JP3334468B2 - Quantization bit number conversion apparatus and method - Google Patents

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a quantization bit number conversion device suitable for use in, for example, a digital video tape recorder (digital VTR) and the like.
The present invention relates to an image signal quantization bit number conversion apparatus and method for converting an externally supplied image signal into an image signal having a larger quantization bit number and outputting the image signal.

[0002]

2. Description of the Related Art At present, the flow of digitization in the field of video is steadily spreading, and there are some digital signal exchanges which have already been standardized and put to practical use. One of them is CCIR Rec. 601 and the like. This defines the format of the Y / U / V digital component signal, and each pixel is defined by an 8-bit digital signal. After that, it became necessary to define each pixel with 10 bits due to requirements of an image process and the like, and a signal standard of 10-bit data was determined. One example is the serial digital interface SMPTE 259M. Therefore, when exchanging signals between different signal standards,
Rec. It is necessary to convert an 8-bit signal defined by 601 or the like into a 10-bit signal.

As described above, one of the necessary techniques for switching signals between different digital signal formats is quantization bit number conversion. Here, as an example, conversion of an 8-bit digital signal into a 10-bit digital signal will be considered. FIG. 7 shows the relationship between the signal values.
Shown in In this example, an 8-bit digital signal value Q8
Is a quantized representative value, and the original analog signal value (true value) of the 8-bit signal value Q8 is included in the true value existence section in the figure. Therefore, in order to convert an 8-bit digital signal to a 10-bit digital signal, the 8-bit signal value Q8 is converted to a 10-bit digital signal value Q10 _0.
～Q10 ₃ of the four by selecting one will output. An example of conversion from a general 8-bit signal value to a 10-bit signal value is to add zero to the lower 2 bits. As a result, the 10-bit signal value Q10 ₀
Will always be output.

[0004]

However, this 1
0-bit signal value Q10 ₀ is because it reflects the attributes of a 8-bit signal value, DVE in the image process (Digita
l Video Effector), chroma key, switcher, etc., there is a problem that if a certain signal level width is expanded, the image quality is significantly degraded due to quantization noise.

SUMMARY OF THE INVENTION Accordingly, an object of the present invention has been made in view of the above-mentioned problems, and not only simply increases the number of quantization bits, but also reduces the number of quantization bits using a classification adaptive process. It is an object of the present invention to provide a quantization bit number conversion device and method capable of converting a signal into a signal corresponding to an increase in the amount of information.

[0006]

Means for Solving the Problems The first aspect of the present invention, a class of detecting a class <br/> and classification based on the level distribution pattern of the pixel of interest and peripheral pixels in the input digital signal Detected by detection means and learned in advance
The class consisting of a prediction coefficient storage means which corresponds to the class are read out of the obtained prediction coefficients, the predicted value generation means for generating a predicted value by using a calculation using the pixel of interest and surrounding pixels and the prediction coefficients the classification adaptive processing means comprises a plurality of stages, the classification adaptive processing means comprising a rear stage, have rows classification from the preceding output value and the target signal, at least the last stage
Classifying adaptive processing means that the output signal is
Output with more bits than digital signal
A quantization bit number conversion device characterized by generating a digital signal .

[0007] The invention of claim 6 includes the steps of detecting a class by class classification based on the level distribution pattern of the pixel of interest and peripheral pixels in the input digital signal, previously obtained by learning processing prediction coefficient
The step corresponding to the class is read, and the calculation using the prediction coefficient and the pixel of interest and the surrounding pixels is performed.
Classification adaptive processing comprising the step of generating a predicted value Te of a plurality of stages, the classification adaptive processing means comprising a subsequent stage, have rows classification from the preceding output value and the target signal, low
Output the classification adaptation processing that is at least the last stage
Signal has more bits than input digital signal
And a method of converting the number of quantization bits, wherein a digital signal to be output to a digital signal is generated .

