JPH06160992A - Color data conversion device for image processor - Google Patents

Abstract

PURPOSE:To convert the data indicating the densities of the colors of red light, green light and blue light of a color original image into data indicating hue, saturation and lightness at a high speed and low cost. CONSTITUTION:Color data on three colors for picture elements obtd. by photometry of the original image are averaged by an adder 50 and a divider 52 and the lightness values corresponding to the color data on three colors are outputted. The color data of the green light and the blue light are standardized by the total sum of the color data on three colors outputted from the adder 50. HLUT 60 is indexed by standardized color data (g), (b) on the two standardized colors and the hue values corresponding to the color data on three colors are outputted. SLUT 58 is indexed by the standardized color data g', b' obtd. by decreasing data quantity from the standardized color data (g), (b) on the two standardized colors by a subtractor 56, by which the saturation values corresponding to the color data on three colors are outputted.

Description

Detailed Description of the Invention

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a color data conversion device for an image processing device, and more particularly to a color data conversion device for an image processing device for converting color data obtained from a color original image into a hue value, a saturation value and a lightness value. The present invention relates to a data converter.

[0002]

2. Description of the Related Art Conventionally, an image display device that reads an image from a color original image such as a film or a print, reproduces the color original image based on the read data, and displays the color original image based on the read data As described above, there is an image processing device such as an image recording device that determines an exposure amount and records an image.

For example, one that extracts density data of a main image such as a person's face in an original image of a color film and determines an exposure amount based on the extracted density data so that a face color is appropriately printed. is there. Such techniques are disclosed in Japanese Patent Laid-Open Nos. 62-115430 and 62-11543.
No. 1, JP 62-115432, JP 6
2-189456, JP-A-62-189457, JP-A-63-138340, JP-A-63-
There is one described in Japanese Patent No. 178222.

Generally, in the image processing apparatus as described above, red light, green light and blue light are used in order to easily perform a calculation for obtaining a composite color when color-correcting a color original image and a calculation for obtaining a color tone of the color original image. R data, G data, and B data representing the densities of the three colors (so-called three primary colors R, G, and B) of the hue (H), saturation (S), and lightness (L) used in the developing system. It is converted into H data, S data, and L data representing each.

Known as this conversion are the conversion by the HSL6 pyramid color model and the conversion based on the definition of Heiden and Rheins.

[0006]

However, since the above conversion is a complicated calculation using a large number of arithmetic expressions, the processing time becomes long. Therefore, in the image processing device as described above,
There is a problem that the processing time dedicated to the conversion becomes long, and the processing time of the original image is added, so that the processing time of the entire apparatus becomes significantly long.

In order to solve this problem, if a high-speed type CPU is used, the calculation time is shortened, but the cost of the device is increased. In addition, the result of calculation of the above conversion is stored in a table in advance, and R is stored using this table.
Each data of HSL corresponding to each data of GB is output. As a result, the calculation at the time of conversion becomes unnecessary, so that the calculation time can be shortened and the processing speed of the device can be increased.

However, since each HSL data is output corresponding to each combination of RGB data, it is necessary to make a table for each HSL. Also, the index that specifies the output data of the table is R
Since there are as many combinations of GB data as possible, the storage capacity becomes large. That is, the gradation of the image is increased in order to reduce the difference between the densities of the image. For example, when the gradation of an image is represented by 8 bits (0 to 255), HSL
Each table that outputs the data of (2) requires a (2 ⁸ ) ^3- bit index. Therefore, when each output data of HSL is 8 bits, the storage capacity of each table is about (2 ²⁴ ) bytes. Therefore, the total of the HSL tables requires a large capacity of about 50 Mbytes for (2 ²⁴ ) -3, which is not practical.

The present invention has been made to solve the above-mentioned problems, and it is a high-speed and low-cost method for converting data representing color densities such as red light, green light, and blue light of a color original image into hue,
An object of the present invention is to provide a color data conversion device for an image processing device, which can be converted into data representing saturation and lightness.

[0010]

In order to achieve the above object, the invention according to claim 1 divides a color original image into a large number of pixels and each pixel is divided into three colors of red light, green light and blue light. Color data output means for decomposing and photometrically outputting color data of three colors, computing means for normalizing at least two color data and outputting standardized color data, and brightness based on the color data of the three colors. A lightness conversion unit that calculates and outputs a value and a saturation table that represents the relationship between the standardized color data and the saturation value of the pixel are provided, and the saturation value corresponding to the input standardized color data is output. And a hue conversion unit that outputs a hue value corresponding to the input standardized color data, and a hue table that represents the relationship between the standardized color data and the hue value of the pixel. ing.

