JP2000004449A - Image interpolating method - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide natural interpolated data composed of a hue having high correlation with nearby pixels by operating the respective pixel components of interpolated pixels for each component based on the respective pixel components of pixels existent in the determined interpolation axis direction. SOLUTION: A composite video signal is converted into a luminance signal and two color difference signals with a decoder 1 and A/D converted. Luminance data Y and color difference data R-Y and B-Y converted into digital signal form are successively written in a memory part 2. Next, the luminance data Y and color difference data R-Y and B-Y are successively read out and converted into RGB data by an RGB color converting part 3 but the luminance data Y are simultaneously inputted to an interpolation axis detecting part 6. Based on these luminance data Y, the interpolation axis detecting part 6 outputs an interpolation axis information signal for each interpolated pixel, and based on the interpolation axis information signal from the interpolation axis detecting part 6, a blur correcting part 4 performs interpolating processing to the RGB data outputted from the color converting part 3 while using the same interpolation axis for each interpolated pixel.

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to an image interpolation method for interpolating one field image in a video signal of an interlaced scanning system with another field image.

[0002]

2. Description of the Related Art In a video signal of an interlaced scanning system, when a signal for one frame is extracted from a moving image signal and displayed or printed as a still image, a field image of a fast moving portion has a comb-like shape. It looks blurred. Therefore, in order to correct the blur, there is known an image interpolation method in which one field signal is generated by calculation from the other field signal to obtain a frame image without blur.

FIG. 13 is a block diagram for explaining an image interpolation device using a conventional image interpolation method. First, a composite video signal of the interlaced scanning system is input to the decoder unit 1. The decoder unit 1 converts this signal into a luminance signal and two color difference signals, and performs A / D conversion at a predetermined sampling frequency to output. .

[0004] The luminance data (Y) and the two color difference data (RY, BY) converted into digital signal forms are:
The data is sequentially written to the memory unit 2 under the control of a CPU (not shown). At this time, the CPU (not shown) controls the luminance data (Y) and the color difference data (RY, BY) for two consecutive predetermined field periods to be written into the memory 2. The luminance data (Y) for one minute and the color difference data (RY, BY) for one frame are written.

Next, the luminance data (Y) and the chrominance data (RY, BY) are sequentially read from the memory 2 to read RGB data.
In the color conversion unit 3, three primary colors of additive color mixture, that is, RGB data composed of R, G, and B (hereinafter, independently referred to as “R data”,
The data is converted into “B data” and “G data”, and the blur correction unit 4 performs an interpolation process for each color data.

[0006] Then, the RGB data which has been subjected to the interpolation processing is subjected to YMC consisting of three subtractive primary colors by a YMC color conversion unit 5.
The data is converted to MC data and output to recording means such as a video printer (not shown) and output to display means such as a monitor (not shown) without passing through the YMC color conversion unit 5.

Here, the ON / OFF control of the interpolation processing in the blur correction unit 4 is selectively performed according to an instruction from a CPU (not shown).
The off state may be switched by, for example, the operator confirming the image on the display means, or may be switched in accordance with the result of calculating the blur in the screen by the CPU or other hardware.

Next, an example of an image interpolation method performed by the blur correction unit 4 will be described. FIG. 14 illustrates an example in which the blur correction unit 4 obtains the interpolation data Xh at the interpolation pixel X. Here, the pixel X is added to the interpolation line of the even field between the i-th line and the (i + 1) -th line of the field (odd field) to be the source of the interpolation operation.
Is interpolated first, a total of 10 pixels (A) existing on the i-th line or the (i + 1) -th line and adjacent to the pixel X
Focusing on the adjacent pixels constituted by i to Ei and Ai + 1 to Ei + 1), two adjacent pixels (for example, Ai and Ei + 1 or Bi and Di + 1) that are point-symmetric about the pixel X Is used as a point-symmetric pixel, and the correlation of pixel data in five axial directions (first to fifth interpolation axes) connecting these point-symmetric pixels is evaluated.

