JP4259428B2 - Color conversion apparatus and color conversion method - Google Patents

Description

  The present invention relates to image processing used in printing-related equipment such as an image display device that displays a color image, a color printer, a color scanner, and the like, and in particular, corrects color data representing a color image in accordance with the characteristics of a device to be used. It relates to color conversion processing.

  In color image display devices, color printers, color scanners, and the like, color conversion processing is performed to correct color data in accordance with device characteristics so that desired color reproduction can be obtained. The desired color reproduction is a 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 human memory colors, the sky color and green grass tend to be stored as brighter, more saturated and lighter colors than actual colors. Therefore, processing for increasing the brightness and saturation of such specific color components is performed. In addition, there are many cases where processing for increasing brightness and saturation is performed for faithful color reproduction.

An example of a conventional color conversion device is described in Patent Document 1 below. The color conversion device described in Patent Document 1 performs color conversion processing by matrix calculation using calculation terms effective for six hue components of red, green, blue, yellow, magenta, and cyan in a color image. It is characterized by. The input color data can be adjusted independently for each of the hue components of red, green, blue, yellow, magenta, and cyan by appropriately setting matrix coefficients related to the calculation terms effective for the six hue components. it can.
Japanese Patent No. 3128429

  According to the color conversion device described in Patent Document 1, color conversion processing for increasing the brightness and saturation of color data by setting matrix coefficients that increase the brightness and saturation of a specific hue component is performed using red, This can be performed for each hue component of green, blue, yellow, magenta, and cyan.

  However, various noise components are added to the color data input to the image display device or the like in the transmission process. When the processing for increasing the saturation and lightness is performed on the color data including the noise component, the lightness and saturation of the noise component are increased together with the lightness and saturation of the original color data. As described above, when the color data includes a noise component, if the processing for increasing the saturation or brightness of the color data is performed, the influence of the noise component is further emphasized, and the image quality is deteriorated. It was.

In addition, in order to reduce the influence of noise components included in the color data, when the color data is input to the color conversion means via the noise removal means, the noise components are removed, but the high-frequency components constituting the contour portion Since the image is also lost, there is a problem that the image is blurred.
In particular, in current image display devices, a high-frequency noise component close to the pixel frequency of image data is often noticeable. This is because in the pixel interval of the current image display device, if adjacent pixel data frequently changes without being related to each other due to the influence of the noise component, it is visually recognized as a large noise component by humans. Here, the pixel frequency is a frequency when continuous pixel data changes every other pixel, for example, 0, 1, 0, 1, 0... 1 / of the clock frequency of the image data. 2.
Such noise components can be removed by a low-pass filter that blocks or attenuates high-frequency components included in the image data and transmits low-frequency components. However, since the high-frequency component that constitutes the original image together with the noise component is also blocked, there arises a problem that the image is blurred.

  The present invention has been made in view of the above problems, and provides a color conversion apparatus and a color conversion method capable of adjusting the saturation and brightness of a desired color component without enhancing the noise component. Objective.

The color conversion device according to the present invention converts the lightness and / or saturation of first color data representing a color image for each pixel and outputs second color data corresponding to the first color data. In the conversion device,
Means for outputting an identification code obtained by quantizing the hue of the color represented by the first color data;
Using the first color data, in the color image represented by the first color data, it becomes a value other than Oite 0 to a specific hue component, a 0 in the hue components other than the specific hue component Hue area data calculating means for calculating a plurality of hue area data;
Based on the identification code, among the plurality of hue region data, select the data to be a value other than Oite 0 to the color of each pixel represented by the first color data, first effective hue region Means for outputting as data;
According to a preset method, a frequency characteristic of a signal input as the first effective hue area data is converted according to the identification code, and a second effective hue area corresponding to the first effective hue area data is converted. Means for outputting data;
Coefficient generating means for outputting a predetermined matrix coefficient set for each of the second effective hue area data;
The lightness and / or saturation of the first color data is calculated by performing a matrix operation including multiplication by using the second hue region data as an operation term and multiplying the second hue region data by the matrix coefficient. Matrix computing means for calculating a correction amount for independently correcting each hue component;
Color correction means for calculating the second color data obtained by correcting the first color data based on the correction amount.