According to a second aspect of the present invention, in the color data conversion apparatus for an image processing apparatus according to the first aspect, the arithmetic means is standardized by a value which is a predetermined multiple of the lightness value. .

The invention according to claim 3 is the invention according to claim 1 or 2.
In the color data conversion device for an image processing device described in the paragraph 1, the standardized color data input to the saturation table is smaller than the data amount of the standardized color data input to the hue table.

[0013]

According to the present invention, in the color data conversion device for an image processing device, the color data output means divides the color original image into a large number of pixels and separates each pixel into three colors of red light, green light and blue light. Then, the light is measured and the color data of three colors is output. The arithmetic means standardizes at least two of the color data and outputs the standardized color data. The standardized color data can be used by combining all three colors of red light, green light, and blue light, or by combining any two of the three colors of red light, green light, and blue light. The lightness conversion means calculates and outputs a lightness value based on the color data of the three colors. For example, it is possible to obtain an average value of color data of three colors and output this average value as a brightness value. Further, the color data conversion device for an image processing device includes a saturation conversion unit having a saturation table and a hue conversion unit having a hue table. The saturation table represents the relationship between the standardized color data and the saturation value of the pixel, and the saturation conversion means outputs the saturation value corresponding to the input standardized color data. The hue table represents the relationship between the standardized color data and the hue value of the pixel, and the hue conversion means outputs the hue value corresponding to the input standardized color data. For example, standardized color data can be used as an index of the hue table and the saturation table. As described above, the color data conversion device for an image processing device obtains a lightness value from each color data of three colors for each pixel of a color original image by calculation, and at least 2
The hue value and the saturation value are output from the hue table and the saturation table according to the standardized color data in which the color data of the color is standardized. Therefore, the calculation required for the conventional conversion is only a simple calculation for obtaining the lightness value, and the conversion time does not increase. In addition, since the hue value and the saturation value are obtained by using the standardized data of at least two colors, a table is provided for each of the lightness, the hue, and the saturation, and three colors of red light, green light, and blue light are provided. There is no increase in capacity for inputting color data.

The above-mentioned standardization can be standardized by a value that is a predetermined multiple of the lightness value as in the invention described in claim 2. By doing so, even when the color data of two colors is standardized, if these two standardized color data are used, the three color components of red light, green light and blue light are included,
The amount of data when designating the hue table and the saturation table can be reduced.

Further, the saturation is less likely to show a delicate change than the hue. Therefore, the saturation value may not require a higher resolution than the hue value. Therefore, as in the third aspect of the invention, the standardized color data input to the saturation table is made smaller than the data amount of the standardized color data input to the hue table. By doing so, the sizes of the hue table and the saturation table can be made different, and the tables can be set according to the resolution of each value at the time of conversion.

[0016]

Embodiments of the present invention will now be described in detail with reference to the drawings. In this embodiment, the present invention is applied to an auto printer. As shown in FIG. 1, the auto printer of this embodiment includes a conveyance roller 12 that conveys a color negative film 10. Below the color negative film 10 transported by the transport roller 12, the light source 1
4. A color correction filter 16 such as a light control filter and a diffusion box 18 are arranged in order. In addition, above the negative film 10, the light rays transmitted through the negative film 10 are
Distributing prisms 20 that distribute in directions are arranged.
A projection optical system 22, a black shutter 23 and a color paper (printing paper) 24 are sequentially arranged on one optical path distributed by the distribution prism 20, and a projection optical system 26 and a CCD image sensor are arranged on the other optical path. 28 are arranged in order.

The CCD image sensor 28 divides one screen (one frame) of the negative film 10 into a large number of pixels (for example, 256 × 256 pixels) and divides each pixel into R (red),
The light is separated into three colors of G (green) and B (blue) for photometry. C
The CD image sensor 28 includes an amplifier 30 for amplifying a CCD image sensor output and an analog-digital (A /
D) The converter 32 is connected to a 3 × 3 matrix circuit 34 for sensitivity correction of the CCD image sensor.
The 3 × 3 matrix circuit 34 is connected to a proper exposure amount calculation circuit 40 via a smoothing circuit 35 for removing noise, a color conversion circuit 37 (details will be described later), and a face extraction circuit 36 composed of a microcomputer. With 1
It is connected to an appropriate exposure amount calculation circuit 40 via an average density calculation circuit 38 for calculating the average density of the entire screen. The appropriate exposure amount calculation circuit 40 is connected to the color correction filter 16 via a driver 42 that drives the color correction filter. The smoothing circuit 35 may be composed of a microcomputer or a circuit.

