JP5075804B2 - Noise reduction device - Google Patents

Description

The present invention relates to noise reduction equipment to reduce the superimposed noise into a digital video signal.

  In general, in addition to the original video itself, noise added via a transmission path or the like is superimposed on the analog video signal. Conventionally, as a technique for reducing such noise, there is a technique called a noise reducer, and various methods have been proposed so far (see, for example, Patent Document 1).

  For example, in a noise reducer (noise reduction circuit) described in Patent Document 1, a median (MEDIAN) value of a pixel point signal sequence of an analog video signal obtained by a median filter and a pixel value of a pixel point signal of an original video signal And based on the comparison result, the noise level of the medium-high frequency superimposed on the video signal detected in advance is added to or subtracted from the pixel value of the pixel point signal of the original video signal to We are reducing.

Digital video signals, on the other hand, are less likely to degrade than analog video signals on the transmission line because the video signal data is discrete values, and even when transmission noise is superimposed, noise is reduced by error correction etc. Can be made.
However, in recent years, noise (camera noise) superimposed on a signal output from a high-definition camera has become a problem in digital video signals. Conventionally, the noise mixed on the transmission path has surpassed the camera noise, so that the noise has not been a problem. However, as the super-high-definition solid-state image pickup device (CCD, CMOS sensor, etc.) with high definition is reduced in area, the light energy obtained per unit photoreceptor is reduced. In addition to noise, low frequency noise like clamp noise is mixed.

Conventionally, as in the invention described in Patent Document 1, there is a technique for reducing the noise level in the mid-high range. However, as described above, there is currently no technology for reducing noise from a video signal in which mid-high frequency noise is multiplexed with low-frequency noise.
Japanese Patent No. 3299026

As described above, the recent digital video signal has a problem in that the mid-high frequency noise is multiplexed and superimposed on the low-frequency noise in accordance with the higher definition of the video.
Such noise has fine sand particles and moves on the screen, which degrades the image quality of the video. Further, the fact that the particles are fine means that they are high-frequency components, and this noise increases the amount of information during encoding. For this reason, when a video including a lot of noise is encoded, there is a problem that transmission bits are occupied to transmit the noise, and the decoded video becomes a video that is further deteriorated than the original video.

Further, as in the invention described in Patent Document 1, using a technique for reducing noise based on a comparison result between the median value of the pixel point signal sequence and the pixel value of the pixel point signal of the original video signal, low noise is reduced. When noise is multiplexed, there are the following problems.
For example, as shown in FIG. 23A, the pixel values in the pixel point sequence of the ideal video signal are p ₁ , p ₂ , and p ₃ at times t ₁ , t ₂ , and t ₃ , respectively. When the magnitude is p ₂ <p ₁ <p ₃ , the pixel value p _{1 at} time t ₁ must be selected as the median value.

However, when a low-frequency signal (low-frequency noise) as shown in FIG. 23B is superimposed on the video signal in an inclined manner, the pixel point sequence of the video signal is as shown in FIG. A noise component is added to each pixel value, and the pixel value of each pixel is p ₁ ′, p ₂ ′, p ₃ ′ at time t ₁ , t ₂ , t ₃ , respectively, and the magnitude thereof is p ₁ ′ <p ₂ ′ <p ₃ ′, and the pixel value p ₂ ′ at time t ₂ is erroneously selected as the median value. As described above, the conventional method has a problem that noise cannot be accurately reduced because the noise level is added to or subtracted from the video signal based on an erroneous determination.

The present invention has been made to solve the above-described problems, and even when low frequency noise and middle high frequency noise are multiplexed and superimposed on a video signal, the noise is accurately reduced. and to provide a noise reduction equipment that is capable of.

The present invention was devised to achieve the above object. First, the noise reduction device according to claim 1 is a noise reduction device for reducing noise superimposed on a digital video signal, a signal input unit, a noise level detecting means, a first delay means, and the low frequency component removing filter comprising a median filter, a second delay means, and comparing means, and selecting subtraction unit, the noise level detecting means There was a block dividing unit, and the orthogonal transform unit, a high-frequency component detecting unit, a high-frequency component cumulative means, and the noise level determining means, configured to Ru with a.

  In such a configuration, the noise reduction device inputs a digital video signal from the outside by the video signal input means. And a noise reduction apparatus detects the magnitude | size of the noise level for every flame | frame superimposed on the digital video signal as a level value by a noise level detection means. Further, the noise reduction device delays the digital video signal by the frame processing time in the noise level detection means by the first delay means.

  Then, the noise reduction device removes a predetermined low frequency component of the digital video signal by a low frequency component removal filter. Thereby, the noise reduction device can remove the low-frequency noise superimposed on the video signal in an inclined manner.

And a noise reduction apparatus selects the median value of a pixel value for every predetermined number of pixels in the signal from which the low frequency component was removed by the low frequency component removal filter by the median filter. Accordingly, the noise reduction device can flatten the noise level for each pixel column while maintaining the rising or falling edge of the signal.
Furthermore, the noise reduction apparatus delays the digital video signal delayed by the first delay means by the pixel selection time in the median filter by the second delay means.

  Then, the noise reduction device sequentially compares the pixel value and the median value selected by the median filter for each pixel of the digital video signal delayed by the second delay means by the comparison means. Thereby, the noise reduction device can obtain the amplitude direction of the signal level of the pixel value accompanying the noise.

  Then, the noise reduction device, when the addition / subtraction means determines in the comparison means that the pixel value of the digital video signal is larger than the median value, outputs a signal obtained by subtracting the level value from the pixel value, When it is determined that the pixel value is smaller than the median value, a signal obtained by adding the level value to the pixel value is used as an output signal, and when the pixel value is determined to be equal to the median value, the pixel value is Output as an output signal. As a result, the noise reduction device can reduce the mid-high frequency noise component amplitude in the output signal.

In this configuration, the noise level detection means of the noise reduction device divides the digital video signal into blocks having a predetermined size for each screen by the block dividing means. The block obtained by dividing the screen includes at least a flat portion with little change in pixel value in the screen. The noise level detecting means orthogonally transforms the blocks divided by the block dividing means by the orthogonal transform means for each block. As a result, the pixel value of the block is converted into the coefficient of the frequency component.

  The noise level detection means averages the coefficient of the high frequency component excluding the DC component of the block transformed by the orthogonal transformation means and the vicinity of the predetermined DC component by the high frequency component detection means within the block, It is detected as the level of the high frequency component.

  Further, the noise level detection means accumulates the number of blocks corresponding to the level of the high frequency component detected by the high frequency component detection means for each level by the high frequency component accumulation means. Note that the high-frequency component of a block composed of a flat portion with little change in pixel value is almost “0” if there is no noise, so the high-frequency component of the flat block can be regarded as noise.

  Therefore, the noise level detection means determines the average value corresponding to the number of blocks corresponding to a predetermined number from the low level value of the high frequency component level accumulated by the high frequency component accumulation means as the level value by the noise level determination means and outputs it. To do. Note that the low level value used for the determination may be the minimum value of the high-frequency component level, or a value that is larger than the minimum value by a predetermined level may be used. Thereby, the high-frequency component of the flat block can be detected with high accuracy, and the level value can be determined.

The noise reducing device according to claim 2, the same video signal input means and the noise reducing device according to claim 1, a noise level detecting means, a first delay means, and the low frequency component removing filter, A median filter, second delay means, comparison means, and selective addition / subtraction means; and a noise level detection means, a line extraction means, an inter-line noise level detection means, a level accumulation means, and a noise level determination. Means.

