JP2005094152A - Image processing apparatus and method - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To convert an SD image into an HD image with an optional high resolution wherein the enhancement of the frequency characteristic in horizontal and vertical directions is balanced while preventing problems of S/N and preshoot/overshoot. <P>SOLUTION: The frequency adjustment circuit 15 of the image processing apparatus 1 attenuates the frequency characteristic of a received video signal much more toward high frequency components in accordance with the band and the S/N of the received video signal. An image conversion section 20 converts a 525i signal into a 1050i signal whose number of lines is twice that of the 525i signal by carrying out adaptive processing to obtain the predicted value of an HD image through linear combination between an SD image and a prescribed predicted coefficient. In this case, the image conversion section 20 varies the resolution axis intensity so that the frequency characteristic at an output stage is almost made flat. A horizontal/vertical enhancement circuit 22 adjusts the enhancement in the horizontal/vertical directions to balance the enhancement in the horizontal and vertical directions as a whole. <P>COPYRIGHT: (C)2005,JPO&NCIPI

Description

  The present invention relates to an image processing apparatus and method suitable for use when, for example, converting a standard resolution or low resolution image into an arbitrary high resolution image.

  For example, a standard resolution or low resolution image (hereinafter referred to as “SD (Standard Definition) image” as appropriate) is converted into a high resolution image (hereinafter referred to as “HD (High Definition) image” as appropriate), or an image is displayed. In the case of enlarging the pixel value, interpolation (compensation) of the missing pixel value is performed by a so-called interpolation filter or the like.

  However, even if pixel interpolation is performed using an interpolation filter, it is difficult to restore a high-resolution image because the HD image component (high frequency component) that is not included in the SD image cannot be restored. .

  Therefore, the applicant of the present application has previously proposed an image conversion device that converts an SD image into an HD image that also includes a high frequency component that is not included therein (see, for example, Patent Documents 1 to 3 below).

JP-A-7-193789 JP-A-11-55630 JP 2000-134586 A

  By the way, in this image conversion apparatus, the SD image can be converted into an HD image of an arbitrary high resolution by changing the resolution axis intensity. However, if the resolution axis strength is increased too much, the frequency characteristics are emphasized from the middle range, and preshoot / overshoot occurs in the output signal. Further, since the high frequency component included in the input signal is emphasized, the degree to which the resolution axis strength is increased is limited from the viewpoint of S / N (Signal / Noise). Furthermore, since the conventional resolution conversion apparatus does not achieve the enhancement balance in the horizontal direction and the vertical direction, the obtained HD image is one in which the horizontal and vertical sharpness and fineness are not balanced.

  The present invention has been proposed in view of such a conventional situation, and even if the resolution axis strength is increased, no problem occurs in terms of S / N and preshoot / overshoot, and the horizontal and vertical directions are further improved. An object of the present invention is to provide an image processing apparatus and method for achieving enhancement balance.

  In order to achieve the above-described object, the image processing apparatus according to the present invention provides the first image signal according to the type and / or S / N of the first image signal having the first number of pixels. A first frequency characteristic adjusting unit that adjusts a frequency characteristic in a horizontal direction of an image signal, and the first image signal whose frequency characteristic is adjusted by the frequency characteristic adjusting unit is greater than the first pixel number. The image conversion means for converting to a second image signal having the number of pixels of 2 and the final output signal so that the frequency characteristics of the final output are approximately the same in the horizontal and vertical directions. Second frequency characteristic adjusting means for adjusting the frequency characteristics in the horizontal direction and the vertical direction, and the image converting means is configured so that the frequency characteristics in the horizontal direction at the output stage of the image converting means are substantially flat. Set the second number of pixels A.

  In order to achieve the above-described object, the image processing method according to the present invention performs the first processing according to the type and / or S / N of the first image signal composed of the first number of input pixels. A first frequency characteristic adjusting step for adjusting a horizontal frequency characteristic of one image signal, and the first image signal whose frequency characteristic is adjusted in the frequency characteristic adjusting step, based on the first number of pixels. The second image signal is converted into a second image signal having a large number of second pixels, and the final output frequency characteristics are approximately the same in the horizontal and vertical directions. A second frequency characteristic adjusting step for adjusting the horizontal and vertical frequency characteristics of the image signal. In the image converting step, the horizontal frequency characteristics at the output stage of the image converting means are substantially flat. Set the second number of pixels so that Than it is.

  In such an image processing apparatus and method, the horizontal frequency characteristics of the first image signal are adjusted according to the type and / or S / N of the input first image signal, and the horizontal direction The first image signal is converted into a second image signal having a larger number of pixels than the first image signal so that the frequency characteristic of the output is substantially flat. The frequency characteristics of the second image signal in the horizontal direction and the vertical direction are adjusted so as to be approximately the same in the vertical direction.

  According to the image processing apparatus and the method thereof according to the present invention, the horizontal frequency characteristics of the first image signal are adjusted according to the type and / or S / N of the input first image signal. The first image signal is converted into a second image signal having a larger number of pixels than the first image signal so that the horizontal frequency characteristic becomes substantially flat, and the final output frequency characteristic By adjusting the frequency characteristics of the second image signal in the horizontal direction and the vertical direction so that the horizontal direction and the vertical direction are approximately the same, the first image signal is converted into the horizontal direction and the vertical direction. Can be converted into a high-definition second image signal with an enhanced S / N balance and improved S / N.