(Structure of Color Conversion Circuit) FIG. 2 shows the structure of the color conversion circuit 37 as a block. The color conversion circuit 37 receives R and G after noise removal in the smoothing circuit 35. , B3 color photometric data to H
It is a circuit for converting into (hue value), L (lightness value), and S (saturation value). In this embodiment, 8-bit data is used as the R, G, and B three-color photometric data, and data transmission / reception is performed with 8-bit data in a portion not specified.

The smoothing circuit 35 is connected to the adder circuit 50, and the photometric data of three colors of R, G and B are individually input. The adder circuit 50 is connected to the face extraction circuit 36 via a divider 52 for dividing by a predetermined value (3 in this embodiment) and a synchronization circuit 62, and also the normalization circuit 5
4 is connected. In this embodiment, the data output from the divider 52 is used as the lightness value L. Therefore,
The lightness value L is equivalent to the calculation by the following formula (1).

L = (R + G + B) / 3 (1) where R, G, and B are photometric data for each of the three colors.

The normalizing circuit 54 includes a smoothing circuit 35.
To G and B two-color photometric data are separately input. The normalization circuit 54 is composed of a divider, and outputs standardized data g and b which are standardized by dividing each of the input photometric data of G and B two colors by photometric data of R, G and B three colors. To do. That is, the standardization circuit 54 has a circuit configuration equivalent to the calculation based on the following equation (2). The standardized data g and b are the saturation look-up table (hereinafter referred to as SLUT) 58 and the hue look-up table (hereinafter referred to as HLU) which will be described later.
It is used as an index of 60).

G = kG / (R + G + B) b = kB / (R + G + B) (2) where k is a constant for making the calculated value an integer (for example, 1
000).

The normalization circuit 54 is connected to the SLUT 58 and the HLUT 60 via a subtractor 56. The number of output bits (the number of effective bits) of the standardized data g, b output from the standardization circuit 54 is determined by increasing the resolution required by the HLUT 60. In the present embodiment, the input 8-bit data is input. The calculation result of is output as 10-bit data. Therefore, the subtractor 56 and the HLUT
To the 60, standardized data g and b of 10 bits are input.

The subtractor 56 outputs the standardized data g, b as 8-bit standardized data g ', b'by bit shift of the shift register or the like. The subtractor 5
6 can have the same structure as the above-mentioned bit shift operation by connecting the upper 8 bit data line. Note that the 10-bit standardized data g, b is output as the 8-bit standardized data g ', b'when the saturation is lower than the normal hue and the resolution is slightly lowered. This is because there is virtually no effect.

The SLUT 58 is composed of a storage element such as SRAM, and stores a large number of saturation values S. Each of the plurality of saturation values S is stored for each part of the address designated by the index, and the 8-bit standardized data g ′, b ′ described above is used for this index. Therefore, the saturation value S can be selected corresponding to each value of the standardized data g ', b'. For example, S
The 16-bit address of the LUT 58 is connected so that the standardized data g ′ is input to the upper 8 bits and the standardized data b ′ is input to the lower 8 bits. As a result, when each of the standardized data g ′ and b ′ is calculated, the saturation value stored in the part of the address corresponding to the calculated value is output. Thus, the SLUT 58 has an index of 8+
8 = 16 bits, ie 2 ¹⁶ relationships are stored. The saturation value data stored in this SLUT 58 is 8
In terms of bits, the storage capacity of the SLUT 58 is 2 ¹⁶ bytes, that is, about 65 kilobytes.

The HLUT 60 is composed of a storage element such as SRAM, and stores a large number of hue values H. Each of the large number of hue values H is stored for each part of the address designated by the index, and the 10-bit standardized data g and b described above is used for this index. Therefore, the hue value H can be selected corresponding to each value of the standardized data g and b. For example, HLUT
Regarding the address 20 bits of 60, the standard data g is input to the upper 10 bits and the standard data b is input to the lower 10 bits. As a result, when each of the standardized data g and b is calculated, the saturation value stored in the part of the address corresponding to the calculated value is output. In this way, the HLUT 58 has an index of 10 + 10 = 20.
Bits, ie 2 ²⁰ relationships are stored. If the data of the hue value stored in the HLUT60 is 8 bits, the storage capacity of HLUT60 2 ²⁰ bytes, i.e. approximately 1.05 megabytes.

The SLUT 58 and the HLUT 60 are connected to the face extraction circuit 36 via the synchronization circuit 62. The synchronization circuit 62 includes a latch circuit (not shown) that temporarily stores data, and includes a divider 52 and an SLUT 58.
And the data output from the HLUT 60 are output in synchronization with each pixel.