The correlation between pixel data is determined, for example, by comparing pixel data (Ai to Ei) of an adjacent pixel existing on the i-th line with i
The pixel data of the adjacent pixel existing on the (+1) th line (A
i + 1 to Ei + 1), and when the correlation evaluation indexes in the first to fifth interpolation axis directions are Δ1 to Δ5, respectively, Δ1 is | Ai−Ei + 1 |, Δ2 is | Bi−Di + 1
|, Δ3 is | Ci−Ci + 1 |, Δ4 is | Di−Bi + 1 |, Δ
5 is calculated by | Ei−Ai + 1 |. Then, the values of the correlation evaluation indices Δ1 to Δ5 are compared, and the interpolation axis H is determined based on the axis having the smallest value. Interpolated data was obtained.

FIG. 15 is a diagram showing a conventional i-type image interpolation method.
According to the R data of 9 pixels existing on the i-th line and 9 pixels existing on the (i + 1) -th line,
An example is shown in which interpolation R data of five pixels (pixels V to Z) in an interpolation line between the (+1) th line and the first line is obtained.

Similarly, FIGS. 16 and 17 show the i-th line and the (i + 1) -th line based on the G data and the B data of the nine pixels existing on the i-th line and the nine pixels existing on the (i + 1) -th line. 5 shows an example in which interpolation G data and interpolation B data of five pixels (pixels V to Z) in an interpolation line between the two are obtained.

Here, as shown in FIGS.
RGB data of 9 pixels on the i-th line and i + 1
The RGB data of 9 pixels in the second line are as follows from the left. i line R data: 2, 3, 7, 10, 7, 3, 1, 0, 0 i line G data: 2, 3, 7, 10, 7, 4, 1, 0, 0 i line B data: 2 , 3, 7, 10, 7, 4, 2, 0, 0 i + 1 line R data: 0, 2, 4, 8, 10, 8, 4, 2, 0 i + 1 line G data: 0, 1, 3, 8 , 10, 9, 4, 2, 0 i + 1 line B data: 0, 1, 3, 8, 10, 8, 4, 3, 0

First, the values of the correlation evaluation indices Δ1 to Δ5 of the five pixels (pixels V to Z) in the respective axes along the interpolation line for the R data are compared with each other using FIG. The values of the evaluation indexes Δ1 to Δ5 are 8, 5,
Since the values are 3, 8, and 7, the third interpolation axis having the minimum value is determined as the interpolation axis H. Similarly, the fifth interpolation axis for the pixel W, the second interpolation axis for the pixel X, and the pixel Y Is set as the second interpolation axis, and the pixel Z is set as the second interpolation axis H.

Since the interpolation R data of the pixels V to Z is obtained by the average value of the point symmetric pixels on the interpolation axis H determined respectively, the interpolation R data Vh of the pixel V is
The average of the R data 7 and 4 of the point symmetric pixel on the third interpolation axis becomes 5.5, and similarly, the interpolation R of the pixel W
The data Wh is 2.5, and the interpolation R data Xh of the pixel X is 9.
0, the interpolated R data Yh of the pixel Y is 5.5, and the interpolated R data Zh of the pixel Z is 2.5.

Further, comparing the values of the correlation evaluation indices Δ1 to Δ5 of the five pixels (pixels V to Z) in the respective axes in the interpolation line for the G data with reference to FIG. The values of the evaluation indexes Δ1 to Δ5 are 8, 5,
Since the values are 4, 9, and 7, the third interpolation axis having the minimum value is determined as the interpolation axis H. Similarly, the third interpolation axis for the pixel W, the second interpolation axis for the pixel X, and the pixel Y Is set as the second interpolation axis, and the pixel Z is set as the interpolation axis H.

Since the interpolation G data of the pixels V to Z is obtained from the average value of the point symmetric pixels on the interpolation axis H determined respectively, the interpolation G data Vh of the pixel V is
The average of 7 and 3, which are the G data of the point symmetric pixel on the third interpolation axis, becomes 5.0.
The data Wh is 9.0, and the interpolation G data Xh of the pixel X is 9.0.
5, the interpolation G data Yh of the pixel Y is 5.5, and the interpolation G data Zh of the pixel Z is 3.0.