  In such a configuration, the noise level detection unit of the noise reduction device sequentially extracts the target line, which is a line constituting the screen, and the adjacent line adjacent to the target line as a correlation line group by the line extraction unit. . The correlation line group (target line and adjacent line) may be a horizontal line or a vertical line with respect to the screen.

  Then, the noise level detection means averages the pixel value difference of the corresponding pixels between the target line and the adjacent line in the correlation line group extracted by the line extraction means by the inter-line noise level detection means. Here, the corresponding pixel means a pixel at the same horizontal position when the line is selected horizontally, and a pixel at the same vertical position when the line is selected vertically.

  Note that the difference between the pixel values of adjacent lines has a smaller image component and a larger uncorrelated noise component. In this case, the uncorrelated noise is usually about twice (3 dB) higher than the image component. Therefore, the inter-line noise level detection means detects the average value of the pixel value difference between the target line and the adjacent line as the inter-line noise level in the correlation line group of the target line. Here, the adjacent line is one of the target lines on the screen (up or down for a horizontal line, right or left for a vertical line), or both. May be.

  The noise level detection means accumulates the number of lines corresponding to the noise level between lines detected by the noise level detection means between lines for each noise level between lines. Of the accumulated inter-line noise levels, the smaller the level is, the more noise can be regarded as noise generated by uncorrelated noise.

  Therefore, the noise level detection means determines and outputs an average value corresponding to a predetermined number of lines from the low level value of the inter-line noise level accumulated by the level accumulation means as the level value by the noise level determination means. . Note that the low level value used for the determination may be the minimum value of the inter-line noise level, or a value that is larger than the minimum value by a predetermined level may be used. As a result, the uncorrelated noise component can be accurately detected, and the level value can be determined.

The present invention has the following excellent effects.
According to the first and second aspects of the present invention, even in the case of a video signal in which the mid-high frequency noise is multiplexed and superimposed on the low-frequency noise, the mid-high frequency noise is removed after removing the low-frequency noise in advance. Therefore, noise can be accurately removed from the video signal. This can also reduce camera noise superimposed on the digital video signal, and can accurately remove noise even in high-definition video such as Super Hi-Vision.

Further, according to the invention described in claim 1, be a digital video signal is absent blanking period, it is possible to detect the noise level superimposed on a digital video signal, the accuracy of the digital video signal Noise can be removed well.

[ Reference example : Configuration of noise reduction device]
First, the configuration of the noise reduction device of the reference example will be described with reference to FIG. FIG. 1 is a block diagram illustrating a configuration of a noise reduction device according to a reference example .

  The noise reduction device 1 is for reducing noise from a digital video signal on which noise is superimposed (hereinafter simply referred to as a video signal). Here, the noise reduction device 1 connects a noise level detection device 2 and a delay device 3.

  The noise level detection device 2 detects the noise level superimposed on the video signal. Here, it is assumed that the noise level detection device 2 detects the noise level superimposed on the digital video signal in units of frames. The noise level detection device 2 will be described in detail later.

The delay device 3 compensates for the signal delay time in the noise level detection device 2. Here, the delay device 3 delays the video signal by the frame processing time for the noise level detection device 2 to detect the noise level in units of frames.
As described above, the noise reduction device 1 receives the noise level (level value) from the noise level detection device 2 and inputs the video signal from the delay device 3 for each frame in which the noise level is detected.

Hereinafter, a detailed configuration of the noise reduction device 1 will be described. Here, the noise reduction apparatus 1 includes a low-frequency component removal filter 10, a median filter 20, a delay unit 30, a comparison unit 40, and a selective addition / subtraction unit 50. The noise reduction device 1 inputs the noise level (level value) output from the noise level detection device 2 via the input terminal (noise level input means) _TIN2 and is output from the delay device 3. A video signal is input via an input terminal (video signal input means) _TIN1 . The noise reduction device 1 outputs the video signal with reduced noise to the outside via an output terminal (video signal output means) _TOUT .

The low-frequency component removal filter 10 removes a predetermined low-frequency component from the video signal input via the input terminal (video signal input means) _TIN1 . The video signal from which the low frequency component has been removed is output to the median filter 20.
As this low-frequency component removal filter 10, for example, a high-pass filter (HPF) that cuts components below a predetermined frequency can be used. Alternatively, a band pass filter (BPF) that partially cuts a component in a predetermined low frequency region may be used. In the following description, the low-frequency component removal filter 10 will be described as the HPF 10.

  Since the HPF 10 removes a low frequency component from the video signal, as shown in FIG. 2A, when the low frequency signal (noise component) is superimposed on the video signal that is the original image, the HPF 10 As shown in 2 (b), a video signal from which the noise component has been removed can be generated.

In the example of FIG. 2A, at the times t ₁ , t ₂ , and t ₃ , the pixel values are p ₁ ′, p ₂ ′, and p ₃ ′, respectively, and the magnitude is p ₁ ′ <p. _{Since 2} ′ <p ₃ ′, the pixel value p ₂ ′ at time t ₂ is selected in the subsequent median filter 20 unless the HPF 10 is passed. However, the low-frequency signal is removed from the video signal by passing through the HPF 10, and as shown in FIG. 2B, the respective pixel values are p ₁ , t ₁ , t ₂ , and t ₃ . Since the magnitudes of p ₂ and p ₃ are p ₂ <p ₁ <p ₃ , the subsequent median filter 20 selects the pixel value p _{1 at} time t ₁ .

The median filter 20 selects a median pixel value for each predetermined number of pixels in the signal from which the low-frequency component has been removed by the HPF 10. The median pixel value selected by the median filter 20 is output to the comparison means 40. Note that the center value output from the median filter 20 (median value) in FIG. 1 are represented by E _2.

  Here, a configuration example of the median filter 20 will be described with reference to FIG. FIG. 3 is a block diagram showing the configuration of a 3-tap median filter that is an example of the median filter. As shown in FIG. 3, the median filter 20 includes sample delay means 201 (201a, 201b) and intermediate value selection means 202.

  The sample delay means 201 delays the video signal in units of pixels, and is connected in series by the number of “tap number−1” (here, “3-1 = 2”). In the case of FIG. 3, the sample delay means 201a delays the input video signal by one pixel signal, and outputs it to the intermediate value selection means 202 and the sample delay means 201b. Further, the sample delay unit 201 b further delays the video signal by one pixel signal and outputs it to the intermediate value selection unit 202.

The intermediate value selection unit 202 selects and outputs the pixel value delayed by the sample delay units 201a and 201b and the median value of the three pixel signals of the pixel signal input at the present time.
As a result, as shown in FIG. 4, the median filter 20 allows the pixel values p _{1 to} p _{3 at} the times t _{1 to} t _{3 to} be, for example, p ₂ <p in the pixel point sequence of the sequentially input video signals. _{When 1} <p ₃ in order of magnitude, p ₁ which is the median value is selected and output.

Here, the median filter 20 has been described as a horizontal 3-tap median filter, but is not limited to this configuration, and a horizontal N-tap median filter (N: an integer of 3 or more) can be used. . Alternatively, a vertical N-tap median filter (N: an integer of 3 or more) may be used. In this case, a sufficient effect can be obtained when N is about 3 to 7 taps. The median filter 20 is not limited to a one-dimensional (horizontal, vertical) filter, and a two-dimensional filter may be used. Also in this case, a sufficient effect can be obtained with a two-dimensional median filter of about 3 × 3 to 7 × 7.
Returning to FIG. 1, the description of the configuration of the noise reduction device 1 will be continued.