The color conversion method according to the present invention converts the lightness and / or saturation of the first color data representing a color image for each pixel and outputs the second color data corresponding to the first color data. In the conversion method,
Outputting an identification code obtained by quantizing the hue of the color represented by the first color data;
Using the first color data, in the color image represented by the first color data, it becomes a value other than Oite 0 to a specific hue component, a 0 in the hue components other than the specific hue component Calculating a plurality of hue area data;
Based on the identification code, among the plurality of hue region data, select the data to be a value other than Oite 0 to the color of each pixel represented by the first color data, first effective hue region Outputting as data,
According to a preset method, a frequency characteristic of a signal input as the first effective hue area data is converted according to the identification code, and a second effective hue area corresponding to the first effective hue area data is converted. Outputting the data;
Outputting a predetermined matrix coefficient set for each of the second effective hue area data;
The lightness and / or saturation of the first color data is calculated by performing a matrix operation including multiplication by using the second hue region data as an operation term and multiplying the second hue region data by the matrix coefficient. Calculating a correction amount for independently correcting each hue component;
Calculating the second color data obtained by correcting the first color data based on the correction amount.

  The color conversion device and the color conversion method according to the present invention select the hue area data effective for a plurality of specific hue components in the color image represented by the first color data, and select the first effective The brightness of the first color data is output by matrix calculation using the second effective hue area data calculated by outputting the hue area data and converting the frequency characteristics of the first effective hue area data. And / or calculating a correction amount for independently correcting the saturation for each hue component, and calculating the second color data based on the correction amount, so that a desired color component can be obtained without enhancing the noise component. You can adjust the saturation and brightness.

Hereinafter, a color conversion apparatus according to the present invention will be described in detail with reference to the drawings.
FIG. 1 is a block diagram showing an embodiment of a color conversion apparatus according to the present invention. As shown in FIG. 1, the color conversion apparatus according to the present invention includes a color correction amount calculation unit 1 and a color correction amount addition means 2. The color correction amount calculation unit 1 and the color correction amount addition means 2 receive first color data Ri, Gi, Bi representing a color image.
The color correction amount calculator 1 includes an αβ calculator 3, a chromatic color component data calculator 4, a hue area data calculator 5, a first effective hue area data calculator 6, a second effective hue area data calculator 7, The matrix calculation means 8 and the coefficient generation means 9 are comprised. The first color data Ri, Gi, Bi input to the color correction amount calculation unit 1 are sent to the αβ calculation means 3 and the chromatic color component data calculation means 4, respectively.

  The αβ calculating means 3 selects and outputs the maximum value β and the minimum value α of the first color data Ri, Gi, Bi, and relates to the hue of the color represented by the first color data Ri, Gi, Bi. An identification code S1 representing information is output. This identification code S <b> 1 is input to the first effective hue area data calculating means 6, the second effective hue area data calculating means 7, and the coefficient generating means 9. The maximum value β and the minimum value α are input to the chromatic color component data calculation unit 4. The minimum value α is also input to the matrix calculation means 8. Here, the minimum value α is data representing an achromatic (gray) component included in the first color data Ri, Gi, Bi.

FIG. 2 is a diagram showing the magnitude relationship between the value of the identification code S1 and the color data Ri, Gi, Bi. FIG. 3 shows the value of the identification code S1 and the hue of the first color data Ri, Gi, Bi. It is a figure which shows the relationship. As shown in FIG. 3, when the first color data Ri, Gi, Bi represents a hue color from red to yellow, 1 is output as the identification code S1. Similarly, S1 = 3 for yellow to green, S1 = 2 for green to cyan, S1 = 4 for cyan to blue, S1 = 5 for blue to magenta, S1 for magenta to red. = 0 is output as an identification code. Further, when the color data Ri, Gi, Bi of the first color represents a red hue, 6 is output as the identification code S1. Similarly, S1 = 11 for yellow, S1 = 7 for green, S1 = 9 for cyan, S1 = 8 for blue, and S1 = 10 for magenta. .
When Ri = Gi = Bi, the first color data represents an achromatic color, that is, gray. In this case, 12 is output as the identification code S1.

  The chromatic color component data calculation means 4 is represented by the first color data based on the first color data Ri, Gi, Bi and the maximum value β and the minimum value α which are the outputs from the αβ calculator means 3. Color data r, g, b, y, m, c representing the size of each color component of red, green, blue, yellow, magenta, and cyan of a color (chromatic color) obtained by removing the achromatic color component from the generated color Is calculated. These chromatic color data are obtained by subtraction processing of r = Ri−α, g = Gi−α, b = Bi−α, y = β−Bi, m = β−Gi, and c = β−Ri.

