristics corresponding to the restriction on the frequency band, using a certain optimum bandwidth r2.

In the next step S18, the data B(1) of the original image are filtered using the computed matrix of the optimal filter coefficients in order to generate data B(r2) is optimum filtered image for execution of a given PSNR.

In the next step S19 issued generated data B(r2) is optimum filtered image and ends with the corresponding operation.

As described above, the device 1 forming optimalnooperating image of this variant implementation performs only two filtering process, applied to the data B(1) of the original image in order to generate data B(r2) is optimum filtered image for execution of a given PSNR.

The above-described operation below will be explained in detail with specific examples, which are images of 1-5, with characteristics shown in Fig.6.

In accordance with the process of the above-described step S10 size 1920×1080 pixels" is defined as the size of the V data B(1) of the original image. Then, in accordance with the process above described step S11 refer to table 10 determining the first bandwidth having the data structure shown in figure 2, so that C2 is defined as the first bandwidth r1.

If C2=0.5, then in accordance with the processes of the above-described steps S12-S14, then the matrix of the first filter coefficients for implementing frequency characteristics corresponding to the restriction of bandwidth, using r1(=0.5)is used to generate the data B(0,5) of the first filtered image for each image 1-5 (with characteristics figure 6), and set P(0,5) as PSNR of each data B(0,5) of the first filtered image.

In accordance with the setting, as shown in Fig.6, P(0,5)=34,5 for image 1; P(0,5)=42,3 for image 2; P(0,5)=40,6 for image 3; P(0,5)=42,7 for image 4 and P(0,5)=45,3 for image 5.

Then the accordance with the process of the above-described step S14, the formula "X=51,2/P(r1)is calculated with the to: distribution coefficient X=1.48 for the image 1; the distribution coefficient X=1,21 for image 2; factor X=1,26 for image 3; the distribution coefficient X=1,20 for image 4; and the distribution coefficient X=1,13 for image 5.

Then, in accordance with the process of the above-described step S16 refer to table 11 to determine the optimal bandwidth with the data structure shown in figure 4, using the calculated coefficient of X distribution as the key so that the value of Biwas determined as the optimum bandwidth r2 in accordance with the value of the coefficient X of the distribution.

As described above, table 11 determination of the optimal bandwidth has the following data structure:

0<A1<A2<A3< ... <An-2<An-1

0<B1<B2<B3< ... <Bn-2<Bn-1<Bn<1

So most optimum bandwidth r2 is assigned to the data B(1) of the original image having the greater coefficient of the X distribution, and the smaller the optimum bandwidth r2 is assigned to the data B(1) of the original image having a smaller coefficient of the X distribution.

Namely, as is clear from the formula "X=51,2/P(r1), the data B(1) of the original image having the greater coefficient of X distribution has Mansour(r1) (which shows the low level signal); therefore, to implement a given PSNR require more optimum bandwidth r2 (i.e. will be implemented by the limitation on the frequency band). In contrast, the data B(1) of the original image having a smaller coefficient of the X distribution, have a large P(r1); therefore, for the realization of a given PSNR requires low bandwidth r2.

Considering the above, to specify that the most optimum bandwidth r2 is assigned to the data B(1) of the original image having the greater coefficient of the X distribution, and small optimum bandwidth r2 is assigned to the data B(1) of the original image having a smaller coefficient of the X distribution, table 11 determination of the optimal bandwidth is the data structure

0<A1<A2<A3< ... <An-2<An-1

0<B1<B2<B3< ... <Bn-2<Bn-1<Bn<1

The optimum bandwidth r2 is as defined above, is bandwidth to generate the data B(r2) is optimum filtered image that implement a given PSNR. Therefore, in accordance with the processes described above, steps S16-S17 calculates the matrix of optimal filter coefficients for implementing frequency characteristics corresponding to the restriction of bandwidth, using optimal the percent bandwidth r2, and the data B(1) of the original image are filtered using the matrix of optimal filter coefficients, whereby forming data B(r2) is optimum filtered image to implement a given PSNR.