The delay means 30 compensates for the signal delay time in the median filter 20. The delay means 30 delays the pixel of the video signal input from the input terminal _{TIN1 according} to the predetermined number of taps of the median filter 20. The video signal delayed by the delay means 30 is output to the comparison means 40 and the selective addition / subtraction means 50. Incidentally, in FIG. 1 the pixel values of the video signal output from the delay unit 30 is expressed by E _1.

The comparison unit 40 sequentially compares the pixel value with the median value selected by the median filter 20 for each pixel of the video signal delayed by the delay unit 30. Here, the comparison means 40 comprises a subtraction means 41 performs a median E ₂ of the video signal input from the median filter 20, a subtraction process between the pixel value E ₁ of the video signal input from the delay unit 30 The sign (“+”, “−”, “0”) indicating the magnitude relationship is output to the selective addition / subtraction means 50 as an identification signal.

Selecting subtraction unit 50, based on a comparison result of the comparison means 40 (identification signal), to the signal level (pixel value) E ₁ for each pixel of the input video signal from the delay means 30 from the input terminal T _IN2 input noise level detector level value of noise level 2 has detected the (pixel value) E ₃ is for addition and subtraction. Here, the selective addition / subtraction means 50 includes an addition means 51, a subtraction means 52, and a selection means 53.

The adding means 51 adds the level value E ₃ of the noise level to the pixel value E ₁ of the video signal input from the delay means 30 (E ₁ + E ₃ ). Further, subtracting unit 52 is to subtract the level value _{E 3} from the pixel value _{E 1} of the video signal _(E 1 -E _3).

When the signal level of E ₁ is smaller than the signal level of E ₂ (E ₁ <E ₂ ), the selection unit 53 adds the unit 51. On the contrary, if the addition result (E ₁ + E ₃ ) is larger (E ₁ > E ₂ ), the subtraction result (E ₁ −E ₃ ) of the subtracting means 52 is equal, and if equal (E ₁ = E the _2), selects a pixel value _{E 1} of the video signal. The signal (pixel value) selected by the selective addition / subtraction means 50 is output as a noise-reduced video signal to the outside via the output terminal _TOUT .
Thereby, the selective addition / subtraction means 50 can reduce the mid-high frequency noise by reducing the amplitude of the noise wave in the signal from the video signal in which the low frequency noise is reduced.

As described above, the noise reduction device 1 removes the low-frequency noise from the video signal by the HPF 10 and then removes the mid-high frequency noise by the processing after the median filter 20. For this reason, the noise reduction apparatus 1 can reduce the influence by low frequency noise, and can reduce middle and high frequency noise accurately.
The noise reduction apparatus 1 can be operated by a noise reduction program that causes a general computer to function as each of the means described above.

[ Reference example : Operation of noise reduction device]
Next, the operation of the noise reduction device of the reference example will be described with reference to FIG. FIG. 5 is a flowchart showing the operation of the noise reduction device of the reference example .
First, the noise reduction device 1 inputs the noise level (level value) detected by the noise level detection device 2 via the input terminal T _IN2 and the noise of the noise level detection device 2 via the input terminal T _IN1. A video signal synchronized with the level detection timing is input from the delay device 3 (step S1).

Then, the noise reduction device 1 removes the low frequency signal (low frequency noise) from the video signal input in step S1 by the HPF 10 (step S2). Further, the noise reduction device 1, the median filter 20, the signal low frequency signal is removed in the step S2, sequentially selects the median value of the pixel value (median) E ₂ (step S3).

Thereafter, the noise reduction device 1, the comparing means 40, and the median value E ₂ selected at step S3, is input in step S1, the processing time of the median filter 20, the pixel of the video signal delayed by the delay means 30 the magnitude of the value E ₁ is compared for each pixel (step S4). More specifically, the noise reduction apparatus 1 uses the subtraction means 41 to set the code (identification signal) based on the subtraction result of E ₁ and E ₂ as the comparison result.

When the comparison result is (E ₁ > E ₂ ), the noise reduction apparatus 1 selects and outputs the subtraction result (E ₁ −E ₃ ) by the subtraction means 52 by the selective addition / subtraction means 50 (step S5). ). If the comparison result is (E ₁ = E ₂ ), the noise reduction apparatus 1 selects E ₁ by the selective addition / subtraction means 50 and outputs it as it is (step S6). Furthermore, when the comparison result is (E ₁ <E ₂ ), the noise reduction apparatus 1 selects and outputs the addition result (E ₁ + E ₃ ) by the addition means 51 by the selective addition / subtraction means 50 (step S7).

  When the noise reduction device 1 has not performed noise reduction of the video signal for one frame (the number of pixels in one frame) (No in step S8), the noise reduction device 1 returns to step S2 and sequentially performs noise reduction for each pixel. Do. On the other hand, when the noise reduction apparatus 1 performs noise reduction of the video signal for one frame (Yes in step S8), and when the video signal of the next frame is further input (Yes in step S9), step S1 is performed. Returning to FIG. 5, the noise reduction operation is continued for the next frame, and when the input of the video signal is completed (No in step S9), the operation is ended.

In the noise reduction device 1 of the reference example, the low frequency component removing filter (HPF or BPF) 10, has been one provided structure in front of the median filter 20, as the noise reduction device 1B of FIG. 6, A low-frequency component removal filter (second low-frequency component removal filter: HPF or BPF) 10B may be further provided on the path from the delay unit 30 to the comparison unit 40 of the noise reduction apparatus 1. The low-frequency component removal filter 10B has a function similar to that of the low-frequency component removal filter 10.

In this case, the comparison means 40 sequentially outputs the pixel value E ₁ ′ and the median value E ₂ selected by the median filter 20 for each pixel of the signal from which the low-frequency component is removed by the low-frequency component removal filter 10B. Compare
Thus, by providing the low-frequency component removal filter 10B, one of the signals input to the comparison means 40 is a signal from which the low-frequency component has been removed by the low-frequency component removal filter 10, and the other is the low-frequency component. Since the signal from which the low-frequency component is removed by the removal filter 10B is obtained, the identification accuracy of the high-frequency component in the comparison unit 40 using the median value can be further increased, and noise can be reduced more accurately.

In the reference example , the noise level detection device 2 and the delay device 3 are provided outside the noise reduction device 1 (1B). However, instead of the noise level detection device 2, the level value is set manually. The level value may be input via an adjustable input device (not shown). In this case, the delay device 3 is not necessary.
Moreover, it is good also as a structure provided with the noise level detection apparatus 2 and the delay apparatus 3 in the inside of the noise reduction apparatus 1 (1B). Hereinafter, this configuration will be described as an embodiment of the present invention .

[Embodiment of the Invention : Noise Reduction Device]
Referring to FIG. 7, the configuration of the noise reducing device according to implementation embodiments of the present invention. Figure 7 is a block diagram showing a configuration of a noise reduction apparatus according to the implementation embodiments of the present invention. Here, the noise reduction device 1C includes a noise level detection unit 2, a low-frequency component removal filter 10, a median filter 20, a first delay unit 30A, a second delay unit 30B, a comparison unit 40, and selective addition / subtraction. Means 50.