  The chromatic color component data obtained as described above has a property that at least one of r, g, and b and at least one of y, m, and c are zero. For example, when the maximum value β is Ri and the minimum value α is Gi (β = Ri, α = Gi), g = 0 and c = 0 are obtained by the above subtraction process, and the maximum value β is Ri and the minimum value α. Is Bi (β = Ri, α = Bi), b = 0 and c = 0. That is, depending on the combination of Ri, Gi, and Bi that is the maximum and minimum, at least one of r, g, and b, and any one of y, m, and c in total will be zero.

  Six chromatic color component data r, g, b, y, m, c output from the chromatic color component data calculation means 4 are sent to the hue area data calculation means 5. FIG. 4 is a block diagram illustrating an example of an internal configuration of the hue area data calculation unit 5. The hue area data calculation unit 5 includes a plurality of minimum value selection units 11a to 11f that select and output the smaller value of two input chromatic color component data.

  The minimum value selection means 11a selects the smaller value of the chromatic color component data r and b and outputs it as the hue area data h1m. Similarly, the minimum value selection unit 11b selects the smaller one of the chromatic color component data r and g and outputs it as the hue area data h1y, and the minimum value selection unit 11c selects the smaller one of the chromatic color component data g and b. Select and output as hue area data h1c, the minimum value selecting means 11d selects the smaller one of the chromatic color component data y and c, and outputs it as the hue area data h1g, and the minimum value selecting means 11e is the chromatic color component data. The smaller one of y and m is selected and output as hue area data h1r, and the minimum value selection means 11f selects the smaller of chromatic color component data m and c and outputs it as hue area data h1b.

  The calculation of the hue area data h1r, h1g, h1b, h1c, h1m, h1y can be expressed by the following equation.

ただし、ｍｉｎ（Ａ，Ｂ）はＡとＢのうち小さい方の値を表す。

However, min (A, B) represents the smaller value of A and B.

  5A to 5F schematically show the relationship between the hue area data h1r, h1y, h1g, h1c, h1b, and h1m and the six hues of red, yellow, green, cyan, blue, and magenta. It is a figure. As shown in FIG. 5, the hue area data h1r, h1g, h1b, h1c, h1m, and h1y are the maximum in the hues of red, green, blue, cyan, magenta, and yellow, respectively, and the magnitudes in the other hues are large. 0. That is, the hue area data h1r, h1g, h1b, h1c, h1m, and h1y are data effective for hue components of red, green, blue, cyan, magenta, and yellow in the color image represented by the first color data. I can say that.

  The hue area data h1r, h1g, h1b, h1y, h1m, and h1c calculated by the hue area data calculation means 5 are input to the first effective hue area data calculation means 6. Based on the identification code S1, the first effective hue area data calculation means 6 selects data that is valid for each pixel from the hue area data h1r, h1 gram, h1b, h1c, h1m, h1y, and the first Are output as effective hue area data h1p1 and h1q1. If the effective hue area data is 1 or 0, one of the first effective hue area data h1p1 and h1q1 is set to 0, or both are output as 0.

FIG. 6 is a diagram illustrating the relationship between the identification code S1 and the first effective hue area data h1p1 and h1q1 selected based on the identification code S1. When the identification code S1 = 1, since the first color data Ri, Gi, Bi represent the hue colors from red to yellow, only the hue area data h1r effective for red and the hue area data h1y effective for yellow are effective. Data, that is, non-zero data, and other hue area data are zero. Accordingly, the first effective area data calculation unit 6 selects h1r and h1y as the first effective hue area data h1p1 and h1q1, respectively. Further, when the identification code S1 = 11, the first color data Ri, Gi, Bi represents the color of the hue of yellow, so only the hue area data h1y effective for yellow becomes effective data, and other hue area data Becomes 0. Accordingly, the first effective area data calculation unit 6 selects h1y as the first effective hue area data h1q1, and sets h1p1 = 0.
The first effective hue area data h1p1 and h1q1 output from the first effective hue area data calculation means 6 are sent to the second effective hue area data calculation means 7.

  FIG. 7 is a block diagram showing an internal configuration of the second effective hue area data calculation means 7. The second effective hue area data calculation means 7 includes frequency characteristic conversion means 10a and 10b. The first effective hue area data h1p1 and h1q1 are input to the frequency characteristic conversion means 10a and 10b, respectively. The frequency characteristic conversion means 10a, 10b removes noise components by converting the spatial or temporal frequency characteristics of the first effective hue area data h1p1, h1q1, and outputs the second effective hue area data h1p2, h1q2. To do.