  Hereinafter, specific embodiments to which the present invention is applied will be described in detail with reference to the drawings. In this embodiment, a standard resolution or low resolution image (hereinafter referred to as an “SD (Standard Definition) image”) composed of an input video signal is used as an arbitrary high resolution image. The image processing apparatus is appropriately converted to an “HD (High Definition) image” and further converted into the number of pixels of the display and displayed.

  First, FIG. 1 shows a schematic configuration of an image processing apparatus according to the first embodiment. As shown in FIG. 1, an image processing apparatus 1 according to the present embodiment includes a tuner 10, an image processing unit 11, an image conversion unit 20, a pixel number conversion unit 21, a Y system processing unit 23, and a C system. The processing unit 24, a matrix circuit 25, a drive / cutoff adjustment unit 26, a display display 27, and a microcomputer 28 are included.

  The image processing unit 11 includes a Y / C separation circuit 12, a chroma demodulation circuit 13, a selector 14, a frequency adjustment circuit 15, an amplitude level adjustment circuit 16, and an S / N detection circuit 17. Various video signals such as an RF signal demodulated into a CVBS (Composite Video Burst Signal) signal, a CVBS signal from an external video device, a YUV signal, and an S (Y and C) signal are input.

  The Y / C separation circuit 12 decodes the CVBS signal and the S signal into a YC signal, supplies the Y signal to the selector 14, and supplies the C signal to the chroma demodulation circuit 13. The chroma demodulation circuit 13 demodulates the C signal into a U signal and a V signal, and supplies them to the selector 14 respectively. The selector 14 supplies the Y signal to the frequency adjustment circuit 15 and the S / N detection circuit 17 among the CVBS signal and the S signal decoded into the YUV signal and the YUV signal whose input is the YUV signal, and the U signal And the V signal are supplied to the amplitude level adjustment circuit 16. The frequency adjustment circuit 15 adjusts the horizontal frequency characteristics of the Y signal in accordance with the resolution axis strength in the subsequent image conversion unit 20 as described below, and supplies the adjusted Y signal to the amplitude level adjustment circuit 16. . Then, the amplitude level adjustment circuit 16 adjusts the amplitude level of the YUV signal and outputs it as a 525i signal (interlaced signal having 525 lines). The S / N detection circuit 17 detects the S / N of the Y signal supplied from the selector 14 and supplies the detection result to the microcomputer 28. The microcomputer 28 controls the adjustment of the frequency characteristic in the frequency adjustment circuit 15 according to the detection result of the S / N. The microcomputer 28 controls the adjustment of the frequency characteristics in the frequency adjustment circuit 15 based on the type of the input video signal. Details of the control by the microcomputer 28 will be described later.

  The image conversion unit 20 converts the 525i signal into, for example, a 1050i signal having twice the number of lines by performing an adaptive process for obtaining a prediction value of the HD image by linear combination of the SD image and a predetermined prediction coefficient. At this time, the image conversion unit 20 can convert the image into an arbitrary high-resolution HD image by changing the resolution axis strength. The image conversion unit 20 supplies the converted YUV signal to the pixel number conversion unit 21. The image conversion process in the image conversion unit 20 will be described in detail later.

  The pixel number conversion unit 21 converts the 1050i signal supplied from the image conversion unit 20 into, for example, a 1050p signal (progressive signal having 1050 lines), and further converts the video format to match the number of pixels of the display 27. (Number of pixels in one screen) is converted. Further, the pixel number conversion unit 21 incorporates a horizontal / vertical emphasis circuit 22, and the horizontal / vertical emphasis circuit 22 enhances frequency characteristics in the horizontal direction and the vertical direction as described later. Of the YUV signals subjected to the above processing, the pixel number conversion unit 21 supplies the Y signal to the Y system processing unit 23 and supplies the UV signal to the C system processing unit 24.

  The Y-system processing unit 23 includes a DC transmission rate circuit, a black expansion circuit, a sharpness circuit (all not shown), and performs predetermined processing on the Y signal. On the other hand, the C-system processing unit 24 includes a color level adjustment circuit (not shown) and the like, and performs predetermined processing on the UV signal. The Y-system processor 23 and the C-system processor 24 supply the processed Y signal and UV signal to the matrix circuit 25, respectively.

  The matrix circuit 25 converts the YUV signal supplied from the Y system processing unit 23 and the C system processing unit 24 into an RGB signal, and supplies the converted RGB signal to the drive / cutoff adjustment unit 26. Then, the drive / cut-off adjustment unit 26 adjusts the drive level and the cut-off level for the display display 27 and displays them on the display display 27.

  Here, in the image conversion unit 20 described above, high-frequency components not included in the SD image are performed by performing an adaptive process for obtaining a prediction value of the HD image by linear combination of the SD image and a predetermined prediction coefficient. Has been made to be restored.

That is, for example, the predicted value E [y] of the pixel value y of a pixel constituting the HD image (hereinafter referred to as “HD pixel” as appropriate) is used as the pixel value of several SD pixels (pixels constituting the SD image). (Hereinafter referred to as “learning data” as appropriate.) Obtained by a linear linear combination model defined by a linear combination of x ₁ , x ₂ ,... And predetermined prediction coefficients w ₁ , w ₂ ,. Think about it. In this case, the predicted value E [y] can be expressed by the following formula (1).