Similarly, comparing the values of the correlation evaluation indices Δ1 to Δ5 of the five pixels (pixels V to Z) in the respective axes in the interpolation line for the B data with reference to FIG. , The values of the correlation evaluation indexes Δ1 to Δ5 are respectively
8, 5, 4, 9, and 7, the third interpolation axis having the minimum value is determined as the interpolation axis H, and similarly, the third interpolation axis for the pixel W and the fifth interpolation axis for the pixel X. The axis and the pixel Y are referred to as a second interpolation axis, and the pixel Z is referred to as a second interpolation axis H.

Since the interpolation B data of the pixels V to Z is obtained by the average value of the point symmetric pixels on the interpolation axis H determined respectively, the interpolation B data Vh of the pixel V is
The average of 7 and 3 which are the B data of the point symmetric pixel on the third interpolation axis becomes 5.0.
Data Wh is 9.0, and interpolation B data Xh of pixel X is 2.
5, the interpolation B data Yh of the pixel Y is 5.5, and the interpolation B data Zh of the pixel Z is 3.5.

[0019]

FIG. 18 shows the interpolation R data, interpolation G data, interpolation B data of the pixels V to Z in the interpolation line obtained in this way, and 9 pixels in the i line and the i + 1 line. 19 shows R data, G data, and B data, respectively.
Is the pixel V in the interpolation line thus obtained.
9 is a graph showing R, G, and B separately for interpolation data of .about.Z, pixel data of 9 pixels in the i-th line, and pixel data of 9 pixels in the (i + 1) -th line. Here, (a) shows a graph for R data, (b) shows a graph for G data, and (c) shows a graph for B data.

As shown in FIG. 18 and these graphs, the RGB data of 9 pixels on the i-th line and i + 1
The RGB data of the 9 pixels on the 9th line are R data, G
Data and B data have almost the same value (the difference is 1 or less) and the correlation is high. That is, 9 of the i-th line
In the pixel data of the pixel, a peak (mountain) is generated in the pixel data for both RGB and the correlation is high, and the pixel data of 9 pixels in the (i + 1) th line has a peak (mountain) in the pixel data for both RGB. The correlation is high. On the other hand, interpolation R data, interpolation G data, interpolation B
It can be seen that the data have different values and a low correlation, and that appropriate interpolation has not been performed.

The R of 9 pixels in the i-th line is
GB data and R of 9 pixels in the (i + 1) th line
Since the values of the RGB data are substantially equal among the R data, the G data, and the B data, the colors of these adjacent pixels are close to white when the values of the respective RGB data are large, and the colors of the RGB pixels are different. If the data values are both small, the value should be close to black, and if both the RGB pixel data values are intermediate values, the color should be gray.

Therefore, the average value of the R data, G data, and B data values is calculated for each pixel, and the colors of these adjacent pixels are represented as follows. When the average value of R data, G data, and B data <2 Black 2 ≦ When the average value of R data, G data, and B data ≦ 8 Gray 8 <R data, G data, and B data values For the average value of white

FIG. 20 shows i obtained by such color discrimination.
FIG. 21 is a diagram illustrating colors of 9 pixels in the i-th line and 9 pixels in the (i + 1) -th line. FIG. 21 illustrates colors of pixels V to Z in an interpolation line by respective interpolation R data,
The example obtained by the average value of interpolation G data and interpolation B data is shown.

As shown in FIG. 21, the pixels V, Y, and Z are gray and almost appropriately interpolated.
Pixel W is light blue (because of R dataＲG data and B data) and pixel X is yellow (R data and G data).
It can be seen that the interpolation is not performed properly because of the data (B data).

[0025]

In order to solve the above-mentioned problems, an image interpolation method according to the present invention is directed to a method in which a plurality of mutually different pixel components constitute one pixel of pixel data in a linear and substantially parallel manner. Between the two pixel signal columns arranged in
An image interpolation method for generating a new pixel signal sequence, wherein when obtaining interpolation data of an interpolation pixel in the new pixel signal sequence, at least one of pixel data in a plurality of pixels on the two pixel signal sequences is obtained. Using a pixel component, determine an interpolation axis direction at the interpolation pixel, and calculate each pixel component at the interpolation pixel for each component based on each pixel component of a pixel existing in the determined interpolation axis direction. The interpolation data of the interpolation pixel is obtained by the following.

