JP2006340395A - Color conversion device and color conversion method - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide a color conversion device and a color conversion method which can also suppress the occurrence of flurring of a picture by suppression of impact of a noise component, without emphasizing further impact of the noise component contained in a color data, when performing a processing which raises intensity of color data. <P>SOLUTION: The color conversion device comprises a formation means (1,1c,1d) of a color correction amount which calculates the color correction amount to perform a color correction amount calculation (4, 4c,4d) and noise removal (3,11,12) based on the above first image data, and a summing means (2) to require the second image data to add the amount of color correction to the first image data. A noise removal means rejects a high frequency component in an inputted data, and is configured with a low pass filter which penetrates a low frequency component. <P>COPYRIGHT: (C)2007,JPO&INPIT

Description

  The present invention relates to data processing used in printers, video printers, scanners and other full-color printing-related equipment, or display devices such as monitors, and in particular, image data expressed by a plurality of color data is used in equipment used. The present invention also relates to a color conversion apparatus and method for performing color conversion processing together.

An example of a conventional color conversion apparatus and method is described in Patent Document 1 below. In this conventional color conversion apparatus and method, means for calculating the minimum value α and the maximum value β of the first color data Ri, Gi, Bi constituting the first image data, the first color data Ri, A means for calculating hue data using Gi, Bi, the minimum value α and the maximum value β, a means for generating first comparison data between the hue data using the hue data, and a first comparison data are used. Means for generating second comparison data between hue data, calculation means for calculating based on hue data, means for generating a predetermined matrix coefficient, first and second comparison data, and calculation means , The hue data r, g, b, and the matrix coefficient are used to perform a predetermined matrix calculation, and the calculated image data is output, and a minimum value α is added to the output image data. Second image after color conversion Means for synthesizing and outputting image data.
(See Patent Document 1)
JP 2000-287074 A

  In a conventional color conversion apparatus or color conversion method, when noise components are included in the color data, if the processing for increasing the saturation or brightness of the color data is performed, the influence of the noise components will be further emphasized. There was a problem that the image was very difficult to see. In order to reduce the influence of the noise component included in the color data, when the color data is input to the color conversion means via the noise removal means, the noise component is removed, but the change portion of the original data is reduced. The change also becomes gradual, and there is a problem that the image is blurred.

  Hereinafter, the above points will be described in more detail. The color conversion is performed in order to obtain a desired color reproduction in the image display apparatus. The desired color reproduction includes “faithful color reproduction” and “preferred color reproduction”. The “faithful color reproduction” is color reproduction that is faithful to the actual color, and as a method for realizing it, it is conceivable to perform color reproduction using a standard or standard color space such as NTSC or sRGB. On the other hand, “preferable color reproduction” is color reproduction that humans feel more preferable in consideration of human visual characteristics and memory colors, and does not necessarily match “faithful color reproduction”. In color reproduction when displaying a moving image such as a television image, “preferable color reproduction” is often performed. Human memory colors tend to be stored as colors with higher saturation and lightness than the actual colors, such as sky colors and green grass. Therefore, in order to realize “preferable color reproduction”, color conversion processing that increases the saturation and brightness of the color is often performed on the input color data. Even in faithful color reproduction, color conversion processing that increases color saturation and lightness is often performed on input color data. This is due to the narrow color reproduction range of the image display means used.

  On the other hand, the first color data input to the image display device or the like does not necessarily match the original color data generated on the color data generation side such as a camera. This is because in the process of transmitting color data, it is affected by various noises. Consider a case where original color data generated by a camera is transmitted via a transmission path and input to an image display apparatus. The original color data output from the camera is Rs, Gs, and Bs, and color data representing red, green, and blue, respectively. The first color data input to the image display device is Ri, Gi, Bi. If there is no influence of noise in the transmission line and the transmission and reception procedures are correctly performed during transmission, Rs = Ri, Gs = Gi, and Bs = Bi should be obtained. However, in actuality, the transmission path may be affected by noise. Assuming that noise components, which are the effects of noise on each color data, are Rn, Gn, and Bn, the first color data Ri, Gi, and Bi input to the image display device are Ri = Rs + Rn, Gi = Gs + Gn, Bi = It can be expressed as Bs + Bn.

  When color conversion processing is performed on the first color data Ri, Gi, Bi input to the image display device to increase the color saturation and brightness in order to realize “preferable color reproduction”. As a result, the saturation and brightness of the original color data component are increased, and the saturation and brightness of the noise component are also increased.

  When the noise component included in the pixel data is white noise, the noise component has a component in a wide frequency range from a low frequency to a high frequency. In addition, there may be a case where a noise component included in pixel data has a strong component at a specific frequency. For example, in the case of a noise component due to the influence of a carrier wave used during transmission, the noise component has a strong component near the frequency of the carrier wave.

  When the noise component has a strong component at a specific frequency, the noise removing unit is configured as a filter that removes (attenuates) the frequency component. On the other hand, when the noise component included in the image data is white noise, it is effective to configure as a filter that removes (attenuates) a particularly visually noticeable frequency component of the noise component. Strictly speaking, the visually conspicuous frequency component is specified in consideration of the pixel interval and viewing distance of the display device, but in the current image display device, the pixel frequency of the image data among the noise components In many cases, high frequency components close to are conspicuous. This is considered to be perceived as a large noise by humans when the data of adjacent pixels frequently changes unrelated to each other due to the influence of noise components at the pixel interval of the current image display device. Here, the pixel frequency is, for example, that image data of continuous pixels is 0, 1, 0, 1, 0,. . . And the frequency when the frequency changes, which is ½ of the clock frequency of the image data.