  Therefore, in order to generalize, a matrix W composed of a set of prediction coefficients w, a matrix X composed of a set of learning data, and a matrix Y ′ composed of a set of predicted values E [y] are expressed by the following equations (2) to ( When defined in 4), the observation equation as shown in equation (5) is established.

  Then, it is considered to apply the least square method to this observation equation to obtain a predicted value E [y] close to the pixel value y of the HD pixel. In this case, a matrix Y composed of a set of true pixel values y of HD pixels serving as teacher data and a matrix E composed of a set of residuals e of predicted values E [y] with respect to the pixel values y of HD pixels are expressed by the following equations. When defined by (6) and (7), a residual equation such as expression (8) is established from the above expression (5).

In this case, the prediction coefficient w _i for obtaining the predicted value E [y] close to the pixel value y of the HD pixel can be obtained by minimizing the square error as shown in the equation (9), for example.

Therefore, when the above-mentioned square error differentiated by the prediction coefficient w _i is 0, that is, the prediction coefficient w _i satisfying the following expression (10) is the prediction value E [y near the pixel value y of the HD pixel. ] Is the optimum value.

Therefore, first, the following equation (11) is established by differentiating the equation (8) by the prediction coefficient w _i .

  Then, from the equations (10) and (11), the following equation (12) is obtained.

  Further, considering the relationship among the learning data x, the prediction coefficient w, the teacher data y, and the residual e in the residual equation of the equation (8), the normality as shown in the following equation (13) is obtained from the equation (12). An equation can be obtained.

  The normal equation of the equation (13) can be established by the same number as the number of prediction coefficients w to be obtained. Therefore, by solving the equation (13) (however, to solve the equation (13), the equation In (13), the matrix composed of the coefficients related to the prediction coefficient w needs to be regular), and the optimal prediction coefficient w can be obtained. In solving equation (13), for example, a sweep-out method (Gauss-Jordan elimination method) or the like can be applied.

  As described above, the optimum prediction coefficient w is obtained, and further, using the prediction coefficient w, it is adaptive to obtain the prediction value E [y] close to the pixel value y of the HD pixel by the equation (1). It is processing.

  Note that the adaptive processing is different from the interpolation processing in that the components included in the HD image that are not included in the SD image are reproduced. In other words, the adaptive process is the same as the interpolation process using a so-called interpolation filter as far as Expression (1) is seen, but the prediction coefficient w corresponding to the tap coefficient of the interpolation filter is determined using the teacher data y. In other words, since it is obtained by learning, the components included in the HD image can be reproduced. That is, a high-resolution image can be easily obtained. From this, it can be said that the adaptive process is a process having an effect of creating an image (its resolution).

  FIG. 2 shows an internal configuration example of an image conversion unit that converts an SD image into an HD image by the adaptive processing as described above.

  The SD image is supplied to the class classification unit 101 and the adaptive processing unit 104. The class classification unit 101 includes a class tap generation circuit 102 and a class classification circuit 103, where an HD pixel (a focused HD pixel) for which a prediction value is to be obtained by adaptive processing (hereinafter referred to as a “target pixel” as appropriate). Is classified into a predetermined class based on the property of the pixel of the SD image corresponding to the target pixel.

  That is, the class tap generation circuit 102 is used for classifying the target pixel. As an SD pixel corresponding to the target pixel (hereinafter referred to as “class tap” as appropriate), for example, a plurality of SD pixels having a predetermined positional relationship with the target pixel are supplied to the class classification unit 101. And is supplied to the class classification circuit 103. The class classification circuit 103 detects the pixel value pattern (pixel value distribution) of the SD pixels constituting the class tap from the class tap generation circuit 102, and the value assigned in advance to the pattern is used as the class of the target pixel. To the adaptive processing unit 104.

Specifically, for example, when an HD image is composed of pixels (HD pixels) indicated by x in FIG. 3, and an SD image is composed of pixels (SD pixels) indicated by circles in FIG. To do. In FIG. 3, the SD image is configured so that the number of horizontal or vertical pixels of the HD image is halved. Here, in FIG. 3 (the same applies to FIGS. 4 to 6 described later), the (i + 1) th SD pixel from the left and the (j + 1) th SD pixel from the top (indicated by a circle in the figure) is represented as X _{i, j.} Similarly, the i ′ + 1-th HD pixel from the left and the j ′ + 1-th HD pixel from the top (portion indicated by a cross in the figure) is represented as Y _{i ′, j ′} . In this case, the position of the SD pixel X _{i, j} matches the position of the HD pixel Y _{2i, 2j} .

Now, for example, if an HD pixel Y _4,4 that coincides with the position of X _2,2 is a pixel of interest as an SD pixel, the class tap generation circuit 102 uses the SD pixel corresponding to the HD pixel Y _4,4. as, for example, SD HD pixels _{Y 4, 4} and 3 × 3 centered on the SD pixels _{X 2, 2} correlation coincides with the position of HD pixel _{Y 4, 4} which are expected to be high for (horizontal × vertical) Pixels X _1,1 , X _2,1 , X _3,1 , X _1,2 , X _2,2 , X _3,2 , X _1,3 , X _2,3 , X _3,3 (dotted line in FIG. 3) SD pixels in a range surrounded by) are extracted, and are used as class taps of the _target pixel (HD pixel) _Y4,4 .