The filter characteristics of the frequency characteristic conversion means 10a and 10b are set according to the properties of the noise components included in the first color data Ri, Gi and Bi, respectively.
For example, when removing high-frequency noise components in the vicinity of the pixel frequency of image data, the high-frequency components in the input data are blocked or greatly attenuated, and the frequency characteristic conversion means 10a and 10b are transmitted by low-pass filters that transmit low-frequency components. Can be configured. Specifically, it is possible to use a low-pass filter that cuts off or has an attenuation band at 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.

  As a low-pass filter having a simple configuration, one that calculates a simple average value of image data in each of a plurality of continuous pixels can be considered. In this case, the filter characteristic is determined by the number of pixels used for calculating the simple average value.

  FIG. 8 is a block diagram illustrating an example of the internal configuration of each of the frequency characteristic conversion units 10a and 10b. As shown in FIG. 8, the frequency characteristic conversion units 10 a and 10 b include a data shift unit 12 including a plurality of data storage units 14 a to 14 i and a weighted addition unit 13. The first effective hue area data h1p1 and h1q1 input to the frequency characteristic conversion units 10a and 10b are sent to the data storage unit 14a and the weighting addition unit 13. The data storage units 14 a to 14 i are cascade-connected to each other, and each time the first effective hue area data is input, the input data is simultaneously shifted to the subsequent stage and output to the weighted addition means 13. Note that after the final data is input to the frequency characteristic conversion means 10a and 10b, the same data as the final data is continuously input to the data storage unit 14a.

The identification code S1 is input to the weighted addition means 13 together with the data output from the data storage units 14a to 14i. The weighted addition means 13 performs weighted addition on the data output from the data storage units 14a to 14i, and outputs the result of the weighted addition as second effective hue area data h1p2 and h1q2. When the weighting addition means 13 sets the weighting coefficients to the same value (1/9), a simple average value is calculated and a smoothing process is performed. This weighting coefficient is determined based on the identification code S1. By selecting a weighting coefficient based on the identification code S1, the frequency conversion characteristic can be changed according to the hue of the first color data Ri, Gi, Bi.
The second effective hue area data h1p2 calculated by the frequency characteristic conversion means 10a is expressed by the following equation.

In the above formula (2), h1p1 [n] is the first effective hue area data inputted n-th, and h1p1 [n + 4]... H1p1 [n-4] is output by the data storage units 14a to 14i. Represents data. The function fs1 represents weighted addition when the value of the identification code S1 is sn.
In the above equation (2), if the weighting coefficient of h1p1 [n] is 1 and the others are 0, the frequency characteristic conversion process is not performed and h1p1 = h1p2 is obtained, and the input first effective hue area data is output as it is. The
Note that the other first effective hue area data h1q1 is also expressed in the same manner as in the above equation (2).

  The second effective hue area data h1p2 and h1q2 are input to the matrix calculation means 8 together with the minimum value α. The matrix calculation means 8 uses the second effective hue area data and the minimum value α as calculation terms, and the first color by matrix calculation using the calculation coefficient U (Eij) output from the coefficient generation means 9 as a matrix coefficient. Color correction amounts R1, G1, and B1 for adjusting the lightness and / or saturation of data for each hue component of red, green, blue, yellow, cyan, and magenta are calculated.

FIG. 9 is a block diagram showing the internal configuration of the matrix calculation means 8. As shown in FIG. 9, the matrix calculation means 8 is configured by multiplication means 15a to 15c and addition means 16a and 16b. The multiplying units 15a to 15c multiply the second effective hue area data h1p2 and h1q2 and the minimum value α of the first color data by the operation coefficient U (Eij), respectively. The adding means 16a adds the outputs of the multiplying means 15a and 15b, and the adding means 16b adds the outputs of the multiplying means 15c and the adding means 16b and outputs the result as a color correction amount R1 (G1 or B1).
In FIG. 10, the calculation coefficient U (Eij) is given for each of the color correction amounts R1, G1, and B1, and the color correction amounts for R1, G1, and B1 are sequentially calculated. By providing three similar circuits, You may comprise so that parallel processing may be performed.

  The above calculation in the matrix calculation means 8 is expressed by the following equation.