As described above, the configuration of the noise removing unit is determined by the characteristics of the noise component. Hereinafter, a case where a noise component having a high frequency component close to the pixel frequency of image data is removed will be described as an example. In this case, the high-frequency component in the input data is blocked or greatly attenuated, and the noise removal unit is configured by a low-pass filter that transmits the low-frequency component, and the first color data is sent to the color conversion unit via the noise removal unit. It is possible to reduce noise component enhancement. However, in this case, the high-frequency component of the original color component is also blocked together with the noise component included in the first color data, causing a problem that the image is blurred. For example, when the noise removing unit is configured as a filter that removes a noise component having a frequency equal to or higher than 1/8 of the pixel frequency of the image data, the original color component is also equal to or higher than 1/8 of the image data pixel frequency. The frequency is removed, and the image is blurred.
In general, the frequency components removed by the noise removing unit 3 are removed from the original color components.

  An object of the present invention is to further enhance the influence of the noise component included in the color data and to suppress the influence of the noise component even when processing such as increasing the saturation and brightness of the color data is performed. An object of the present invention is to provide a color conversion apparatus and method capable of suppressing the occurrence of image blur.

The color conversion device according to the present invention provides:
In a color conversion device that performs color conversion on first image data that is image information for each pixel that includes a plurality of first color data, and obtains second image data that includes a plurality of second color data,
Noise removing means for removing noise from the first image data;
A color correction amount calculating means for calculating a color correction amount for converting at least one of brightness and saturation of the first image data using the output of the noise removing means;
And adding means for adding the color correction amount calculated by the color correction amount calculating means to the first image data to obtain the second image data.

  According to the present invention, even when processing for increasing the saturation and brightness of color data is performed, the influence of the noise component included in the color data is not further emphasized, and the influence of the noise component is suppressed. It is also possible to suppress the occurrence of image blur.

Hereinafter, the present invention will be specifically described with reference to the drawings showing embodiments thereof.
Embodiment 1 FIG.
FIG. 1 is a block diagram showing an example of the configuration of a color conversion apparatus according to an embodiment of the present invention.

The first color data Ri, Gi, Bi are input to the color correction amount generation means 1 and the color correction amount addition means 2. The color correction amount generation unit 1 includes a noise removal unit 3 and a color correction amount calculation unit 4, and the first color data Ri, Gi, Bi is input to the noise removal unit 3. The noise removing unit 3 removes the noise component in the first color data and outputs the color data R2, G2, and B2 from which noise has been removed. The noise-removed color data R2, G2, and B2 are input to the color correction amount calculation unit 4, and the color correction amount calculation unit 4 calculates the color correction amounts R1, G1, and B1. The color correction amount adding means 2 receives the first color data Ri, Gi, Bi and the color correction amounts R1, G1, B1, and calculates the second color data Ro, Go, Bo by adding them. Is done.
Note that the color correction amount does not always have a positive value, but may have a negative value.

  Since the first color data is affected by various noises in the process of transmission, it is composed of the original color data components generated during image generation and the noise components generated by the subsequent noise effects. . The original color data is Rs, Gs, and Bs, and color data representing red, green, and blue, respectively. The first color data is Ri, Gi, Bi. If there is no influence of noise, Rs = Ri, Gs = Gi, and Bs = Bi. However, it is actually affected by noise. Assuming that noise components, which are the effects of noise on each color data, are Rn, Gn, and Bn, the first color data Ri, Gi, and Bi input to the image display device are Ri = Rs + Rn, Gi = Gs + Gn, Bi = It can be expressed as Bs + Bn. That is, the first color data Ri, Gi, Bi input to the image display device is represented by the sum of the original color data components Rs, Gs, Bs and the noise components Rn, Gn, Bn. Will be.

As described in the prior art, the configuration of the noise removing unit 3 is determined according to the characteristics of the noise component. Hereinafter, a case where a noise component having a high frequency component close to the pixel frequency of image data is removed will be described as an example. In this case, the noise removing unit 3 can be configured by a low-pass filter that blocks or greatly attenuates high-frequency components in the input data and transmits low-frequency components.
More specifically, a low-pass filter that cuts off or greatly attenuates a frequency component of about 1 / 4.5 or more of the pixel frequency, that is, 1/9 or more of the clock frequency of the pixel data is used.
In addition, as a low-pass filter with a simple configuration, a configuration that calculates a simple average value of image data of a plurality of continuous pixels is conceivable. At this time, the filter characteristic is determined by the number of pixels used for calculating the simple average value.

FIG. 2 is a block diagram illustrating an example of a partial configuration of the noise removing unit 3, that is, a configuration of a portion that calculates R2 from the color data Ri. The part for calculating G2 from Gi and the part for calculating B2 from Bi can also have the same configuration. As shown in the figure, the noise removing unit 3 includes a data shift unit 5 including a plurality of data storage units 5 a to 5 h and a weighted addition unit 6.
The first color data Ri input to the noise removing unit 3 is input to the data storage unit 5a. The data storage units 5a to 5h are cascade-connected to each other, and every time the first color data is input, the data in the data storage unit 5a is transferred to the data storage unit 5b, and the data in the data storage unit 5b is transferred to the data storage unit. Shift data to 5c all at once. Data held in the data storage units 5 a to 5 h is input to the weighted addition means 6.

  The weighted addition means 6 performs weighted addition on the data from the data storage units 5a to 5h and outputs the data as color data R2 from which noise has been removed. If the weighting performed by the weighting addition means 6 is made equal, that is, the weighting coefficients are set to the same value, and a simple average value is calculated. Therefore, the noise-removed color data R2 calculated by the noise removing unit 3 is expressed by the following equation (1).

In Equation (1), Ri [n] represents the first color data input nth, and the function f represents weighted addition.
It is assumed that when the first data is input to the noise removing unit, the same data as the first data is input to the data storage units 5a to 5h all at once. In addition, after the last data is input to the noise removing unit, the same data as the last data input to the noise removing unit is continuously input to the data storage unit 5a.