Also, for example, when the HD pixel Y _5,4 right next to the HD pixel Y _4,4 that coincides with the position of X _2,2 is the target pixel, the class tap generation circuit 102 causes the HD pixel Y _As SD pixels corresponding to ₅ and ₄ , for example, as shown by being surrounded by a dotted line in FIG. 4, SD pixels X _{1 and 1} in the class tap formed when the HD pixel Y _{4 and 4} is the target pixel are shown. Instead of ₂ pixels, those including SD pixels X ₄ and ₂ are extracted, and the 9 SD pixels are used as class taps of the _target pixel (HD pixel) Y _{5 and 4} .

Further, for example, when the HD pixel Y _4,5 adjacent below the HD pixel Y _4,4 coinciding with the position of X _2,2 is set as the target pixel, the class tap generation circuit 102 causes the HD pixel As an SD pixel corresponding to Y _4,5 , for example, as shown by being surrounded by a dotted line in FIG. 5, the SD pixel X ₂ in the class tap formed when the HD pixel Y _4,4 is set as the target pixel. ₁ and SD pixels X ₂ and ₄ are extracted, and the nine SD pixels are used as class taps for the _target pixel (HD pixel) Y _4,5 .

For example, when the HD pixel Y _5,5 adjacent to the lower right of the HD pixel Y _4,4 that coincides with the position of X _2,2 is the target pixel, the class tap generation circuit 102 As the SD pixels corresponding to the HD pixels Y ₅ and ₅ , for example, as shown by being surrounded by a dotted line in FIG. 6, the SD pixels in the class tap formed when the HD pixels Y _{4 and 4} are the target pixels. Instead of X _1,1 , those including SD pixels X _4,4 are extracted, and the nine SD pixels are used as class taps of _target pixels (HD pixels) Y _5,5 .

  The class classification circuit 203 detects a pattern of nine SD pixels (pixel values) as the class tap configured by the class tap generation circuit 102, and outputs a value corresponding to the pattern as a class of the target pixel. Is done.

  This class is supplied to an address terminal (AD) of a coefficient ROM (Read Only Memory) 107 in the adaptive processing unit 104.

Here, 8 bits or the like are generally assigned to the pixels constituting the image. Now, assuming that 8 bits are allocated to the SD pixel, for example, even if only the 3 × 3 pixel square class tap shown in FIG. 3 is considered, the number of pixel value patterns is (2 ⁸ ) ^9. The number of streets becomes enormous, and it is difficult to speed up subsequent processing.

  Therefore, as preprocessing before class classification, the class tap is subjected to, for example, ADRC (Adaptive Dynamic Range Coding) processing, which is processing for reducing the number of bits of SD pixels constituting the class tap.

That is, in the ADRC process, first, among the nine SD pixels constituting the class tap, the maximum value (hereinafter referred to as “maximum pixel” as appropriate) and the minimum value (hereinafter referred to as “minimum pixel” as appropriate). Is detected). Then, a difference DR (= MAX−MIN) between the pixel value MAX of the maximum pixel and the pixel value MIN of the minimum pixel is calculated, and this DR is set as a local dynamic range of the class tap. Based on the dynamic range DR, Each pixel value constituting the class tap is requantized to K bits smaller than the original number of assigned bits. That is, the pixel value MIN of the minimum pixel from each pixel value is subtracted forming the class taps, each subtraction value is divided by DR / 2 ^K.

As a result, each pixel value constituting the class tap is expressed by K bits. Accordingly, for example, when K = 1, the number of patterns of the pixel values of the nine SD pixels is (2 ¹ ) ^nine , and the number of patterns is very small compared to the case where ADRC processing is not performed. It can be.

  On the other hand, the adaptive processing unit 104 includes a prediction tap generation circuit 105, a prediction calculation circuit 106, and a coefficient ROM 107, where adaptive processing is performed.

  In other words, in the prediction tap generation circuit 105, a plurality of pixels having a predetermined positional relationship with respect to the target pixel, which are used for obtaining the predicted value of the target pixel in the prediction calculation circuit 106 from the SD image supplied to the adaptive processing unit 104. SD pixels are extracted and supplied to the prediction calculation circuit 106 as prediction taps.

Specifically, for example, HD pixel _{Y 4, 4} is a pixel of interest, when the class tap as described in FIG. 3 constituted, the prediction tap generating circuit 105, for example, an HD pixel _{Y 4, 4} 5 × 5 SD pixels centered on the SD pixel X _2,2 that coincides with the position of the target pixel Y _{4,4 in} the range surrounded by the solid line in FIG. Are extracted and used as prediction taps for the target pixel (HD pixel) _Y4,4 .

Further, for example, when the HD pixel _{Y 5,4} is a pixel of interest, the prediction tap generating circuit 105, for example, as shown enclosed by a solid line in FIG. 4, HD pixel _{Y 4, 4} and the pixel of interest In the prediction tap formed in this case, SD pixels including X pixels ₅ and ₂ are extracted in place of the SD pixels X ₀ and ₂ , and the 25 SD pixels are extracted as pixels of interest (HD pixels). Y _{5 and 4} are class taps.