As described above, in the device 1 forming optimally filtered images are first pre-determined bandwidth in accordance with the image size of the original image data, and based on the size of the image formed data of the preview image in order to install PSNR. Then we can calculate the dimensionless parameter, such as the distribution coefficient, on the basis of established PSNR and refer to table 11 to determine the optimal bandwidth by using the calculated distribution coefficients as a key in order to determine the optimal bandwidth for the implementation of a given PSNR, where the table 11 to determine the optimal bandwidth has the structure of a data conversion means which provides a higher distribution coefficient of the original image data, the majority determined the optimal bandwidth. Based on the optimal bandwidth to derive optimum filtered image data of the original image.

In accordance with the mustache is a device 1 forming the optimum filtered image, with the above structure, the source image data will be subject only to two filtering processes in order to develop data optimally filtered image to implement a given PSNR.

Therefore, in accordance with the device 1 forming the optimum filtered image process adaptive filtering can be automatically performed without performing the encoding process so as to realize a simplified process adaptive filtering and all the frames in the video had a high subjective image quality and the same value of the objective assessment of image quality.

Although the present invention has been explained according to the embodiment, having the drawings, the present invention is not limited to the embodiment.

For example, in the present embodiment, the PSNR is used as the value of the objective assessment of image quality. However, you may use the value of the objective assessment of image quality, other than the PSMR.

Also in the present embodiment, the image size of 1920×1080 pixels is shown as an example. However, when prepared table 10 determining the first bandwidth (see Figure 2), which manages the first bandwidth r1 corresponding to the different size of the image (any size, for example, the so-called 4k×2k, HD, SD, VGA, CIF and QCIF), previously the present invention can be applied to images of any size.

In addition, although not provided a detailed explanation of the above embodiment, when prepared in advance and stored table 11 determination of optimal bandwidths corresponding to different predetermined PSNR, you can execute image processing for the implementation of the optional quality control images using the present invention.

Also in the present embodiment, for the first bandwidth r1 and optimum bandwidth r2 is the same bandwidth is determined for the horizontal and vertical directions. However, similar effects are obtained when different bandwidth are determined for the horizontal and vertical directions. In the video, showing an image of a natural distance or relationship, there is a large change in the allocation in the vertical direction compared to the horizontal direction, since there is relative movement in the vertical direction. For a positive use of this effect, different bandwidths are assigned to the horizontal and vertical directions.

Also in the present embodiment, on which westline specific explanation is not provided for a number of signals of the digital filter. However, similar effects are obtained when the present invention is applied to a digital filter, having any number of signals. In addition, the specific limitation imposed on the method for designing a digital filter implementations have restrictions on bandwidth. For example, the desired shape of the frequency characteristics can be subjected to inverse Z transform in order to obtain and develop a matrix of filter coefficients of the digital filter having the appropriate frequency response.

Also in the present embodiment, the value of 51.2" is used in the Formula (1). However, the value depends on the characteristics of the digital filter and will accordingly be modified when using a different digital filter.

Also in the present embodiment, the processing bandwidth applies only to the selected component. However, the processing bandwidth can also be used for component with different colors. In this case, the coding efficiency can further be improved.

Industrial applicability

The present invention is provided for implementing a filter for converting the original image into an image having a specific value of the objective assessment of image quality by aproxen the process. Accordingly, a simplified process may implement adaptive filtering to convert all video frames having a high subjective image quality and the same value of the objective quality of the image.