上記式（３）のマトリクス係数は、Ｅｉｊ（ｉ＝１〜３，ｊ＝１〜３）である。

The matrix coefficient of the above formula (3) is Eij (i = 1 to 3, j = 1 to 3).

  The color correction amounts R1, G1, and B1 output from the matrix calculating unit 8 are sent to the color correction amount adding unit 2. The color correction amount adding means 2 calculates the second color data Ro, Go, Bo by adding the color correction amounts R1, G1, B1 to the first color data Ri, Gi, Bi.

  The above formula (3) can be rewritten into the following formula (4). In Expression (4), the operation terms h1r2, h1g2, h1b2, h1c2, h1m2, and h1y2 are obtained by converting temporal or spatial frequency characteristics of the hue area data h1r, h1g, h1b, h1c, h1m, and h1y. Data, corresponding to the second effective hue area data h1p2, h1q2. The matrix coefficient is Fij (i = 1 to 3, j = 1 to 7).

  The color conversion apparatus according to the present invention includes effective hue area data selected based on the identification code S1, that is, hue area data relating to the color of each pixel represented by the first color data Ri, Gi, Bi. Since the second effective hue area data h1p2 and h1q2 obtained by converting the frequency characteristics are used, the amount of calculation in the matrix calculation can be reduced. Further, the number of multiplication means and addition means of the matrix calculation means 8 can be reduced, and the circuit scale can be reduced.

  The operation of the color conversion device according to the present invention will be described below. The first color data Ri, Gi, Bi are affected by various noises in the process of transmission. Therefore, if the original color data at the time of image generation is Rs, Gs, Bs, and the magnitude of the noise component for each color data is Rn, Gn, Bn, the first color data is Ri = Rs + Rn, Gi = Gs + Gn, Bi = Bs + Bn. That is, the first color data Ri, Gi, Bi input to the color conversion device is represented by the sum of Rs, Gs, Bs that are original color data components and Rn, Gn, Bn that are noise components. Will be.

  FIG. 10 is a diagram illustrating an example of original color data Rs, Gs, and Bs. In FIG. 10, the horizontal axis represents the pixel position, and the vertical axis represents the size of the color data Rs, Gs, Bs at each pixel position. At pixel positions 0 to 16, Rs = 48, Gs = 160, and Bs = 48, representing a uniform green color (including a gray component). At pixel positions 17 to 42, Rs = 160, Gs = 48, and Bs = 48, which represent a uniform red color (including a gray component). At pixel positions 43 to 63, Rs = 48, Gs = 48, and Bs = 48, which represents uniform gray.

  FIG. 11 is a diagram showing color data when noise components Rn, Gn, Bn are added to the original color data Rs, Gs, Bs, that is, first color data Ri, Gi, Bi including noise components. . In FIG. 11, the values of the color data Gi at the pixel positions 12 and 13 are indicated by arrows a and b, and the values of the color data Ri at the pixel positions 26 and 27 are indicated by arrows c and d. It is. The value of the color data Gi at each pixel position is Gi = 146 (Ri = 54, Gi = 54) at the pixel position 12, and Gi = 168 (Ri = 62, Gi = 54) at the pixel position 13. Further, the value of the color data Ri at each pixel position is Ri = 146 (Gi = 40, Bi = 38) at the pixel position 26 and Ri = 174 (Gi = 46, Bi = 60) at the pixel position 27. The value of the color data Gi at the pixel positions 12 and 13 and the value of the color data Ri at the pixel positions 26 and 27 should be equal to each other, but the values are different at each pixel position due to the influence of noise components. .

  Here, the saturation of the first color data can be expressed by dividing the difference between the maximum value and the minimum value of the color data Ri, G, i, Bi by the maximum value, and the brightness is expressed by the maximum value. be able to. According to this, the saturation of the first color data at the pixel position 12 is 0.63, the brightness is 146, the saturation of the first color data at the pixel position 13 is 0.68, the brightness is 168, and the pixel position is 26. The saturation of the first color data is 0.74, the lightness is 146, the saturation at the pixel position 27 is 0.74, and the lightness is 174.

  FIG. 12 is a diagram showing hue area data calculated based on the first color data Ri, Gi, Bi shown in FIG. As shown in FIG. 12, the hue area data h1r and h1g are calculated for the first color data shown in FIG.