Further, for example, when the HD pixel Y _4,5 is set as the target pixel, the prediction tap generation circuit 105 sets the HD pixel Y _{4,4 as} the target pixel as shown by a solid line in FIG. In the prediction tap formed in this case, SD pixels X _2,5 are extracted instead of SD pixels X _2,0 , and the 25 SD pixels are extracted as the target pixel (HD pixel). The prediction tap is Y _4,5 .

For example, when the HD pixels Y _{5 and 5} are the target pixels, the prediction tap generation circuit 105 determines that the HD pixels Y _{4 and 4} are the target pixels as indicated by a solid line in FIG. In the class tap formed in this case, the SD pixel X _0,0 including the SD pixel X _5,5 is extracted instead of the SD pixel X _0,0 , and the 25 SD pixels are extracted as the _target pixel (HD pixel). The prediction tap is Y _5,5 .

  In addition to the prediction tap supplied from the prediction tap generation circuit 105, the prediction calculation circuit 106 is also supplied with the prediction coefficient from the coefficient ROM 107.

  That is, the coefficient ROM 107 stores the prediction coefficient obtained by learning in advance for each class, and when a class is supplied from the class classification circuit 103, the coefficient ROM 107 stores it in an address corresponding to the class. The prediction coefficient is read and supplied to the prediction calculation circuit 106.

As a result, the prediction calculation circuit 106 is supplied with the prediction tap corresponding to the target pixel and the prediction coefficient for the class of the target pixel. In the prediction arithmetic circuit 106, prediction coefficients w, w ₂ ,... From the coefficient ROM 107 and prediction taps (constituting SD pixels) x ₁ , x ₂ ,. Is used to calculate the predicted value E [y] of the pixel of interest (HD pixel) y, and this is output as the pixel value of the HD pixel.

  The above processing is performed with all the HD pixels as the target pixel, whereby the SD image is converted into an HD image. Note that the class tap generation circuit 102 and the prediction tap generation circuit 105 perform processing using the same HD pixel as the pixel of interest.

  Next, FIG. 7 shows a configuration example of a learning apparatus that performs a learning process for calculating a prediction coefficient to be stored in the coefficient ROM 107 of FIG.

  The HD image to be the teacher data y in learning is supplied to the thinning circuit 201 and the teacher data extraction circuit 204. In the thinning circuit 201, for example, the number of pixels of the HD image is thinned out. Thus, an SD image is obtained. That is, in the thinning circuit 201, the number of horizontal or vertical pixels of the HD image is halved, whereby an SD image is formed. This SD image is supplied to the class classification unit 202 and the prediction tap generation circuit 203.

  In the class classification unit 202 or the prediction tap generation circuit 203, the same processing as in the class classification unit 101 or the prediction tap generation circuit 105 in FIG. 2 is performed, and thereby the class or prediction tap of the pixel of interest is output, respectively. . The class output from the class classification unit 202 is supplied to the address terminals (AD) of the prediction tap memory 205 and the teacher data memory 206, and the prediction tap output from the prediction tap generation circuit 203 is supplied to the prediction tap memory 205. Note that the class classification unit 202 and the prediction tap generation circuit 203 perform processing using the same HD pixel as the target pixel.

  In the prediction tap memory 205, the prediction tap supplied from the prediction tap generation circuit 203 is stored at the address corresponding to the class supplied from the class classification unit 202.

  On the other hand, in the teacher data extraction circuit 204, the HD pixel that is the target pixel in the class classification unit 202 and the prediction tap generation circuit 203 is extracted from the HD image supplied thereto and supplied to the teacher data memory 206 as teacher data. Is done.

  The teacher data memory 206 stores the teacher data supplied from the teacher data extraction circuit 204 at an address corresponding to the class supplied from the class classification unit 202.

  The above processing is sequentially performed with all HD pixels constituting all HD images prepared for learning in advance as the target pixel.

  As a result, the same address in the teacher data memory 206 or the prediction tap memory 205 is located at the position where the HD pixel of the class corresponding to the address or the prediction tap described with reference to FIGS. SD pixels are stored as teacher data y or learning data x, respectively.

  Note that the prediction tap memory 205 and the teacher data memory 206 can store a plurality of pieces of information at the same address, so that a plurality of learnings classified into the same class can be stored at the same address. Data x and teacher data y can be stored.

  Thereafter, the arithmetic circuit 207 reads out the prediction tap or the HD pixel as the teacher data as the learning data stored at the same address from the prediction tap memory 205 or the teacher data memory 206, and uses them, for example, the minimum self A prediction coefficient that minimizes an error between the predicted value and the teacher data is calculated by multiplication. That is, in the arithmetic circuit 207, the normal equation shown in Expression (13) is established for each class, and the prediction coefficient is obtained by solving this.

  As described above, the prediction coefficient for each class obtained by the arithmetic circuit 207 is stored at an address corresponding to each class in the coefficient ROM 107 of FIG.

  In the learning process as described above, there may occur a class in which the number of normal equations necessary for obtaining the prediction coefficient cannot be obtained. For such a class, for example, the class is ignored and the normal equation is ignored. The prediction coefficient obtained by solving the above is used as a default prediction coefficient.