1. A method of processing an image containing the steps are:
determine a first bandwidth based on the image size of the input original image data;
compute a matrix of the first filter coefficients for implementing frequency characteristics corresponding to the restriction of bandwidth, using the first bandwidth;
form data is first filtered image by performing a filtering process of the original image data using a first matrix of filter coefficients;
get the value of the objective assessment of image quality data of the first filtered image by comparing the original image data with the data of the first filtered image and calculate the distribution coefficient used for determining an optimum bandwidth, based on the value of the objective assessment of image quality;
determine the optimum bandwidth corresponding to the calculated distribution coefficient by referring to the table determine the optimal floor of the si transmission, which defines the relationship between the distribution coefficient and the optimum bandwidth;
compute a matrix of optimal filter coefficients for implementing frequency characteristics corresponding to the restriction of bandwidth, using a certain optimal bandwidth; and
form data is optimally filtered image by performing the filtering process of the original image data using a matrix of optimal filter coefficients;
moreover, at the stage of determining the first bandwidth determines the first bandwidth corresponding to the image size of the original image data, by reference to the table of determining the first bandwidth, which defines the ratio between the image size and the first bandwidth;
at the stage of determining the optimal bandwidth, when given multiple tables determine the optimal bandwidths in accordance with the size of the image and the target value for the objective assessment of image quality, select the table to determine the optimal bandwidth, which corresponds to the image size of the original image data and a specific target value for the objective assessment of quality, and determine the optimal band pass the tion, corresponding to the calculated distribution coefficient, by referring to the selected table to determine the optimal bandwidth; and
at the step of calculating the distribution coefficient calculate the distribution coefficient by dividing the value of the objective assessment of image quality, which is obtained when the source image data not subjected to the restriction of bandwidth, the resulting value of the objective assessment of image quality.

2. The imaging device containing
the device determining the first bandwidth based on the image size of the input original image data;
the device calculate the matrix of the first filter coefficients for implementing frequency characteristics corresponding to the limit of the frequency band, using a first bandwidth;
the processing unit data of the first filtered image by performing the filtering process of the original image data using a first matrix of filter coefficients;
the device receiving the values of the objective assessment of image quality data of the first filtered image by comparing the original image data with the data of the first filtered image, and calculating a coefficient of distribution is, used for determining an optimum bandwidth, based on the value of the objective assessment of image quality;
the device determining the optimum bandwidth corresponding to the calculated distribution coefficient, by reference to the table determine the optimal bandwidth, which defines the relationship between the distribution coefficient and the optimum bandwidth;
the device calculate the matrix of optimal filter coefficients for implementing frequency characteristics corresponding to the restriction of bandwidth, using a specific optimal bandwidth; and
device data generating optimum filtered image by performing the filtering process of the original image data using a matrix of optimal filter coefficients;
moreover, the device determining the first bandwidth, the first bandwidth corresponding to the image size of the original image data, by reference to the table of determining the first bandwidth, which defines the ratio between the image size and the first bandwidth;
moreover, when given multiple tables determine the optimal bandwidths in accordance with razmara the image and the target value for the objective assessment of image quality, device for determination of the optimal bandwidth selects the table to determine the optimal bandwidth, which corresponds to the image size of the original image data and a specific target value for the objective assessment of image quality; and
determines the optimum bandwidth corresponding to the calculated distribution coefficient, by referring to the selected table to determine the optimal bandwidth; and
the device calculate the distribution coefficient calculates the distribution coefficient by dividing the value of the objective assessment of image quality, which is obtained when the source image data not subjected to the restriction of bandwidth, the resulting value of the objective assessment of image quality.

3. The computer-readable storage medium that stores an image processing application, which instructs the computer to perform the process of implementation of the method of image processing according to claim 1.



 

Same patents:

FIELD: physics; image processing.

SUBSTANCE: present invention can be used, for instance, in resolution transformation. In the device and method for processing images, gradient direction v1 of the edges with the largest gradient of pixel values is detected, as well as direction v2 of the edges, orthogonal to the gradient direction v1 of the edges. Processing is done for improving and smoothing out in the gradient direction v1 and direction v2 of the edges, respectively, so as to generate image output data D2.

EFFECT: prevention of loss of high-frequency components and appearance of stepped images.

10 cl, 9 dwg

FIELD: information technology.

SUBSTANCE: pixels data of the current video sequence frame is recorded to the buffer of current frame, and the pixels data of the previous video sequence frame is recorded to the buffer of previous frame; motion between current and previous video sequence frames is determined in the differences calculation block; the calculated motion between previous and current frames is recorded to the motion data buffer; the data from the current frame buffer is recorded to the previous frame buffer; partial derivatives for every channel of the current frame are determined in the partial derivative calculation blocks for X-direction and Y-direction; spatial averaging of directional derivatives for each channel of the current frame, as well as channels smoothing in the first and the second smoothing blocks are performed; direction of the edge for every pixel position of the current frame is calculated in the arctangent calculator; anisotropic filter for every pixel position of the current frame is formed and recorded to the memory of filter values; anisotropic filtering of all pixels of the current frame, their motion exceeding predefined limit in the anisotropic filtering block, is done.