  FIG. 3 is a diagram illustrating an example of original color data Rs, Gs, and Bs. In the figure, the horizontal axis represents the pixel position where each data exists, and the vertical axis represents the size of each data. Further, in order to focus on the data Rs representing red, the change in Rs is particularly shown by a bold line. In pixel positions 0 to 16 and 43 to 63, Rs = 80, Gs = 48 and Bs = 48, and in pixel positions 17 to 42, Rs = 160, Gs = 48 and Bs = 48. This means that red is present in the pixel positions 17 to 42 with higher saturation and lightness than both sides. Here, the saturation can be expressed by dividing the difference between the maximum value and the minimum value of the color signal by the maximum value, and the lightness can be expressed by the maximum value of the color signal. According to this, the saturation at the pixel positions 0 to 16 and 43 to 63 is expressed as 0.4 and the brightness is expressed as 80, the saturation at the pixel positions 17 to 42 is expressed as 0.7, and the brightness is expressed as 160.

  FIG. 4 shows color data when noise Rn, Gn, Bn is added to the original color data Rs, Gs, Bs, that is, first color data Ri input to the image display apparatus when noise components are present. It is a figure showing an example of Gi, Bi. In the figure, the horizontal axis represents the pixel position where each data exists, and the vertical axis represents the size of each data. Further, in order to pay attention to the data Ri representing red, the change in Ri is particularly shown by a thick line. In the figure, the arrows indicate Ri at pixel positions 26 and 27, and the values are 146 and 174, respectively. The Ri at the pixel positions 26 and 27 should originally be the same value, and the difference in values is due to the influence of noise components. At this time, in the pixel positions 26 and 27, Gi is 40 and 46, and Bi is 38 and 60.

Here, it is assumed that the weighted addition means 6 calculates a simple average value.
FIG. 5 shows noise-removed color data R2, G2, and B2 obtained by the noise removal means 3 described with reference to FIG. 2 by using the first color data Ri, Gi, and Bi shown in FIG. Show. In the figure, the horizontal axis represents the pixel position where each data exists, and the vertical axis represents the size of each data. As shown in FIG. 5, in the color data R2, G2, and B2 from which noise has been removed, the change in the data changes gradually, but the noise component is also removed.

  The color correction amount calculation means 4 calculates the color correction amounts R1, G1, B1 using the color data R2, G2, B2 from which noise is removed, and the calculated color correction amounts R1, G1, B1 are the color correction amount addition means. Output to 2. The first color data Ri, Gi, Bi is also input to the color correction amount adding means 2, and the color correction amount adding means 2 adds the color correction amounts R1, G1, B1 to the second color data Ro, Go. Bo is calculated.

  The color correction amount calculation means 4 performs a calculation for obtaining a correction amount for increasing at least one of lightness and saturation of at least one color, for example, a linear calculation represented by the following equation (2), for example. Thus, color correction amounts R1, G1, and B1 are obtained.

  The coefficient shown in the following formula (3) is an example of a coefficient used for the formula (2).

  The coefficient shown in the above equation (3) is a coefficient that increases the lightness and saturation of red, which is one specific color of the image data. For simplification, it is assumed that the values of the color data R2, G2, and B2 from which noise has been removed have the same values as the first color data Ri, Gi, and Bi. For example, the color data from which noise has been removed has R2 = 80, Consider the case where G2 = 48 and B2 = 48. At this time, R1 = 24, G1 = 0, and B1 = 0, and the second color data is Ro = R2 + R1 = 104, Go = G2 + G1 = 48, and Bo = B2 + B1 = 48. When the color data from which noise is removed is R2 = 160, G2 = 48, and B2 = 48, R1 = 48, G1 = 0, and B1 = 0, and the second color data is Ro = R2 + R1 = 208, Go = G2 + G1 = 48 and Bo = B2 + B1 = 48. For the color data from which noise has been removed, the former has a saturation of 0.4 and a lightness of 80, and the latter has a saturation of 0.7 and a lightness of 160. On the other hand, for the second color data, the former has a saturation of 0.54 and a lightness of 104, and the latter has a saturation of 0.77 and a lightness of 208. Therefore, by performing color conversion, both brightness and saturation of the image data are increased.

  FIG. 6 is a diagram illustrating an example of the second color data Ro, Go, and Bo. The first color data Ri, Gi, and Bi shown in FIG. 4 are used as inputs, and the coefficients shown in Expression (3) are used. Is the second color data calculated in the case of In the figure, the horizontal axis represents the pixel position where each data exists, and the vertical axis represents the size of each data. Further, in order to pay attention to the data Ro representing red, the change in Ro is particularly shown by a thick line. The arrows in the figure indicate Ro at pixel positions 26 and 27, and the values are 193 and 220, respectively.

  Here, the difference between the values of the second color data Ro at the pixel positions 26 and 27 is 220-193 = 27. On the other hand, the difference between the values of the first color data Ri at the pixel positions 26 and 27 is 174-146 = 28. The difference between the values of the first color data Ri at the pixel positions 26 and 27 is caused by the influence of the noise component.

  Therefore, according to the color conversion device or the color conversion method of the present embodiment, processing for increasing the saturation and brightness of color data is performed without further enhancing the influence of noise components included in the color data. Yes. When the influence of the noise component in the image data is emphasized, the image displayed on the image display device becomes very difficult to see, but in the color conversion device or the color conversion method in the present embodiment, the second color The magnitude of the noise component in the data is the same as that in the first color component, avoiding an increase in the influence of the noise component.

  In the above example, the color correction amount calculation means 4 performs calculations to increase the brightness and saturation of one color, red. However, the color correction amount calculation means 4 increases the brightness and saturation of two or more colors. Color correction amount calculation may be performed. The present invention can also be applied to color correction that lowers the brightness and saturation instead of increasing the brightness and saturation. When the brightness and saturation are lowered, the color correction amount becomes a negative value.