  Here, the process in the image conversion unit 20 described above performs an adaptive process for obtaining a predicted value of the HD image by linear combination of the SD image and a predetermined prediction coefficient, so that a high frequency that is not included in the SD image is obtained. Since the component is restored, the output signal from the image conversion unit 20 has a higher frequency component emphasized than the input signal. That is, the image conversion unit 20 has a frequency characteristic that increases as the high frequency component increases. As a result, as schematically shown in FIG. 8, even if the input signal of the image conversion unit 20 is flat, the output signal from the image conversion unit 20 is emphasized as the high frequency component is increased. .

  However, when the high frequency component is emphasized in this way, the preshoot / overshoot of the output signal is conspicuous and the S / N is deteriorated, which may cause deterioration in image quality.

  Therefore, in the image processing apparatus 1 according to the present embodiment, the frequency characteristics in the image conversion unit 20 are taken into consideration, and the image processing unit 11 (specifically, the frequency adjustment circuit 15) in the preceding stage shows a specific reverse characteristic. Specifically, the frequency characteristics are adjusted so as to show the characteristics of an LPF (Low-Pass filter). As a result, as schematically shown in FIG. 9A, when the input signal of the image processing unit 11 is flat, the output signal from the image processing unit 11 (the input signal of the image conversion unit 20) is The higher frequency components are attenuated, and the output signal from the image conversion unit 20 is flat.

  At this time, compared with the S / N of the input signal of the image processing unit 11, the S / N of the output signal is improved by the amount of attenuation of the frequency characteristic of the high frequency component. Further, since the frequency component newly created by the image conversion unit 20 contains almost no random pattern signal, the S / N improvement is ensured even in the output signal from the image conversion unit 20. Is done. That is, as shown in FIG. 10, in the frequency adjustment circuit 15 of the image processing unit 11, the S / N of the portion indicated by the hatching in the figure is improved by setting the frequency characteristics so that the higher frequency component attenuates. It becomes.

  In order to flatten the frequency characteristic of the output of the image conversion unit 20 in FIG. 9A, a frequency characteristic that compensates for the frequency characteristic of the image conversion unit 20 is required for the image processing unit 11 in the previous stage. However, strictly speaking, it is difficult for the image processing unit 11 in the previous stage to compensate the frequency characteristics up to a high frequency range created by the image conversion unit 20. Therefore, as a practical method, a filter having a frequency characteristic as shown in FIG. 9B may be used in the image processing unit 11 and substituted.

  Furthermore, by significantly increasing the attenuation amount of the high frequency component by the frequency adjustment circuit 15 (for example, near 6 dB), it is possible to reduce the ringing of the transient part included in the input signal of the image conversion unit 20. That is, generally, in the band compression / decompression process, the bit distribution in the transient part is less than that in the flat part. When the image conversion unit 20 converts an SD image into an HD image, it has the same property. If the frequency characteristics of the high frequency component are significantly attenuated, the information of the high frequency component falls uniformly. The degree of drop in the output signal from the conversion unit 20 is larger in the transient part due to the bit distribution. Therefore, by attenuating the frequency characteristic in the frequency adjustment circuit 15 as the high frequency component in this way, it is possible to reduce the ringing in the transient portion that is likely to occur in the RF signal to a considerable extent.

  In addition, not only ringing, but also mosquito noise (a type of noise that occurs during MPEG (Moving Picture Experts Group) decoding) generated around signal waveforms with medium amplitude or higher (especially character signals) can be similarly reduced. It is. Note that this reduction effect increases as the frequency characteristic of the high frequency component is attenuated in the frequency adjustment circuit 15.

  Here, in the above description, the frequency characteristic of the frequency adjustment circuit 15 is adjusted so as to indicate the inverse characteristic of the frequency characteristic of the image conversion unit 20. The frequency characteristics may be adjusted in accordance with the band or S / N of the video signal, and the image conversion unit 20 may change the resolution axis intensity by the adjustment amount.

  For example, the frequency adjustment circuit 15 attenuates the frequency characteristics of higher frequency components, but among the input video signals, a video signal with a narrower band and a smaller amount of information of higher frequency components has a negative effect due to attenuation of the frequency characteristics. It is thought that there are few. Therefore, the microcomputer 28 according to the present embodiment uses the frequency characteristics in the frequency adjustment circuit 15 in the order of 1) S signal and YUV signal, 2) CVBS signal, and 3) RF signal based on the type of the input video signal. The attenuation is controlled to be large. Specifically, for example, the wide band S signal and YUV signal are attenuated by about 2 dB, the CVBS signal is attenuated by about 2 to 3 dB, and the narrow band RF signal is attenuated by about 6 dB. At this time, the image conversion unit 20 increases the resolution axis intensity by the attenuation amount of the frequency characteristic.

  Further, as described above, the S / N of the video signal can be improved by increasing the attenuation amount of the high-frequency component by the frequency adjustment circuit 15 and increasing the resolution axis strength by the image conversion unit 20. If the S / N of the video signal is poor, it is considered effective to increase the attenuation amount of the frequency characteristic in the frequency adjustment circuit 15 and increase the resolution axis strength in the image conversion unit 20. Therefore, the S / N detection circuit 17 in the present embodiment detects the S / N of the Y signal supplied from the selector 14 and supplies the detection result to the microcomputer 28. The microcomputer 28 adjusts the attenuation amount of the frequency characteristic in the frequency adjustment circuit 15 according to the S / N detection result. For example, the RF signal often has a poor S / N, but by increasing the attenuation amount of the frequency characteristic in the frequency adjustment circuit 15 and increasing the resolution axis strength in the image conversion unit 20 as described above, the S / N can be reduced. Significant improvement is possible.