EFFECT: increased quality of dynamic video picture due to changes in anisotropic filtering with adaptive mask.

7 cl, 2 dwg

FIELD: information technology.

SUBSTANCE: filtration of noise from digital images is based on defining a local structure of the image and on non-local averaging in accordance with the defined structure. The local structure of the image is determined by successively rolling up predefined templates with neighbouring pixels and by selecting a RPC template which gives the least error after rolling up. Noise is filtered from the digital image through weighted averaging of pixel values in the search window.

EFFECT: fast filtration of noise in digital images which provides high quality of noise suppression without causing distortions.

4 cl, 16 dwg

FIELD: information technology.

SUBSTANCE: size of coding unit relative the required printer resolution is evaluated. If the size of the unit is discernible by the human eye, the following steps are carried out: the approximate metric of discernibility of a distortion caused by the Gibbs effect is determined for each coding unit and stored in memory; the approximate metric of discernibility of block distortion is determined for each border of the unit; if the corresponding element of the approximate metric of discernibility of unit distortion exceeds a predefined threshold value, a filter which can suppress block distortions is applied to the given border of the unit; for each coding unit, if the corresponding element of the approximate metric of discernibility of distortion caused by the Gibbs effect exceeds a predefined threshold value, a filter which can suppress distortions is applied to the coding unit in order to suppress distortions caused by the Gibbs effect.

EFFECT: preservation of image sharpness.

7 cl, 6 dwg

FIELD: physics.

SUBSTANCE: invention proposes to use an imaging model with separation of effects associated with reflecting power of the surface R and effects associated with scene illumination characteristics L, for which: quality of a recorded image is evaluated and if there is need to correct the image, noise is filtered off; a smaller copy of the image is formed; borders for subsequent contrast enhancement at the correction step are defined on the smaller copy; the luminance channel of the initial image is selected and filtered; the image is corrected in accordance with an empirical equation of the LR imaging model:

where A is the lower boundary of contrast enhancement of the smaller copy of the image; Φ and ψ are lower and upper boundaries of contrast enhancement of the converted image; JB is the brightness component of the initial image after bilateral filtering; γ is a non-linear conversion parameter and JF is the brightness component of the enhanced image; and the resultant image is converted to RGB colour space.

EFFECT: higher image quality.

7 cl, 19 dwg, 2 tbl

FIELD: information technology.

SUBSTANCE: coding device has definition apparatus for determining image area data meant for processing in order to counter reconstruction implied by granular noise arising in image data coded based on said image data and apparatus for countering reconstruction, designed for processing in order to counter reconstruction for image area data, defined using definition apparatus when coding image data, where when the said image data are coded in data unit data modules, the said definition apparatus determines unit data which form the said image data as the said image area data, and apparatus for countering reconstruction forcibly sets the orthogonal transformation coefficient to zero, which becomes equal to zero when quantisation is carried out using the said unit data, among orthogonal transformation coefficients of unit data defined using the said definition apparatus.

EFFECT: improved quality of the decoded image.

14 cl, 18 dwg

FIELD: information technologies.

SUBSTANCE: low-pass filter is used to filter interpolated video data where extent of filtering by low-pass filter is determined on the basis of boundary strength value determined for interpolated video data and adjacent video data (interpolated and/or not interpolated). Boundary strength is determined on the basis of control video data proximity for interpolated video data and adjacent video data.

EFFECT: noise reduction for interpolated data and reduction of interpolation distortion.

45 cl, 12 dwg

FIELD: information technologies.

SUBSTANCE: invention is related to the field of processing and coding of images, in particular to method for space filtration in processing and coding of images. Technical result is achieved by the fact that method of spatial filtration for coding and processing of image includes stage of definition of efficient value of blockiness to detect efficient value of code block blockiness in compliance with number of similar pixels of edge area of code block subject to filtration; stage of image real edge detection to decide whether edge of code block is actual edge of image in compliance with difference of pixel values at two sides of code blocks edge; stage of filtration for filtration of code block in mode of filtration, which corresponds to efficient value of code block blockiness, when edge of code block is not an actual edge of image.