Further, according to the image interpolation method of the present invention, a pixel component of pixel data of one pixel is arranged between two pixel signal strings in which pixels composed of a luminance component and a color component are arranged linearly and substantially in parallel. An image interpolation method for generating a new pixel signal sequence, and calculating a plurality of pixel components color-separated from the luminance component and the color component when obtaining interpolation data of an interpolation pixel in the new pixel signal sequence. , At least one of pixel data in a plurality of pixels on the two pixel signal columns
Using the three pixel components, the interpolation axis direction at the interpolation pixel is determined, and the color-separated pixels at the interpolation pixel are determined based on the color-separated pixel components of the pixels existing in the determined interpolation axis direction. The interpolation data of the interpolation pixel is obtained by calculating the components for each component.

Further, in the image interpolation method according to the present invention, the interpolation axis direction at the interpolation pixel in the image interpolation method is as follows:
The two pixel signal strings are determined based on G color components in a plurality of pixels on the two pixel signal strings, and the two pixel signal strings are arranged on one field in an interlace scanning video signal. A part of a pixel signal, wherein the direction of the interpolation axis in the interpolation pixel is determined based on a pixel component of the video signal that contains the most high-frequency components on one screen, and The direction of the interpolation axis at the interpolation pixel is determined based on the coordinate value in the uniform color space calculated by the pixel component.

[0028]

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS An image interpolation method according to the present invention comprises:
The interpolation axis for interpolating the pixel X in the interpolation line existing between the line and the (i + 1) th line is common to the respective color data, so that more natural interpolation processing can be performed.

FIG. 1 is a block diagram for explaining an image interpolation apparatus using the image interpolation method according to the embodiment of the present invention. The image interpolation apparatus using the conventional image interpolation method described with reference to FIG. And an interpolation axis detecting unit 6 added thereto. The same components as those of the conventional image interpolation device are denoted by the same reference numerals.

In an image interpolating apparatus using an image interpolating method according to an embodiment of the present invention, a composite video signal is input to a decoder unit 1 as in a conventional image interpolating device, and the decoder unit 1 converts this signal into a luminance signal and a luminance signal. The signal is converted into two color difference signals, A / D converted at a predetermined sampling frequency, and output.

The luminance data (Y) and the chrominance data (RY, BY) converted into digital signal forms are obtained.
Are sequentially written to the memory unit 2 under the control of a CPU (not shown). At this time, the CPU (not shown) controls the luminance data (Y) and the color difference data (RY, BY) for two consecutive predetermined field periods to be written into the memory 2. Minute luminance data (Y)
And color difference data (RY, BY) for one frame are written.

Next, the luminance data (Y) and the chrominance data (RY, BY) are sequentially read from the memory 2,
The color data is converted into RGB data by the color conversion unit 3, and the luminance data (Y) from the memory 2 is simultaneously input to the interpolation axis detection unit 6. The interpolation axis detection unit 6 outputs an interpolation axis information signal for each interpolation pixel based on the input luminance data (Y), and the shake correction unit 4 outputs RGB signals output from the color conversion unit 3.
The data is interpolated on the same interpolation axis for each interpolation pixel based on the interpolation axis information signal from the interpolation axis detector 6.

The RGB data that has been subjected to the interpolation processing is converted into YMC data by a YMC color conversion unit 5 and output to recording means such as a video printer (not shown), and without passing through the YMC color conversion unit 5. It is output to display means such as a monitor (not shown).

The ON / OFF control of the interpolation processing in the blur correction section 4 is selectively performed according to an instruction from a CPU (not shown), and the ON / OFF of the interpolation processing in the blur correction section 4 is determined by the operator. May be switched by confirming the image on the display means, or may be switched according to the result of calculating the blur in the screen by the CPU or other hardware.