  Further, the frequency adjusting circuit 15 has been described as adjusting the frequency characteristic in the horizontal direction, but in this case, there is a problem that the enhancement balance in the horizontal direction and the vertical direction cannot be achieved.

  In view of this, in the image processing apparatus 1 according to the present embodiment, the horizontal / vertical emphasis circuit 22 is built in the pixel number conversion unit 21, and the following processing is performed in order to balance enhancement in the horizontal and vertical directions as a whole. I do.

  For example, when the input video signal is a YUV signal, an S signal, or a CVBS signal, the attenuation amount of the horizontal frequency characteristic in the frequency adjustment circuit 15 of the image processing unit 11 is set to 2 to 2 as schematically shown in FIG. The resolution is increased to about 3 dB and the image conversion unit 20 increases the resolution axis intensity until the frequency characteristics of the output signal become flat. As a result, the horizontal frequency characteristics in the output stage from the image converter 20 are substantially flat, but the frequency characteristics of the vertical component are not attenuated in the frequency adjustment circuit 15, and therefore in the output stage from the image converter 20. The frequency characteristic in the vertical direction is such that the high frequency component is enhanced by the increase in the resolution axis strength of the image conversion unit 20.

  In the subsequent stage of the image conversion unit 20, the frequency characteristics in the vertical direction deteriorate during progressive conversion in the pixel number conversion unit 21, and the frequency characteristics in the horizontal direction are high in the sharpness circuit (not shown) of the Y system processing unit 23. It increases as In view of this, the horizontal / vertical emphasis circuit 22 of the pixel number conversion unit 21 increases the frequency characteristics in the vertical direction as the frequency increases in order to achieve the enhancement balance in the horizontal direction and the vertical direction as a whole.

  On the other hand, when the input video signal is an RF signal, as schematically shown in FIG. 12, the horizontal frequency characteristic attenuation amount in the frequency adjustment circuit 15 of the image processing unit 11 is set to about 6 dB, and the image conversion unit At 20, the resolution axis strength is increased until the frequency characteristics of the output signal become flat. As a result, the horizontal frequency characteristics in the output stage from the image converter 20 are substantially flat, but the frequency characteristics of the vertical component are not attenuated in the frequency adjustment circuit 15, and therefore in the output stage from the image converter 20. The frequency characteristic in the vertical direction is such that the high frequency component is enhanced by the increase in the resolution axis strength of the image conversion unit 20.

  In the subsequent stage of the image conversion unit 20, in the sharpness circuits of the pixel number conversion unit 21 and the Y system processing unit 23, the frequency characteristics in the horizontal direction increase as the frequency increases. On the other hand, since the frequency characteristic in the vertical direction is sufficiently increased in the image conversion unit 20, the horizontal / vertical emphasis circuit 22 of the pixel number conversion unit 21 does not perform any further enhancement, and as a whole the horizontal direction and the vertical direction. Balance the direction of enhancement.

  In this way, the enhancement method that balances the enhancement in the horizontal direction and the vertical direction does not change the sharpness in normal video even if the enhancement in the horizontal direction is reduced, compared with the enhancement method that relies on the horizontal direction only. The video expression is many and easy to see. Furthermore, the S / N at high frequencies is improved by the amount that the horizontal enhancement can be reduced.

  Note that when the input video signal is an HD signal, the image conversion unit 20 does not perform the conversion process, so the frequency adjustment circuit 15 does not adjust the frequency characteristics. Therefore, only the horizontal / vertical enhancement in the horizontal / vertical emphasis circuit 22 and the horizontal enhancement in the sharpness circuit of the Y-system processing unit 23 contribute to the enhancement. Here, in the progressive conversion in the pixel number conversion unit 21, the frequency characteristic in the vertical direction deteriorates, and in the sharpness circuit of the Y-system processing unit 23, the frequency characteristic in the horizontal direction increases as the frequency increases. In view of this, the horizontal / vertical emphasis circuit 22 of the pixel number conversion unit 21 increases the frequency characteristics in the vertical direction as the frequency increases as shown in FIG. Let's make it. As a result, the enhancement in the horizontal direction can be suppressed as compared with the enhancement method relying only on the horizontal direction, and the S / N at high frequencies can be improved. Also, by enhancing the vertical enhancement, the stereoscopic effect of the image can be increased.

  Although the best mode for carrying out the present invention has been described above, the present invention is not limited to the above-described embodiment, and various modifications can be made without departing from the scope of the present invention. Of course.

  According to the present invention described above, an SD image is converted into an arbitrary high-resolution HD image that balances horizontal and vertical sharpness and fineness while preventing problems of S / N and preshoot / overshoot. Can be converted.