EFFECT: adaptive removal of blockiness artifacts in even area of image.

10 cl, 4 dwg

FIELD: physics; image processing.

SUBSTANCE: invention relates to video compression systems, particularly to a filter for eliminating modularity. A video encoder is proposed, which is based on several layers, which uses the filter for eliminating modularity, containing: apparatus for encoding the input frame; apparatus for decoding the said encoded frame; apparatus for selecting filtration intensity for eliminating modularity in accordance with whether a unit, included in the decoded frame, was coded using internal BL mode, where if, at least the current unit and the neighbouring unit were encoded using a mode other than internal BL mode, the given filtration intensity is selected with respect to the boundary between the current unit and the neighbouring unit; and if the current unit and the neighbouring unit were encoded using internal BL mode, filtration intensity, which is lower than filtration intensity, selected with respect to the boundary, is selected; and filtering apparatus for eliminating modularity with respect to the boundary between a unit and a neighbouring unit in accordance with the selected filtration intensity for eliminating modularity.

EFFECT: more accurate selection of filtration intensity for eliminating modularity taking into account internal base layer (BL) mode.

7 cl, 19 dwg

FIELD: physics; image processing.

SUBSTANCE: invention relates to processing digital images, particularly, for reducing distortions. A method is proposed for enhancing digital images, involving analysis of image parametres with subsequent extraction of brightness components, construction of a correcting filter and image correction. Images with distortions, superimposed with previous corrections, are corrected without use and analysis of parametres of the original image. Construction of the correcting filter is done on an averaged projection profile with construction of a set of local projections of brightness of the image in the direction defined by the gradient on the selected sections. The image is corrected by selecting pixels of the image in the local region, neighbouring the edges, except pixels directly on the edges. The selected sections are corrected using the constructed correcting filter with regulation of weight coefficients of the filter on local values of the gradient amplitude.

EFFECT: efficient reduction and removal of distortions in digital images with superimposed distortions.

6 dwg

FIELD: information technology.

SUBSTANCE: invention relates to digital photography, more specifically to analysis of digital image quality. The method of detecting distortions caused by Gibbs effect in JPEG coding involves evaluation of the size of the coding unit with relative the given resolution of the printing device; determination for each coding unit, whether the size of the unit makes it distinctive to the human eye with the required printing resolution, of the approximate metric of distortion distinctiveness, caused by Gibbs effect; setting to zero corresponding elements of the approximate metric of distortion distinctiveness, caused by Gibbs effect if their values are below the preferred threshold; calculation for zero elements of the approximate metric of distortion distinctiveness, caused by Gibbs effect, of the corresponding distortion dispersion; dispersion is zeroed for the rest of the elements.

EFFECT: invention can be used in detecting distortions during JPEG coding.

4 cl, 6 dwg

FIELD: physics; image processing.

SUBSTANCE: invention concerns systems of video compression, and, in particular, to the modularity elimination filter, used in the multilayered video decoder. The video decoder which is based on set of layers, using filtration for modularity elimination is offered, and the video decoder contains: agent of restoration of a video frame from an incoming bit stream; agents of choosing modularity elimination filtration intensity according to, whether there was a block included in a reconstructed frame, one is coded by means of an internal BL mode, and if, at least, of: the current block and the next block - is coded by means of the mode, differing from the internal BL mode, selection of filtration intensity in relation to border between the current block and the next block takes place; and if the current block together with the next block has been coded by means of the internal BL mode, selection of filtration intensity, which is lower than filtration intensity chosen in relation to border takes place; and an agent of modularity elimination filtration in relation to the border between the block and the next block according to the chosen modularity elimination filtration intensity.

EFFECT: maintenance of more exact choice of modularity elimination filtration intensity at the account of an internal base layer (BL) mode.

7 cl, 19 dwg

FIELD: digital processing of images, possible use for global and local correction of brightness of digital photographs.