Next, a description will be given of an image interpolation method according to an embodiment of the present invention, which is performed by the shake correction unit 4 and the interpolation axis detection unit 6. The interpolation axis detector 6 first determines the interpolation axis based on the luminance data (Y) read from the memory 2 by the interpolation axis determination method described with reference to FIG. That is, the correlation between the pixel data and the pixel data (Ai + 1 to Ei + 1) of the adjacent pixel existing in the i-th line and the pixel data (Ai + 1 to Ei + 1) of the adjacent pixel existing in the (i + 1) -th line are calculated. , The degree of correlation in each axis direction is evaluated, and the axis having the smallest value of the correlation evaluation index in the first to fifth interpolation axis directions is determined as the interpolation axis H.

The interpolation axis information of the interpolation axis H determined by the interpolation axis detection unit 6 is input to the blur correction unit 4, and the blur correction unit 4 outputs R data, G data, Interpolation processing is performed on the B data based on the interpolation axis H from the interpolation axis detector 6. That is, the interpolation R data at the interpolation pixel,
The interpolation G data and the interpolation B data are obtained by an interpolation operation based on the same interpolation axis H determined by the interpolation axis detection unit 6.

As shown in FIG. 2, in the case of an image interpolation device in which the luminance data (Y) does not exist in the memory 2, a luminance component calculating section 7 is provided. The luminance data (Y) may be calculated from the data and the B data as follows. Y = 0.3R + 0.59G + 0.11B

FIG. 3 shows an example in which the luminance data (Y) on the i-th line and the (i + 1) -th line is obtained from the R data, G data, and B data on the i-th line and the (i + 1) -th line. . Here, the RGB data in the i-th line and the (i + 1) -th line are the same as the pixel data used in describing the conventional image interpolation method, and the luminance data (Y) in the i-th line is 2.0, 3.0, 7.0, 10.
0, 7.0, 3.70, 1.11, 0, 0, and i +
The luminance data (Y) in the first line is 0, 1.30, 3.3, 8.0, 10.0, 8.5 in order from the left.
9, 4.0, 2.11, 0.

In this manner, the luminance data (Y) on the i-th line and the (i + 1) -th line read from the memory 2 in FIG. 1 or i obtained by the calculation by the luminance component calculator 7 in FIG. FIG. 4 shows an example in which the interpolation axis H at five pixels (pixels V to Z) in the interpolation line is determined based on the luminance data (Y) on the i-th line and the (i + 1) -th line.

First, regarding the pixel V, the correlation evaluation index Δ
Since the values of 1 to Δ5 are 8, 5, 3.7, 8.7, and 7, respectively, the third interpolation axis having the minimum value is determined as the interpolation axis H,
Similarly, a third interpolation axis is set for the pixel W, a second interpolation axis is set for the pixel X, a second interpolation axis is set for the pixel Y, and a second interpolation axis is set for the pixel Z.

FIGS. 5 to 7 show the interpolation R data (FIG. 5), the interpolation G data (FIG. 6), and the interpolation B data (FIG. 6) at the interpolation pixels V to Z based on the interpolation axis H thus determined. Fig. 7) shows an example obtained.

First, referring to FIG. 5, the interpolation R for the pixels V to Z will be described.
An example of obtaining data will be described.
h is 5.5 by averaging 7 and 4 which are R data of the point symmetric pixel on the third interpolation axis. Similarly, the interpolation R data Wh of the pixel W is 9.0 and the interpolation R data of the pixel X is 9.0. Data Xh
Is 9.0, interpolation R data Yh of pixel Y is 5.5, pixel Z
Is 2.5.

Further, referring to FIG. 6, the interpolation G at the pixels V to Z will be described.
To explain an example of obtaining data, the interpolation G data V of the pixel V
h is 5.0 by averaging 7 and 3, which are the G data of the point symmetric pixel on the third interpolation axis, and similarly, the interpolation G data Wh of the pixel W is 9.0 and the interpolation G data of the pixel X is Data Xh
Is 9.5, the interpolation G data Yh of the pixel Y is 5.5, and the pixel Z is
Of the interpolation G data Zh is 3.0.