The noise level detection means 2 corresponds to the noise level detection device 2 described in FIG. In this case, the noise level detection means 2 detects the noise level of the video signal input via the input terminal (video signal input means) _TIN1, and then outputs the noise level (level value) to the selective addition / subtraction means 50. To do.

The first delay means 30A corresponds to the delay device 3 described in FIG. In this case, the first delay means 30A delays the video signal input via the input terminal (video signal input means) _TIN1 by the frame unit processing time for the noise level detection means 2 to detect the noise level. Let This delayed video signal is output to the low-frequency component removal filter 10 and the second delay means 30B.

The second delay means 30B is the same as the delay means 30 described in FIG. Other configurations are the same as those of the noise reduction apparatus 1 described with reference to FIG.
The operation of the noise reduction device 1C is basically the same as the operation of the noise reduction device 1 (FIG. 1) described in FIG.

  Accordingly, the noise reduction device 1C can generate a video signal in which noise is reduced from the video signal with a configuration of one input and one output. Also in this configuration, a low-frequency component removal filter (second low-frequency filter) is further provided in the path from the second delay unit 30B to the comparison unit 40 of the noise reduction device 1C as in the noise reduction device 1D illustrated in FIG. (Component removal filter: HPF or BPF) 10B may be provided. The operation and effect in this case are the same as those of the noise reduction device 1B described in FIG.

[Configuration of noise level detection device (noise level detection means)]
Next, the configurations of the noise level detection device 2 and the noise level detection means 2 (hereinafter described as the noise level detection device 2) described with reference to FIGS. 1 and 6 to 8 will be described in detail.
The noise level detection device 2 detects a noise level from a video signal on which noise is superimposed. Here, as an example of the noise level detection device (noise level detection means) 2, two configurations will be described as examples.

In the first example, the noise level detection device 2 searches for a flat portion in which at least a change in pixel value is small for each predetermined block in a frame (screen) constituting an image, and is superimposed on that portion. The detected signal component is detected as a noise level (level value E ₃ ; see FIG. 1).
In the second example, the noise level detection device 2 detects the noise level (level value E ₃ ; see FIG. 1) superimposed in the video signal based on the correlation of the lines in the frame (screen) constituting the video. To do.

(First example: configuration of noise level detection device [block type noise level detection])
First, the configuration of the noise level detection apparatus 2 according to the first example will be described with reference to FIG. FIG. 9 is a block diagram showing the configuration of the noise level detection apparatus according to the first example.
Usually, a video screen (image) has a flat portion with little change in pixel value, for example, an infinite region such as the sky, a region where the camera is not in focus, or the like. These areas are areas where the frequency components of the image have few high frequency components. On the other hand, noise is a high-frequency component with fine particles. That is, in the screen, the high frequency component in the region with a low high frequency component can be regarded as being superimposed by noise.

Therefore, the noise level detection device 2 detects the noise level from the high frequency component in the region where the high frequency component is small in the screen.
Here, the noise level detection device 2 includes blocking means 21, orthogonal transform means 22, high frequency component detection means 23, high frequency component accumulation means 24, and noise level determination means 25.

  The blocking unit 21 converts the video signal into data for each block having a predetermined size. The blocking unit 21 receives a video signal from the outside, and outputs the divided blocks to the orthogonal transform unit 22. Here, the blocking unit 21 includes a block dividing unit 21a and a block selecting unit 21b.

  The block dividing means 21a divides the video signal into blocks of a predetermined size for each screen, that is, for each field or frame constituting the screen. Although the size of this block is not particularly limited, for example, the block is divided into sizes of about 4 × 4 pixels to 128 × 128 pixels.

  The block selection means 21b is for partially selecting the blocks divided by the block dividing means 21a so that the ratio is equal to or higher than a predetermined ratio with respect to the entire screen in the screen. The block selected by the block selection unit 21 b is output to the orthogonal transformation unit 22. For example, as shown in FIG. 10A, the block selection unit 21b provides a gap for each block B in the screen D and covers an area of 50% or more (more preferably 70% or more) of the entire screen. A plurality of blocks B, B,. As a result, a block including a region that is a candidate for a flat portion with a small change in pixel value in the screen is selected.

  Here, the block selection unit 21b partially selects blocks so that the ratio is equal to or greater than a predetermined ratio from the screen. However, as shown in FIG. It is good also as selecting. In this case, the block selecting unit 21b is omitted from the configuration, and all the blocks divided by the block dividing unit 21a are output to the orthogonal transform unit 22.

The orthogonal transform unit 22 performs orthogonal transform on the block output from the blocking unit 21 for each block. This orthogonal transform means 22 transforms each block into a frequency component by performing orthogonal transform on each block. This frequency component is output to the high frequency component detection means 23. For example, the orthogonal transform unit 22 transforms a block into a frequency component by a discrete cosine transform (DCT) which is a kind of orthogonal transform. Accordingly, as shown in FIG. 11, for example, the pixel values (p _{11 to} p ₁₄ , p _{21 to} p ₂₄ , p _{31 to} p ₃₄ , p _{41 to} p ₄₄ ) of the block B of 4 × 4 pixels are represented by pixels. It is converted into a value (DCT coefficient; x _{11 to} x ₁₄ , x _{21 to} x ₂₄ , x _{31 to} x ₃₄ , x _{41 to} x ₄₄ ) indicating the same number of frequency components as the number. The DCT coefficients in the figure, the upper left coefficient x ₁₁ represents a DC component, lower right, it becomes higher frequency toward a lower left direction.

  In addition, as long as this orthogonal transformation transforms the discrete signal of a block into a frequency domain, you may use transformations other than DCT transformation. For example, discrete sine transform (DST), fast Fourier transform (FFT), Hadamard transform, or the like can be used. Thus, by converting the discrete signal of a block into the frequency domain, it becomes possible to analyze which frequency component each block has.

  The high frequency component detection means 23 averages the coefficients of the high frequency components excluding the DC component of the block converted by the orthogonal transform means 22 and the predetermined vicinity of the DC component in the block, and obtains the level of the high frequency component of the block. It is to detect.

  In the orthogonally transformed frequency component, the low frequency component including the DC component is a component including a large amount of the frequency component of the video signal. Therefore, here, the high frequency component detecting means 23 sets the coefficient of the low frequency component to “0” and averages the coefficient of the high frequency component in the other region within the block, thereby obtaining the level of the high frequency component of the block. I will consider it. This averaging can be calculated by the square root of the mean square sum of the coefficients of each high-frequency component, the absolute value average, or the like.

  Here, referring to FIG. 12 (refer to FIG. 9 as appropriate), a method in which the high-frequency component detection unit 23 detects the level of the high-frequency component from the frequency component of the block will be described. FIG. 12 is a schematic explanatory diagram for explaining a method of detecting the level of the high frequency component from the frequency component of the block. FIG. 12A shows a coefficient (corresponding to FIG. 11B) of a frequency component of a certain block obtained by the orthogonal transform means 22, and FIG. 12B shows only the high frequency component extracted from the frequency component. Indicates the state. Here, in order to simplify the description, the block size is assumed to be 4 × 4 pixels.