In the apparatus and method for changing the number of quantization bits according to the present invention, class classification is performed based on a level distribution pattern of input 8-bit pixel data, and a prediction coefficient corresponding to the class is read. The input 8-bit pixel data and the prediction coefficient are converted into a 10-bit prediction value, and the classification is performed based on the converted 10-bit prediction value and the level distribution pattern of the input pixel data. Is read out, and 10-bit pixel data is generated from the input 8-bit pixel data and the prediction coefficient.

[0009]

BRIEF DESCRIPTION OF THE DRAWINGS FIG. 1 is a block diagram showing an embodiment of a quantization bit number converter according to the present invention. FIG. 1 is a block diagram of a quantization bit number conversion device according to the present invention. An 8-bit digital pixel data d0 is supplied from an input terminal indicated by 1. The pixel data d0 is supplied to the first-stage processing unit 2 and the next-stage processing unit 3. The first-stage processing unit 2 includes a class classification unit 5, a prediction coefficient ROM 6, and a prediction calculation unit 7, and the next-stage processing unit 3 includes a class classification unit 8, a prediction coefficient ROM
9 and a prediction operation unit 10.

The pixel data d0 supplied to the first stage processing unit 2
Is supplied to the classifying unit 5 and the prediction calculating unit 7.
The class classification unit 5 extracts pixels x _{0 to} x ₈ of 3 pixels × 3 lines (hereinafter referred to as (3 × 3) blocks) from the supplied pixel data d0 as shown in FIG. _{As 4} , the class is classified. The class is d
1 is supplied to the prediction coefficient ROM 6.

As shown in FIG. 3, nine prediction coefficients w _{0 to} w ₈ corresponding to the supplied class d1 are read out from the prediction coefficients ROM 6 as shown in FIG. FIG. 3 is an example using a class in which each of the nine pixels x _{0 to} x ₈ shown in FIG. 2 is represented by one bit, and has 512 types of classes, and nine prediction coefficients for each class. w ₀ ~w ₈ it is stored. Read prediction coefficient d
2 is supplied to the prediction calculation unit 7. In the prediction calculation unit 7,
Using 8-bit pixel data d0 and prediction coefficient d2,
A 10-bit predicted value d3 is generated from a linear linear combination expression described later. The generated 10-bit prediction value d3 is supplied to the next-stage processing unit 3 as an output of the first-stage processing unit 2.

In the next-stage processing unit 3, the 8-bit pixel data d0 from the input terminal 1 is supplied to the classifying unit 8 and the prediction calculating unit 10, and the 10-bit predicted value d3 from the first-stage processing unit 2 is classified. It is supplied to the unit 8. The class classification unit 8 classifies classes from nine pixels x _{0 to} x ₈ and a 10-bit predicted value d3 as shown in FIG. The classified class is supplied to the prediction coefficient ROM 9 as d4. Further, the class classification section 8, eight pixels excluding the pixels _{x 4 x 0 ~x 3, x} 5 ~x
It is also possible to generate a class using the predicted value d3 of ₈ and 10 bits.

[0013] In the prediction coefficient ROM 9, as shown in FIG. 4, the nine prediction coefficients w ₀ to w ₈ corresponding to the class d4 supplied from the pre-stored prediction coefficients are read. In FIG. 4, first, the first class is classified according to the 10-bit predicted value d3 from the first-stage processing unit 2. As shown in FIG. 7, the 10-bit pixel data predicted from the 8-bit pixel data Q8 is Q10 _{0 to} Q10 _3.
And the first class of 0, 1, 2, 3 is determined depending on which of the prediction values d3 corresponds to Q10 ₀ to Q10 ₃ . In addition, 512 types of classes corresponding to the level distribution pattern of the nine pixels x _{0 to} x ₈ are determined for each first class. Then, the prediction coefficients of nine for each class w ₀ to w ₈ are stored. The read prediction coefficient d5 is supplied to the prediction calculation unit 10. Prediction operation unit 10
Then, 10-bit pixel data d6 is generated from a linear linear combination equation using 8-bit pixel data d0 and a prediction coefficient d5. Generated 10-bit pixel data d6
Is output from the output terminal 4 as an output of the next stage processing unit 3.