  FIG. 13 is a diagram showing first effective hue area data h1p1 obtained by selecting the hue area data shown in FIG. 12 based on the identification code S1. As shown in FIG. 11, at the pixel positions 0 to 16, the maximum value β of the first color data is Gi. Therefore, from the relationship shown in FIG. 2, the identification code S1 is any one of 2, 3, and 7 at the pixel positions 0 to 16. In this case, the first hue area data is h1p1 = h1g. At pixel positions 17 to 42, the maximum value β of the first color data is Ri. Therefore, from the relationship shown in FIG. 2, the identification code S1 is 0, 1, or 6 at the pixel positions 17 to 42. In this case, the first hue area data is h1p1 = h1r.

  Here, when the identification code S1 is 0, 1, 6 (that is, when the first color data represents magenta to red color), the weighted addition means 13 of the frequency characteristic conversion means 10a performs the simple averaging to perform the first averaging. 2 effective hue area data is calculated, and if the identification code S1 has another value, the frequency characteristic conversion process is not performed and the first effective hue area data h1p1 is output as it is as the second effective hue area data h1p2. It shall be. Further, in the weighted addition means 13 of the frequency characteristic conversion means 10b, the frequency characteristic conversion process is not always performed regardless of the value of the identification code S1, and the first effective hue area data h1p1 is directly used as the second effective hue area data. Assume that h1p2 is output. In this case, the effect of frequency characteristic conversion appears only for colors from magenta centering on red to yellow.

  FIG. 14 is a diagram showing second effective hue area data h1p2 obtained by performing the above process in the second effective hue area data calculating means 7. As shown in FIG. 14, noise components are removed at pixel positions 17 to 42 representing magenta to red colors.

  FIG. 15 shows a second calculation calculated by adding the color correction amounts R1, G1, and B1 calculated using the matrix coefficients shown in the following formula (5) to the first color data Ri, Gi, and Bi shown in FIG. It is a figure showing color data Ro, Go, and Bo. The matrix coefficient shown in the following formula (5) is a coefficient that increases the brightness and saturation of the hue components of red, yellow, and green in the first color data.

図１５中、矢印ａ’，ｂ’により示しているのは、画素位置１２，１３における色データＧｏの値であり、矢印ｃ’，ｄ’により示しているのは画素位置２６，２７における色データＲｏの値である。色データＧｏの上記各画素位置における値は、画素位置１２においてＧｏ＝１７３（Ｒｏ＝５４，Ｇｏ＝５４）、画素位置１３においてＧｏ＝２０２（Ｒｏ＝６８，Ｇｏ＝５４）となっている。また、色データＲｏの上記各画素位置における値は、画素位置２６においてＲｏ＝１７７（Ｇｉ＝４０，Ｂｉ＝３８）、画素位置２７においてＲｏ＝２０４（Ｇｉ＝４６，Ｂｉ＝６０）である。

In FIG. 15, the arrows a ′ and b ′ indicate the values of the color data Go at the pixel positions 12 and 13, and the arrows c ′ and d ′ indicate the colors at the pixel positions 26 and 27. This is the value of data Ro. The values of the color data Go at the respective pixel positions are Go = 173 (Ro = 54, Go = 54) at the pixel position 12 and Go = 202 (Ro = 68, Go = 54) at the pixel position 13. The value of the color data Ro at each pixel position is Ro = 177 (Gi = 40, Bi = 38) at the pixel position 26, and Ro = 204 (Gi = 46, Bi = 60) at the pixel position 27.

  Accordingly, the saturation and lightness at the pixel position 12 of the second color data are 0.69 and 173, the saturation and lightness at the pixel position 13 are 0.73 and 202, and the saturation and lightness at the pixel position 26 are 0.79. , 177 and the saturation and lightness at the pixel position 27 are 0.77 and 204, respectively. Thus, it can be seen that the brightness and saturation of the second color data Ro, Go, Bo are higher than those of the first color data Ri, Gi, Bi.

  The difference between the first color data Gi at the pixel positions 12 and 13 is 168-146 = 22, but the difference between the second color data Go at the pixel positions 12 and 13 at the same pixel position is 202-173 = 29. It turns out that it is increasing. On the other hand, the difference between the first color data Ri at the pixel positions 26 and 27 is 174-146 = 28, but the difference between the second color data Ro at the pixel positions is 204-177 = 27, which is almost the same. It has not changed. The difference between the first color data Gi and Ri at each pixel position is caused by the influence of the noise component, and the influence of the noise component is emphasized for the second color data Go. It can be seen that the second color data Ro is processed to increase the saturation and lightness without enhancing the influence of the noise component.