  In the color conversion apparatus or the color conversion method according to the present embodiment, the feature is that the color correction amount generation unit 1 includes the noise removal unit 3, and the color correction amount generation unit 1 performs color correction amount calculation and noise removal to perform color removal. A correction amount is obtained and supplied to the color correction amount adding unit 2, and the color correction amount adding unit 2 performs the color correction amount that has undergone the noise removal as described above and the first image data that has not undergone the noise removal as described above. To obtain the second image data. Hereinafter, the effect will be described in more detail by comparison with the case where the color correction amount generating unit 1 does not include the noise removing unit 3.

  In the color conversion apparatus shown in FIG. 1, if the noise removal means 3 is configured such that R2 = Ri, G2 = Gi, and B2 = Bi, it is equivalent to the case where the noise removal means 3 is not provided.

  As in the previous case, consider a case where the first color data Ri, Gi, Bi shown in FIG. 4 in which noise components are added to the original color data Rs, Gs, Bs shown in FIG. 3 are input. . That is, at the pixel positions 26 and 27, the original color data are Rs = 160, Gs = 48, and Bs = 48. At the pixel positions 26 and 27, the first color data is Ri = 146, Gi = 40, Bi = 38, Ri = 174, Gi = 46, and Bi = 60.

  Since the noise removal unit 3 is not provided, R2 = Ri, G2 = Gi, and B2 = Bi are input to the color correction calculation unit 4. The color correction amount calculation means 4 obtains the color correction amounts R1, G1, and B1 by the matrix calculation shown in the above equation (2) using the coefficient shown in the above equation (3). The color correction amount adding means 2 adds the first color data Ri, Gi, Bi and the color correction amounts R1, G1, B1 to calculate the second color data Ro, Go, Bo.

  FIG. 7 is a diagram illustrating the second color data Ro, Go, Bo when the noise removing unit 3 is not provided and R2 = Ri, G2 = Gi, and B2 = Bi. In the figure, the horizontal axis represents the pixel position where each data exists, and the vertical axis represents the size of each data. Compared to FIG. 6 showing the second color data Ro, Go, Bo obtained when the noise removing means 3 is provided, it can be seen that the noise component is also enhanced by the color conversion processing. In the figure, the arrows indicate the second color data Ro at the pixel positions 26 and 27, and the values are 189 and 226, respectively. On the other hand, the values of the first color data Ri at the pixel positions 26 and 27 are 146 and 174, respectively. The difference between the values of the first color data Ri at the pixel positions 26 and 27 is 174-146 = 28, and this value represents the magnitude of the noise component included in the first color data at the pixel position. On the other hand, the difference between the values of the second color data Ro at the pixel positions 26 and 27 is 226−189 = 37, and the value is large. This represents that the noise component included in the first color data is emphasized. When the influence of the noise components Rn, Gn, Bn in the image data is emphasized, the image displayed on the image display device becomes very difficult to see.

  Here, as described above, the difference between the values of the second color data Ro at the pixel positions 26 and 27 obtained by the color conversion apparatus according to the present embodiment including the noise removing unit 3 is 27, and the noise component is Not emphasized.

  As a method of reducing the influence of the noise component included in the first color data, it is also conceivable to input the first color data to the color conversion means via the noise removal means. FIG. 8 is a block diagram showing an example of a conventional color conversion apparatus to which noise removal means is added, and is configured to input first color data to the color conversion means 7 via the noise removal means 3. . The first color data Ri, Gi, Bi are input to the noise removing unit 3. The noise removing unit 3 removes the noise component in the first color data and outputs the color data R2, G2, and B2 from which noise has been removed. The color data R2, G2, B2 from which noise has been removed is input to the color conversion means 7. The color conversion unit 7 includes a color correction amount calculation unit 4 and a color correction amount addition unit 2. Similarly to the above-described case, the color correction amount calculation means 4 uses the matrix calculation shown in the above equation (2) using the coefficient shown in the above equation (3), from the color data R2, G2, and B2 from which noise has been removed. Color correction amounts R1, G1, and B1 are calculated. The color correction amount adding means 2 adds the color data R2, G2, B2 from which noise has been removed and the color correction amounts R1, G1, B1, respectively, to calculate second color data Ro, Go, Bo. Therefore, in the color conversion apparatus shown in FIG. 8, the second color data Ro, Go, Bo is expressed by the following formula (4).

  The noise removal means 3 can be configured as described with reference to FIG. Here, it is assumed that a simple average value is calculated in the weighted addition means 6 of the noise removal means 3.

  FIG. 9 shows second color data Ro, Go, Bo obtained by the color conversion device shown in FIG. At this time, the first color data in which a noise component is added to the original color data shown in FIG. 4 is input. In the figure, the horizontal axis represents the pixel position where each data exists, and the vertical axis represents the size of each data. Although the noise components in the second color data Ro, Go, and Bo are removed from FIG. 9, the change of the data change part is also gradual as surrounded by the broken line in the figure. When the change of the data change portion also becomes gradual, it is recognized as an image blur. For example, blurring occurs in characters and boundary portions in the image.

  In particular, when color data that is not affected by noise, that is, color data that does not include a noise component, is input, the blur in the image becomes a problem. That is, for color data having no noise component, only the drawback of image blurring occurs.

  On the other hand, in the second color data Ro, Go, Bo (FIG. 6) obtained by the color conversion apparatus (FIG. 1) of the present embodiment, no image blur is generated. That is, the data change from the pixel position 16 to 17 and the data change from the pixel position 42 to 43 are not gentle. This is because the first image data Ri, Gi, Bi is supplied to the color correction amount adding means 2 without removing noise.