As shown in FIG. 12A, it is assumed that a certain block is orthogonally transformed by the orthogonal transformation means 22 and the same number of coefficients as the number of pixels of the block (here, 4 × 4) are obtained.
At this time, the high frequency component detection means 23 regards the DC component and the coefficients of the horizontal frequency component and the vertical frequency component including the DC component as “0” as the coefficients in the vicinity of the DC component and the DC component (FIG. )), And extract other coefficients as high-frequency component coefficients. That is, the coefficients x ₁₁ , x ₁₂ , x ₁₃ , x ₁₄ , x ₂₁ , x ₃₁ , x ₄₁ are regarded as “0”, and the other coefficients x ₂₂ , x ₂₃ , x ₂₄ , x ₃₂ , x ₃₃ , x _{34 are considered.} , X ₄₂ , x ₄₃ , x ₄₄ are extracted as high frequency component coefficients.

  Then, the high frequency component detection means 23 averages each coefficient extracted as a high frequency component coefficient within the block, thereby obtaining the level of the high frequency component of the block. Here, when the averaging is performed using the square root of the mean square sum, the high frequency component detecting means 23 calculates the level L of the high frequency component by the following equation (1).

  Alternatively, in the case of averaging by absolute value averaging, the high frequency component detection means 23 calculates the level L of the high frequency component by the following equation (2).

  In the above equations (1) and (2), the coefficient 16/9 is a normalization coefficient, and is a coefficient for normalizing nine high frequency component coefficients to 16 coefficients of the entire block. It is. By normalizing in this way, the level of the high frequency component for the entire block can be obtained.

Here, as the DC component and the coefficient in the vicinity of the DC component, the horizontal and vertical frequency components including the DC component are regarded as “0”, and the level of the high frequency component of the block is calculated, but the coefficient regarded as “0” Various patterns can be used as long as they contain a DC component. For example, as shown in pattern A of FIG. 13A, the high-frequency component detection means 23 positions the coefficient of the minimum vertical frequency component and the maximum horizontal frequency component, and the position of the coefficient of the maximum vertical frequency component and the minimum horizontal frequency component. Are divided into two regions R ₁ and R ₂ , the coefficient of the region R _{1 including} the DC component is regarded as “0”, and the level of the high frequency component is calculated by the coefficient of the other region R ₂ To do.

For example, as shown in FIG. 13B, the diagonal line shown in FIG. 13A may be curved so as to include more high-frequency components and divided into two regions R ₁ and R _2. . As a result, more high-frequency components in the image can be extracted. Further, for example, as shown in FIG. 13C, a region R ₁ partially including the coefficient on the DC component side is compared with the region on the high frequency component side excluding the horizontal frequency component and the vertical frequency component including the DC component. it may be classified blocks in the region R ₂ otherwise. As a result, among the high frequency components in the image, it is possible to remove the components close to the DC component and extract the high frequency components.
Returning to FIG. 9, the description of the configuration of the noise level detection apparatus 2 will be continued.

  The high frequency component accumulating means 24 accumulates the number of blocks corresponding to the level of the high frequency component detected by the high frequency component detecting means 23 for each level. The high-frequency component accumulating unit 24 accumulates (histograms) the number of blocks corresponding to the level of the high-frequency component for each field or frame unit constituting the screen (see FIG. 14). As a result, it is analyzed which level of which high-frequency component is distributed in the screen. The accumulated value for each level accumulated by the high frequency component accumulating unit 24 is output to the noise level determining unit 25.

The noise level determination unit 25 uses a low level value of the high frequency component level accumulated by the high frequency component accumulation unit 24 or an average value corresponding to a predetermined number of blocks from the low level value as a noise level (level value). Judgment. As described with reference to FIG. 12, the coefficients detected by the high frequency component detecting means 23 as the high frequency components (for example, the coefficients x ₂₂ , x ₂₃ , x ₂₄ , x ₃₂ , x ₃₃ , x ₃₄ , x ₄₂ , x ₄₃ , x ₄₄ ) is almost “0” if it is a flat part with little change in the pixel value of the screen, so if noise is superimposed on the flat part, It can be said that the high frequency component is noise. Therefore, the noise level determination unit 25 is a low level value of the level of the high frequency component or a predetermined number (for example, 2 to 5) of the block having the high frequency component accumulated by the high frequency component accumulation unit 24. The average value (weighted average value) according to the number of blocks is determined as the noise level.

  Here, the minimum value of the high frequency component level accumulated by the high frequency component accumulating means 24 is used as the low level value of the high frequency component level. Note that the noise level of an image captured by a CCD or the like may differ depending on the brightness of the screen. When the minimum value of the high-frequency component level is used, the noise level may be determined at a low level. Therefore, the low level value is not necessarily limited to the minimum value, and a value larger than the minimum value within a predetermined range from the minimum value may be used.

  Here, the noise level determination method of the noise level determination means 25 will be described with reference to FIG. FIG. 14 is a graph showing a cumulative value (frequency) of blocks for each level of high-frequency components as a histogram. Note that this graph is a diagram for visually explaining the accumulated value of the block, and does not mean that the high-frequency component accumulating unit 24 generates the graph.

Usually, the high frequency component includes the high frequency component of the image and noise at the same time. At this time, in the graph of FIG. 14, the histogram on the right from the center of the graph having a large high-frequency component is considered to be a high-frequency component of the video (including noise). On the other hand, several histograms at the left end of the graph are considered to be almost noise-only components that do not include high-frequency components of video.
Therefore, the noise level determination means 25 calculates an average value from cumulative values corresponding to several histograms on the left end of the graph and determines it as a noise level.

For example, as shown in FIG. 14, when determining the noise level from three histograms, the noise level determination means 25 selects L ₁ , L ₂ , L ₃ with low levels of high frequency components, and the respective frequencies. An average value (weighted average value) is obtained from C ₁ , C ₂ , and C ₃ by the following equation (3), and set as a noise level N _L. This noise level N _L corresponds to the level value E ₃ .

Here, the noise level determination means 25 calculates the noise level by the weighted average of the levels of a plurality of high frequency components, but the noise level may be determined by the arithmetic average of the levels of the high frequency components without considering the frequency. Good. Further, the noise level determination means 25 may simply set the minimum high-frequency component level (L _{1 in} the case of FIG. 14) as the noise level.

Then, the noise level determination means 25 outputs the determined noise level (level value E ₃ ) to the noise reduction devices 1 and 1B (FIGS. 1 and 6). In addition, when the noise level detection apparatus 2 is incorporated in the noise reduction apparatus 1, the noise level determination means 25 uses the determined noise level (level value E ₃ ) as the noise reduction apparatuses 1C and 1D (FIGS. 7 and 8). ) To the selective addition / subtraction means 50.

  Here, the example in which the noise level detection device 2 detects the noise level of the video signal in units of screens (in the spatial direction) has been described. However, as shown in FIG. 15, a plurality of screens (a plurality of fields or frames) are detected. ) (Space + time direction). That is, the noise level detecting device 2 accumulates blocks corresponding to the level of the high frequency component for a predetermined time (a time corresponding to a plurality of fields and a plurality of frames) in the high frequency component accumulating unit 24, and the noise level determining unit 25. Thus, the noise level may be determined based on the accumulated result.

  Further, here, the noise level detection device 2 uses the low level value of the level or the low level value in the histogram of the block for each level of the high frequency component accumulated by the high frequency component accumulation unit 24 by the noise level determination unit 25. An average value (such as a weighted average) according to the predetermined number of blocks is determined as the noise level. However, the noise level determination means 25 is simply a low level value (minimum value) of a high frequency component level detected by the high frequency component detection means 23 or a predetermined number of times from the low level value, regardless of the number of blocks. The average value of the level values may be determined as the noise level.