An example of obtaining the interpolation B data at the pixels V to Z will be described with reference to FIG. 7. The interpolation B data Vh of the pixel V is the B data of the point symmetric pixel on the third interpolation axis.
Is 5.0 by the average of the pixel W and the pixel W.
Is 9.0, the interpolation B data X of the pixel X is 9.0.
h is 9.0, the interpolation B data Yh of the pixel Y is 5.5, and the interpolation B data Zh of the pixel Z is 3.5.

FIG. 8 shows the R data, G data, and B data of the pixels V to Z on the interpolation line obtained in this way, and the R data, G data, and B data of 9 pixels on the i and i + 1 lines. FIG. 9 shows the interpolation data of the pixels V to Z in the interpolation line, the pixel data of 9 pixels in the i-th line, and the 9 pixels in the i + 1-th line. 3 is a graph showing pixel data of R, G, and B separately. Here, (a) shows a graph for R data, (b) shows a graph for G data, and (c) shows a graph for B data.

As shown in FIG. 8 and these graphs, interpolation R data, interpolation G data,
Both data are the RGB data of the i-th line and i +
Interpolated R data as well as RGB data of the first line,
The values are almost equal between the interpolation G data and the interpolation B data, and the interpolation data has high correlation with peaks (peaks) occurring in the pixel data. It can be seen that appropriate interpolation is performed.

Further, as shown in FIG.
According to the B interpolation data, the colors at the pixels V to Z are gray, white, white, gray, and gray in order from the left, and it can be seen that appropriate interpolation has been performed.

Here, an example in which the interpolation axis H in the interpolation pixel is determined based on the luminance data (Y) has been described.
For example, it is possible to determine the interpolation axis H based on the G data using the fact that the human eye has the highest sensitivity to the wavelength band of the G data, and perform the interpolation processing of the RGB data using the interpolation axis H.

As described above, the luminance data (Y) or G
When the interpolation axis H is determined based on the data, it is possible to detect a desired interpolation axis direction for a general image, but it does not include a component of extremely hue-biased, for example, G data. An unnatural interpolation direction may be selected for an image.

In such a case, an image interpolation device using the present image interpolation method is configured as shown in FIG.
CPU 9 performs a fast Fourier transform on the RGB data for one frame written in, calculates the frequency distribution of each color, and determines the interpolation axis H using the pixel data of the color having the highest frequency component. Is also possible.

As described above, if the interpolation axis H is determined based on the pixel data of the color having the largest amount of high frequency components, an image having an extremely uneven hue has a large number of high frequency components, in other words, a boundary portion or a steep step. Interpolation based on colors with many
An interpolated image with less unnaturalness can be obtained.

In the above embodiments, each processing is shown by a hardware block diagram for easy understanding, but the features of the present invention are not limited to the hardware configuration. For example, as shown in FIG. 12, a decoder 1, a memory 2, and a CPU 9 are connected to each other via a data bus 10.
U9 may be processed by software.

Although the example in which the interpolation data is calculated for the RGB data has been described, the same calculation result can be obtained by calculating the interpolation data for the luminance and color difference data. Further, even if the luminance data (Y) is calculated from the RGB data and the interpolation axis H is not detected from the correlation of the luminance data, the RGB data can be calculated.
Data is stored in a uniform color space, for example, L, a, b space (JISZ
8729), and 2 in this uniform color space.
The correlation between the interpolation axes may be evaluated from the point-to-point distance (color difference). Further, an interpolation operation may be performed by using YMC data instead of RGB data.

[0054]

According to the present invention, when pixel data of one pixel is composed of a plurality of data representing colors or luminances, a direction to be interpolated is detected from the luminance data or selected predetermined color data, and this detection result is obtained. By calculating the interpolation data of each color according to, it is possible to obtain natural interpolation data having a hue highly correlated with neighboring pixels.

[Brief description of the drawings]

FIG. 1 is a block diagram for explaining an image interpolation device using an image interpolation method according to an embodiment of the present invention.

FIG. 2 is a block diagram for explaining another image interpolation apparatus using the image interpolation method according to the embodiment of the present invention.

FIG. 3 is a diagram showing luminance data on an i-th line and an (i + 1) -th line.

FIG. 4 is a diagram illustrating an interpolation axis determined based on luminance data.

FIG. 5 is a diagram showing interpolation R data based on an interpolation axis detected from luminance data.