  As described above, according to the color conversion device or the color conversion method of the present embodiment, even when processing for increasing the saturation and brightness of color data is performed, the influence of noise components included in the color data is further emphasized. In addition, it is possible to suppress the occurrence of image blur due to suppression of the influence of noise components.

  In recent years, there have been increasing cases of viewing television images and video images recorded on DVDs with a personal computer. In general, a video image or a television image often includes a large noise component. On the other hand, a graphic screen generated by a personal computer has a very small noise component, and there are many displays that require high definition such as character information. On the other hand, in an image display device, there are generally cases where there is no means for determining whether input image data represents a video image or a television image or character information. Therefore, when a video image or television image is viewed on a personal computer, enhancement of noise components in the video image or television image can be suppressed, and blurring of the image on the graphic screen generated by the personal computer can also be suppressed. The color conversion apparatus or color conversion method of the present invention is very useful.

  In the above, the configuration of the color correction amount calculation means is the matrix calculation method, but the same applies to the table conversion method. The table conversion method has the advantage that various conversion characteristics can be realized because the converted data can be specified for each set of input color data, but the temporal and spatial changes of the data, That is, the influence of noise is not particularly improved as compared with the matrix calculation method. Therefore, the effect that the color conversion processing can be performed without further emphasizing the influence of the noise component included in the color data by providing the color correction amount generation means with the noise removal means is the same as that of the matrix calculation method.

  Further, although cases have been described with the present embodiment where the processing is realized by hardware, similar processing can also be realized by software.

Embodiment 2. FIG.
In the first embodiment, the color correction amount generation unit is configured to obtain the color correction amount using the data from which noise has been removed by the noise removal unit. Instead of doing this, the color correction amount generating means may be configured to calculate the color correction amount from the image data and then perform noise removal by the noise removing means to obtain the color correction amount.

  FIG. 10 is a block diagram showing an example of the configuration of a color conversion apparatus according to Embodiment 2 of the present invention. The first color data Ri, Gi, Bi are input to the color correction amount generation unit 1b and the color correction amount addition unit 2. The color correction amount generation unit 1b includes a color correction amount calculation unit 4b and a noise removal unit 3, and the first color data Ri, Gi, Bi are input to the color correction amount calculation unit 4b. The color correction amount calculation unit 4 b calculates the color correction amounts R 3, G 3, B 3 from the first color data Ri, Gi, Bi and outputs them to the noise removal unit 3. The noise removing unit 3 removes noise components included in the color correction amounts R3, G3, and B3 output from the color correction amount calculating unit 4b, and outputs the noise-removed color data as color correction amounts R1, G1, and B1. To do. The color correction amount adding means 2 receives the first color data Ri, Gi, Bi and the color correction amounts R1, G1, B1, and calculates the second color data Ro, Go, Bo by adding them. Is done.

  As in the first embodiment, the first color data includes Ri = Rs + Rn, Gi from the original color data components Rs, Gs, Bs generated at the time of image generation and noise components Rn, Gn, Bn. = Gs + Gn, Bi = Bs + Bn. The color correction amount calculation means 4b is the same as the color correction amount calculation means 4 in the first embodiment except that the first color data Ri, Gi, Bi are input and the color correction amounts R3, G3, B3 are output. It can be set as this structure. That is, the color correction amount calculation means 4b obtains the color correction amounts R3, G3, and B3 by the linear calculation shown in the following equation (5) similar to the above equation (2). That is, the calculation of equation (5) is also for obtaining a correction amount for increasing at least one of the brightness and saturation of at least one color, as in the above equation (2).

  As the calculation coefficient in the above formula (5), the same coefficient as the following formula (3) used in the first embodiment can be used. The coefficient shown in the following formula (3) is a coefficient that increases the brightness and saturation of red, which is one specific color of image data.

  Consider the case where the first color data Ri, Gi, and Bi shown in FIG. 4 are input as in the first embodiment. That is, at the pixel positions 26 and 27, the first color data is Ri = 146, Gi = 40, Bi = 38, and Ri = 174, Gi = 46, Bi = 60.

  FIG. 11 is a diagram showing the color correction amounts R3, G3, and B3 output from the color correction amount calculation unit 4b. In the figure, the horizontal axis represents the pixel position where each data exists, and the vertical axis represents the size of each data. At pixel positions 26 and 27, the color correction amounts are R3 = 43, G3 = 0, B3 = 0, and R3 = 52, G3 = 0, B3 = 0.

The noise removing unit 3 can have the same configuration as that described with reference to FIG. However, the color correction amounts R3, G3, and B3 output from the color correction amount calculation unit 4b are input, and the color correction amounts R1, G1, and B1 from which noise is removed are output.
Here, it is assumed that a simple average value is calculated in the weighted addition means 6 of the noise removal means 3.

  FIG. 12 is a diagram illustrating the color correction amounts R1, G1, and B1 output from the noise removing unit 3. In the figure, the horizontal axis represents the pixel position where each data exists, and the vertical axis represents the size of each data. At pixel positions 26 and 27, the color correction amounts are R1 = 47, G1 = 0, B1 = 0, R1 = 46, G1 = 0, and B1 = 0.

The color correction amounts R1, G1, and B1 output from the noise removing unit 3 are added to the first color data Ri, Gi, and Bi by the color correction amount adding unit 2, and the second color data Ro, Go, and Bo are added. Is calculated.
FIG. 13 is a diagram illustrating an example of the second color data Ro, Go, Bo output from the color correction amount adding unit 2. In the figure, the horizontal axis represents the pixel position where each data exists, and the vertical axis represents the size of each data. Further, in order to pay attention to the data Ro representing red, the change in Ro is particularly shown by a thick line. The arrows in the figure indicate Ro at pixel positions 26 and 27, and the values are 193 and 220, respectively.