SUBSTANCE: system and method for correcting dark tones in digital photographs contain global contrasting module, module for conversion from RGB color system, module for determining dark tone amplification coefficient, bilateral filtration module, dark tone correction module, module for conversion to RGB color system, random-access memory block, displaying device. Global contrasting module is made with possible correction of global image contrast, module for conversion from RGB color system is made with possible conversion of image from RGB color system to three-component color system, one component of which is image brightness, and two others encode color, module for conversion to RGB color system is made with possible conversion from three-component color system, one of components of which is image brightness, and two others encode color, back to RGB color system, module for determining dark tone amplification coefficient is made with possible computation of global image brightness bar graph and can determine dark tone amplification coefficient based on analysis of signs, calculated from global image brightness bar graph, bilateral filtration module is made with possible execution of bilateral filtration of image brightness channel, dark tone correction module is made with possible correction of dark tones in image brightness channel.

EFFECT: absence of halo-effect.

2 cl, 17 dwg

FIELD: methods for removing noise in an image, possible use for improving quality of image.

SUBSTANCE: in accordance to the invention, effect is achieved due to conversion of brightness of image pixels with noise by means of solving the diffusion equation in non-divergent form, which ensures simultaneous suppression of noise and preservation of image edges.

EFFECT: simplified noise removal and increased quality of resulting digital image.

4 cl, 1 dwg

FIELD: photographic equipment engineering, image processing methods, in particular, methods for automatically correcting red-eye effect.

SUBSTANCE: method includes analysis of additional information about an image; creation of at least one array for storing image point marks; for each image point a color mark is recorded into mark array, if the color of point is a typical color for red eyes and is not a typical color for human skin; filtration of one-component image; for each point of image a boundary mark is recorded into array of marks, and also filter number is recorded into array of marks; on basis of array of marks, connected areas of points are determined; for each connected area of points with consideration of neighborhood, computation of fixed row of features; on basis of features, classification of connected areas of points onto red-eye areas and false areas; connection to red-eye connected area of image points neighboring with points of given area and close in color; connection to connected area of red-eye of image points positioned inside external contour of given area; and change of color of points in connected red-eye area.

EFFECT: ensured high quality of automatic correction of red-eye effect.

10 cl, 8 dwg

FIELD: video technology.

SUBSTANCE: invention refers to video compression, particularly to the image block compression systems. The method of image processing is suggested, which includes the definition of whether the two image blocks are adjacent or not, and whether the two blocks are subdivided or not. If the two blocks are adjacent, then the filtration of block smoothing at one or more border pixels of the two adjacent blocks occurs if it is defined that both of the adjacent blocks are not subdivided.

EFFECT: increase in the block smoothing efficiency with the use of border information.

33 cl, 24 dwg

FIELD: electric engineering.

SUBSTANCE: method includes change of image size in accordance with photocopy size and resolution of printing device, histograms are calculated for absolute values of border images, where border images are produced as a result of high-frequency filtration with convolution kernels of different size, border histogram, logarithm integral is calculated for every histogram, criteria are calculated from array of border histogram logarithm integrals, decision is taken on the basis of criteria about photograph sharpness, user is warned about possibility of printing out of focus picture, if picture as classified as out of focus.

EFFECT: detection of low quality, out of focus digital images, and their automatic exclusion from the process of printing with account of preset size of print and resolution of print.

6 cl, 4 dwg

FIELD: information technologies.

SUBSTANCE: invention refers to video compression technology, specifically to blocking effect correction filter applied in multilayered video coder/decoder. Decision mode of blocking effect correction filtration intensity is offered for frame containing block set, including the stages as follows: making decision on current block and adjacent block corrected for blocking effect; estimating whether current block and adjacent block are coded by means of internal BL mode; and if at least one either current block or adjacent block is coded by means of mode other than internal BL mode, making decision on preset filtration intensity relative to border between current block and adjacent block; and if both current block and adjacent block are coded by means of internal BL mode, making decision on filtration intensity which is lower than that chosen relative to border.

EFFECT: development of decision mode blocking effect correction filtration intensity for frame containing block set which provides proper choice of blocking effect correction filter intensity according to that whether certain block to which blocking effect correction filter is applied, uses internal base layer (BL) mode in video coder/ decoder based on layer set.