(First Example: Operation of Noise Level Detection Device)
Next, the operation of the noise level detection apparatus 2 according to the first example will be described with reference to FIG. FIG. 16 is a flowchart showing the operation of the noise level detection apparatus according to the first example.

First, the noise level detection apparatus 2 divides the video signal into blocks of a predetermined size (for example, 4 × 4 pixels to 128 × 128 pixels) by the block dividing unit 21a of the blocking unit 21 (step S11). .
Furthermore, the noise level detection apparatus 2 uses the block selection unit 21b of the blocking unit 21 so that the block divided in step S11 becomes a predetermined ratio (for example, 70%) or more with respect to the entire screen in the screen. Thus, a partial selection is made (step S12). The block selected in this way includes a block including a flat portion in which a change in pixel value is small in the screen.

  And the noise level detection apparatus 2 converts each block into a frequency component by orthogonal transformation for every block selected by step S12 by the orthogonal transformation means 22 (step S13). Thereafter, the noise level detection device 2 uses the high frequency component detection means 23 to average the coefficients of the high frequency components excluding the direct current component and the predetermined vicinity of the direct current component in the frequency component obtained in step S13. The high frequency component level of the block is detected (step S14). As a result, the noise level detection device 2 detects both the high-frequency component level of the video on which the noise is superimposed and the high-frequency component level generated only by the noise.

  Then, the noise level detection apparatus 2 accumulates the number of blocks corresponding to the level of the high frequency component detected in step S14 by the high frequency component accumulating unit 24 for each level (step S15). Thereby, the noise level detection apparatus 2 can analyze the distribution of the level of the high frequency component of each block. At this time, it can be said that the block having a low level of the high frequency component generates the high frequency component only by noise.

Therefore, the noise level detection device 2 uses the noise level determination unit 25 to obtain a minimum value (low level value) of the high-frequency component level accumulated in step S15 or a predetermined number (for example, 2 to 5) from the minimum value. The average value corresponding to the number of blocks is determined as the noise level in the screen (step S16).
Thereby, the noise level detection apparatus 2 can detect the noise level superimposed on the video signal from the video itself.

(Second Example: Configuration of Noise Level Detection Device [Line Correlation Noise Detection])
Next, the configuration of the noise level detection apparatus 2 according to the second example will be described with reference to FIG. FIG. 17 is a block diagram showing the configuration of the noise level detection apparatus according to the second example.
Normally, in a video screen (image), the difference between pixel values of adjacent lines has a small image component and a large uncorrelated noise component. Therefore, the smaller the pixel value difference is, the more the difference can be regarded as noise generated by uncorrelated noise.

  Therefore, the noise level detection device 2 detects the noise level from the difference between the pixel values of adjacent lines in the screen. Here, the noise level detection device 2 includes a line extraction unit 26, an inter-line noise level detection unit 27, a level accumulation unit 28, and a noise level determination unit 29.

  The line extraction unit 26 correlates a target line, which is a line constituting a screen from a video signal, and an adjacent line adjacent to the target line along a scanning line with a correlation line group (more specifically, for each correlation line group). Pixel). Here, it is assumed that the line extraction unit 26 partially extracts lines so that the ratio is equal to or higher than a predetermined ratio with respect to the entire screen in the screen. The pixel value of each pixel in the line extracted by the line extraction unit 26 is output to the inter-line noise level detection unit 27.

Here, with reference to FIG. 18 (refer to FIG. 17 as appropriate), the lines (correlation line group) extracted by the line extraction means 26 will be described. FIG. 18 is an explanatory diagram for explaining a method of extracting a correlation line group of two lines in the line extraction unit.
As shown in FIG. 18 (a), the line extraction means 26 is an Nth line (target line) and an (N + 1) th line (adjacent line) in the main scanning unit of the screen D (two-dimensional image). Two adjacent lines are sequentially extracted as a correlation line group. At this time, the line extraction unit 26 provides a gap for each adjacent correlation line group, and extracts a plurality of correlation line groups so as to cover an area of 50% or more (more preferably 70% or more) of the entire screen D. . Thereby, the line extraction unit 26 can extract a correlation line group in which a change in pixel values between lines is small in the screen D.

18A shows an example in which the line extracting unit 26 extracts a line corresponding to the horizontal width of the screen as a line. However, as shown in FIG. It is good also as extracting a part of short line partially.
Furthermore, as shown in FIG. 18C, the line extraction unit 26, on a screen (frame) continuous in the time direction, is a line at the same horizontal position, which is the N _tth line of the screen at time t, The _{Nt +} 1th line on the screen at time (t + 1) may be extracted as a correlation line group. Also in this case, as in FIG. 18B, the line need not necessarily use the horizontal width of the screen and may be partially extracted.

In addition, here, an example is shown in which lines are extracted in the horizontal direction along the scanning lines, but lines may be extracted in the vertical direction.
Here, the line extraction means 26 partially extracts the correlation line group from the screen so that the ratio is equal to or higher than the predetermined ratio. However, the line extraction unit 26 may extract the correlation line group of the entire screen D. Good. Returning to FIG. 17, the description of the configuration of the noise level detection apparatus 2 will be continued.

  The inter-line noise level detection means 27 averages the pixel value differences of the corresponding pixels (here, the pixels at the same horizontal position) for each adjacent correlation line group in the line extracted by the line extraction means 26. The noise level is detected as the inter-line noise level in the correlation line group of the target line. This averaging can be calculated by the square root of the mean square sum of pixel value differences in each correlation line group, the absolute value average, or the like.

  Here, with reference to FIG. 19 (refer to FIG. 17 as appropriate), a method in which the inter-line noise level detection unit 27 detects the inter-line noise level from the pixel values of adjacent lines will be described. FIG. 19 is a schematic explanatory diagram for explaining a method of detecting an inter-line noise level from pixel values of adjacent lines.

As shown in FIG. 19, it is assumed that the pixel of the Nth line and the pixel of the (N + 1) th line adjacent to it are extracted by the line extraction means 26. Each line has M pixels in the horizontal direction. Here, the pixel values of the pixels of the Nth line are p _{N, 1} , p _{N, 2} , p _{N, 3} ,..., P _{N, M,} and the pixel values of the pixels of the (N + 1) th line are pN _{+ 1,1} , pN _{+ 1,2} , pN _{+ 1,3} ,..., pN _{+ 1, M.}

  The inter-line noise level detection means 27 averages the pixel value difference of each pixel at the same horizontal position between the N-th line and the (N + 1) -th line, so that the inter-line noise level in the correlation line group Use noise level. Here, when averaging is performed using the square root of the mean square sum, the line-to-line noise level detection means 27 calculates the line-to-line noise level L using the following equation (4).

  Alternatively, in the case of averaging by absolute value averaging, the inter-line noise level detection means 27 calculates the inter-line noise level L by the following equation (5).

Here, the method for detecting the noise level between lines on the same screen (frame) has been described. However, as shown in FIG. 18C, even if the noise level between lines is detected between adjacent frames. Good. In this case, in the above-described equations (4) and (5), the pixel values p _{N, i} , p _{N + 1, i} only become the pixel values p _{Nt, i} , p _{Nt + 1, i} corresponding to the time t, respectively. Similarly, the noise level between lines can be calculated.
Returning to FIG. 17, the description of the configuration of the noise level detection apparatus will be continued.