  Here, the difference between the values of the second color data Ro at the pixel positions 26 and 27 is 220-193 = 27. On the other hand, the difference between the values of the first color data Ri at the pixel positions 26 and 27 is 174-146 = 28. The difference between the values of the first color data Ri at the pixel positions 26 and 27 is caused by the influence of the noise component.

  As described above, also in the color conversion device or the color conversion method according to the present embodiment, the process of increasing the saturation or brightness of the color data is performed without further enhancing the influence of the noise component included in the color data. .

Embodiment 3 FIG.
FIG. 14 is a block diagram showing an example of the configuration of a color conversion apparatus according to Embodiment 3 of the present invention. The first color data Ri, Gi, Bi are input to the color correction amount generating unit 1c and the color correction amount adding unit 2. The color correction amount generation unit 1c includes an achromatic color component calculation unit 9, a chromatic color component calculation unit 10, a noise removal unit 3, and a color correction amount calculation unit 4c. The first color data Ri, Gi, Bi are achromatic colors. Input to the component calculation means 9 and the chromatic color component calculation means 10. The achromatic color component calculating means 9 selects the minimum value of the first color data Ri, Gi, Bi and outputs it as achromatic color component data α. The achromatic color component data α is data representing an achromatic color component of the first color data, that is, a gray component.

  FIG. 15 is a diagram illustrating the achromatic color component data α. In the figure, the vertical axis represents the data size. The size of the achromatic color component data α is the size at which the first color data Ri, Gi, Bi are present in equal amounts, and represents the size of the gray component included in the first color data.

The achromatic color component data α is input to the chromatic color component calculation means 10 and the color correction amount calculation means 4c. The chromatic color component calculation means 10 subtracts the achromatic color component data α from the first color data Ri, Gi, Bi, that is,
R4 = Ri-α
G4 = Gi-α
B4 = Bi-α
Thus, the chromatic color component data R4, G4, and B4 are calculated. The chromatic color component data is data obtained by removing the achromatic color component from the first color data, and is data relating to hue and saturation information included in the first color data. The chromatic color component data is not related to the gray component included in the first color data.

  The chromatic color component data R4, G4, and B4 are input to the noise removing unit 3, and the noise removing unit 3 calculates the chromatic color component data R5, G5, and B5 from which noise has been removed. The noise removing unit 3 may be configured as described with reference to FIG. 2 with respect to the first embodiment (a configuration including three circuits illustrated in FIG. 2). In this case, the first image data Ri, Gi , Bi, chromatic color component data R4, G4, B4 are input signals, and noise-removed chromatic color component data R5, G5, B5 are output signals. The chromatic color component data R5, G5, and B5 from which noise has been removed are input to the color correction amount calculation unit 4c, and the color correction amount calculation unit 4c calculates the color correction amounts R1, G1, and B1. The color correction amount calculation means 4c calculates the color correction amounts R1, G1, and B1 by the calculation shown in the following equation (6). The calculation of Expression (6) is also for obtaining a correction amount for increasing at least one of the brightness and saturation of at least one color, as in Expression (2) above.

  The color correction amount adding means 2 receives the first color data Ri, Gi, Bi and the color correction amounts R1, G1, B1, and calculates the second color data Ro, Go, Bo by adding them. Is done.

  Consider the case where the first color data Ri, Gi, and Bi shown in FIG. 4 are input as in the first embodiment. That is, at the pixel positions 26 and 27, the first color data is Ri = 146, Gi = 40, Bi = 38, and Ri = 174, Gi = 46, Bi = 60.

  FIG. 16 is a diagram illustrating an example of the chromatic color component data R4, G4, and B4. The chromatic color component data R4, G4, and R4 obtained for the first color data Ri, Gi, and Bi shown in FIG. B4 is represented. In the figure, the horizontal axis represents the pixel position where each data exists, and the vertical axis represents the size of each data. At pixel positions 26 and 27, the chromatic color component data is R4 = 108, G4 = 2, B4 = 0, and R4 = 128, G4 = 0, B4 = 14.

  Here, it is assumed that a simple average value is calculated in the weighted addition means 6 of the noise removal means 3. At this time, noise-removed chromatic color component data R5, G5, and B5 are shown in FIG.

  In the color correction amount calculation means 4c, the color correction amounts R1, G1, and B1 are calculated by the calculation shown in the above equation (6). The same equation (3) as the coefficient used in the first embodiment is used as a coefficient. Can be used. The coefficient shown in the following formula (3) is a coefficient that increases the brightness and saturation of red, which is one specific color of image data.

  FIG. 18 is a diagram illustrating the second color data Ro, Go, and Bo when the coefficient shown in the above equation (3) is used. In the figure, the horizontal axis represents the pixel position where each data exists, and the vertical axis represents the size of each data. Further, in order to pay attention to the data Ro representing red, the change in Ro is particularly shown by a thick line. The arrows in the figure indicate Ro at pixel positions 26 and 27, and the values are 180 and 207, respectively. Here, the difference between the values of the second color data Ro at the pixel positions 26 and 27 is 207−180 = 27. On the other hand, the difference between the values of the first color data Ri at the pixel positions 26 and 27 is 174-146 = 28. The difference between the values of the first color data Ri at the pixel positions 26 and 27 is caused by the influence of the noise component.

At the pixel position 26, the second color data is Ro = 180, Go = 40, Bo = 38, the saturation is 0.79, and the lightness is 180. On the other hand, at the pixel position 26, the first color data is Ri = 146, Gi = 40, Bi = 38, the saturation is 0.74, and the lightness is 146. Therefore, in the second color data, both saturation and brightness are higher than in the first color data.
Further, when gray data such as Ri = 100, Gi = 100, Bi = 100 is input as the first color data, the second color data also becomes Ro = 100, Go = 100, Bo = 100, and color conversion is performed. Not affected.