Each of the standardized data g and b is
Since the R, G, and B color photometric data are obtained by division by the sum, the R color photometric data component is included, and one standardized data r corresponds to these standardized data g and b. Therefore, even if only two pieces of standardized data g, b are used, they can be handled in the same manner as the case of using three pieces of standardized data r, g, b. For this reason, the relationship between the standardized data g ′ and b ′ and the saturation value S and the relationship between the hue value H and the standardized data g and b are values calculated in advance based on the three-color photometric data. Standardized data g,
Correspondence can be made by storing b or g ', b'as an index. Further, it is not limited to the combination of the standardized data g, b, and any two of the three standardized data r, g, b may be used in combination.

For calculating the hue value H and the saturation value S, there is a method according to the following equation (3). S = 1-min (r, g, b) H = H '/ 2Pi (3) However, R, G, and B each have a minimum value of 0 as shown in the three-dimensional color coordinates in FIG. 3 standardized so that the maximum value is 1.
The color photometric data, min (), represents the minimum value of the numerical values in (). H'is given by the following equation (4), and Pi (i
Is one of R, G, and B) is P in FIG.

[0030]

[Equation 1]

However,

[0032]

[Equation 2]

Next, the operation of this embodiment will be described. Light source 14
The light beam emitted from the laser beam passes through the color correction filter 16, the diffusion box 18, and the color negative film 10, is distributed by the distribution prism 20, and is received by the CCD image sensor 28 via the projection optical system 26. At this time, the black shirt 23 is closed. By this light reception, the CCD image sensor 28 divides the entire screen into a large number of pixels, decomposes each pixel into R, G, and B colors, and performs photometry, and outputs a photometric data signal. The photometric data signal is amplified by the amplifier 30 and then converted into a digital signal by the A / D converter 32, the sensitivity of the image sensor is corrected by the 3 × 3 matrix circuit 34, and the smoothing circuit 35 and the average density calculation circuit 38 are supplied. Is entered. The average density calculation circuit 38 calculates the average density of one screen. The smoothing circuit 35 removes noise from the three-color photometric data. The noise-removed three-color photometric data is subjected to a hue conversion value H, a saturation value S, and a lightness value L in a color conversion circuit 37 for facilitating the calculation when estimating a face part described later.
Is converted to. Each of the converted hue value H, saturation value S, and lightness value L is input to the face extraction circuit 36. The face extraction circuit 36 estimates the part of the face of the person in one screen, and outputs R, G, B three-color photometric data of the part estimated to be the face.
The exposure amount calculation circuit 40 calculates the exposure amount using the three-color photometric data output from the face extraction circuit 36 and the average density calculated by the average density calculation circuit 38, and the color correction filter 16 is calculated via the driver 42. Is controlled and the black shutter 23 is opened and closed for printing. When the average density calculated by the average density calculation circuit 38 is used, the exposure correction amount for the average density can be calculated. When the exposure correction amount is not calculated, the average density calculation circuit 38 is not necessarily required, and the exposure amount may be calculated directly from the three-color photometric data output from the face extraction circuit 36.

As shown in FIG. 2, the noise-removed R, G, and B color photometric data input to the color conversion circuit 37 are input to the adder 50 and the total sum is input to the divider 52. Is output. In this divider 52, the total sum of the R, G, B three-color photometric data is divided into the number of colors separated (in this case, R, G,
Divide by 3 in 3). Therefore, the R, G, and B photometric data are averaged by the operation of the adder 50 and the divider 52, and this average value is output as the lightness value L to the synchronization circuit 62.

Further, the total sum of the R, G, B three-color photometric data obtained by the adder 50 is input to the standardization circuit 54, and in the standardization circuit 54, the G, B2 color photometric data is converted into R,
It is standardized into 10-bit standardized data g and b by the sum of the photometric data of three colors G and B. The 10-bit standardized data g, b is used as an index for outputting the hue value H stored in the HLUT 60, and the hue value H corresponding to the standardized data g, b is sent to the synchronization circuit 62. Output. Therefore, 8 from the HLUT 60
Bit normalized data g ', b', that is, R specified by an index based on 20-bit data,
The hue value H corresponding to the G, B three-color photometric data is output.

On the other hand, since the resolution may be lower than the hue with respect to the saturation, the 10-bit standardized data g, b is converted into 8-bit standardized data g ',
b '. These normalized data g ′, b ′ are used as an index for outputting the saturation value S stored in the SLUT 58, and the saturation value S corresponding to the normalized data g ′, b ′ is synchronized with the synchronization circuit 62. Output to. Therefore, the SLUT 58 outputs the saturation value S corresponding to the 8-bit standardized data g ', b', that is, the R, G, B three-color photometric data designated by the index of the 16-bit data.