13 cl, 20 dwg

FIELD: physics; image processing.

SUBSTANCE: present invention pertains to image processing, and in particular, to the method of complexing digital multispectral half-tone images. Method of complexing digital multispectral half-tone images, including obtaining the initial images, involves breaking down each initial image to low frequency and high frequency components, separate processing of low and high frequency component images, complexing of the components, based on the principle of weighted summation for each pixel, and formation of the resultant image. Each initial image is subjected to multiple-level decomposition by the Haar wavelet through fast discrete static two-dimensional wavelet-transformation with the objective obtaining an approximate component, which is a low frequency image component, and a family of detail components, which are high frequency image components. The values of the matrix of energy characteristics of pixels are determined at all decomposition levels for each image. All detail components are filtered and the detail components are corrected through adaptive change of the values of the detail components in accordance with the inter-level dynamics of their energy characteristics. The noise microstructure is removed through adaptive threshold cut of the values of detail components on each decomposition level. The correcting brightness function and the correcting contrast function are calculated for each decomposition level, the parameter of which is a value of the approximate component. Brightness of ranges of each decomposition level is smoothed out through transformation of the approximate components by correcting brightness functions. The detail components of the contrast correcting function are transformed. A weight function is calculated for each decomposition level, the parameter of which is a value of the energy characteristic. The component of each synthesised image for each pixel at each decomposition level is calculated by weighted summation of the corresponding components of decomposing initial images using weight functions. All detail components of the synthesised image are filtered, and the detail components are corrected through adaptive change of the values of detail components in accordance with the inter-level dynamics of their energy characteristics. Noise microstructures are eliminated through adaptive threshold cut of the values of detail components at each decomposition level. The brightness correcting function and the contrast correcting function are calculated, the parameter of which is the value of approximate components of the synthesised image. The approximate component of the correcting brightness function is transformed. The detail components of the contrast correcting function are transformed. The synthesised image is formed through reconstruction using reverse fast discrete static two-dimensional wavelet-transformation, applied to the detail components of the synthesised image and approximate component of the synthesised image. The brightness range of the resulting image is matched with parameters of the video system.

EFFECT: obtaining a high quality image, containing informative image elements of the same scene, obtained in different spectral ranges.

9 dwg

FIELD: physics; processing of images.

SUBSTANCE: invention is related to the field of digital X-ray images processing. Input image is exposed to gamma-correction: extraction of square root for approximation of Poisson noise by model of additive noise distributed according to normal law; for multiplicative model of noise logarithmic conversion is performed; single-level wavelet transform of input image is done, on the basis of which wavelet coefficients are partitioned block-by-block, and standard deviation of noise for every block is assessed; prepared block ratings of noise are smoothened and interpolated by size of initial image, which gives continuously changing and locally adapting assessment of noise for the whole image; initial image is exposed to packet stationary wavelet transform by preset number of decay levels; on the basis of noise level assessment calculated at stage 2, coefficients of transform are exposed to processing with adaptive non-linear operator, which performs threshold suppression of noise and separation of image parts; reverse stationary wavelet transform is done, at that produced image with reduced level of noise and highlighted parts is exposed to reverse gamma-transform.

EFFECT: simultaneous suppression of noise and higher contrast of X-ray images.

5 dwg

FIELD: physics, processing of images.

SUBSTANCE: invention concerns numeral photo, and in particular, the analysis of quality of the numeral image. Method of revealing of unitised contortions is offered at JPEG-coding, at which: estimate the size of the coding block concerning the demanded resolution of a press; spot for each boundary of the block the approximate metric of discernability of contortion at the coding block transformation in case, the size of the coding block is a distinguishable human eye; Classify, in a case when discernability of contortions at coding transformation exceeds the predetermined threshold, boundary of the block or as boundary which demands correction for elimination of unitised contortions or as boundary which is not subject to unitised contortions, by application of the binary qualifier to the vector of the characteristic signs calculated by means of use proquantised DCT of coefficients of the adjacent blocks and a matrix of quantisation of the image.

EFFECT: increase of reliability of detection of unitised contortions at use of the underload computing and temporary resources.

4 cl, 4 dwg

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