As described above, in the color conversion apparatus or the color conversion method according to the present embodiment, the processing for increasing the saturation and lightness of the color data is performed without further emphasizing the influence of the noise component included in the color data. Since the color correction amount is obtained after the first color data is separated into the achromatic and chromatic components, the hue and saturation of the color data are not affected without affecting the color reproduction of the achromatic color (gray). And brightness can be adjusted.
In addition, since noise removal is performed only on the chromatic color components, the influence of noise removal does not occur on black and white image data. For example, when a color video image is displayed on a personal computer and a document expressed in black and white is displayed, the video image shows noise reduction effect, but the black and white document has no blur due to the noise reduction effect. Does not occur.

Embodiment 4 FIG.
In the third embodiment, the first color data is separated into the achromatic color component and the chromatic color component, and noise is removed only from the chromatic color component. Instead of doing this, it is also possible to remove noise separately from the chromatic color component as well as the chromatic color component.

  FIG. 19 is a block diagram showing an example of the configuration of a color conversion apparatus according to Embodiment 4 of the present invention. The first color data Ri, Gi, Bi are input to the color correction amount generation unit 1d and the color correction amount addition unit 2. The color correction amount generation unit 1d includes an achromatic color component calculation unit 9, a chromatic color component calculation unit 10, a chromatic color noise removal unit 11, an achromatic color noise removal unit 12, and a color correction amount calculation unit 4c, and includes first color data. Ri, Gi, and Bi are input to the achromatic color component calculation unit 9 and the chromatic color component calculation unit 10. The achromatic color component calculating means 9 selects the minimum value of the first color data Ri, Gi, Bi and outputs it as achromatic color component data α.

  The achromatic color component data α is input to the chromatic color component calculating means 10 and the achromatic color noise removing means 12. The chromatic color component calculation means 10 calculates chromatic color component data R4, G4, B4 by subtracting the achromatic color component data α from the first color data Ri, Gi, Bi. The chromatic color component data R4, G4, and B4 are input to the chromatic color noise removing unit 11, and the chromatic color noise removing unit 11 calculates the chromatic color component data R5, G5, and B5 from which noise has been removed. Further, the achromatic color noise removing unit 12 calculates the achromatic color component data from which noise has been removed.

The chromatic noise removing unit 11 can have the same configuration as the noise removing unit 3 of the first embodiment (having three configurations shown in FIG. 2). In this case, the chromatic color noise removing unit 11 receives the chromatic color component data R4, G4, and B4 output from the chromatic color component calculating unit 10 and outputs the chromatic color component data R5, G5, and B5 from which noise is removed. . The achromatic color noise removing unit 12 can be configured as shown in FIG. 2 used in the description of the first embodiment, and the minimum value α as the achromatic color component output from the achromatic color component calculating unit 9 is input. The achromatic component from which the noise component is removed is output.
The noise-removed chromatic color component data R5, G5, and B5 and the noise-removed achromatic color component data are input to the color correction amount calculation unit 4c. The color correction amount calculation unit 4c receives the color correction amounts R1, G1, B1 is calculated. The color correction amount adding means 2 receives the first color data Ri, Gi, Bi and the color correction amounts R1, G1, B1, and calculates the second color data Ro, Go, Bo by adding them. Is done.

  In the color conversion device according to the present embodiment, not only the chromatic color component data but also the achromatic color component data is configured to remove noise separately from the chromatic color component data. Different from the color conversion device. Achromatic color component data is related to saturation information in combination with chromatic color component data, but it has only brightness information by itself and differs from chromatic color data for human visual characteristics. It can be considered to have characteristics. Therefore, by removing noise from the chromatic color component data and the achromatic color component data separately, it is possible to select the influence of noise removal and the magnitude of the effect according to the visual characteristics.

It is a block diagram which shows an example of a structure of the color conversion apparatus by Embodiment 1 of this invention. It is a block diagram showing an example of a partial structure of the noise removal means 3 in the color conversion apparatus by Embodiment 1 of this invention. It is a figure showing an example of original color data Rs, Gs, and Bs. It is a figure showing an example of the 1st color data input into an image display apparatus in case a noise component exists. It is a figure showing an example of the color data from which noise was removed. It is a figure showing an example of the 2nd color data in Embodiment 1 of this invention. It is a figure showing an example of the 2nd color data at the time of comprising a noise removal means so that it may become R2 = Ri, G2 = Gi, and B2 = Bi. It is a block diagram showing an example of the conventional color conversion apparatus which added the noise removal means. It is a figure showing an example of the 2nd color data calculated | required by the conventional color conversion apparatus which added the noise removal means. It is a block diagram which shows an example of a structure of the color conversion apparatus by Embodiment 2 of this invention. It is a figure which shows an example of the color correction amount output from the color correction amount calculating means in the color conversion apparatus by Embodiment 2 of this invention. It is a figure which shows an example of the color correction amount output from the noise removal means in the color conversion apparatus by Embodiment 2 of this invention. It is a figure showing an example of the 2nd color data output from the color correction amount addition means in the color conversion device by Embodiment 2 of this invention. It is a block diagram which shows an example of a structure of the color conversion apparatus by Embodiment 3 of this invention. It is a figure showing achromatic color component data (alpha). It is a figure showing an example of the chromatic color component data in the color conversion apparatus by Embodiment 3 of this invention. It is a figure showing an example of the chromatic color component data from which the noise was removed in the color conversion device by Embodiment 3 of this invention. It is a figure showing an example of the 2nd color data in the color conversion apparatus by Embodiment 3 of this invention. It is a block diagram which shows an example of a structure of the color conversion apparatus by Embodiment 4 of this invention.

