JPH09167240A - Digital image signal processing device and method therefor - Google Patents

Abstract

PROBLEM TO BE SOLVED: To detect an edge based on the pixel difference value to reduce hardware. SOLUTION: A pixel extraction circuit 42 extracts the pixels of (3×3) blocks out of the digital image data. The difference detection circuits 43a to 43h detect the differences (a) to (h) and their directions based on a remarked pixel and the pixels (a) to (h) and supply them to a maximum difference detection circuit 44. The circuit 44 detects the absolute maximum difference, and the maximum difference and its direction are supplied to a threshold processing circuit 46 and a selection circuit 45 respectively. The circuit 45 selects the difference of the direction opposite to the direction of the maximum difference and supplies the difference to a threshold processing circuit 47. The circuit 46 compares the maximum difference with the threshold (T1), and the circuit 47 compares the difference of the opposite direction with the threshold value (T2). Based on these comparison results, an edge detection circuit 48 outputs the edge data.

Description

Detailed Description of the Invention

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a digital image signal processing apparatus and method capable of extracting an edge of an image by combining pixel differences of digitized image data.

[0002]

2. Description of the Related Art Conventionally, image edge extraction has been performed by performing DCT and FFT on an image. D
As a result of CT and FFT, the edge portion is obtained by utilizing the fact that the frequency characteristic of the edge portion is biased to a high frequency portion. Further, it has also been performed that the variation of the image data is obtained by examining the first-order differential characteristic, and thereby the edge is detected.

[0003]

However, the DC
There is a problem that a large-scale hardware is required to perform frequency expansion such as T and FFT and to investigate the first-order differential characteristics.

Therefore, an object of the present invention is to perform edge determination by checking the pixel difference with simpler hardware without requiring large-scale hardware for first-order differentiation and frequency expansion. It is an object of the present invention to provide a digital image signal processing device and method capable of processing.

[0005]

According to a first aspect of the present invention, there is provided a pixel extraction means for extracting pixels from digitized image data, and a difference between the extracted target pixel and peripheral pixels is calculated. A first maximum difference detecting means for converting the calculated difference into an absolute value and detecting a maximum first difference absolute value and a second difference absolute value in the opposite direction of the first difference absolute value. And a comparing means for detecting the presence / absence of an edge and the direction of the edge by performing threshold value determination on each of the first and second absolute difference values. A digital image signal processing device characterized by the above.

Further, according to a second aspect of the present invention, a digital image signal processing device is adapted to sample a digital image signal, receive a signal whose number of pixels is reduced by sampling, and interpolate a thinned pixel. A class classification means for detecting the presence or absence of an edge with respect to the peripheral pixels of the target pixel existing in the received digital image signal, and determining the class of the target pixel based on the presence or absence of the edge and the level distribution of the peripheral pixel. , The error between the created value and the true value of the target pixel when the value of the target pixel is created by the linear linear combination of the values of multiple pixels and the coefficient included in the input digital image signal in the vicinity of the target pixel. And a linear linear combination of the coefficient and a value of a plurality of pixels near the pixel of interest, for generating a coefficient for each class that minimizes Thus, a digital image signal processing apparatus characterized by comprising a calculating means for generating the value of the pixel of interest.

According to the invention described in claim 4, the step of extracting pixels from the digitized image data, the difference between the extracted target pixel and peripheral pixels is calculated, and the calculated difference is absolute. First converted to a value and maximized
Detecting a difference absolute value of, and detecting a second difference absolute value in a direction opposite to the first difference absolute value,
A digital image signal processing method characterized by comprising steps for detecting the presence / absence of an edge and the direction of the edge by performing threshold value judgments on the first and second absolute difference values respectively. is there.