Here, the quantization bit number conversion device of the present invention will be specifically described. The class classification used in the class classification units 5 and 8 includes a method of setting a class generation tap for the input image data d0 and generating a class by forming a waveform of an input signal. The following example is proposed as a class classification method based on the characteristics of a signal waveform.

1) Method for directly using PCM (Pulse Code Modulation) data 2) Method for applying ADRC (Adaptive Dynamic Range Coding) 3) Method for applying DPCM (Differential PCM) 4) BTC (Block Trancation Coding) Method of applying 5) Method of applying VQ (Vector Quantization) 6) Frequency domain class (DCT (Descrete Cosine Transcription)
sform Coding), Hadamard transform, Fourier transform, etc.)

In this embodiment, as an example, the class classification unit 5 uses a class classification method that mainly considers the time domain, and the class classification unit 8 uses a class classification method that considers the frequency domain. As described above, it is preferable that different classification methods are used in the classification units 5 and 8.
As an example, the class classification unit 5 performs a class classification using ADRC, and the class classification unit 8 performs a class classification using DCT.

The adaptive processing is performed for each of the classified classes. There are a method using a product-sum operation using a prepared prediction coefficient and a method of outputting a prepared prediction value. For example, as shown in FIG.
In the 9-tap prediction bit of the input pixel x ₀ ~x _8, considering a case of predicting the 10-bit signal value of the target pixel x _4, the prediction equation is represented by the formula (1).

[0018]

(Equation 1) x': 10-bit prediction value of the target pixel x ₄ x _i: 8-bit input pixel value w _i: prediction coefficients

Next, another embodiment of the present invention is shown in FIG. The input terminal 11 supplies digital pixel data d10 composed of 8 bits. The pixel data d10 is
It is supplied to the first stage processing unit 12. This first stage processing unit 12
It comprises a classification unit 15, a prediction coefficient ROM 16 and a prediction calculation unit 17. The next-stage processing unit 13 includes a class classification unit 18, a prediction coefficient ROM 19, and a prediction calculation unit 2.
It consists of 0.

The pixel data d supplied to the first stage processing unit 12
10 is supplied to the class classification unit 15 and the prediction calculation unit 17. The classification unit 15, the supplied pixel data d10 was 2 as shown in (3 × 3) 9 pixels x ₀ ~x ₈ included in the block are extracted, as a pixel of interest x _4, class being classified. The class is supplied to the prediction coefficient ROM 16 as d11.

As shown in FIG. 3, nine prediction coefficients w _{0 to} w ₈ corresponding to the supplied class d11 are read out from the prediction coefficient ROM 16 as shown in FIG. The read prediction coefficient d12 is calculated by the prediction operation unit 17
Supplied to The prediction operation unit 17 generates a 10-bit prediction value d13 from the linear linear combination equation (1) using the 8-bit pixel data d10 and the prediction coefficient d12. The generated 10-bit prediction value d13 is stored in the first-stage processing unit 12
Is supplied to the next-stage processing unit 13 as the output of.

In the next stage processing unit 13, the 10-bit prediction value d 13 from the first stage processing unit 12 is supplied to the class classification unit 18 and the prediction calculation unit 20. In the classifying unit 18,
The first class corresponding to the predicted value is classified from the supplied 10-bit predicted value d13, and the class is further classified from the level distribution defined by the predicted value d13. That is, using the nine prediction values corresponding to each of the nine pixels x ₀ ~x _8, classes are classified in the same manner as in the above example. The classified first class and class are supplied to the prediction coefficient ROM 19 as d14.

As shown in FIG. 4, nine prediction coefficients w _{0 to} w ₈ corresponding to the supplied class d14 are read out from the prediction coefficient ROM 19 as shown in FIG. The read prediction coefficient d15 is calculated by the prediction calculation unit 20.
Supplied to The prediction operation unit 20 calculates the linear linear combination expression (1) from the prediction value d13 and the prediction coefficient d15 from
The bit pixel data d16 is generated. Generated 1
The 0-bit pixel data d16 is output from the output terminal 14 as an output of the next stage processing unit 13.