The lightness value L, the saturation value S and the hue value H are
The face extraction circuit 3 is synchronized with each pixel in the synchronization circuit 62.
6 is output.

As described above, the lightness value is obtained by an average which is a simple operation, and the hue value and the saturation value can be obtained by designating each look-up table, that is, the SLUT 58 and the HLUT 60 by an index, and thus, it is conventionally used. The time required for conversion becomes unnecessary, and the conversion processing time becomes short.

Further, since the lightness value can be obtained by a simple calculation, a memory or the like having a storage capacity for lightness which is conventionally stored in a look-up table becomes unnecessary. A look-up table, that is, the SLUT 58 and the HLUT 60 are used for the hue value and the saturation value. The indexes for designating the SLUT 58 and the HLUT 60 respectively are for the two colors standardized based on the R, G, and B color photometric data. By using the standardized data of
The number of indexes decreases as compared with the case where all the R, G, and B color photometric data are used, and it becomes possible to use the SLUT 58 and the HLUT 60 with a small storage area for each data.

As is well known, resolution is required for hue values. Therefore, the HLUT 60 requires a large storage capacity, but in the present embodiment, the standardizing circuit 54 sets the data length based on the resolution of the hue value L, that is, the number of bits. On the other hand, for the saturation value S, the hue value L
The resolution can be reduced compared to. Therefore, the number of bits is reduced by the subtractor to obtain sufficient resolution. In this way, since data can be handled with an optimum number of bits according to the required resolution, each SLUT
The storage capacities of 58 and HLUT 60 can be optimally set.

According to the present embodiment, the storage capacity of about 50 megabytes, which is conventionally required when the lookup table stores 8-bit data for each of the hue value, the saturation value, and the lightness value, is about 65. About 1.1 with a kilobyte SLUT 58 and about 1.05 megabyte HLUT 60
The storage capacity of megabytes can be covered, and the storage capacity can be reduced by 1/50.

FIG. 3 shows a face extraction routine by the face extraction circuit 36. In this face extraction circuit 36, processing is performed on the basis of the hue value, the saturation value and the lightness value which have been denoised and converted.

In step 104, as shown in FIG. 5A, a two-dimensional histogram of the hue value and the saturation value is obtained by using a coordinate system composed of a hue value axis, a saturation value axis and a pixel number axis which are orthogonal to each other. Then, as will be described later in step 106, the obtained two-dimensional histogram is divided into mountains, that is, clustering of the two-dimensional histogram is performed. In the next step 108, clustered 2
A large number of pixels are clustered based on the peaks of the dimensional histogram, the screen is divided based on this clustering, and areas that are candidates for human faces are extracted from the divided areas. In the next step 110, the face area is estimated from the area extracted as the face candidate, and R, G, B three-color photometric data of the area estimated as the face is output. Then, in step 112, it is judged whether or not the printing of all the frames is completed, and when it is judged that the printing is completed, this routine is ended.

Next, details of steps 106 to 110 will be described. FIG. 6 shows the details of step 106. In step 120, an area to be evaluated is cut out from the two-dimensional histogram of the hue value and the saturation value.
In FIG. 5, one frame is set as the evaluation area for simplification of description. In step 122, it is determined whether there is an evaluation area. When the evaluation area cannot be cut out in step 120, that is, when the evaluation of all areas is completed, there is no evaluation area, and this routine is ended. If there is an evaluation area, the X and Y axes for creating the mountain cutting histogram are determined in step 124. In other words, the evaluation area is rotated around an axis parallel to the pixel number axis, and when the mountains of the histogram are viewed from the side, priority is given to multimodality and the position where the mountains are the sharpest is obtained. Determine the X and Y axes. When the processing time needs to be shortened, the accuracy is somewhat deteriorated, but the axes that maximize the variance of the histogram may be used as the X and Y axes. In the example of FIG. 5 (1), when the four mountains with the reference numbers 1 to 4 are viewed from the side, priority is given to the multi-modality, and the peaks are the most sharp positions because the three mountains are visible. The X axis is defined in the direction orthogonal to the viewing direction, and the Y axis is defined in the direction orthogonal to the X axis.

In the next step 126, the two-dimensional histogram is projected on the X and Y axes to create one-dimensional histograms. In the example of FIG. 5 (1), when viewed from the direction orthogonal to the X axis, the peaks with the reference numerals 1 and 2 appear to overlap, so the one-dimensional histogram for the X axis has the peaks and the reference numerals with the reference numeral 3. Three mountains, a mountain with 1 and 2 and a mountain with symbol 4, appear, and when viewed from the direction orthogonal to the Y axis, the mountains with symbols 1 to 4 appear to overlap, so one dimension about the Y axis. One mountain appears in the histogram. In the next step 128, the histogram is converted into an evaluation function H (a) by the following equation (5), and mountains are cut out from the histogram on the X axis based on this evaluation function.