  The level accumulating means 28 accumulates the number of lines corresponding to the inter-line noise level detected by the inter-line noise level detecting means 27 for each level. The level accumulating means 28 accumulates the number of lines corresponding to the inter-line noise level for each level in a screen unit (see FIG. 20). Thus, it is analyzed how much noise level between lines is distributed in the screen. The accumulated value for each level accumulated by the level accumulating unit 28 is output to the noise level determining unit 29.

  The noise level determination unit 29 determines the low level value of the inter-line noise level accumulated by the level accumulation unit 28 or the average value corresponding to the number of lines corresponding to a predetermined number from the low level value as the noise level (level value). To do. The inter-line noise level detected by the inter-line noise level detecting means 27 has a smaller image component between adjacent lines, so that the smaller the value, the closer to the noise level. Therefore, the noise level determination means 29 is a predetermined number (for example, 2 to 5) from the minimum value or minimum value of the inter-line noise level in the total number of lines for each inter-line noise level accumulated by the level accumulating means 28. The average value (weighted average value) corresponding to the number of lines is determined as the noise level.

  Here, the minimum value of the inter-line noise level accumulated by the level accumulating means 28 is used as the low level value of the inter-line noise level. In addition, since the minimum value of the noise level between lines may be detected by chance regardless of noise depending on the image, the low level value is not necessarily limited to the minimum value. A value larger than the minimum value may be used within a predetermined range.

  Here, the noise level determination method of the noise level determination unit 29 will be described with reference to FIG. FIG. 20 is a graph showing a cumulative value (frequency) of the number of lines for each noise level between lines as a histogram. This graph is a diagram for visually explaining the cumulative value of the number of lines, and does not mean that the level cumulative unit 28 generates the graph.

The pixel value difference between the lines includes the image component difference and the noise component at the same time. At this time, in the graph of FIG. 20, it is considered that the histogram on the right from the center of the graph having a large inter-line noise level includes a large amount of differences in image components of video (including noise). On the other hand, several histograms at the left end of the graph are considered to be almost noise-only components that do not include differences in image components of the video.
Therefore, the noise level determination means 29 calculates an average value from the cumulative values corresponding to several histograms on the left end of the graph and determines it as the noise level.

For example, as shown in FIG. 20, when determining the noise level from three histograms, the noise level determination means 29 selects L ₁ , L ₂ , L ₃ having low inter-line noise levels, and the respective frequencies. An average value (weighted average value) is obtained from C ₁ , C ₂ , and C ₃ by the following equation (6), and set as a noise level N _L This noise level N _L corresponds to the level value E ₃ .

Here, the noise level determination means 29 calculates the noise level by weighted average of a plurality of inter-line noise levels. However, the noise level may be determined by arithmetic average of the inter-line noise levels without considering the frequency. Good. Further, the noise level determination means 29 may simply set the minimum inter-line noise level (L _{1 in} the case of FIG. 20) as the noise level.

  Then, the noise level determination means 29 outputs the determined noise level (level value) to the noise reduction devices 1 and 1B (FIGS. 1 and 6). When the noise level detection device 2 is incorporated in the noise reduction device 1, the noise level determination unit 29 uses the determined noise level (level value) of the noise reduction devices 1C and 1D (FIGS. 7 and 8). It outputs to the selective addition / subtraction means 50.

  Here, the noise level detection device 2 sequentially extracts a total of two lines, that is, a target line and one adjacent line as a correlation line group by the line extraction unit 26, and the noise level detection unit 27 between the lines extracts two lines. The noise level between lines was detected from the pixel value of the line. However, the correlation line group is not limited to two lines. For example, the inter-line noise level may be detected by a total of three lines including the target line and two adjacent lines.

  Here, with reference to FIG. 21 (refer to FIG. 17 as appropriate), a method of detecting the inter-line noise level with three lines will be described. FIG. 21 is an explanatory diagram for explaining a method of extracting a three-line correlation line group in the line extraction unit.

  As shown in FIG. 21A, for example, the line extraction means 26 is the main scanning unit of the screen D (two-dimensional image), the Nth line (target line), (N−1) and (N + 1). Three adjacent lines of the first line (adjacent line) are sequentially extracted as a correlation line group. At this time, the line extraction unit 26 provides a gap for each adjacent correlation line group, and extracts a plurality of correlation line groups so as to cover an area of 50% or more (more preferably 70% or more) of the entire screen D. .

Further, as shown in FIG. 21B, a part of lines shorter than the horizontal width of the screen may be partially extracted as three lines.
Furthermore, as shown in FIG. 21 (c), the line extraction unit 26, on the screen (frame) continuous in the time direction, is a line at the same horizontal position, and is the N _tth line (target) at the time t. Line) and N _t-1 and N _{t + 1} second lines (adjacent lines) on the screen at times (t−1) and (t + 1) may be extracted as a correlation line group. Also in this case, as in FIG. 21B, it is not always necessary to use the width of the line in the horizontal direction of the screen, and the line may be partially extracted.

  As described above, when the line extraction unit 26 sequentially extracts three correlation line groups, the noise level detection device 2 uses the inter-line noise level detection unit 27 to select one of the target lines for each corresponding pixel of the correlation line group. The difference between the pixel value of the target line and 1/2 of the pixel value of the line adjacent to the first line (first adjacent line) and the line adjacent to the other (second adjacent line) and the pixel value of the target line is obtained. A value obtained by averaging is used as the noise level between lines.

That is, the inter-line noise level detection means 27 multiplies the pixel values of the corresponding pixels of the three lines by (−1/2, 1, −1/2) or (1/2, −1, 1/2). Then, the difference between the target line and the adjacent line is obtained. Then, the inter-line noise level detection means 27 calculates the inter-line noise level in the correlation line group from the difference by the above-described equation (4) or (5).
In this way, by detecting the noise level with three lines, the image components are dispersed and the noise level can be detected with higher accuracy than when the noise level is detected with two lines.

  Also, here, in the histogram of the number of lines for each inter-line noise level accumulated by the level accumulation means 28 by the noise level determination means 29, the noise level detection device 2 uses the low level value of the level or the low level value. An average value (such as a weighted average) corresponding to the predetermined number of lines is determined as the noise level. However, the noise level determination means 29 is merely a low level value (minimum value) or a predetermined number from the low level value among the noise levels between lines detected by the noise level detection means 27 between lines regardless of the number of lines. The average value of the minute level values may be determined as the noise level.

(Second Example: Operation of Noise Level Detection Device)
Next, the operation of the noise level detection apparatus 2 according to the second example will be described with reference to FIG. FIG. 22 is a flowchart showing the operation of the noise level detection apparatus according to the second example.

  First, the noise level detection apparatus 2 extracts a line (correlation line group) from the video signal along the scanning line by the line extraction unit 26 (step S21). The line extracting unit 26 may extract lines of the entire screen, or may partially extract lines so that a predetermined ratio (for example, 70%) or more with respect to the entire screen in the screen. It is good also as selecting.

  And the noise level detection apparatus 2 averages the difference of the pixel value in the same horizontal position for every adjacent line (correlation line group) in the line selected by step S21 by the noise level detection means 27 between lines, It is detected as a noise level between lines in the correlation line group (step S22). The inter-line noise level is a level in which a difference between image components and a noise component between video lines are mixed.