Next, an example of learning of the prediction coefficients stored in the above-described prediction coefficient ROM will be described with reference to the flowchart of FIG. In this flowchart, the control of the learning process starts from step S1, and in the learning data formation in step S1, for example, learning data is formed from 8-bit pixel data and 10-bit pixel data from one frame. The value of the peripheral pixel in the field or the frame is adopted as the learning data. The true value of the target pixel and the values of the plurality of peripheral pixels are a set of learning data.

Wherein the dynamic range of a block composed of peripheral pixels is smaller than a predetermined threshold value;
That is, control is performed so that a program with low activity 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. Step S2
At the end of the data, if the processing of all the input data, for example, the data of one frame has been completed, the control is shifted to the prediction coefficient determination in step S5, and if not, the control is shifted to the class determination in step S3. Move on.

In the class determination in step S3, as described above, a class is determined based on predetermined 8-bit pixel data in a field or a frame. In the normal equation generation in step S4, a normal equation of Expression (9) described later is created. After the processing of all data is completed, the control shifts to step S5 from the end of data in step S2. In the prediction coefficient determination in step S5, this normal equation is solved using a matrix solution method to determine a prediction coefficient. Step S
In the prediction coefficient store of 6, the prediction coefficients are stored in the memory,
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. The pixel data consisting of 10 bits of the pixel of interest and y, and the estimated value y ', the value of the surrounding pixels when the x ₁ ~x _n, of n tap according to the coefficient w ₁ to w _n for each class setting the linear combination _{y'= w 1 x 1 + w} 2 x 2 + ··· + w n x n (2). Learning ago, w _i is undetermined coefficients.

As described above, learning is performed for each class.
When the number of data is m, equation (2) is represented by 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, so that the elements of the error vector E are predicted by the respective learning data x _j1 , x _j2 ,..., X _jn , y _j error as e _j, defined as 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) The coefficients to be minimized are determined, and the optimal prediction coefficients w ₁ , w ₂ ,..., W _n in the least squares method are determined.

[0030]

(Equation 2)

That is, when the partial differential coefficient based on w _i in equation (5) is obtained, it becomes as shown in equation (6). (I = 1, 2,..., N) in equation (6).

[0032]

(Equation 3)

Since it is sufficient to determine each w _i so that equation (6) is set to 0,

[0034]

(Equation 4)

Using a matrix,

[0036]

(Equation 5)

Is as follows. This equation is generally called a normal equation. The normal equation is a simultaneous equation having exactly n unknowns. Thereby, the undetermined coefficients w ₁ , w ₂ ,..., W _n which are the most probable values can be obtained. Specifically, since the matrix on the left side of Expression (9) is generally positive definite symmetric, the simultaneous equation of Expression (9) can be solved by a method called the Cholesky method, and the undetermined coefficient w _i is obtained, and the class The coefficient w _i is stored in a memory using the code as an address.

In this embodiment, the pixels used for the class classification and the pixels used for the prediction calculation are the same, but it is not always necessary. Further, the number of prediction coefficients stored in the prediction coefficient ROM does not have to be the same between the first-stage processing unit and the next-stage processing unit.

Further, in this embodiment, 10-bit pixel data is generated by using a linear linear combination equation, but it is also possible to use a predicted value prepared for each class in advance.

Further, in this embodiment, the first stage processing unit and the next stage processing unit have a two-stage configuration.
A multi-stage configuration such as a stage configuration is also possible.

[0041]

According to the present invention, a 10-bit predicted value is generated from inputted 8-bit digital pixel data by using the class classification processing, and a 10-bit pixel is generated based on the predicted value. Because data is generated,
It is possible to improve the performance of the quantization bit number conversion.

[Brief description of the drawings]

FIG. 1 is a block diagram of an embodiment of a quantization bit number conversion device according to the present invention.