According to a fifth aspect of the present invention, there is provided a digital image signal processing method for sampling a digital image signal, receiving a signal whose number of pixels is reduced by sampling, and interpolating a thinned pixel. A step for detecting the presence or absence of an edge with respect to the peripheral pixels of the target pixel existing in the received digital image signal, and determining the class of the target pixel based on the presence or absence of the edge and the level distribution of the peripheral pixel; When the value of the pixel of interest included in the image signal is created by linearly combining the values of a plurality of pixels and the coefficient in the vicinity of the pixel of interest, the error between the created value and the true value of the pixel of interest is minimized. To generate a coefficient for each class and a linear linear combination of the coefficient and the values of multiple pixels near the pixel of interest. A digital image signal processing method characterized by comprising the step of generating a value of the unit.

Since the difference between the pixel values of the digitized image data is obtained and the edge is detected by the combination of the respective differences, the hardware can be reduced. Also, by comparing the absolute value of the difference in the direction opposite to the direction in which the absolute value of the difference is maximum with the threshold value,
The accuracy of edge detection can be improved.

[0010]

BEST MODE FOR CARRYING OUT THE INVENTION An embodiment of the present invention will now be described in detail with reference to the drawings. An example of a MUSE decoder to which the present invention can be applied is shown in FIG. A MUSE baseband signal is supplied from an input terminal 11 and the supplied baseband signal is supplied to an LPF (low-pass filter) 12. In the LPF 12, the supplied baseband signal is subjected to predetermined band limitation, and then digitized by the A / D conversion circuit 13. The sampling frequency of the A / D conversion circuit 13 is 16.2 MHz and digitized.

Image data is supplied to the de-emphasis circuit 14 from the digitized base band signal, subjected to de-emphasis processing by the de-emphasis circuit 14, and then supplied to the inverse γ correction (γ ^-1 ) circuit 15. , Inverse γ correction is performed. The image data from the inverse γ correction circuit 15 is supplied to the motion part / motion amount detection circuit 18, the interframe interpolation circuit 19 and the class classification adaptive interpolation circuit 23.

The motion part / motion amount detection circuit 18 detects the motion area and the motion amount of the supplied image data based on the motion vector of the entire screen supplied from the terminal 17. The motion area and motion amount detected by the motion portion / motion amount detection circuit 18 are mixed (MIX) circuit 25.
In the above, it is a reference for the mixing ratio of each pixel of the still area and the moving area. The detected motion area and motion amount are supplied to the mixing circuit 25.

The still area from the image data is interframe-interpolated by the interframe interpolation circuit 19 using the image data of the previous frame. However, when the entire image moves, like the panning of the camera, a process of moving and superimposing the image one frame before according to the motion vector transmitted as the control signal is performed. The output signal of the interframe interpolation circuit 19 is supplied to the LPF 20.
The LPF 20 has 12M for the supplied image data.
A band limitation of Hz is applied, and in the frequency conversion circuit 21, the sampling frequency of the image data is changed from 32.4 MHz to 24.
Frequency converted to 3MHz. Inter-field interpolation circuit 22
Then, similarly to the interframe interpolation circuit 19, interfield interpolation using image data of one field before is performed. The output signal of the inter-field interpolation circuit 22 is supplied to the mixing circuit 25.

A motion classification from the image data is subjected to spatial interpolation, that is, frame interpolation or field interpolation by the class classification adaptive interpolation circuit 23 together with the edge data as described later. Class classification adaptive interpolation circuit 23
Is supplied to the frequency conversion circuit 24. In the frequency conversion circuit 24, the sampling frequency of the image data is frequency-converted from 32.4 MHz to 48.6 MHz. The output signal is supplied to the mixing circuit 25. The mixing circuit 25 controls the mixing ratio of the still image and the moving image based on the signal from the moving part / movement amount detecting circuit 18. The output signal of the mixing circuit 25 is supplied to a TCI decoder (not shown) and separated into Y, Pr and Pb signals. Furthermore, D / A is changed, inverse matrix operation is performed,
After γ correction is performed, R, G, B signals are obtained.