Explanation of symbols

1, 1b, 1c, 1d color correction amount generation means, 2 color correction amount addition means, 3 noise removal means, 4, 4b, 4c color correction amount calculation means, 5 data shift means, 5a to 5h data storage section, 6 weighting Addition means, 7 color conversion means, 10 chromatic color component calculation means, 11 chromatic color noise removal means, 12 achromatic color noise removal means.

Claims (16)

In a color conversion device that performs color conversion on first image data that is image information for each pixel including a plurality of first color data, and obtains second image data including a plurality of second color data,
Noise removing means for removing noise from the first image data;
A color correction amount calculation means for calculating a color correction amount for converting at least one of brightness and saturation of the first image data using the output of the noise removal means;
A color conversion apparatus comprising: an adding unit that adds the color correction amount calculated by the color correction amount calculating unit to the first image data to obtain the second image data. In a color conversion device that performs color conversion on first image data that is image information for each pixel that includes a plurality of first color data, and obtains second image data that includes a plurality of second color data,
Means for calculating achromatic component data representing an achromatic component in the first image data;
Means for calculating chromatic color component data from the first image data and the achromatic color component data;
Noise removing means for removing noise from the chromatic color component data;
Color correction amount calculation means for obtaining a color correction amount for converting at least one of brightness and saturation of the first image data using the achromatic color component data and the chromatic color component data from which noise has been removed; ,
A color conversion apparatus comprising: an adding unit that adds the color correction amount calculated by the color correction amount calculating unit to the first image data to obtain the second image data. In a color conversion device that performs color conversion on first image data that is image information for each pixel that includes a plurality of first color data, and obtains second image data that includes a plurality of second color data,
Means for calculating achromatic component data representing an achromatic component in the first image data;
Means for calculating chromatic color component data from the first image data and the achromatic color component data;
Achromatic color noise removing means for removing noise from the achromatic color component data and outputting the noise-free achromatic color component data;
Chromatic color noise removing means for removing noise from the chromatic color component data and outputting the noise-removed chromatic color component data;
A color correction amount for obtaining a color correction amount for converting at least one of lightness and saturation of the first image data using the noise-removed achromatic color component data and the noise-removed chromatic color component data. Computing means;
A color conversion apparatus comprising: an adding unit that adds the color correction amount calculated by the color correction amount calculating unit to the first image data to obtain the second image data.   The achromatic color component data is calculated as a minimum value of the first color data constituting the first image data, and the chromatic color component data is obtained by replacing the achromatic color component data with the first color data. The color conversion apparatus according to claim 2, wherein the color conversion apparatus is calculated as a reduced value.   3. The color conversion apparatus according to claim 1, wherein the noise removing unit is a low-pass filter that blocks high-frequency components in input data and transmits low-frequency components.   4. The low-pass filter according to claim 3, wherein each of the achromatic color noise removing unit and the chromatic color noise removing unit is a low-pass filter that blocks high-frequency components in input data and transmits low-frequency components. Color conversion device.   The color conversion apparatus according to claim 5 or 6, wherein the low-pass filter blocks a frequency component of about 1/9 or more of a clock frequency of image data.   4. The color conversion apparatus according to claim 1, wherein the color correction amount calculation is to obtain a correction amount for increasing the lightness or saturation of at least one color. In the color conversion method for color-converting first image data, which is image information for each pixel composed of a plurality of first color data, and obtaining second image data composed of a plurality of second color data,
A noise removing step for removing noise from the first image data;
A color correction amount calculation step for calculating a color correction amount for converting at least one of brightness and saturation of the first image data using the data from which noise has been removed by the noise removal step;
A color conversion method comprising: an addition step of adding the color correction amount calculated in the color correction amount calculation step to the first image data to obtain the second image data. In the color conversion method for color-converting first image data, which is image information for each pixel composed of a plurality of first color data, and obtaining second image data composed of a plurality of second color data,
Calculating achromatic component data representing an achromatic component in the first image data;
Calculating chromatic color component data from the first image data and the achromatic color component data;
A noise removal step for removing noise from the chromatic color component data;
A color correction amount calculation step for obtaining a color correction amount for converting at least one of brightness and saturation of the first image data using the achromatic color component data and the chromatic color component data from which noise is removed; ,
A color conversion method comprising: an addition step of adding the color correction amount calculated in the color correction amount calculation step to the first image data to obtain the second image data. In the color conversion method for color-converting first image data, which is image information for each pixel composed of a plurality of first color data, and obtaining second image data composed of a plurality of second color data,
Calculating achromatic component data representing an achromatic component in the first image data;
Calculating chromatic color component data from the first image data and the achromatic color component data;
Removing noise from the achromatic color component data and outputting the achromatic color component data from which noise has been removed;
A chromatic noise removal step for removing noise from the chromatic color component data and outputting the chromatic color component data from which noise has been removed;
A color correction amount for obtaining a color correction amount for converting at least one of lightness and saturation of the first image data using the noise-removed achromatic color component data and the noise-removed chromatic color component data. A calculation step;
A color conversion method comprising: an addition step of adding the color correction amount calculated in the color correction amount calculation step to the first image data to obtain the second image data.   The achromatic color component data is calculated as a minimum value of the first color data constituting the first image data, and the chromatic color component data is obtained by replacing the achromatic color component data with the first color data. The color conversion method according to claim 10 or 11, wherein the color conversion method is calculated as a reduced value.   11. The color conversion method according to claim 9, wherein the noise removing step performs low-pass filtering that blocks high frequency components in input data and transmits low frequency components.   12. The achromatic color noise removal step and the chromatic color noise removal step each perform low pass filtering that blocks high frequency components in input data and transmits low frequency components. Color conversion method.   The color conversion method according to claim 13 or 14, wherein the low-pass filtering cuts off a frequency component of about 1/9 or more of a clock frequency of image data.   12. The color conversion method according to claim 9, wherein the color correction amount calculation is to obtain a correction amount for increasing the lightness or saturation of at least one color.

Images