  Then, the noise level detecting device 2 accumulates the number of lines corresponding to the noise level between lines detected in step S22 by the level accumulating unit 28 for each level in units of screen (step S23). Thereby, the noise level detection device 2 can analyze the distribution of the noise level between lines in the screen. At this time, it can be said that the noise level between lines with a low level is almost only a noise component.

Therefore, the noise level detection device 2 uses the noise level determination unit 29 to obtain a minimum value (low level value) of the inter-line noise level accumulated in step S23 or a predetermined number (for example, 2 to 5) from the minimum value. The average value corresponding to the number of lines is determined as the noise level in the screen (step S24).
Thereby, the noise level detection apparatus 2 can detect the noise level superimposed on the video signal from the video itself.

It is a block block diagram which shows the structure of the noise reduction apparatus of a reference example . It is explanatory drawing for demonstrating the method of removing the low frequency signal by a low frequency component removal filter. It is a block block diagram which shows the structure of the 3-tap type median filter which is an example of a median filter. It is explanatory drawing for demonstrating operation | movement of a median filter. It is a flowchart which shows operation | movement of the noise reduction apparatus of a reference example . It is a block block diagram which shows the structure which further added the low-pass component removal filter to the noise reduction apparatus of a reference example . It is a block diagram showing a configuration of a noise reduction apparatus according to the implementation embodiments of the present invention. It is a block diagram showing a configuration of further adding a low frequency component removing filter in the noise reducing device according to implementation embodiments of the present invention. It is a block block diagram which shows the structure of a noise level detection apparatus (1st example). It is a schematic diagram which shows the area | region which selects the block in a screen, Comprising: (a) is a figure which shows the case where it selects with the clearance gap between blocks, (b) selects the whole screen as a block. It is a figure which shows the relationship between the pixel value of a block, and the frequency component by orthogonal transformation. It is typical explanatory drawing for demonstrating the method to detect the level of a high frequency component from the frequency component of a block. It is a figure which shows the example of a pattern for demonstrating the area | region of the frequency component used as the object in order to detect the level of a high frequency component. It is the graph which represented the cumulative value of the block for every level of a high frequency component as a histogram. It is a figure which shows the example which extracts a block over a some field or frame. It is a flowchart which shows operation | movement of a noise level detection apparatus (1st example). It is a block block diagram which shows the structure of a noise level detection apparatus (2nd example). It is explanatory drawing for demonstrating the extraction method of the correlation line group of 2 lines in a line extraction means. It is typical explanatory drawing for demonstrating the method of detecting the noise level between lines from the pixel value of an adjacent line. It is the graph which represented the total value of the number of lines for every noise level between lines as a histogram. It is explanatory drawing for demonstrating the extraction method of the correlation line group of 3 lines in a line extraction means. It is a flowchart which shows operation | movement of a noise level detection apparatus (2nd example). It is explanatory drawing for demonstrating operation | movement of the median filter in the conventional noise reduction apparatus.

Explanation of symbols

1, 1B, 1C, 1D Noise reduction device 10 Low-frequency component removal filter (HPF, BPF)
10B Second low-frequency component removal filter (HPF, BPF)
DESCRIPTION OF SYMBOLS 20 Median filter 30 Delay means 30A 1st delay means 30B 2nd delay means 40 Comparison means 50 Selective addition / subtraction means 2 Noise level detection apparatus (noise level detection means)
DESCRIPTION OF SYMBOLS 21 Blocking means 22 Orthogonal transformation means 23 High frequency component detection means 24 High frequency component accumulation means 25 Noise level determination means 26 Line extraction means 27 Inter-line noise level detection means 28 Level accumulation means 29 Noise level determination means 3 Delay device T _IN1 video input Means T _IN2 noise level input means T _OUT video signal output means

Claims (2)

A noise reduction device for reducing noise superimposed on a digital video signal,
Video signal input means for inputting the digital video signal;
Noise level detection means for detecting the level of the noise level for each frame superimposed on the digital video signal input by the video signal input means as a level value;
First delay means for delaying the digital video signal by a frame processing time in the noise level detection means;
A low-frequency component removal filter that removes a predetermined low-frequency component of the digital video signal delayed by the first delay means;
In the signal from which the low-frequency component is removed by the low-frequency component removal filter, for each predetermined number of pixels, a median filter that selects a median pixel value;
Second delay means for further delaying the digital video signal delayed by the first delay means by a pixel selection time in the median filter;
Comparison means for sequentially comparing the pixel value and the median value selected by the median filter for each pixel of the digital video signal delayed by the second delay means;
In this comparison means, when it is determined that the pixel value of the digital video signal is larger than the median value, a signal obtained by subtracting the level value from the pixel value is used as an output signal, and the pixel value is greater than the median value. If the pixel value is determined to be smaller, the signal obtained by adding the level value to the pixel value is used as an output signal, and if the pixel value is determined to be equal to the median value, the pixel value is output as the output signal. Selective addition / subtraction means,
The noise level detection means includes
Block dividing means for dividing the digital video signal into blocks of a predetermined size for each screen;
Orthogonal transform means for orthogonally transforming the blocks divided by the block dividing means for each block;
High-frequency component detection means for averaging the coefficient of the high-frequency component excluding the DC component of the block transformed by the orthogonal transform means and the predetermined vicinity of the DC component in the block and detecting it as the level of the high-frequency component of the block; ,
High-frequency component accumulating means for accumulating the number of blocks corresponding to the level of the high-frequency component detected by the high-frequency component detecting means for each level;
Noise level determination means for determining an average value of a predetermined number of levels from a low level value of the high frequency component level accumulated by the high frequency component accumulation means as the level value;
Noise reduction device you comprising: a. A noise reduction device for reducing noise superimposed on a digital video signal,
Video signal input means for inputting the digital video signal;
Noise level detection means for detecting the level of the noise level for each frame superimposed on the digital video signal input by the video signal input means as a level value;
First delay means for delaying the digital video signal by a frame processing time in the noise level detection means;
A low-frequency component removal filter that removes a predetermined low-frequency component of the digital video signal delayed by the first delay means;
In the signal from which the low-frequency component is removed by the low-frequency component removal filter, for each predetermined number of pixels, a median filter that selects a median pixel value;
Second delay means for further delaying the digital video signal delayed by the first delay means by a pixel selection time in the median filter;
Comparison means for sequentially comparing the pixel value and the median value selected by the median filter for each pixel of the digital video signal delayed by the second delay means;
In this comparison means, when it is determined that the pixel value of the digital video signal is larger than the median value, a signal obtained by subtracting the level value from the pixel value is used as an output signal, and the pixel value is greater than the median value. If the pixel value is determined to be smaller, the signal obtained by adding the level value to the pixel value is used as an output signal, and if the pixel value is determined to be equal to the median value, the pixel value is output as the output signal. Selective addition / subtraction means,
The noise level detection means includes
Line extraction means for sequentially extracting a target line which is a line constituting the screen and an adjacent line adjacent to the target line as a correlation line group from the digital video signal;
In the correlation line group extracted by the line extraction means, the difference between the pixel values of the corresponding pixels of the target line and the adjacent line is averaged and detected as an inter-line noise level in the correlation line group of the target line. Noise level detection means between lines;
Level accumulation means for accumulating the number of lines of the target line corresponding to the noise level between lines detected by the noise level between lines for each noise level between the lines;
Noise level determination means for determining, as the level value, an average value of the noise levels between lines from a low level value of the noise level between lines accumulated by the level accumulation means;
Noise reduction device you comprising: a.

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