An example of the class classification adaptive interpolation circuit 23 to which the present invention is applied is shown in FIG. The moving area of the image data is supplied from the input terminal 31. The moving area of the image data is supplied from the inverse γ correction circuit 15 as described above. The motion area is supplied to the level detection circuit 32 and the interpolation circuit 36. The level detection circuit 32 detects the level of the supplied motion area, for example, the level distribution pattern for each 3 pixels × 3 lines (hereinafter, referred to as (3 × 3) block). As an example, the level distribution pattern for each block is detected by binarizing the level of each pixel of the (3 × 3) block by, for example, comparing it with the average value of the block.

The class creating circuit 34 creates a class from the edge data supplied from the terminal 33 and the level distribution pattern for each detected (3 × 3) block. Specifically, in this example, the edge data is
It consists of edge amount (2 bits), edge direction (3 bits), and a total of 1 together with the level distribution pattern (9 bits).
A block class is created with 4 bits. Here, the edge amount (2 bits) is a stepwise difference in the detected edge direction, such as `00 '.
Indicates no edge, `01 'indicates small difference value,` 10' indicates medium difference value, and `11 'indicates large difference value. These 14-bit classes are transferred from the class creating circuit 34 to the coefficient ROM 35.
Supplied to

The coefficient ROM 35 reads the coefficient data corresponding to the address using the supplied class as an address. This coefficient data is calculated in advance as described later. The read coefficient data is supplied from the coefficient ROM 35 to the interpolation circuit 36. In the interpolation circuit 36, for example, a linear linear combination equation is used to create interpolation data from the supplied coefficient data,
Spatial, ie frame or field, interpolation is performed. The interpolated image data is supplied to the frequency conversion circuit 24 via the output terminal 37.

FIG. 3 shows an embodiment of the edge detection circuit of the digital image signal processing apparatus according to the present invention. Digital image data is supplied from an input terminal 41. In the pixel extraction circuit 42, with respect to the inputted image data as shown in FIG.
Pixels are extracted. The extracted target pixel is supplied from the pixel extraction circuit 42 to the difference detection circuits 43a to 43h, and the pixels a to pixel h located around the target pixel are supplied to the difference detection circuits 43a to 43h, respectively.

The difference detection circuits 43a to 43h detect differences Δa to Δh between the pixel of interest and the pixels a to pixel h, and the differences Δa to Δh are supplied from the difference detection circuits 43a to 43h to the maximum difference detection circuit 44. It This difference Δa is obtained by Δa = Ly−La (1), where the level of the pixel of interest is Ly and the level of the pixel a is La. Similarly, the differences Δb to Δh are obtained by subtracting the values of the pixels b to h from the value of the target pixel y.

The maximum difference detection circuit 44 converts the differences Δa to Δh from the difference detection circuits 43a to 43h into absolute values, and obtains the maximum difference and its direction from | Δa | to | Δh |. The obtained maximum difference is supplied from the maximum difference detection circuit 44 to the threshold value processing circuit 46, and the direction of the maximum difference is supplied to the selection circuit 45. In the threshold value processing circuit 46, it is determined whether or not it is an edge by using the threshold value T1 in which the maximum difference supplied is set as shown in FIG. 5A, and it is determined that it is larger than the threshold value T1 (difference). A), it is determined that it is an edge, and when it is determined that it is smaller than the threshold value T1 (difference B), it is determined that it is not an edge. The selection circuit 45 selects the difference in the direction opposite to the direction of the maximum difference from the maximum difference detection circuit 44. That is, the differences Δa to Δh from the difference detection circuits 43a to 43h.
Is selected and supplied to the threshold value processing circuit 47.

In the threshold value processing circuit 47, it is judged whether or not there is an edge by using the supplied threshold value T2 in which the difference is set as shown in FIG. 5B, and it is judged that it is larger than the threshold value T2. If it is (difference C), it is determined that it is not an edge, and if it is determined that it is smaller than the threshold value T2 (difference D), it is determined that it is an edge. However, the threshold processing circuit 46
In the case of what was judged to be not an edge,
Irrespective of the determination result of the threshold value processing circuit 47, it is not an edge. The edge detection circuit 48 is supplied with the judgment results from the threshold value processing circuits 46 and 47, and detects the presence / absence of an edge, the edge direction, the difference amount, etc. as edge data from the judgment results. The detected edge data is output from the output terminal 49.