[0046]

[Equation 3]

However, f (a) is the number of pixels when the value (feature amount) in the X-axis direction is a, and x is the displacement from the feature amount a.

That is, the average value T of the evaluation function H (a) is calculated, and the range (valley, skirt existence range) equal to or smaller than the average value T of the evaluation function H (a) is calculated. Next, the position of the minimum histogram within this range is set as the valley or skirt of the histogram. Then, a histogram is cut out at the obtained valley or skirt.

Explaining the cutout of the mountain with reference to FIG. 7, when the evaluation function H (a) is obtained from the histogram represented by the solid line SI, it becomes as shown by the broken line in the figure. The range of the average value T or less with respect to the negative portion of the evaluation function H (a) is the range of feature amounts v0 to v1 and v2 to v3. The position where the frequency of the histogram in this range is the minimum is the range v
Av0 = v0 in 0 to v1, av1 in the range v2 to v3
And av0 is obtained as a skirt and av2 is obtained as a valley, and the histogram is cut out at this position.

In step 130, the mountain peak of the Y axis is cut out in the same manner as the mountain peak cut of the X axis. Next step 132
Then, an area in which the peaks of the one-dimensional histogram for the X-axis and the Y-axis cut out as described above overlap on the two-dimensional histogram is obtained, and the peak is cut out from the two-dimensional histogram of the hue value and the saturation value. Area E in FIG. 5 (1)
Reference numeral 1 shows an example of a mountain cut out as described above.

In the next step 134, it is judged whether or not the mountain cut out from the two-dimensional histogram is a single peak, and if it is not a single peak, steps 124 to 134 are repeated until the mountain cut out from the two-dimensional histogram becomes a single peak. repeat. A region E2 in FIG. 5C shows an example of a single-peak mountain cut out as described above.

In the next step 136, a process (labeling) for attaching a label for identifying the cut-out single-peak mountain is performed. In step 138, the labeled mountain is masked and the process returns to step 120. Then, the above steps are repeated to divide the entire area of the two-dimensional histogram for the hue value and the saturation value into single peaks.

FIG. 8 shows the details of step 108 in FIG. 3, and in step 140, the range XR (FIG. 5 (3)) and Y of the single-peak mountain divided as described above in the X-axis direction. The range YR in the axial direction (FIG. 5 (3)) is obtained for each single peak, and it is determined whether the hue value and the saturation value of each pixel of the original image belong to these ranges, and the pixel clustering is performed. At the same time, the pixels belonging to the range surrounded by the ranges XR and YR are collected, and the original image is divided so that the collected pixels form one area on the original image. Also, the divided areas are numbered. FIG. 5 (2) shows an example in which the original image is divided, and the pixels in each area denoted by reference numerals 1 to 4 are included in the unimodal peaks denoted by reference numerals 1 to 4 in FIG. 5 (1). Corresponds to the pixels that are displayed. Pixels belonging to the same single-peak mountain in FIG. 5 (1) are divided into different regions in FIG. 5 (2). This is due to the hue value range of the single-peak mountain in FIG. This is because the pixel has a saturation value range, but the region is divided in FIG.

At the next step 142, the small area is removed by judging the area of the divided area, and the numbering is performed again. In the next step 144, a contraction process for deleting all the boundary pixels of the region to remove one skin, and an expansion process for expanding the boundary pixels in the direction of the background pixels to enlarge the skin by one conversely to the contraction process are performed. It separates a small area that grows up from a large area. In the next step 146, small areas are removed and renumbering is performed in the same manner as in step 142, and in order to separate the weakly bonded areas in step 148, the same contraction and expansion processing is performed, In step 150, the small area is removed and renumbering is performed as described above.

FIG. 9 shows details of step 110. In step 162, one area is selected from the areas extracted in step 108, that is, the routine of FIG. 8, as the area of interest, and the horizontal fillet diameter of the area of interest is selected. And the size of the attention area is standardized by performing the enlargement / reduction processing of the attention area so that the vertical fillet diameter becomes a predetermined value.
The density value or the brightness value is standardized according to the following equation (6).