FIG. 2 is a schematic diagram used for describing a quantization bit number conversion device of the present invention.

FIG. 3 is an example of a prediction coefficient ROM applied to the present invention.

FIG. 4 is an example of a prediction coefficient ROM applied to the present invention.

FIG. 5 is a block diagram of another embodiment of the quantization bit number conversion device of the present invention.

FIG. 6 is a flowchart illustrating learning of a prediction coefficient applied to the present invention.

FIG. 7 is a schematic diagram used for describing a quantization bit number conversion device of the present invention.

[Explanation of symbols]

 2 First stage processing unit Third stage processing unit 5, 8 Classification unit 6, 9 Prediction coefficient ROM 7, 10 Prediction calculation unit

────────────────────────────────────────────────── ─── Continuation of the front page (56) References JP-A-7-85287 (JP, A) JP-A-8-307836 (JP, A) JP-A 8-265711 (JP, A) (58) Field (Int.Cl. ⁷ , DB name) H03M 7/38 H04N 5/14 H04N 5/92 H04N 7/24

Claims (6)

(57) [Claims] 1. A based on the level distribution pattern of the pixel of interest and peripheral pixels in the input digital signal classification
The class of by the class detection means for detecting a class, the prediction coefficient obtained in advance by learning processing
Prediction coefficient storage means which corresponds to the scan is read from the predicted value generation means for generating a predicted value by using an arithmetic had use <br/> and the prediction coefficient and the pixel of interest and the peripheral pixels comprising the classification adaptive processing means comprises a plurality of stages, the above classification adaptive processing means comprising a subsequent stage, have rows classification from the preceding output value and the target signal, at least the last stage and becomes the classification adaptive processing means
Means that the output signal is greater than the input digital signal.
A quantization bit number conversion device for generating a digital signal output so as to have a bit number. 2. Based on the level distribution pattern of the pixel of interest and peripheral pixels in the input digital signal classification
A plurality of stages of class classification adaptive processing means including class detection means for detecting a class by performing the above operation, and a predicted value storage means for reading a predicted value corresponding to the class, wherein the subsequent class classification adaptive processing means includes: There line classification from the preceding output value and the target signal, at least the last stage and becomes the classification adaptive processing means
Means that the output signal is greater than the input digital signal.
A quantization bit number conversion device for generating a digital signal output so as to have a bit number. 3. The quantization bit number conversion device according to claim 1, wherein said class classification adaptive processing means at a subsequent stage performs class classification from an output value at a previous stage and said input digital signal. Characteristic quantization bit number conversion device. 4. The quantization bit number conversion device according to claim 1, wherein said class classification adaptive processing means at a subsequent stage comprises a level distribution defined by an output value of a preceding stage and an output value of said preceding stage. A quantization bit number conversion device for performing a class classification based on the pattern. 5. Based on the level distribution pattern of the pixel of interest and peripheral pixels in the input digital signal classification
A class detection means for detecting a class by the above classes of prediction coefficients found in advance by learning processing
Prediction coefficient storage means for reading out the one corresponding to the input signal, the number of bits being larger than the input digital signal by an operation using the prediction coefficient, the pixel of interest and the peripheral pixel
Quantization bit number conversion apparatus characterized by comprising a prediction value generating means for generating a predicted value is a digital signal having a. 6. Based on the level distribution pattern of the pixel of interest and peripheral pixels in the input digital signal classification
The class of by detecting a class, the prediction coefficient obtained in advance by learning processing
A step that corresponds to the scan is read, the classification adaptive processing comprising the step of generating a predicted value by using an arithmetic had use <br/> and the prediction coefficient and the pixel of interest and the peripheral pixels a plurality of stages, the above classification adaptive processing means comprising a subsequent stage, have rows classification from the preceding output value and the target signal, the classification adaptive processing that is at least the last stage, out
The number of bits to be output is larger than that of the input digital signal.
Generating a digital signal to be output so as to have a quantization bit number conversion method.