Specifically, for example, if | Δc | between the pixel of interest and pixel c is the maximum difference, then | Δc | is supplied from the maximum difference detection circuit 44 to the threshold value processing circuit 46.
Further, the direction of | Δc | is supplied from the maximum difference detection circuit 44 to the selection circuit 45. In the selection circuit 45, the absolute difference value | Δg | in the opposite direction to the pixel c is supplied to the threshold value processing circuit 47. The threshold value processing circuit 46 compares the supplied | Δc | with the threshold value T1, and the threshold value processing circuit 47 compares the supplied | Δg | with the threshold value T2.
Are compared. These threshold processing circuits 46 and 4
7 shows that | Δc | is larger than the threshold value T1,
If | Δg | is smaller than the threshold value T2, it is determined that there is an edge between the pixel of interest and the pixel c. In other cases, it is determined that it is not an edge.

Next, an example of the learning method of the coefficient data stored in the above-mentioned coefficient ROM 35 will be described with reference to the flowchart of FIG. This flowchart is based on step S
The control of the learning process starts from 1, and in the learning data formation in step S1, learning data corresponding to a known image is formed. The values of peripheral pixels in the field or frame are adopted as learning data. The true value of the pixel of interest and the values of a plurality of peripheral pixels are a set of learning data.

Here, control is performed so that the dynamic range of the block composed of peripheral pixels is smaller than a predetermined threshold value is not treated as learning data. This is because a dynamic range having a small dynamic range is likely to be affected by noise and an accurate learning result may not be obtained. At the end of the data in step S2, if the processing of all the input data, for example, one frame of data is completed, the control shifts to the prediction coefficient determination in step S5, and if not completed, the class determination in step S3 is performed. Control is transferred.

As described above, the class determination in step S3 is based on the value of a predetermined pixel in the field or frame and the edge data. In the normal equation generation in step S4, the normal equation of Expression (9) described later is created. After the processing of all the data is completed, the control is transferred from the data end of step S2 to step S5. In the determination of the prediction coefficient in step S5, this normal equation is solved using the matrix solution method to determine the prediction coefficient. The predictive coefficient store in step S6 stores the predictive coefficient in the memory, and the learning flowchart ends.

Step S4 in FIG. 6 (normal equation generation)
The process of step S5 (determination of prediction coefficient) will be described in more detail. Let y be the true value of the pixel of interest and y be its estimated value.
′ And the values of the surrounding pixels are x ₁ to x _n ,
Setting the coefficient for each class w ₁ linear combination of n taps by _{~w n y'= w 1 x 1} + w 2 x 2 + ··· + w n x n (2). Learning ago, w _i is undetermined coefficients.

As mentioned above, learning is done for each class,
When the number of data is m, the equation (2) is represented by the equation (3). y _j ′ = w ₁ x _j1 + w ₂ x _j2 + ... + w _n x _jn (3) (where j = 1, 2, ..., M)

When m> n, w _{1 to} w _n are not uniquely determined, and therefore the elements of the error vector E are predicted in the respective learning data x _j1 , x _j2 , ..., X _jn , y _j . The error is defined as e _{j and} defined as in the following equation (4). e _j = y _j − (w ₁ x _j1 + w ₂ x _j2 + ... + w _n x _jn ) (4) (where j = 1, 2, ..., m) Next, equation (5) The coefficient to be minimized is obtained, and the optimum prediction coefficients w ₁ , w ₂ , ..., W _n in the least square method are determined.

[0029]

[Equation 1]

That is, when the partial differential coefficient by w _i of the equation (5) is obtained, the equation (6) is obtained. In Expression (6), (i = 1, 2, ..., N).