  This is because the characteristics of the frequency characteristic conversion means 10a and 10b are set so that noise removal processing is performed only for colors from magenta centering on red to yellow. If the weighted addition means 13 of the frequency characteristic conversion means 10a is set so that the simple average value is calculated when the value of the identification code S1 is 2, 3 and 7, the process from cyan to yellow centered on green is performed. Noise removal processing is also performed on colors.

  As described above, the color conversion device according to the present invention includes each of the hue area data h1r, h1g, h1b, h1y, h1c, and h1m effective for the hue components of red, green, blue, yellow, cyan, and magenta. Second data obtained by selecting data effective for the pixel color as first effective hue area data h1p1, h1q1, and converting the frequency characteristics of the selected first effective hue area data based on the identification code S1. Since color conversion processing for increasing brightness and / or saturation is performed by matrix calculation using the effective hue area data h1p2 and h1q2 as calculation terms, the brightness and / or saturation of a specific color component is increased without enhancing noise components. It is possible.

  In addition, since data effective in each pixel among the six hue area data is selected as the first effective hue area data, the frequency characteristics are converted for the selected first effective hue area data. The number of frequency characteristic conversion means can be reduced as compared with the case where frequency characteristic conversion is performed for each of the two hue area data.

  Further, color correction amounts R1, G1, and B1 for adjusting the brightness and / or saturation of the first color data for each hue component are calculated using the second effective hue area data h1p2 and h1q2. By adding the color correction amount to the first color data Ri, Gi, Bi to perform the process of increasing the brightness and / or the saturation, it is possible to prevent the image from being blurred due to the noise removal process. That is, the influence of the noise removal process appears only in the color correction amounts R1, G1, and B1, and the contour information of the first color data is maintained, so that the brightness and / or saturation is improved without enhancing the noise component. be able to. As shown in FIG. 15, it can be seen that the changes in the values of the second color data from the pixel positions 16 to 17 and the pixel positions 42 to 43 are not gradual, and the contour information of the image is maintained.

In the above description, the matrix calculation means 8 performs the calculation using the matrix coefficient (Fij) that increases the brightness and saturation of the three hue components of red, yellow, and green. Alternatively, a color correction amount calculation that increases the saturation may be performed. Moreover, you may perform correction | amendment which suppresses a brightness or saturation according to display characteristics, such as a display.
Here, the adjustment amount of lightness and / or saturation, and the hue component to be adjusted can be appropriately set by the matrix coefficient Fij in Expression (3).

  Furthermore, in the above description, the second hue area data calculation unit is used for the purpose of noise removal, but it may be configured to perform conversion of frequency characteristics that suppress or enhance arbitrary frequency components.

1 is a block diagram illustrating a configuration of a color conversion apparatus according to an embodiment of the present invention. It is a figure which shows the magnitude relationship between an identification code and 1st color data. It is a figure which shows the relationship between an identification code and a hue. It is a block diagram which shows the structure of a hue area | region data calculation means. It is a schematic diagram which shows the relationship between hue area | region data and a hue. It is a figure which shows the relationship between an identification code and 1st effective hue area | region data. It is a block diagram which shows the structure of a 2nd effective hue area | region data calculation means. It is a block diagram which shows the structure of a frequency characteristic conversion means. It is a block diagram which shows the structure of a matrix calculating means. It is a figure which shows an example of the original data of 1st color data. It is a figure which shows an example of 1st color data. It is a figure which shows an example of hue area data. It is a figure which shows an example of 1st effective hue area | region data. It is a figure which shows an example of 2nd effective hue area | region data. It is a figure which shows an example of 2nd color data.

Explanation of symbols

1 color correction amount calculating means, 2 color correction amount adding means, 3 αΒ calculating means, 4 chromatic color component data calculating means, 5 hue area data calculating means, 6 first effective hue area data calculating means, 7 second effective Hue area data calculating means, 8 matrix calculating means, 9 coefficient generating means,

Claims (10)