It is a block diagram which shows schematic structure of the image processing apparatus in this Embodiment. It is a block diagram which shows the example of an internal structure of the image conversion part in the image processing apparatus. It is a figure for demonstrating the formation method of a class tap and a prediction tap. It is a figure for demonstrating the formation method of a class tap and a prediction tap. It is a figure for demonstrating the formation method of a class tap and a prediction tap. It is a figure for demonstrating the formation method of a class tap and a prediction tap. It is a block diagram which shows the example of an internal structure of the learning apparatus which learns the prediction coefficient memorize | stored in coefficient ROM of the image conversion part. It is a schematic diagram which shows the frequency characteristic in the signal before and behind the conventional image conversion part. It is a schematic diagram which shows the frequency characteristic in the signal before and behind the image conversion part in this Embodiment, The same figure (A) shows the example in case an image process part has an ideal frequency characteristic, The figure (B) ) Shows an example where the image processing unit has practical frequency characteristics. It is a figure which shows the signal component replaced by the image conversion part. It is a schematic diagram which shows the frequency characteristic in each block of an image processing apparatus in case the input video signal is a YUV signal, S signal, or CVBS signal. It is a schematic diagram which shows the frequency characteristic in each block of an image processing apparatus in case the input video signal is an RF signal. It is a schematic diagram which shows the frequency characteristic in each block of an image processing apparatus in case the input video signal is an HD signal.

Explanation of symbols

  DESCRIPTION OF SYMBOLS 1 Image processing apparatus, 10 tuner, 11 Image processing part, 12 Y / C separation circuit, 13 Chroma demodulation circuit, 14 Selector, 15 Frequency adjustment circuit, 16 Amplitude level adjustment circuit, 17 S / N detection circuit, 20 Image conversion part , 21 pixel number conversion unit, 22 horizontal / vertical emphasis circuit, 23 Y system processing unit, 24 C system processing unit, 25 matrix circuit, 26 drive / cutoff adjustment unit, 27 display display, 28 microcomputer

Claims (10)

First frequency characteristic adjusting means for adjusting the horizontal frequency characteristic of the first image signal according to the type and / or S / N of the first image signal composed of the input first pixel number. When,
Image converting means for converting the first image signal whose frequency characteristic is adjusted by the frequency characteristic adjusting means into a second image signal having a second pixel number larger than the first pixel number;
Second frequency characteristic adjusting means for adjusting the horizontal and vertical frequency characteristics of the second image signal so that the final output frequency characteristics are substantially the same in the horizontal and vertical directions; With
The image processing apparatus, wherein the image conversion means sets the second number of pixels so that a horizontal frequency characteristic in an output stage of the image conversion means becomes substantially flat. The image conversion means includes
Class classification means for classifying a target pixel, which is a pixel of the first image signal of interest, into a predetermined class according to the nature of the second image signal corresponding to the target pixel; ,
The image processing apparatus according to claim 1, further comprising: a predicting unit that predicts the predicted value of the target pixel corresponding to the class of the target pixel. The prediction means is
Prediction coefficient storage means for storing, for each class, a prediction coefficient for calculating a prediction value of the pixel of interest by linear combination with the pixels of the second image signal;
The image processing apparatus according to claim 2, further comprising: a predicted value calculation unit that calculates a predicted value of the target pixel from the prediction coefficient for the target pixel and the pixel of the second image. The first image signal is an S signal, a YUV signal, a CVBS signal, or an RF signal,
The image processing apparatus according to claim 1, wherein the first frequency characteristic adjusting unit increases the attenuation amount of the frequency characteristic in the order of the S signal, the YUV signal, the CVBS signal, and the RF signal.   5. The image processing apparatus according to claim 4, wherein the first frequency characteristic adjusting means increases the attenuation amount of the frequency characteristic as the S / N of the first image signal is smaller. A first frequency characteristic adjustment step of adjusting the horizontal frequency characteristic of the first image signal according to the type and / or S / N of the first image signal composed of the input first pixel number. When,
An image conversion step of converting the first image signal having the frequency characteristic adjusted in the frequency characteristic adjustment step into a second image signal having a second pixel number larger than the first pixel number;
A second frequency characteristic adjusting step for adjusting the horizontal and vertical frequency characteristics of the second image signal so that the final output frequency characteristics are substantially the same in the horizontal direction and the vertical direction; Have
In the image conversion step, the second pixel number is set so that a horizontal frequency characteristic in an output stage of the image conversion means is substantially flat. The image conversion step
A class classification step for classifying a target pixel, which is a pixel of the first image signal of interest, into a predetermined class according to the nature of the second image signal corresponding to the target pixel; ,
The image processing method according to claim 6, further comprising: a prediction step of predicting the predicted value of the target pixel corresponding to the class of the target pixel. The prediction process is
From the prediction coefficient for calculating the prediction value of the pixel of interest by linear combination with the pixel of the second image signal stored in the prediction coefficient storage means for each class, and the pixel of the second image The image processing method according to claim 7, further comprising a predicted value calculation step of obtaining a predicted value of the target pixel. The first image signal is an S signal, a YUV signal, a CVBS signal, or an RF signal,
7. The image processing method according to claim 6, wherein in the first frequency characteristic adjustment step, the attenuation amount of the frequency characteristic is increased in the order of the S signal, the YUV signal, the CVBS signal, and the RF signal.   10. The image processing method according to claim 9, wherein, in the first frequency characteristic adjustment step, the attenuation amount of the frequency characteristic is increased as the S / N of the first image signal is smaller.

Images