[0031]

(Equation 2)

Since each w _i may be determined so that the equation (6) becomes 0,

[0033]

(Equation 3)

If a matrix is used as

[0035]

(Equation 4)

It becomes This equation is generally called a normal equation. Normal equations are simultaneous equations with exactly n unknowns. As a result, the undetermined coefficients w ₁ , w ₂ , ... W _n that are the most probable values can be obtained. Specifically, since the matrix on the left side of the equation (9) is generally positive definite symmetric, the simultaneous equations of the equation (9) can be solved by a method called Cholesky method, and the undetermined coefficient w _i is obtained, The coefficient w _i is stored in the memory using the code as an address.

FIG. 7 shows another embodiment of edge extraction according to the present invention. In another embodiment, only vertical and diagonal edges are sought. That is, diagonal edges 1 and 3 and vertical edge 2
Ask for. First, as shown in FIG. 7A, the larger difference absolute value of the two difference absolute values in the horizontal direction is compared with the threshold value T1, and as shown in FIG.
Detecting the edge limited to the vertical direction and the diagonal direction by finding the smallest difference absolute value in 6 directions excluding one horizontal direction and comparing the difference absolute value with the threshold value T2. You can

Specifically, the larger one of | Δc | and | Δg | is compared with the threshold value T1. Other differences Δ
a, Δb, Δd, Δe, Δf, and Δh are converted into absolute values, and the smallest difference absolute value is obtained from these converted absolute values, and the absolute difference value is the threshold value T2. Compared to. Then, the edges limited to the vertical direction and the diagonal direction are obtained using the respective comparison results.

Further, in the above-described embodiment, for the input image data, 8 (3 × 3) blocks of 8 pixels are provided for each pixel.
This is a method for edge extraction using the absolute value of the difference between directions. However, as shown in FIG. 8, using the pixels of the (5 × 5) block, instead of the difference absolute value in the opposite direction of the maximum difference absolute value from the target pixel and the pixels a to h,
For example, a method is also possible in which an edge extraction is performed by obtaining a difference absolute value in the opposite direction of the maximum difference absolute value from the pixel of interest and the pixels A to H and comparing the difference absolute value with a threshold value T2. . Further, the difference absolute value in the opposite direction of the maximum difference absolute value from the target pixel and the pixels a to pixel h, and the difference absolute value in the opposite direction to the maximum difference absolute value from the target pixel and the pixels A to pixel H It is also possible to use a method of extracting the edge by comparing the sum of and with the threshold value T2.

Furthermore, in the above-described other embodiment, six directions (pixels a, b, d, e, excluding two difference absolute values in the horizontal direction (pixels c, g) compared with the threshold value T1 are excluded.
This is a method in which the minimum difference absolute value is obtained from the difference absolute values of f and h), and the difference absolute value is compared with the threshold value T2 to perform edge extraction. However, the sum of the difference absolute value between the target pixel and the pixel a in the same direction from the target pixel and the difference absolute value between the target pixel and the pixel A, and similarly, each of the target pixel and the pixels b, d, e, f, and h Difference absolute value and the difference absolute value of each of the target pixel and pixels B, D, E, F, and H are calculated, and the calculated 6 in the same direction is calculated.
It is also possible to use a method of performing edge extraction by comparing the minimum sum of absolute differences from the sum of two absolute differences with the threshold value T2.

In this embodiment, the edge extraction is performed using the maximum difference absolute value among the difference absolute values of the target pixel and the peripheral pixels and the difference absolute value in the opposite direction. Is just an example, and the maximum difference absolute value regardless of the direction of the pixel and the minimum difference absolute value are obtained and the threshold determination is performed as described above, thereby performing edge extraction. The accuracy can be improved.

In this embodiment, the class of the block is created from the presence / absence of edges, the direction of the edges, and the level distribution pattern. However, the class may be created using the presence / absence of edges and the level distribution pattern. It is also possible to create a class using the presence / absence of edges, the direction of edges, the pattern of level distribution, and the amount of difference.

[0043]

According to the present invention, since edge detection can be performed with a difference value obtained when processing such as class classification is performed, hardware can be reduced.

[Brief description of the drawings]

FIG. 1 is a block diagram showing an example of a digital image signal processing device to which the present invention can be applied.