[0056]

[Equation 4]

Where dmax: maximum density value (or brightness value) in the area dmin: minimum density value (or brightness value) in the area ds: full-scale density value (or brightness value) of the image sensor d: density value before normalization (Or luminance value) dr: normalized density value (or luminance value)

In step 164, a plurality of types (10 types in this embodiment) of standard face images stored in advance (face image viewed from the front, face image viewed from the side (left and right), downward face image, upward face image) A correlation coefficient r of the attention area with respect to a face image or the like) is calculated by the following expression (7), and this correlation coefficient is used as a feature amount. In this standard face image, even if the data is only the outline of the face, the internal structure of the face (eye, nose,
Oral data) may be added.

[0059]

[Equation 5]

However,

[0061]

[Equation 6]

Where T is the horizontal and vertical fillet diameters of the image (here, the fillet diameters are the same), f
(X, y) represents a region of interest, and g (x, y) represents a standard face image.

Then, in step 166, it is determined whether or not the region of interest is a human face by linear discriminant analysis using the above-mentioned characteristic amount as a variable, and R, G, B photometric data of the region determined to be a face. Is output to the proper exposure amount calculation circuit 40. In step 168, it is determined whether or not the determination as to whether the face is a face has been completed for all the extracted regions. If not, steps 162 to 168 are repeated.

In the above, the correlation coefficient is used as the feature amount used to determine whether the face is a person's face. However, an invariant derived from the normalized central moment around the center of gravity, which will be described below, Autocorrelation functions or geometric invariants may be used.

The central moment μ _pq about the (p + q) th centroid of the image f (x, y) is

[0066]

[Equation 7]

However,

[0068]

[Equation 8]

Then, the normalized central moment around the center of gravity is as follows.

[0070]

[Equation 9]

[0071] However, y = (p + q + 2) / 2 p + q = 2,3, from the above ......, secondary, next seven invariant from the tertiary normalized central moments about the center of gravity [psi _{i ,} (I
= 1, 2, ..., 7) is derived.

[0072]

[Equation 10]

Further, the autocorrelation function R _f is expressed as follows.

[0074]

[Equation 11]

The geometric invariant feature amount is expressed by the following equation.

[0076]

[Equation 12]

The appropriate exposure amount calculation circuit 40 is composed of the face extraction circuit 3
6. The R, G, B photometric data of the face area extracted as described above in 6 and the screen average density D _i of one frame calculated by the average density calculation circuit 38 (i = R, G, or B) ) And are used to calculate an appropriate exposure amount E _i according to the following equation, and output to the driver 42. The driver 42 controls the dimming filter 16 by calculating an exposure control value from the appropriate exposure amount E _i .

L _og E _i = LM _i · CS _i · (DN _i −D _i ) + PB _i + LB _i + MB _i + NB _i + K ₁ + K ₂ (8) However, each symbol represents the following.

LM: Magnification slope coefficient, which is preset according to the enlargement magnification determined by the type of negative and the print size.

CS: Color slope coefficient prepared for each type of negative, there are underexposure and overexposure, and it is determined whether the average density of the frame to be printed is under or over the standard negative density value. Either underexposure or overexposure is selected.

DN: Standard negative density value. D: Average density value of print frame.

PB: A correction balance value for standard color paper, which is determined according to the type of color paper.

LB: For standard print lens. It is a correction lens balance value, which is determined according to the type of printing lens.

MB: A correction value (master balance value) with respect to a change in print light source and a change in paper developing performance.

NB: Negative balance (color balance) value determined by the characteristics of the negative film.

K ₂ : Color correction amount. K ₁ : A density correction amount represented by the following formula.

[0087]

[Equation 13]

Here, K _a and K _b are constants, and FD is the face area average density. Further, the density correction amount K ₁ in the above equation (8) may be used as the correction value obtained by the film inspection apparatus, and the color correction amount K ₂ may be expressed using the face area average density as follows.

[0089]

[Equation 14]

However, K _c is a constant. Further, the density correction amount K ₁ and the color correction amount K ₂ in the above equation (8) are used as the correction amounts obtained by the film inspection device, and the average density D _i of the print frame in the equation (8) is the average density FD of the face area. _i
Alternatively, the exposure amount may be obtained instead.

In the present embodiment, since the judgment is made by using the contour of the area and the internal structure, the face data can be extracted from the image in which the faces, the ground, the trees and the like having similar hues are mixed. .

FIG. 10 shows a modification in which the present invention is applied to an exposure amount determining device separate from the printer or the printer processor. In FIG. 10, parts corresponding to those in FIG. 1 are assigned the same reference numerals and explanations thereof will be omitted. Further, the average density calculation circuit 38 is not always necessary, but instead of this, an integrated transmission density detection circuit for detecting LATD of the entire screen may be used.