FIG. 6 is a diagram showing interpolation G data based on an interpolation axis detected from luminance data.

FIG. 7 is a diagram showing interpolation B data based on an interpolation axis detected from luminance data.

FIG. 8 is a diagram showing an interpolation result by the image interpolation method of the present invention.

FIG. 9 is a graph showing an interpolation result by the image interpolation method of the present invention.

FIG. 10 is a diagram showing the result of interpolation by the image interpolation method of the present invention in color for each pixel.

FIG. 11 is a block diagram illustrating a configuration of an image interpolation device that determines an interpolation axis by calculating a frequency distribution of each color.

FIG. 12 is a block diagram for explaining a configuration of an image interpolation device when the image interpolation method of the present invention is implemented by software processing.

FIG. 13 is a block diagram for explaining an image interpolation device using a conventional image interpolation method.

FIG. 14 is a diagram illustrating a method of determining an interpolation axis of interpolation pixel data in an interpolation line from pixel data in an i-th line and an (i + 1) -th line.

FIG. 15 is a diagram showing interpolation R data based on an interpolation axis detected from the R data.

FIG. 16 is a diagram showing interpolation G data based on an interpolation axis detected from the G data.

FIG. 17 is a diagram showing interpolation B data based on an interpolation axis detected from the B data.

FIG. 18 is a diagram showing an interpolation result by a conventional image interpolation method.

FIG. 19 is a graph showing an interpolation result by a conventional image interpolation method.

FIG. 20 is a diagram showing each pixel in the i-th line and the (i + 1) -th line in color.

FIG. 21 is a diagram showing a result of interpolation by a conventional image interpolation method in color for each pixel.

[Explanation of symbols]

 Δ1: Correlation evaluation index of pixel data in the first interpolation axis direction Δ2: Correlation evaluation index of pixel data in the second interpolation axis direction Δ3: Correlation evaluation index of pixel data in the third interpolation axis direction Δ4: Fourth interpolation Correlation evaluation index of pixel data in the axis direction Δ5 ... Correlation evaluation index of pixel data in the fifth interpolation axis direction

 ──────────────────────────────────────────────────続 き Continued on the front page F term (reference)

Claims (5)

[Claims] An image interpolation for generating a new pixel signal sequence between two pixel signal sequences in which pixels each having a plurality of different pixel components and constituting one pixel data are arranged linearly and substantially in parallel. A method of obtaining interpolation data of an interpolated pixel in the new pixel signal sequence by using at least one pixel component of pixel data in a plurality of pixels on the two pixel signal sequences. The interpolation data of the interpolation pixel is obtained by calculating each pixel component of the interpolation pixel for each component based on each pixel component of the pixel existing in the determined interpolation axis direction. An image interpolation method characterized by the following. 2. A new pixel signal sequence is generated between two pixel signal sequences in which the pixel components of the pixel data of one pixel are composed of a luminance component and a color component in a linear and substantially parallel arrangement. An image interpolation method, wherein when obtaining interpolation data of an interpolated pixel in the new pixel signal sequence, calculating a plurality of pixel components color-separated from the luminance component and the color component; Using at least one pixel component of the pixel data of the plurality of pixels, the interpolation axis direction at the interpolation pixel is determined, and the color separation of the pixels existing in the determined interpolation axis direction is performed based on each pixel component. An image interpolation method, wherein the interpolation data of the interpolation pixel is obtained by calculating each pixel component subjected to color separation at the interpolation pixel for each component. 3. The image interpolation method according to claim 1, wherein an interpolation axis direction of the interpolation pixel is determined based on a G color component of a plurality of pixels on the two pixel signal strings. Method. 4. The two pixel signal trains are a part of a pixel signal on one field in an interlace scanning video signal, and an interpolation axis direction at the interpolation pixel is one of the video signals. 3. The image interpolation method according to claim 1, wherein the image interpolation method is determined based on a pixel component containing the largest number of high-frequency components on the screen. 5. The image interpolation method according to claim 1, wherein an interpolation axis direction of the interpolation pixel is determined based on a coordinate value in a uniform color space calculated by the pixel component.