In a color conversion device that converts the brightness and / or saturation of first color data representing a color image for each pixel and outputs second color data corresponding to the first color data.
Means for outputting an identification code obtained by quantizing the hue of the color represented by the first color data;
Using the first color data, in the color image represented by the first color data, it becomes a value other than Oite 0 to a specific hue component, a 0 in the hue components other than the specific hue component Hue area data calculating means for calculating a plurality of hue area data;
Based on the identification code, among the plurality of hue region data, select the data to be a value other than Oite 0 to the color of each pixel represented by the first color data, first effective hue region Means for outputting as data;
According to a preset method, a frequency characteristic of a signal input as the first effective hue area data is converted according to the identification code, and a second effective hue area corresponding to the first effective hue area data is converted. Means for outputting data;
Coefficient generating means for outputting a predetermined matrix coefficient set for each of the second effective hue area data;
The lightness and / or saturation of the first color data is calculated by performing a matrix operation including multiplication by using the second hue region data as an operation term and multiplying the second hue region data by the matrix coefficient. Matrix computing means for calculating a correction amount for independently correcting each hue component;
A color conversion device comprising: color correction means for calculating the second color data obtained by correcting the first color data based on the correction amount. Hue region data calculation means, in the color image represented by the first color data, red, green, blue, yellow, cyan, becomes each value other than Oite 0 to the hue components of magenta, other than the hue component The color conversion apparatus according to claim 1, wherein hue area data that is 0 in the hue component is calculated. Hue region data calculation means is chromatic color component data representing the size of each of the red, green, blue, yellow, cyan, and magenta color components obtained by removing the achromatic color component from the color represented by the first color data. r, calculated g, b, y, m, and c, using the above chromatic component data, becomes red, green, blue, yellow, cyan, and a value other than Oite 0 to each color component of magenta, the color The color conversion apparatus according to claim 2, wherein the hue area data h1r, h1g, h1b, h1y, h1m, and h1c that are 0 in the hue components other than the components are calculated by the following formula.

The means for outputting the second effective hue area data blocks or attenuates a specific frequency component in the first effective hue area data based on the identification code. The color conversion device according to item 1. The matrix calculation means sets the second effective hue area data obtained by performing frequency conversion on the two first effective hue area data selected from the hue area data based on the identification code as operation terms h1p2 and h1q2, and sets the first effective hue area data in the pixel. 5. The color conversion apparatus according to claim 1, wherein a minimum value in one color data is α, and correction amounts R1, G1, and B1 are calculated by the following formulas.

In a color conversion method for converting the brightness and / or saturation of first color data representing a color image for each pixel and outputting second color data corresponding to the first color data,
Outputting an identification code obtained by quantizing the hue of the color represented by the first color data;
Using the first color data, in the color image represented by the first color data, it becomes a value other than Oite 0 to a specific hue component, a 0 in the hue components other than the specific hue component Calculating a plurality of hue area data;
Based on the identification code, among the plurality of hue region data, select the data to be a value other than Oite 0 to the color of each pixel represented by the first color data, first effective hue region Outputting as data,
According to a preset method, a frequency characteristic of a signal input as the first effective hue area data is converted according to the identification code, and a second effective hue area corresponding to the first effective hue area data is converted. Outputting the data;
Outputting a predetermined matrix coefficient set for each of the second effective hue area data;
The lightness and / or saturation of the first color data is calculated by performing a matrix operation including multiplication by using the second hue region data as an operation term and multiplying the second hue region data by the matrix coefficient. Calculating a correction amount for independently correcting each hue component;
And a step of calculating the second color data obtained by correcting the first color data based on the correction amount. Hue region data in the color image represented by the first color data, becomes red, green, blue, yellow, cyan, and a value other than Oite 0 to each of the hue components of magenta hue components other than the color component The color conversion method according to claim 6, wherein the data is zero . Chromatic color component data r, g, b, y representing the size of each of the red, green, blue, yellow, cyan, and magenta color components of the color represented by the first color data excluding the achromatic color component. , m, calculates c, using the above chromatic component data, red, green, blue, becomes yellow, cyan, and a value other than Oite 0 to each color component of magenta, 0 in the hue components other than the color component become hue region data h1r, h1g, h1b, h1y, h1m, color conversion method according to claim 7, characterized in that calculated by the following equation h1c.

9. The second effective hue area data blocks or attenuates a specific frequency component in the first effective hue area data based on the identification code. Color conversion method. The second effective hue area data obtained by performing frequency conversion on the two first effective hue area data selected from the hue area data based on the identification code are the operation terms h1p2 and h1q2, and the first color data in the pixel The color conversion method according to any one of claims 6 to 9, wherein the correction value R1, G1, B1 is calculated by the following equation, with α being a minimum value of α.

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