In FIG. 11, the face extraction circuit of FIG. 10 is composed of a plurality of face extraction circuits 36 ₁ , 36 ₂ ... 36 n, and the exposure amount is calculated by parallel processing. Face extraction circuit 36
_1, 36 ₂ ··· 36n reads an image according to the time chart of FIG. 12, calculates the exposure amount, and outputs the result. In FIG. 12, t ₁ is the image reading time for one frame, t _1
2 is the exposure amount calculation time for one frame, t ₃ is the exposure amount calculation result transfer time for one frame, and t ₂ >> t ₁ and t ₃ . The face extraction circuit 36 ₁ reads one frame of image at t _1
The exposure amount is calculated in ₂ hours, and the calculation result is transferred in t ₃ hours. At the same time when one frame of image is read by the face extraction circuit 36 ₁ , one frame of film is fed and the face extraction circuit 36 1
1 frame image reading by ₂ is started, and face extraction circuit 3
The exposure amount calculation of 6 ₁ and the image reading of the face extraction circuit 36 ₂ are performed in parallel, and the same applies to the face extraction circuits 36 ₃ and 36 ₄ hereinafter.
... 36n is processed in parallel.

The time Tp required for parallel processing of mxn frames is Tp = m (t ₁ + t ₂ + t ₃ ) + (n-1) t ₁ . On the other hand, the processing time Ts when parallel processing is not performed
Is Ts = m · n (t ₁ + t ₂ + t ₃ ). Therefore,

[0095]

[Equation 15]

Double speeding is possible. This parallel processing device can also be applied to the printer of FIG.

In addition to determining the exposure amount of the photographic printing apparatus, the present invention determines the exposure amount of the digital color printer, the copying condition of the copying machine, the exposure amount of the camera, the display condition of the CRT screen, the hard copy from the magnetic image data. It can also be applied to the determination of the amount of light when creating.

[0098]

As described above, according to the present invention, the lightness value is obtained by a simple calculation based on the measured color data, and the hue value and the saturation value are output by the table. It is possible to output the optimum conversion data from each of the measured color data without increasing the calculation time for converting the hue, saturation and lightness.

Further, since the saturation value and the hue value can be obtained from the hue table and the saturation table by the standardized color data obtained by standardizing at least two color data, the storage capacity of each table can be optimized. You can get the effect.

[Brief description of drawings]

FIG. 1 is a schematic diagram showing a printer according to a first embodiment of the present invention.

FIG. 2 is a schematic block diagram showing the configuration of a color conversion circuit.

FIG. 3 is a flowchart showing a face extraction routine of a face extraction circuit.

FIG. 4 is a diagram showing color coordinates.

FIG. 5 (1) is a diagram showing a two-dimensional histogram of hue values and saturation values. (2) is a diagram showing a state in which the original image is divided. (3) is a diagram showing a state in which a single peak is cut out from a two-dimensional histogram.

FIG. 6 is a diagram showing details of step 106 in FIG.

FIG. 7 is a diagram showing a histogram and an evaluation function.

FIG. 8 is a diagram showing details of step 108 in FIG.

FIG. 9 is a diagram showing details of step 110 in FIG.

FIG. 10 is a schematic diagram of an exposure amount calculation device to which the present invention is applied.

FIG. 11 is a schematic diagram of an exposure amount calculation device that performs parallel processing by a plurality of face extraction circuits.

FIG. 12 is a diagram showing a time chart of parallel processing.

[Explanation of symbols]

 28 CCD image sensor 30 Amplifier 36 Face extraction circuit 37 Color conversion circuit 50 Adder 52 Divider 54 Normalization circuit 58 Saturation lookup table 60 Hue lookup table

Claims (3)

[Claims] 1. A color data output means for dividing a color original image into a large number of pixels, dividing each pixel into three colors of red light, green light, and blue light, measuring the light, and outputting color data of the three colors. An arithmetic unit that standardizes two color data and outputs the standardized color data, a brightness conversion unit that calculates and outputs a lightness value based on the color data of the three colors, and the standardized color data and the pixel A saturation conversion unit that includes a saturation table that represents a relationship with a saturation value and that outputs a saturation value corresponding to the input standardized color data; and a relationship between the standardized color data and the hue value of the pixel. And a hue conversion unit that outputs a hue value corresponding to the input standardized color data, and a hue data conversion apparatus for an image processing apparatus. 2. The color data conversion apparatus for an image processing apparatus according to claim 1, wherein the arithmetic unit normalizes the value by a predetermined multiple of the lightness value. 3. The image processing apparatus according to claim 1, wherein the standardized color data input to the saturation table is smaller than the data amount of the standardized color data input to the hue table. Color data converter.