FIG. 2 is a block diagram showing an example of a class classification adaptive interpolation circuit to which the present invention can be applied.

FIG. 3 is a block diagram showing an embodiment of edge detection of the digital image signal processing device of the present invention.

FIG. 4 is a schematic diagram used to describe an edge detection circuit of the present invention.

FIG. 5 is a schematic diagram used to describe an edge detection circuit of the present invention.

FIG. 6 is a flowchart used for explaining an example of learning of a coefficient ROM of a class classification adaptive interpolation circuit to which the present invention can be applied.

FIG. 7 is a schematic diagram used to describe another example of the edge detection circuit of the present invention.

FIG. 8 is a schematic diagram used to describe another example of the edge detection circuit of the present invention.

[Description of Reference Signs] 42 pixel extraction circuit 43 difference detection circuit 44 maximum difference detection circuit 45 selection circuit 46, 47 threshold processing circuit 48 edge detection circuit

Claims (5)

[Claims] 1. A pixel extracting means for extracting a pixel from digitized image data, a difference between an extracted target pixel and a peripheral pixel is calculated, and the calculated difference is converted into an absolute value to obtain a maximum value. A first maximum difference detecting means for detecting a first difference absolute value, a second maximum difference detecting means for detecting a second difference absolute value in a direction opposite to the first difference absolute value, A digital image signal processing apparatus comprising: a comparing unit for detecting the presence / absence of an edge and the direction of the edge by performing threshold value determination on each of the first and second absolute difference values. 2. A digital image signal is sampled,
In a digital image signal processing device that receives a signal in which the number of pixels is reduced by the sampling and interpolates the thinned pixels, an edge of a peripheral pixel of a pixel of interest existing in the received digital image signal is detected. A class classification means for detecting the presence / absence and determining the class of the pixel of interest based on the presence / absence of the edge and the level distribution of the peripheral pixels, and a classifying means included in the input digital image signal, in the vicinity of the pixel of interest. When the value of the pixel of interest is created by linearly combining the values of a plurality of pixels and the coefficient, a coefficient that minimizes the error between the created value and the true value of the pixel of interest is generated for each class. Generating a value of the pixel of interest by means of a linear primary combination of the coefficient generating means for performing the above and the values of a plurality of pixels in the vicinity of the pixel of interest. Digital image signal processing apparatus characterized by comprising a fit calculation means. 3. The digital image signal processing device according to claim 1 or 2, wherein the target pixel extracted by the second maximum difference detection means, the peripheral pixel and / or the peripheral pixel. Differences between different peripheral pixels and their directions are obtained, the obtained differences are converted into absolute values, and a second difference absolute value in the opposite direction of the maximum first difference absolute value is detected. And a digital image signal processing device. 4. A step of extracting a pixel from digitized image data, a difference between the extracted target pixel and peripheral pixels is calculated, and the calculated difference is converted into an absolute value to obtain a maximum value. The step of detecting the difference absolute value of 1; the step of detecting the second difference absolute value in the opposite direction of the first difference absolute value; and the step of detecting the first and second difference absolute values, respectively. A digital image signal processing method comprising: a step for detecting the presence or absence of an edge and a direction of the edge by performing a threshold value determination. 5. A digital image signal is sampled,
In the digital image signal processing method for receiving a signal in which the number of pixels is reduced by the sampling and for interpolating the thinned pixels, an edge of a peripheral pixel of a target pixel existing in the received digital image signal is detected. A step for detecting the presence / absence, and determining the class of the pixel of interest by the presence / absence of the edge and the level distribution of the peripheral pixels; a plurality of steps included in the input digital image signal, in the vicinity of the pixel of interest. When a value of the pixel of interest is created by a linear linear combination of the pixel value and the coefficient, a coefficient that minimizes the error between the created value and the true value of the pixel of interest is generated for each class. And a step for generating the value of the pixel of interest by a linear linear combination of the coefficient and the values of a plurality of pixels in the vicinity of the pixel of interest. Digital image signal processing method characterized by comprising a flop.